National College of Business
Administration & Economics
Lahore
Factors influencing the use and acceptance of Fin-tech in
Pakistan
BY
Muhammad Nazish Farooq
MASTER OF PHILOSOPHY
IN
BUSINESS ADMINISTRATION
NATIONAL COLLEGE OF BUSINESS
ADMINISTRATION & ECONOMICS
Factors influencing the use and acceptance of Fin-tech in Pakistan
BY
Muhammad Nazish Farooq
A dissertation submitted to
School of Business Administration
In partial fulfillment of the
requirements for the Degree of
MASTER OF PHILOSOPHY
IN
BUSINESS ADMINISTRATION
NATIONAL COLLEGE OF BUSINESS
ADMINISTRATION & ECONOMICS, LAHORE
Factors influencing the use and acceptance of Fin-tech in Pakistan
BY
Muhammad Nazish Farooq
A dissertation submitted to School of Business Administration, in partial
fulfillment of the requirements for the degree of
MASTER OF PHILOSOPHY
IN
BUSINESS ADMINISTRATION
____________________________________________________
Dissertation Committee:
____________________________
Chairman
____________________________
Member
____________________________
Member
____________________________
Rector
National College of Business
Administration & Economics
DECLARATION
I, Muhammad Nazish Farooq, certify that the thesis entitled “Factors influencing the use and
acceptance of Fin-tech in Pakistan.” is an original research work by me and it does not
incorporate (without acknowledgement) any material previously submitted for a degree or
diploma in any University, and that to the best of my knowledge and belief it does not contain
any material. Previously published or written by another person where due reference is not cited
in the text.
Muhammad Nazish Farooq
DEDICATED TO
My late father (may God bless him in eternal
heaven) who had always been a source of
inspiration for me.
ACKNOWLEDGEMENT
First &foremost, I thank Allah for the protection and ability to do work & I ask the Almighty to
keep showering the blessings upon me, my family, teachers, friends & colleagues as always. I
would like to express my special appreciation & gratitude towards my tremendous mentor Dr.
Muhammad Akhter. My deep thanks to him for encouraging my research and guidance. His
priceless advice on both research & my career made my stumbling journey to go rather
smoothly. I would also like to thank my family, friends &classmates for their productive critics,
which made my work challenging & exciting & got me going to hit the final score. A special
thanks to all the staff & leadership of Institute of Business management & Administration
Sciences for setting the grounds & arrangements in completion of this thesis.
Words cannot express how grateful I am to my Educational institute, NCBA & E for taking me
in as a student & making me what i am today. At the end, I would like to express appreciation to
my beloved mother and father (Late) those spent most of their time helping & guiding me
through every minute of my life to achieve my life's goal. They literally made my life's business
their own & supported me day & night practically, economically, socially & emotionally. Their
presence by my shoulders never made me reluctant or afraid thus making it possible to coming to
its happy ending & moving forward to next step in my life.
.
RESEARCH COMPLEITION CERTIFICATE
The research project Topic attached here to entitled" Factors influencing the use and acceptance
of Fin-tech in Pakistan” is conducted under my supervision, to fulfill the requirements for the
degree of MS (2 years) of National College of Business Administration & Economics is hereby
accepted.
(Dr. Muhammad Akhter)
Supervisor
SUMMARY
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Factors influencing the use and acceptance of Fin-tech in Pakistan
SYNOPSIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE
DEGREE OF MS
BY
MUHAMMAD NAZISH FAROOQ
MS (FINANCE)
Reg #122030982
Session 2020-2022
Supervisor
Dr. MUHAMMAD AKHTER
Department of management Sciences
National College of Business Administration & Economics
Abstract
The purpose of this study is to examine the Factors influencing the use and acceptance of Fintech in Pakistan. Increased access to and use of mobile devices has helped propel the growth of
the financial technology (Fin-Tech) sector in Pakistan. Startups and the financial sector see FinTech as a method to increase their access to new markets, but this requires the introduction of
new mobile apps and other technological platforms. Users and developers of financial
technology have reason to be concerned about the development of harmful mobile applications.
In this study, we empirically explore how users and organizations’ expectations regarding factors
including effort, performance, social impact, facilitating conditions, behavioral intentions, and
fin-tech adoption change. The authors of this research offer a model called "Intention to adopt
fin-tech adoption" that uses the UTAUT framework. In total, 700 residents and individuals in
Bahawalpur's industrial sector were questioning for this analysis. An online survey and a paper
survey, developed with the assistance of experts, were using to achieve the study's goals.
Therefore, both the public and the industries that make use of fin-tech stand to benefit greatly
from its widespread adoption. Independent variables found to have a moderate effect on both
behavioral intent and Fin-Tech uptake. Businesses and individuals in Bahawalpur to cut down on
expenses and increase efficiency are using fin-Tech. We may use the study's findings to
encourage more people in the Bahawalpur district of Pakistan to adopt financial technologies.
Keywords
Fin-Tech, Effort Expectancy, Behavioral Intentions, Performance Expectancy, Social Influence,
Facilitating Conditions.
Chapter 1
1.0 Introduction:
1.1 Background:
Due to technological advances, the financial services (FS) industry is changing at a rate quicker
than the rest of the economy. Financial technology, or Fin-Tech, is a catchall term describing
innovative business models in the financial services sector that are enabled by new technology
and which are changing the way financial services benefit the parties involved both financially
and non-financially (Zavolokina, Dolata, & Schwabe, 2016). This Fin-Legacy financial company
is being force by technology to review its aims, acquire new skills, and change its organizational
culture. Financial Stability Board defines Fin-Tech as "technologically enabled financial
innovation that could lead to new business models and applications, together with procedures
and products that have a significant impact on the provision of financial services." to put it
another way. There are number of recent studies that demonstrate the importance of the
relationship between a person's genetic make-up and their ability to learn new things. Startups in
the financial technology sector will benefit from technologically enhanced abilities that have a
greater knowledge intensity and are less reliant on the resources of the company itself (Temel,
Mention, & Yurtseven, 2021). Fin-Tech is transforming the creation, promotion, delivery, and
consumption of financial services, and there is no doubt about it (Dapp, Slomka, AG, &
Hoffmann, 2014). Fin-future, Tech's on the other hand, remains uncertain. As a whole, the new
momentum is enhancing financial infrastructure and promoting behavioral change, however at
the same time eroding current enterprises and business models.
Despite its early phases, study into this socially created Fin-Tech phenomenon is gaining
momentum (Gomber, Koch, & Siering, 2017). While Fin-Tech has been around for some time,
this is the first time it has been examine as a concept or word in its entirety. By leveraging on the
popular media's use of the term "Fin-Tech," Fin-Tech was conceive as a breakthrough in digital
technology focused on generating, altering, improving, and disrupting financial information
technology applications and encouraging competition in the industry. An entrepreneurial
perspective on digital finance has been embraced in recent studies, such as (Gomber et al., 2017)
which looked at the current status of digital finance research from an entrepreneurial perspective.
Financial technology institutions and how they operate should be the subject of future research
based on their results, according to the authors.
Financial technology (Fin-tech) is more than just a location for new ideas to blossom; it is also a
vital platform for enhancing conventional Korean operations, which helps keep the country's
economy flourishing. Globally, 84 percent of all payments are made through the Fin-tech
business. Financial technology not only enhances traditional financial activities, but it also
distinguishes them. They established the long-term economic feasibility of Korea's emerging
Fin-tech platform. Financial technology in the banking business has expanded six times faster in
the US global market over the past six years, according to KPMG's annual report (2019). As a
well-established industry, fin-tech has developed its own set of business models and workflows.
Affordability and low-cost services have been made more widely available thanks to financial
technology (Smith & Gregorio, 2017).
Fin-tech has been implementing by financial institutions and global incumbents, as the PwC
Global Fin-tech (2017) research demonstrates (Rizvi, Naqvi, & Tanveer, 2018). Fin-tech
companies face a wide range of challenges, including regulatory ambiguity, cultural and
management differences, and a wide range of business strategies, according to 34 percent of
respondents. There are 58% of current occupants, which say regulatory uncertainty and IT
security are major concerns. Fin-success tech's in developed economies has spurred us to raise
awareness of the hazards it poses to individuals in developing countries who wish to adopt it.
People in Pakistan still use cash for the majority of their transactions, placing them at risk for
financial exclusion (Rizvi et al., 2018). Strategic risks include regulatory ambiguity and
operational risk in Pakistani society, notwithstanding the favorable and enticing possibilities for
financial technology adoption in Pakistani society. Fin-tech inclusion in Pakistan was rank 16th out of
26 countries by the State of Financial and Digital Inclusion Project in 2017(Rizvi et al., 2018).
