This document is an independent research study report submitted by Gheethu Maria Joy to fulfill the requirements for a Master of Business Administration degree from Cochin University of Science and Technology. The report examines the effectiveness of recruitment apps among potential job applicants who are students at selected colleges in Kerala. The study was conducted under the guidance of