Banking institutions that have been around for a while are being compelled by Fin-Tech to
elaborate on their plans, acquire new skills, and revolutionize their identities (Mention,
2021). Fin-Tech newcomers, on the other hand, can gain from technology-enabled capabilities,
which characterized by greater knowledge intensity and reduced relying on one's own supply of
resources (Mention, 2021). There is no question that financial technology (Fin-Tech) is a gamechanger. aspects of how FS are created, advertised, distributed, and consumed (Sibanda,
Ndiweni, Boulkeroua, Echchabi, & Ndlovu, 2020).
However, the verdict on Fin-Tech's long-term viability is still out. Because of the increasing
momentum,
upgrading
the
financial
sector
is
bringing
with
it
some
unintended
effects. Architecture and facilitating a shift in attitude and behavior on the one hand, and
disturbing on the other end of the spectrum are the incumbent employers, service models, and
legislation.
1.2 Challenges/Problems faced by Fin-tech:
The PwC Global Fin-tech (2017) study Sahabuddin, Muhammad, Yahya, Shah, and Alam (2019)
shows that Fin-tech has been adopted by financial institutions and global incumbents. However,
there are still significant challenges facing Fin-tech, including regulatory ambiguity (48%),
cultural and management variations (55%), and varying business models (40%). In addition,
58% 54% of existing businesses are concerned about regulatory uncertainty and IT security. The
widespread adoption of high-risk financial technology in industrialized nations has prompted us
to highlight the threats it poses to individuals in less developed regions. There is a high risk of
financial exclusion for the 85 percent of Pakistanis who still use cash, as most of their purchases
are made in person (Sahabuddin et al., 2019). Strategic risks include public perception of
concerns including regulatory ambiguity and operational risk, notwithstanding the favorable and
appealing prospects for adoption of financial technology in Pakistani culture. discourages uptake
In 2017, Pakistan was ranked 16th of of 26 countries for Fin-tech inclusion, according to data
compiled by the State of Financial and Digital Inclusion Project (Sahabuddin et al., 2019).
Despite fin-potential tech's to transform the financial sector, widespread adoption has yet to
occur because of consumer skepticism regarding the potential risks involved. Fear of the
unknown, according to Nurlaily, Aini, and Asmoro (2021), is a major barrier to the widespread
adoption of new financial technologies. One of the obstacles to adoption is the risk of financial
loss, additional fees, and transaction expenses; another is legal risk, or the ambiguity of
regulatory requirements for adoption; a third is security risk, or privacy concerns and inadequate
security technology; a fourth is operational risk (inadequacy in processing and systematic
problems with Fin-tech companies). Fin-tech firms are in the arduous throes of reducing these
dangers while simultaneously expanding their product's use. Auditors of a company can play a
crucial role in reducing the dangers associated with the use of digital technology in financial
transactions by creating robust safeguards that can accommodate changes in practise due to
advancements in Fin-tech. According to Nurlaily et al. (2021), banks and other financial
institutions need strong defensive tactics to deal with the inevitable process disruptions brought
on by the development of new financial technologies. Among the moderating duties of an audit
committee are monitoring external financial reporting, enforcing internal control processes,
minimizing risk, and conducting internal and external audits (Magrane & Malthus, 2010).
In order to achieve economic growth and development, many developing nations have recently
shared their perspectives on Blade technology. During the 2008 global financial crisis, many
developing nations looked to update their monetary systems. Experts in the field agree on one
thing: Balance technology is crucial. Before the effects of climate change become apparent, the
poorest countries in the world have positioned themselves around Pakistan, India, and
Bangladesh. Because of this, the environment in these nations is suffering. There are a lot of
things that should have been taken into account while planning for Pakistan's future
development, but because Pakistan is not an industrialized country, it has a lot of environmental,
monetary, political, and social challenges. Many developing countries are on board with the idea
of exporting Blade technology to the West. Lack of public authority support and customer
fascination with energy ventures or emanation reduction exercises, and the problem of blocking
quick loans to places with normally excessive pollution and discharge, such as oil fields and coal
power stations, are other problems in agricultural nations. In addition, in agricultural nations,
there aren't enough early adopters of Blade Technology due to a lack of data on potential
applications (Shafique & Majeed, 2020).
According to Lewis and Weigert (2012), a trust is a multifaceted phenomenon that significantly
affects interactions between businesses. Factors like information organization, accessibility,
respectability of treatment, and reliability of distant connections all play a role in how well
received innovations in financial technology are (Bolaji, 2022). Trust fundamentally based on an
individual's belief that the platform on which they exchange data is secure, as described by
(Bolaji, 2022). Classes like trustworthiness and accessibility can have a significant impact on
trust convictions and goals, as stated by Le (2021) discovered that people are more likely to trust
an organization if they have reason to believe in its foundations; Alshaikh, Ahmad, Maynard,
and Chang (2014) found the same thing to be true for online environments. As shown by (Vance
et al., 2008), there are parts to data innovation mysteries; these parts, which affect framework
quality, are crucial to the idea of trust. Trust and ease of use are inextricably related, as
demonstrated by research (Bogeat-Triboulot et al., 2007). The dedication of portable exchange
and the simplicity of use are bolstering the trust swing in IT growth.
Fin-tech is on its way to becoming a phenomenon on a worldwide scale, thanks to the efforts of
thought leaders and scholars. The controllers are starting to take notice. The term "financial
technology" (or "fin-tech") is used to describe both the services and the associated methods that
are part of the financial sector that are made possible by technological advancements. The term
"financial technology," or "fin-tech," is used to refer to any innovation in the way businesses
interact with, and provide and receive, financial services. Though it has had an impact on
developed economies, it has been felt most strongly by developing nations like China and India
(Busoniu, Babuska, De Schutter, & Ernst, 2017).
1.3 Problem statement of the study:
A study found that 72% of businesses in Pakistan had no idea what financial technology (FinTech) was. It is difficult for Pakistan's financial technology sector to progress. Few financial
technology companies exist in Pakistan, and most of the country's investment in this sector goes
toward banking (Butt & Khan, 2019).
The convergence of numerous technologies has had a significant impact on the uptake of
financial technology. The amount of money being invest globally in financial technology is
surging. On the other hand, Fin-Tech in Pakistan is severely constrained. An investigation has
been launch in Pakistan in order to establish the worth of financial technology and identify the
barriers that the country faces when it comes to adopting the same technology.
Because of this, our study differs from others of its kind in a number of ways. Fin-tech adoption
examine in this study. Users' perceptions of the advantages and dangers of financial technology
were not taken into account in previous studies. In the same way, previous research has missed
the importance of user trust in the realm of financial technology. In order to assess if customers
were interested in Fin-tech adoption, we added the trust component into our research. This is
because a person considers the trust component when making judgments based on risk and
benefit analysis. There has been little attention paid to the Fin-tech industry's demand side in
earlier studies. Fin-tech users' adoption of new goods and services was the focus of this study,
rather than the suppliers. Even for financial technology companies, a conceptual framework
proposed in this study had positive policy consequences.
1.4 Research objectives:

To examine the EE, PE and SI have an impact on BI, and does BI mediate the effect EE,
PE, SI and FC has on Fin-Tech adoption.

To examine the how many people are adopt the Fin-Tech or if not why people don’t
adopt it.
1.5 Research questions:
There are following questions;
Q#1: Do Effort Expectancy, Performance Expectancy and Social Influence have an impact on
Behavioral Intention, and does Behavioral Intention mediate the effect Effort Expectancy,
Performance Expectancy, Social Influence and Facilitating Condition has on Fin-Tech adoption?
Age, gender, and educational attainment were treating as modifiers in the initial UTAUT model,
which was revising in 2012. According to the research, people's previous experiences with new
technology can also influence their views on their acceptability. Age and gender are other factors
that have a soothing effect on online behavior. Since experience is an antecedent, and age and
gender are moderators, we will focus on these factors. These factors will be taken into account as
well.
Q#2: How does people are adopting the Fin-Tech or if not, why people don’t adopt it?
1.6 Research Hypothesis:
H1: Performance Expectancy has significant impact on the Fin-Tech adoption in Pakistan.
H2: Effort Expectancy has significant impact on the Fin-Tech adoption in Pakistan.
H3: Social Influence has significant impact on the Fin-Tech adoption in Pakistan.
H4: Facilitating Condition has significant impact on the Fin-Tech adoption in Pakistan.
H5: Behavioral Intention mediates the relationship of Performance Expectancy, Effort Expectancy, Social
Influence and Facilitating Condition of Fin-tech adoption in Pakistan.
H6: Age moderates the relationship between Effort Expectancy and Behavioral Intentions to
adopt Fin-tech.
H7: Age moderates the relationship between Social Influence and Behavioral Intentions to adopt
Fin-tech.
H8: Age moderates the relationship between Facilitating Conditions and Behavioral Intentions to
adopt Fin-tech.
H9: Voluntariness of Use moderates the relationship between Social Influence and Behavioral
Intentions to adopt Fin-tech.
1.7 Scope of the study:

In order to better, understand Fin-Tech and Behavioral Intentions, as well as to raise
awareness of Fin-Tech in Pakistan.

In Pakistan, I am undertaking a study on financial technology and consumer behavior
that has been done all over the world.

It will support and reinforce the hypothesis that the adoption of Fin-Tech raises the
level of Behavioral Intent.

Fin-Tech adoption is also examined in this research.
1.8 Significance of the study:
The goal of this study is to find out what factors influence Pakistanis' decision to use fintech.
Pakistan has a dearth of financial technology firms. Pakistan's major capital cities, such as
Karachi, Lahore, and Islamabad, are the only places where these companies operate. Weak
economic development coupled with a scarcity of Fin-tech experts has led to underinvestment in
the sector. For the time being, local financial institutions cannot take advantage of Fin-tech to its
full potential.
Chapter: 2
2.1. Literature review:
Financial technology services have recently received a lot of attention, according to (Ryu, 2018).
Because of the enormous risks involved, some experts and practitioners are wary about Fin-tech
adoption, despite its potential to revolutionize the financial sector. We need to learn more about
the factors that influence people's willingness or reluctance to employ Fin-tech. Benefits and
risks of its adoption are considered, and a paradigm based on net valence is proposed. This
framework is theoretically rooted on theory of reasoned action. Based on empirical data from
244 Fin-tech users, this study first investigates whether perceived advantages and dangers
influence Fin-tech adoption intention.
For example, if the perceived benefits and hazards of Fin-tech adoption varies based on user
type, we investigate this. Financial technology adoption intentions are negatively affect by legal
risk, but convenience is positively affected. The differences between early and late adopters are
cause by a variety of reasons.
Customer decision-making processes modeled in this study, providing researchers with valuable
new insights. The benefits and risks that affect adoption decisions, and the extent to which they
do so, are illustrating by our findings. This study adds to our knowledge of the factors that
influence individuals' evaluation of potential gains and losses when choosing a choice.
Therefore, there is more candor and openness in the decision-making procedure. The advantages
and disadvantages of using financial technology vary from user to user. There are two main
factors that influence how quickly people adopt new services built on cutting-edge technologies:
the service's own traits, and those of the people who end up adopting them. Before the
anticipated integrative impacts of positive and negative factors on Fin-tech adoption intention
can occur, it is imperative that enterprises in the sector take into account the demographics of
their customers (Ryu, 2018).
This study aims to investigate the fin-tech concept, create a literature map, and discover
emerging trends and opportunities in the industry. To this end, SLR aims to characterize fin-tech
activity areas, proposes a classification for this literature, and draws attention to major themes
dealt with thus far in the sample articles, as well as highlighting fresh research questions in this
field for further inquiry. For this reason, it is important to conduct a systematic literature review
(SLR). Fin-tech companies, in brief, are those that provide novel financial services by
capitalizing on the widespread availability of the internet and the potential for automated data
processing. Subcategories of research such as network externalities block chain, security all
prominently featured in this study, and they represent the most delicate aspects of digital
transformation on a global scale. Literature focuses on financial services and developments,
addressing themes such as financial industry regulation, local legislation, or the global financial
system as a whole. Several factors in the operating environment of financial services
organizations pose a risk of financial loss, and this is the final topic we will cover (Mehrban et
al., 2020).
Financial technology is growing like wildfire because of all the attention it is receiving. New
jargon has arisen because of the industry's constant growth. The term "Fin-Tech" is one example
of such lingo. This term commonly used in the financial technology sector to refer to a wide
variety of operations. Typically, corporations and other organizations will use IT-based apps to
provide the necessary services to their customers. The concept encompasses a wide range of
delicate issues, such as safety, privacy, threats, and cyber-attacks. It's important to keep in mind
that various emerging technologies, like mobile embedded systems, mobile networks, mobile
cloud computing, big data, data analytics techniques, and the cloud, are necessary for the
development of Fin-Tech. Yet, before this emerging technology can gain widespread user
adoption, a number of security and privacy issues must be addressed (Mehrban et al., 2020).
Due to its quick growth in response to rapid technological change, the new Fin-Tech business is
attracting a flood of modern investors. E-wallets, Bit Coins, Peer-to-Peer (P2P) Crediting,
Mobile Banking, etc. are just a few of the many concepts available in today's industry. Many of
these tools are essentially ubiquitous at this point. There is no longer a need for traditional
financial institutions; people can borrow money in any amount from other Internet users through
specialized services, make purchases using their credit cards on mobile devices, and keep track
of their spending and income with a card, all from the comfort of their own homes. Users can
now get their cash from an ATM without having to make a credit arrangement, exchange
currency, or even travel to a bank. New digital currency is available for use in addition to the
Russian ruble while shopping online. These tools simplify life considerably (Damian & Manea,
2019).
Adopting financial technologies and illustrating the interplay between these two factors. By
providing theoretical and empirical evidence of the effects of perceived benefit and risk, this
research provides a significant contribution to the study of financial technology adoption. It
reveals the thought procedure behind consumer choices, providing researchers with novel
information. The outcomes illustrate the benefits and risks that contribute to the formation of
adoption intentions and the relative importance of each. This study adds to our knowledge of the
factors that influence individuals' evaluation of potential gains and losses when choosing a
choice. Therefore, there is more candour and openness in the decision-making procedure.
Benefits and Dangers of Financial Technology Adoption: A Framework for Understanding (Ryu,
2018).
There is a lot more regulatory uncertainty and strategic risk in the financial sector because of the
widespread adoption of financial technology. This research found that as the Fin-tech revolution
progresses, the number of people planning to adopt it decreases as a result of growing awareness
of the risks involved. Adoption rates are low because potential parents are nervous about the
risks. Auditors specializing in financial technology who employ effective risk-mitigation
practices can significantly lessen these risks. Methods for assessing the moderating effect of
auditors in risk management of financial technology. This research employs a number of
different quantitative methods. Data acquired through surveying 200 professionals working in
Pakistan's financial technology sector. Statistical analysis and hypothesis testing were performed
using Stata 14. The results show that with the emergence of the Fin-tech revolution, risks are
seen as increasing across the board (Sharma, Dwivedi, Metri, & Rana, 2020).
Pakistan is a promising market for fin-tech companies due to the growing popularity of social
media and online shopping among Pakistani customers. The State Bank of Pakistan also has
solid regulations in place, which have helped propel Pakistan's financial technology sector.
Restrictions are necessary, but they may be harmful to a developing sector.
Mobile banking, often known as M-banking, is a flexible kind of banking that utilizes mobile
phones to do a wide range of duties, such as reviewing account activity, sending SMS
notifications, providing card explanations, checking balances, and customizing recharge options
(Vinayagamoorthy & Sankar, 2012).
In order to stay relevant and serve as many customers as possible, banks need to regularly update
their technology. Mobile banking, for instance, gives people in outlying places round-the-clock
access to their funds. This paper shall refer to mobile financial services as "mobile financial
services" throughout. Vinayagamoorthy and Sankar (2012) To stay competitive and meet the
needs of as many clients as possible, financial institutions must invest in cutting-edge
technology. Customers in outlying areas may now do things like check their account balances
whenever they like thanks to mobile banking options. This research looked at the TAM
(Technology Acceptance Model), TPB (Theory Planned Behavior), and the IDT (Innovation
Diffusion Model) and found statistical significance in all 13 components, including perceived
usefulness as well as ease of use and individual originality. Through exploratory factor analysis,
we learned that it's not enough to only introduce M-commerce, and that we need instead focus on
bolstering attributes.
Perceived utility shown to be the most important factor, thus services should work to convince
their customers that they are essential to their busy, modern lifestyles. When consumers lack
confidence in a company, they are less inclined to employ its services. Based on these findings, it
is clear that service providers must priorities customer trust (Choi, Lee, & Williams, 2011).
Personal trustworthiness was also an early and important driver of the expansion of mobile
banking, but this factor has eroded in recent years. M-commerce transactions may only approve
if customers and sellers have complete faith in one another. This was found by a group of
researchers (Belanche, Casaló, & Flavián, 2019). According to studies by Belanche et al. (2019),
customers are more likely to adopt (or maintain) a mobile banking service if they have faith that
the company can effectively develop service delivery strategies and offer adequate protection
against fraud and privacy violations.
Adoption (or continued use) intentions increase when customers have faith that their mobile
banking provider can address their concerns about security and privacy. (Stewart & Jürjens,
2018).
Many obstacles must overcome by a startup in order to create a successful Fin-Tech product or
service. While developing a Fin-Tech product or service, startups confront a number of hurdles,
including: articulating a clear value proposition for intangible/service Fin-tech products;
understanding clients and the product/service market; and identifying and securing funding. Fintech startups have a hard time getting operating loans because of the high initial expenses
associated with securing intellectual property and proving the value of their intangible/service
offerings to potential clients. (Bömer & Schwienbacher, 2018). In order to attract early-stage
funding, Fin-Tech startups need to justify their concept to potential investors. This can be
challenging due to the complexity involved in establishing a profitable business model,
identifying a target market, and profiling potential customers and users. It has been found
(Williamson et al., 2019) that in order for Fin-Tech start-ups to be successful; they need skills in
the area of spotting innovation links.
In order to stay ahead of the competition, Fin-Tech organizations need dynamic screening,
auditing, and road planning, and forecasting tools. However, in order for incumbents to reap the
benefits of important technical innovations in Fin-Tech product and service offerings, they must
make the transition from inefficient old systems to cutting-edge new methods (Zhang et al.,
2019). Unfortunately, a shortage of skilled labor is another challenge facing new Fin-Tech
businesses. Due to the shift from human to digital consumer connection, Fin-Tech firms face a
serious challenge. (Bömer & Schwienbacher, 2018).
This study used behavioral intention as a dependent variable to decode the theory-driven actions
taken during the first stages of adopting Fin-tech because of its novel nature and the early stage
of its implementation. Future research should utilize additional approaches (such as field studies,
longitudinal analysis) to more closely observe and investigate the differences between early and
late adopters in the latter stages of Fin-tech adoption in order to increase measurement reliability.
Fourth, this research was constrained in its sample size because it focused on only four specific
applications of Fin-tech: mobile payment, mobile remittance, peer-to-peer lending, and crowd
funding. Our research may not generalize to other forms of Fin-tech (such as Bitcoin, Ethereum,
internet-based insurance, individual finance, equity financing, retained investment, and Fin-tech
software and hardware). Therefore, additional study is required to generalize these findings to
other Fin-tech offerings.
The importance of customers' worries about a product's safety has grown in studies of innovation
and consumption. Users' inherent aversion to taking any kind of risk is a major roadblock for
widespread adoption of Fin-tech. Perceived risk is defined in this study as "users' impression of
uncertainty and the probable negative implications for the use of Fin-tech." According to the
research on information security, people are less likely to use IT services if they believe there is
some sort of threat associated with doing so (Ryu, 2018). Perceived risks from a variety of
sources, as documented by Abramova and Böhme (2016), have a substantial and deleterious
effect on Bit coin adoption.
2.2 Performance Expectancy:
Perceived usefulness and perceived reliability are the other models' performance expectancy
constructs (TAM, and combined TAM-TPB), and this concept held up in both voluntary and
compulsory circumstances, defining "performance expectancy" as the degree to which an
individual believes that using the system will help him or her to attain gains in job performance.
Research shows that performance expectations have a bigger effect on men, especially younger
workers, but that the impact moderated by age and gender.
Performance Expectancy is a method used to foretell how individuals would feel if their
expectations are not met. The most surprising result was seeing among those who had low
expectations but performed well beyond them. In other words, the dissonance between the
person's expectations and their actual performance was reducing by removing the good
performance and modifying the correct responses. Those who did well on the test were more
likely to adjust their answers than those who had predicted a poor performance but nonetheless
fared poorly.
H1: Performance Expectancy has significant impact on the Fin-Tech adoption in Pakistan.
2.3 Effort expectancy:
You could conceive of "Effort Expectancy" as the level of comfort you feel when employing a
specific method in your daily life. This concept can also represent by variables in other models,
such as those evaluating users' perceptions of the ease with which a system can be utilized
(TAM). The idea was only relevant in context after training, and it was significant in both
voluntary and required training contexts. This was too expected given the body of research that
was conducted on the topic. It would appear that factors such as age, experience, and gender can
mitigate the influence that anticipating an effort has on a person's intentions regarding their
behavior. As a direct consequence of this, young women and experienced professionals are
making a greater impact in their respective industries than their younger contemporaries are at
the same point in their careers.
H2: Effort Expectancy has significant impact on the Fin-Tech adoption in Pakistan.
2.4 Social Influence:
The degree to which an individual believes that prominent others agree that they should utilize
the innovative strategy is an example of social influence. Preexisting models contain concepts
that are analogous to one another, such as subjective norms. Examining the two models led to the
discovery that the notion in question had a purpose that was comparable in both, being of
minimal significance when implemented voluntarily but taking on a role of significant
significance when compelled to do so. This effect appears to be substantial early in one's
experience and when incentives and punishments administered, as is the case in mandatory
situations when the effect is attributed to compliance. Additionally, this effect appears to be large
when rewards and punishments are applied simultaneously. It has hypothesized that gender, age,
experience, and voluntariness all have a moderating effect on the impact that social impacts have
on behavioral intentions. In contrast, social influence in voluntary contexts works by affecting
how people view a particular technological innovation. This can be done by influencing how
people vote (also known as internalization and identification).
H3: Social Influence has significant impact on the Fin-Tech adoption in Pakistan.
2.5 Facilitating conditions:
The term "Facilities Conditions" describes a user's confidence in the reliability of a company's
and a system is underlying hardware and software. This definition is comprised of the
individual's sense of agency and the facilitating environment. While a comparison of models
reveals parallels between voluntary and mandatory training contexts in the first period to
differentiate between one's goal and the framework provided, this effect wanes in the second.
According to the data, facilitating variables lose their significance and are at odds with the
DTPB/TPB enabling situations when both the performance expectation and the effort
expectation constructs are present. With increased technology literacy and the identification of
new resources, this impact is likely to grow. A worker's consumption is less likely to be affected
by good economic conditions the older they are, especially if they have more life experience.
H4: Facilitating Condition has significant impact on the Fin-Tech adoption in Pakistan.
2.6 Behavioral intention:
When a person has a stronger intention to carry out an action, there is a larger chance that they
will go on and do it. That is how the vast majority of individuals feel about the question of
whether or not they believe it is acceptable to take part in the behavior.
Scientists have revealed a number of models that help them in determining the important aspects
that determine the mindset and aims of the Fin-Tech sector. The scientists working in the field
discovered these models. In the following section, we are going to examine these specific
categories of models.
H5: Behavioral Intention mediates the relationship of Performance Expectancy, Effort
Expectancy, Social Influence and Facilitating Condition of Fin-tech adoption in Pakistan.
2.7 IDT, TAM, TRA, TPB and UTAUT Theories:
There are a number of models available to aid in the study of mobile banking, service adoption
behavior. These models incorporate a number of factors that assess a user's intent and attitude
toward mobile banking. These are the makes and models we're talking about: 1) Rational-action
theory (TRA) Acceptance Model for New Technology (TAM) Planned Behavior Theory (TPB)
4) Theory of Innovation Diffusion (IDT) 5) The Unified Theory of Technology Acceptance and
Use (UTUAT).
2.8 Theory of Reasoned Action (TRA)
Ajzen and Fishbein (1975) Proposed a model showing that a person's actual behavior can be
influenced by their behavioral goal, beliefs, and subjective standards about their behavior, as
shown in Figure 1. There are two kinds of standards: those set by the individual, and those set by
the community. When a person is unsure if they should execute a certain activity, they consider
the opinions of others. This is known as "perceiving others' opinions" and "a person's favorable
or negative attitude toward the performed conduct." This means that TRA can be used to explain
a person's real-life behavior.
Source: Fishbien and Ajze (1975)
Figure 01: Theory of Respond Action
2.9 Technology Acceptance Model (TAM)
In 1986, Fred Davis proposed the Technology Acceptance Model (TAM) (see Figure 02). As
defined by Davis (1986), "The degree to which an individual believes that using the particular
system would increase his or her performance" is defined as "the degree to which an individual
believes that using the particular system would be devoid of effort." According to him, a user's
willingness to adopt new technology or an information system is mostly determined by their
impressions of the system's usability and convenience of use.
Source: Davis 1986 p. 24
Figure 2: Technology Acceptance Model
2.10 Theory of Planned Behavior (TPB)
It's an addendum to TRA's Theory of Planned Behavior (see Figure 03), which incorporates one
more construct: Perceived Behavioral Control (PBC). Perceived behavioral control, in other
words, is the belief that a person has the ability to control a given behavior. PBC is influence by
control beliefs and perceive power or perceived facilitation. Control beliefs refer to the
assumption that one's actions are influence by the existence of external factors. Perceived
authority determines a person's capacity to operate a system.
Source:Ajzen 1991
Figure 03: Theory Of Planned Behavior
2.11 Unified Theory of Acceptance and Use of Technology Model (UTUAT)
Model:
Computer Use Model, PC Use Model, Innovation Diffusion Theory, and Social Cognitive
Theory were all used to design this model, which was developed by (Venkatesh, Morris, Davis,
& Davis, 2003). To what extent does a person believe that utilizing the system will enable him or
her to increase their job performance, as described by (Venkatesh et al., 2003). As described by
Social Influence, performance expectancy is the degree to which an individual believes that
using the system would increase their job performance. As a measure of how easy it is to use the
system, effort expectation defined. Venkatesh et al. (2003) defined Facilitating Settings as the
conditions that enable an individual to make progress in their work.
Performance
Expectancy
Effort Expectancy
Behavioral
Intention
Social Influence
Fin-tech
Adoption
Facilitating
Condition
Voluntarine
ss of use
Age
In this chapter, theories and models of technology adoption, notably the eight models that were
combine to form the UTAUT model, were discuses and examined. The review was able to
differentiate between two models. Supporters of TAM may be disappointed to see that such
models lack the comprehensiveness require to be consider acceptable or complete. The models
that include a wide range of components that contribute to acceptance behavior, on the other
hand, are more sophisticated and demanding (such as (Cheung, Chang, & Lai, 2000). Between
these two extremes, the UTAUT is able to account for a bigger proportion than any of its prior
models of usage intentions. This is due to its pared-down and comprehensive character.
H6: Age moderates the relationship between Effort Expectancy and Behavioral Intentions to
adopt Fin-tech.
H7: Age moderates the relationship between Social Influence and Behavioral Intentions to adopt
Fin-tech.
H8: Age moderates the relationship between Facilitating Conditions and Behavioral Intentions to
adopt Fin-tech.
H9: Voluntariness of Use moderates the relationship between Social Influence and Behavioral
Intentions to adopt Fin-tech.
Chapter 03
3.0. Research Methodology:
Data collection procedures are outlined in this section. Creating a research approach tailored to a
specific subject is a crucial part of being a competent researcher. Ultimately, the quality of a
research project may be gauged by looking at this part (Kallet, 2004). The research plan provides
us with genuine connections and crystal-clear justifications. When the methodology of the
experiment is well defined, it is carrying out. Therefore, this section needs to be carefully shaped
with sufficient information so that the audience may decide, if necessary, whether the study
being completed is reproducible or not, and whether or not to endorse the results and end.
Research methodology, as defined by (Abramova & Böhme, 2016), is "the science of
investigating how to conduct scientifically valid research." Using data, samples, and other forms
of analysis, research methods can reveal a way forward in a case. Research questions and
objectives are grounded in the methodology design (Gilbert, 2006). A competent researcher is
also aware of the efficacy, accuracy, and applicability of the methods used in the study. The
scientific method is a valued component of any good research approach (S. Rajasekar,
Philominathan, & Chinnathambi, 2013).
Experts embrace the inquiry to discover the reasons, impacts, and occasionally a reaction for
reasonable and non-legitimate difficulties, but ultimately, implementable results and proposals
are essential for dealing with new challenges that develop. Ethical principles are also critical at
every stage of the assessment process, and researchers must provide assurance of their presence
at every turn. As suggested by the framework, the two should be locked in because it aids in
ensuring the results of the assessment are consistent in quality and authenticity. Data consistency
is communicated by the fact that even after multiple iterations of an evaluation, the results
remain unchanged. Despite this, adopting procedures like those that they have been examining
for what they were supposed to measure is crucial to authenticity.
In this section, we take a closer look at the assessment's important methodological form points
and reasoning as well as the underlying justification applied model, data collecting, and testing.
3.1 Methodology:
The Unified Theory of Acceptance and Use of Technology Model are use in Pakistan to explore
the factors that influence the use and acceptance of Fin-tech in this chapter's research objectives.
Methods of research are discuses in this chapter, including the sort of study conducted, as well as
the population studied and the sample used.
3.2. Research Objective and Rationale:
Research investigates that the Fin-Tech adoption in Pakistan and addressed the gap in literature
by adding a factor (Fin-tech adoption). This study also investigates the Effort Expectancy,
Performance Expectancy and Social Influence have an impact on Behavioral Intention, and does
Behavioral Intention organizational culture and organizational structure affect the public service
performance in public sector of Pakistan. The importance of the study is to work in Pakistani
culture is way of Asian culture, which is different from western culture, and the cultural values
are strong in this region and Pakistani is one amongst all.
3.3. Role of Researcher:
This research is being carried out on the issue of Public service performance affect the public
sectors (Motorway Police) of Pakistan. This project is directing towards the better understanding
of public service performance in services industry. This study also investigates the relationship
between motorway police services in population and other life values as religiosity and
subjective well-being. The information, knowledge, and understanding of researcher about
public service performance and all other constructs and adoption of right kind of questionnaire
based on all observed and frequent variables in local and foreign studies.
3.4 Data Collection:
Gathering data is a necessary part of research. Several methods are used to get the required data
and information. Data gathered through either natural or artificial means. Because data should
only be, accessed ethically, ethical consideration is the most important step in the data collection
process. Ethical considerations were addressing since participants were adequately informed of
the study's goal and topic prior to the start of the investigation. This technique can be broken
down into two basic categories.
3.5. Qualitative and Quantitative Approach:
3.5.1. Qualitative Approach:
Qualitative and quantitative approaches to research are the most common. When it comes to
qualitative research, it focuses on qualitative phenomena like quality and is conducted in the
context of their natural environment (S. Rajasekar et al., 2013). In qualitative research, different
people will have different interpretations of the same findings Different researchers may offer
different interpretations of the construct depending on the context and perspective they are
working from. of individuals and the social environment in which they live. Qualitative research
uses ethnography, and narration, among other qualitative methods. Additionally, case studies can
be applied in a qualitative manner through the utilization of qualitative approaches (Dehghan,
Hashemi, & Ghatee, 2006). The process of coming up with a concept Qualitative research's
primary goal is to learn something new about the data.
3.5.2 Quantitative Approach:
As the name implies, quantitative studies focus on numerical processes that can be expressed as a
set of numbers. In order to create hypotheses and test them, and then to interpret the results,
quantitative research uses statistical methodologies (N. Rajasekar, Kumar, & Venugopalan,
2013). Focusing on "deductive reasoning, the norms of logic, and measurable qualities of human
experience" is central to this method (Graneheim & Lundman, 2004). It is known as hypothesis
testing research. The researcher claims that the purpose of this study is to assess and improve the
level of UTAUT knowledge among early adopters of financial technologies in Pakistan.
The research relies heavily on positivist qualitative techniques (Li, Easterby-Smith, & Bartunek,
2009). Scientific and methodical inquiry of quantitative properties is at the heart of our work.
The researcher in this study did the following to achieve her aims: creates possibilities for further
investigation builds a model amassing information produces measuring tools Take stock of the
outcome.
3.6. Research Strategy
To begin with, scientists need to determine appropriate standards for assessment. This technique zone is
an excellent tool for facilitating the essential response to preset investigative tendency. It ought to be
totally unbiased and based on empirical evidence. Approaches adopted during the execution of
exploration might be influenced by data variety systems and constraints such as those imposed by time,
money, resources, access, and other external considerations (Saunders, Lewis et al., 2007). This study is
primarily a conditions and sensible outcomes study, as we will dissect the potential explanations and their
assets in relation to our proposed study/model.
3.7 Primary Data Sources:
In general, there are two methods of information gathering, as mentioned by (Arbnor & Bjerke,
2008). One is used as the major source of information, while the other is used as a backup.
Information obtained directly from the source (known as "primary source" information) is more
trustworthy than data obtained from other sources. Data collected in the course of the study, be it
through interviews, questionnaires, or surveys. In this investigation, the researcher relied mostly
on the results of a questionnaire.
3.8 Population:
Researchers should include "all members, cases, and aspects of a population" in their definition
of population, as per Bull (2005). Before deciding on a sample size, the researcher must first
decide who the intended audience is and then define that audience (Wilson, 2010). There will be
a focus on financial technology in this study. As a result, the studies demographic comprise all
Pakistanis who have some knowledge of financial technology.
3.9 Sample and Sample Size:
A sample is a selection of individuals from a larger group, or population. An appropriate
definition of a sample is "a part of the population from which this study actually collects the
information and uses a sample to draw conclusions about the full population" (Moore, 2009).
When doing a study like this, researchers employ probability-sampling methods to determine
which samples to use for their analysis.
It is crucially vital to pick a sufficiently big number of participants for generating confidence in
the survey results and making these results representative. By using a 95% confidence level, we
may say that there is a 5% chance that the study's results will be different from the actual results,
which is sufficient for a sufficiently large sample size. A margin of error or confidence interval
of 95% is acceptable (Niles, Wishnok, & Tannenbaum, 2006). The current investigation
followed the standard practice of using a margin of error of 5%. Thanks to the convenience of a
self-administered survey, we anticipate a response rate of 75% in this investigation. The sample
size for this investigation was determined using a combination of a sample size table (Krejcie &
Morgan, 1970) and a sample size calculator (Siegle & Schuler, 2000). With a 95% confidence
level and 5% margin for error, a sample size of 700 is required to represent the entire population.
The aforementioned criteria were used to choose the appropriate sampling units in accordance
with the sample size, and 700 questionnaires were used in the final data analysis.
3.10 Sampling Techniques:
There are two main sampling techniques: probability and non-probability.
Researcher used a type of probability sampling called cluster sampling in this study. Probability
sampling is a sampling technique in which some members of the population have an equal
chance of being chosen or in which the probability of selection is precisely known beforehand.
And the Smart Pls software is used to analyze the data of this study.
3.11 Philosophy of Research.
This research strategy requires a sizable sample to be representative of the whole population.
Due to the way the research was set up, we know that our findings will be correct. It has become
clear that the theory of hypothesis testing is crucial to the field of modern research.
3.12 .Theoretical Framework
In rural areas, the framework sheds light on the steps necessary to launch a business and improve
output. More and more success feeds the cycle of progress, which in turn spurs further success,
which spurs further change, which spurs further strife as people strive to improve themselves and
their economic standing. According to recent studies as (Masnoon and Saeed, 2014) shows good
effect of microfinance. Through a detailed analysis and finding of the previous researches which
lead to conclusion given below:

experimental research on the microfinance impact shows its small and short livingness

unable to establish remarkable good impact denoting important loop holes in techniques
used data collection, respondents, selection.
3.13. Instrument and Scale
The research was conducted solely with the illiterate, and it made use of a questionnaire survey
format. In order to ensure that the research participants could understand the questionnaire, it
was originally developed in English and then translated into Urdu. The questionnaires were
modified to create the final product. The questionnaire was taking by the assessment of many
studies to control the questionnaire bias. The questionnaire consist of 7-points likert scale to
measure the impact of microcredit finance on the socio economic status of small
agriculturalist in Punjab ,Pakistan (Owais Shafique and Rana M. Naeem Khan, 2020) .The
questionnaire scale describes are under:
1-Strongly Disagree,
2- Moderately Disagree,
3- Slightly Disagree,
4- Neutral
5-Slightly Agree,
6-Moderately Agree,
7-Strongly Agree.
3.13.1. Pilot Testing
Eighty rural men and women were asked to fill out a survey after it was developed. The validity
of the data was determined through an evaluation of microfinance participants (46- Male
Participants and 34-Female Participants, from across the entire Bahawalpur region. For the
purpose of validating the effectiveness of the questionnaire through pilot testing. This
questionnaire passed all of its testing with flying coolers, and the appropriate adjustments were
made to ensure that the judging instrument remained objective and focused on its intended goal
throughout the process.
3.14. Technique of Data Collection
The research in this study was carried out with the aid of a questionnaire survey. The survey
technique worked well for gathering information from the clusters. In this survey method,
participants meet in person to fill out questionnaires. More people are likely to respond, and
that's a guarantee, thanks to this strategy.
3.15. Tool for Data Collection
In order to obtain information from respondents, a series of structured questionnaires were
created. The survey questions are all closed-ended. The most common technique of data
collection was a survey. Both microcredit finance participants and those who have never used the
service are encouraged to take part in the studies.
The study's participants and controls filled out different questionnaires to ensure accurate data
collection. The final questionnaires consisted of a set of questions designed to elicit responses
from respondents that would help draw meaningful conclusions from the question's statistical
data. (See Appendices A, B, and C)
3.16. Facet of Organization of the Questionnaire
The questionnaire was comprised on the following sections.
Section A:
I. Personal Information,
Section B:,
I. Knowledge Sharing Ability (KSA),
II. Financial and Legal Awareness (FLA).
3.17. Data Analysis
The data collected from interviews & questionnaire was assessed by the help of statistical tools
SPSS 21 and Smart PLS software version 3.0. This research inclined to use regression, analysis
of variance & structural equation modeling, very rarely used by researchers of the past. The data
was tested to judge the effectiveness of Fin-tech in Pakistan.
3.18. Reliability and Validity Analysis
(Noah Webster 1843.) Repeated testing using the same analysis, investigation, or evaluation
approach should produce consistent results, which is what is meant by reliability. When a theory,
finding, or evaluation can be shown to have a solid foundation and meaningful connections to the
real world, we say that it has broad validity.
3.18.1. Reliability Analysis
Consistency in achieving the same results is reliability. Cronbach's Alpha, according to Field,
offers reliable assessment services for items rated on the Likert scale. Data with an Alpha value
of 0.7 or higher are regarded reliable according to the reliability test, and items with an Alpha
value of 0.7 or higher should be considered consistent 1.
3.18.2. Validity Analysis
A notion, method, or strategy has validity if it can be reduced to a smaller, more manageable size
without losing any of its meaning or utility. However, a few specialists from the Department of
Management Science at the International University of Balochistan (IUB) in Pakistan were
consulted in order to determine the accuracy of the tool's design. Expert guidance was used
throughout questionnaire development, and data collection and analysis on questionnaire
components and variables were conducted to ensure the highest quality.
3.18.3. Normality Test
It can be analyzed with many tests to check the deviation along with skewness & kurtosis that
are proposed by Meyers et al. (2012). In case of the value of skewness & Kurtosis exists between
+/- 1 and +/- 3 . The data indicates the normally disseminated.
3.18.4. KMO and Bartlett Test
KMO experiments are generally an analysis
of the sampling capacity of all variables and for
each variable. The outcomes of KMO analysis creates a table within 0 and 1 that expressed as:
0.90s represent marvelous,
0.80s represent meritorious,
0.70s represent middling,
0.60s represent mediocre,
0 .50s represent miserable,
3.19. Correlation Analysis
According to Field (2013), since variables come from their respective sources, and hence are
unique, correlation is a useful tool for analyzing their relationship. The linear relationship
between two variables can be evaluated by using correlation. Or, to put it another way, a
thorough analysis of the phenomena of evaluating the link between variables.
The analysis of the correlation coefficient reveals the consistent association between the
variables. If the correlation coefficient is between -1 and +1, it is considered valid, while a value
of 0 indicates that there is no relationship between the variables being studied. Conversely, a
value of -1 or +1 represents a negative or positive absolute one-dimensional correlation between
variables.
3.20. Pearson’s Correlation Coefficient
Pearson's Correlation Coefficient, denoted by "r," is one of the most well-known correlation
approaches and is widely utilised in research. This weakens, invalidates, and unsuitably presents
conclusions if the data are regularly distributed, and it also has a detrimental effect on the
association between variables.
3.20.1. Evaluation of PLS-SEM Result:
The results of the factor analysis can be found in this section. According to the third chapter, the
information comes from a variety of different studies. The study assesses the trustworthiness and
accuracy of the data. Testing the outer model and the inner model by testing and screening of the
data follows analysis and verification of the constructs' reliability and validity (Joseph F. Hair et
al., 2013). In this theoretical model, we use Smart PLS 3.0 by (Henseler et al., 2014) was used to
test the relationship between constructs.
3.20.2. The Measurement Model:
A primary step in PLS-SEM analysis is the evaluation of the measurement model (the outer
model). In the outer model, we focus on the component measurement that defines the loading
and interrelationships of the various constructs (items). In other words, if you want to know if
your research question and your model's structure are correct and exact, you should look at the
model's exterior.
Two main keys that are used in PLS-SEM research to test the outer models reliability and
validity (N’dri and Kakinaka, 2020; Owais Shafique and Maria Habib, 2020).Whether or not this
is the case is contingent upon some criterion for evaluating the presence of a relationship
between constructs (inner model). The validity of an external model can be evaluated in a few
different ways: (1) by examining the items' internal consistency reliability, (2) by examining the
variables' convergent validity in relation to the individual constructs using the average variance
extracted (AVE), and (3) by examining the indicators' external loading and the Fornell-Larcker
criterion.
Cronbach’s Alpha 3.20.3.
Unlike CP in Cronbach's alpha, CR does not presume that the indicators will be loaded equally
on the various structures. The CR ranges from 0 to 1, and the minimum acceptable value is 0.60,
with values of 0.70 and higher being the norm. There is more confidence in a number between
0.70 and 0.90.
All items' CR and Cronbach's alpha were calculated in this study. Results from the CR and
Cronbach's alpha are shown in the table, indicating a cutoff value of 0.70. The dependability of
the measurement methodology is reflected in the CR values, which fall between 0.81 and 0.92.
3.20.4. AVE values
A value of 0.50 for the AVE indicates adequate Convergent Validity (CV). In other words,
Variables is latent because it adequately displays convergent validity and accounts for at least
50% of the variation among its components. In this investigation, CV was examined by looking
at AVE values. With AVE values falling between 0.52 and 0.72, convergent validity is
established.
3.20.5. Structural model:
To evaluate hypotheses 1–9 and present a complete picture of the findings, we conduct a
thorough model analysis of the structural model. In Smart PLS 3.0.0, the path coefficient sizes
were calculated using the PLS-SEM algorithm, and the relationship strength was validated using
the PLS-SEM bootstrapping procedure. Bootstrapping was utilised to generate a sample size of
700 based on the original number of cases.
3.20.6. PLS bootstrapping
To begin, the bootstrapping approach estimates the route model of a direct relationship between
the independent factors and the dependent variable, skipping the mediator variable. The PLSSEM algorithm and the bootstrapping procedure are used to calculate the route coefficients and tvalues in these models (Hair Jr. et al., 2013). In the second stage, the path model is estimated
with the mediator variable. The focus is on whether the independent variables and the mediator
and dependent variable relationship are significant.
Chapter 04
4.1 Demographics Analysis:
In the following table, gender respondents are giving to describe the demographic analysis. In
this table, we choose 700 respondents those respond to questionnaire. Out of 700, 465
respondents are male and 235 are females.
.
Demographics
Total 700
Percentage
Gender
Male
465
66.42
Female
235
33.57
<26
397
56.71
26-35
220
31.42
36-45
50
7.14
<45
30
4.28
3
0.42
10
1.42
FSc
115
16.42
Bachelor
267
38.14
Masters
230
32.85
M. Phil
60
8.57
6
0.85
12
1.71
Yes
357
51
No
343
49
Yes
372
53.14
No
328
46.85
Age
Prefer not to say
Education
Matric
PhD
Prefer not to say
Currently use Fin-tech Apps to Make Payments
Currently use Fin-tech Apps to Transfer Money
Measurement Model: 4.2.1
Outer loadings, CR, AVE, discriminate validity, and convergent validity are derived to evaluate
measurement models. It began with a check of the measurement model's convergent validity.
Evidence for this was found in CR and AVE factor loadings (Chung, Kim, Lee, & Kim, 2018).
Everything in Table 2 has a loading greater than the 0.6 threshold (Chin, 1998). The CR values
were higher than the recommended value of 0.7 (Hair et al., 2006), and the AVE values, which
represent the total amount of variance in the displays that can be attributed to the latent construct,
were higher than the recommended value of 0.5. (Hair, Halle, Terry-Humen, Lavelle, & Calkins,
2006).
When comparing the loadings across columns in Table 1, it is required that the loadings for each
indicator's primary construct are always greater than the loadings for any secondary constructs.
These findings support the use of the cross-loadings criterion as an indicator of discriminating
validity across all constructs.
4.2.2 Validity and Reliability for Constructs
Figure 1. PLS algorithm Interaction
Loading AVE
Behavioral Intention to Use
BIAFTA1
BIAFTA3
BIAFTA5
Effort Expectancy
EE1
EE3
Facilitating Conditions
FC1
FC2
FC3
FC4
Performance Expectancy
PE2
PE4
Social Influence
SC1
SC2
CR
Cronbach's Alpha
0.802
0.921
0.876
0.754
0.867
0.886
0.795
0.939
0.914
0.857
0.923
0.833
0.847
0.943
0.909
0.876
0.897
0.964
0.948
0.947
0.890
0.900
0.903
0.872
0.922
0.929
0.930
0.918
SC3
Figure 2: Bootstrapping
0.912
Table 5: Discriminant Validity (Heterotrait-Monotrait Ratio (HTMT)
BI
EE
FC
PE
SI
BI
EE
FC
PE
SI
0.867
0.850
0.896
0.852
0.812
0.885
0.873
0.866
0.857
0.827
Extracted average variance (AVE), Behavioral Intention (BI), Effort Expectation (EE),
Facilitating Conditions (FC), Performance Expectation and Social Influence (PE and SI).
Heterotrait-Monotrait Ratio of Correlation is an additional test of discrimination validity
(HTMT). For this condition to apply, HTMT values cannot exceed 0.90. All latent variables have
HTMT values below the cutoff value of 0.90, as shown in Table 5. This lends further credence to
the notion of selective validity.
Table 7: (Estimated Hypothesis Structural Testing)
Standard
Beta
T
Statistics
P
Values
Decisions
EE -> BI
0.214
3.746
0.000
Supported
FC -> BI
0.186
3.198
0.001
Supported
PE -> BI
0.303
5.268
0.000
Supported
SI -> BI
0.230
4.200
0.000
Supported
Moderating Effect AGE on EE -> BI
0.033
0.566
0.571
NonSupported
Moderating Effect AGE on FC -> BI
-0.080
1.621
0.106
NonSupported
Moderating Effect AGE on PE -> BI
0.115
2.119
0.035
Supported
Moderating Effect AGE on SI -> BI
-0.091
2.155
0.032
Supported
Moderating Effect Voluntariness of
use on SI -> BI
0.0312
2.154
0.321
NonSupported
Chapter 5
5.1. Discussion and Conclusion:
The observations, recommendations, and conclusions from the previous chapter are all summed
up here. This chapter provides a brief overview of the study's findings in light of the literature
review, study aims, research questions, and hypotheses. In this chapter, we explore the scope and
methodology restrictions that hampered the findings of this study, as well as the potential future
directions for similar studies. The chapter ends with a summary of the study's findings. Here, we
take stock of what we learned in the last chapter and how that relates to the overarching aims of
the research. This study looked at the connections between six factors: attitude toward effort,
attitude toward performance, social influence, enabling conditions, behavioral intentions, and
adoption of financial technology. The literature review presented in the preceding chapter
yielded a wide variety of research questions, ranging from H1 to H9, which test hypotheses
about the connection between different factors and Fin-Tech adoption.
5.2 Discussion:
In this study, age serves as a moderator between the dependent variable "behavioral intention"
and the independent variable "age." The connection between these factors is analyzed by the
researcher.
The moderating influence of age on the relationships between these four variables is also
investigated here (EE, PE, SI, and FC). The study found that whereas SI and FC did not have a
significant influence, EE and PE did.
When it comes to the spread of BI and Fin-Tech, the H1 Hypothesis holds true: EE makes a big
difference. According to H2, FC has a highly significant beneficial effect on the spread of BI to
Fin-tech. According to Hypothesis 3, PE has a highly significant favorable effect on businesses'
uptake of BI and Fin-Tech. According to Hypothesis 4, SI has a highly significant beneficial
effect on the spread of BI to Fin-Tech. The H5 hypothesis states that the Moderating Effect of
Age on Adoption of Emerging, Business, and Financial Technology is Positive but Not
Significant. The H6 hypothesis states that the moderating effect of age on the uptake of FC, BI,
and Fin-tech is negative and minor. Age has a positive and significant effect on the uptake of PE,
BI, and Fin-Tech (H7). Adoption of SI, BI, and Fin-tech is moderated by age, according to the
H8 hypothesis, and this has a negative, significant influence. In addition, Voluntariness of Use
moderates the adoption of SI, BI, and Fin-Tech; according to hypothesis, H9 has positive
insignificance relationship. In this study, based on UTAUT theory, we empirically examined the
most critical factors—including effort expectation, performance expectancy, social influence,
conducive settings, and behavioral intentions—that boosted the desire to adopt the Fin-Tech.
5.3 Conclusion:
As promised in the introduction, the findings presented here are a direct result of the aims of the
investigation. Finding out what elements would determine Fin-Tech uptake in the Bahawalpur
area was the primary objective of this research. The results of this study suggest that the
reliability of the aid sector improves as individual components take on more responsibility.
Thanks to this breakthrough, more details about the crucial elements that aid the public and
industries in Bahawalpur in adopting Fin-Tech will become available.
Adoption of Fin-Tech in Bahawalpur is significantly influenced by Behavioral Intention, which
is mediated by the Independent Variables (Personal Experience, Social Influence, Financial
Capability), and moderated by Age and Voluntariness of Use. These results originate in the city
of Bahawalpur's successful manufacturing sector.
Therefore, the general public and the industries that employ it are in a good position to benefit
greatly from the widespread adoption of fin-tech. Independent variables were found to have a
moderate effect on both behavioral intent and the uptake of Fin-Tech. Businesses and individuals
in Bahawalpur can save money and time by taking advantage of Fin-Tech.
Managers in the fin-tech industry need to be aware of the variations between aspects of danger,
which vary with the various users. The ability to make this distinction can be quite useful for
Fin-Tech companies. Must learn about the specifics of each Fin-tech customer in order to better
serve them while satisfying the needs of the buyer and increasing the likelihood of repeat
business service. Our research shows that early adopters are most concerned with the
financial transactions, while those that adopt it later are looking to maximize their financial
gain. As an added complication, early adopters worry about legal repercussions, while late
adopters do not see the rush. Security concerns should be given the most weight. So, Fin-Tech
firms need to while weighing the pros and downsides of Fin-tech, companies should think about
the preferences of their users. Application sees actualization in the marketplace. Our research
will aid Fin-tech firms in making informed investment and investment of sufficient resources
into creating and offering Fin-tech.
This research shows that the likelihood of continuing to use Fin-tech is influencing more by the
perceived advantage than by the perceived danger. Despite the significant risk considerations
being present in the sector, the results also suggested that people were willing to continue using
Fin-tech generally. It appears that despite the Lending Club crisis, users still see significant
benefits in P2P lending and perceive minimal risks. The optimistic view of P2P lending and its
interpretation seem to be in line with the 2017 Bitcoin investing enthusiasm. Strongly positive
user perceptions of Fin-tech that are not accompanied with risk perceptions can be disastrous for
the short-term financial well-being of Fin-tech users and the long-term viability of Fin-tech
business ecosystems. Because of this, our results require caution when being interpreted. Users'
decision-making processes and the Fin-tech industry as a whole will benefit from users'
awareness of both the opportunities and threats presented by Fin-tech. Therefore, improving the
security of financial transactions is more crucial to the long-term success of Fin-tech companies
than is delivering benefits to customers in the short run. Finding risk-mitigation methods that can
help fin-tech firms gain the trust of their customers is a priority.
This study was conducted in Pakistan, however because attitudes about new technologies vary
from country to country, the findings may not generalize. The authors of this study hypothesize
that the findings might be different if the investigation were conducted in Pakistan, where
adoption of Fin-Tech looks to be significantly lower than in Western countries. Accordingly, our
research is restricted to the country of Pakistan and the specific city of Bahawalpur.
5.4 Limitation of the study
Current research has provided a thorough plan for the widespread use of Fin-Tech in
Bahawalpur, Pakistan. The four-factor framework of this investigation includes the mediator
Behavioral Intention and the moderator Age, both of which affect the adoption of Fin-tech. The
residents and businesses of the Bahawalpur area are all impacted. It is no surprise that the
industrial sector, today's most vital operating sector, is fiercely competitive in the Bahawalpur
area.
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Appendices
Questionnaire Questions:
I currently use Fin-tech Apps to make payments.
I currently use Fin-tech Apps to transfer money.
Please mark the Fin-tech Apps that you have used in the past/ are currently using / intend to use Your Online
Banking Service (Bank Transfers)
Amazon Pay
Apple Pay
Facebook Pay
PayPal
Paytm
Google Pay Stripe
Square
Adyen
SamsungPay
M-Pesa
WorldRemit
AMEX Express
AliPay
Transfer Wise
WeChat Wallet
Baidu Wallet
Easypasa
Jazz Cash
Other:
Using Fin-tech apps would increase the quality or output of banking.
Strongly Disagree
1234567
Strongly Agree
Q#1: The adoption of Fin-tech apps would save my time.
Q#2: The adoption of Fin-tech apps would ultimately increase my productivity and efficiency.
Q#3: The adoption of Fin-tech apps would save me money (travel, waiting, banking costs).
Q#4: Using Fin-tech apps enhances my image among friends.
Q#5: Using Fin-tech apps is very convenient for me.
Q#6: Perceived Ease of Use
Q#7: I think that learning to use Fin-tech apps is/would be easy.
Q#8: I think that interaction with Fin-tech apps does not require a lot of mental effort.
Q#9: I think it is easy to use Fin-tech apps to accomplish my banking tasks.
Q#10: My interaction with the Fin-tech apps is clear and understandable.
Q#11: Ease of Use
Q#12: I think everyone perceives Fin-tech Apps as easy-to-use Apps.
Q#13: I can quickly become skillful at using Fin-tech Apps without any help due to their intuitive design.
Q#14: Attitude towards Use
Q#15: I feel using Fin-tech apps is a wise idea.
Q#16: I feel using Fin-tech apps is a good idea.
Q#17: I like the idea of using Fin-tech apps.
Q#18: Using Fin-tech apps is an exciting idea.
Q#19: Behavioral Intension to Adopt Green Banking
Q#20: I intend to use/continue using Fin-tech apps in the future.
Q#21: I prefer to use Fin-tech apps.
Q#22: I believe it is worthwhile for me to use Fin-tech apps
Q#23: I see myself using Fin-tech apps for handling my banking transactions.
Q#24: I would use Fin-tech apps for my banking needs.
Q#25: My intention to continue using Fin-tech Apps is directly related to their performance.
Q#26: Using Fin-tech apps makes the provision of banking services efficient.
Q#27: Using Fin-tech apps can save time in providing banking services.
Q#28: Using Fin-tech apps can make the provision of banking services more convenient.
Q#29: Using Fin-tech apps can be more useful in managing my finances..
Q#30: Effort Expectancy.
Q#31: Learning to use Fin-tech apps is easy for me.
Q#32: Becoming skillful at using Fin-tech apps is easy for me.
Q#33: Becoming skillful at using Fin-tech apps is easy for me.
Q#34: Overall, I think Fin-tech apps is easy to use.
Q#35: Social competition.
Q#36: People who are important to me think that I should use Fin-tech apps.
Q#37: People who are familiar with me think that I should use Fin-tech apps.
Q#38: People who influence my behavior think that I should use Fin-tech apps.
Q#39: Most people surrounding me use Fin-tech apps.
Q#40: Environmental concerns.
Q#41: I believe Fin-tech apps will lead to better care for the environment.
Q#42: I believe Fin-tech apps is a sustainable environmental practice by banks.
Q#43: I believe Fin-tech apps will help to deal with environmental vulnerabilities.
Q#44: I believe Fin-tech apps to be a socially responsible activity of the banks.
Q#45: I think that my financial information is secure when I use Fin-tech apps.
Q#46: I perceive that using Fin-tech apps has more benefits than risks.
Q#47: I think it is safe to use Fin-tech apps due to government regulations.
Q#48: I rather use Fin-tech Apps than traditional channels (e.g.offline Banking).
Q#49: Transaction Efficiency
Q#50: I am certain that transactions through Fin-tech Apps will be applied successfully.
Q#51: Using Fin-tech Apps reduces my overall costs when making a transaction (time saving, moneyefficient, etc).
Q#52: The efficiency of Fin-tech Apps relies heavily on the quality of the Internet connection.
Q#53: Sustainability Purposes.
Q#54: One of the main reasons why I use/intend to use Fin-tech Apps is linked to an attempt to help reduce
paper, gas or my overall carbon footprint.
Q#55: I would be willing to pay a fee per transaction if this fee is used to promote financial inclusion or to
contribute to socio-economic progress in underdeveloped areas of the world.
Q#56: I would be willing to pay a reasonable fee as a contribution to sustainability purposes.
Q#57: Facilitating Conditions’
Q#58: Using Fin-tech apps suits my living environment.
Q#59: Using Fin-tech apps fits into my working style.
Q#60: Using Fin-tech apps is compatible with my life.
Q#61: Help is available if I have problems in using Fin-tech apps.
Q#62: Consumer Innovativeness.
Q#63: In general, I am among the last in my circle of friends to buy a new innovative product when it is
launched.
Q#64: If I heard that a new innovative product was launched, I would be interested enough to buy it.
Q#65: Compared to my friends, I own few new innovative product.
Q#66: I will buy a new innovative product even if it has no reviews.
Q#67: In general, I am among the last in my circle of friends to know that a new innovative product was
launched.
Q#68: I know about new innovative products before other people do.
Q#69: Most people who are important to me would think that using Fin-tech apps is a wise idea.
Q#70: Most people who are important to me would think I should use Fin-tech apps.
Q#71: My family who are important to me would think that using Fin-tech apps is a wise idea.
Q#72: My family who are important to me would think that using Fin-tech apps is a good idea.
Q#73: My family who are important to me would think I should use Fin-tech apps.
Q#74: Self-efficacy.
Q#75: I will be able to achieve most of the goals that I have set for myself.
Q#76: When facing difficult tasks, I am certain that I will accomplish them
Q#77: In general, I think that I can obtain outcomes that are important to me.
Q#78: I believe I can succeed at almost any endeavor to which I set my mind.
Q#79: I will able to successfully overcome many challenges.
Q#80: I am confident that I can perform effectively on many different tasks.
Q#81: Compared to other people, I can do most tasks very well.
Q#82: Even when things are tough, I can perform quite well.
Q#83: Attitude towards behavior.
Q#84: I feel using Fin-tech apps is a wise idea.
Q#85: I feel using Fin-tech apps is a good idea.
Q#86: Perceived Behavioral Control.
Q#87: I would be able to operate Fin-tech apps.
Q#88: I have the resources to use Fin-tech apps.
Q#89: I have the knowledge to use Fin-tech apps.
Q#90: I have the ability to use Fin-tech apps.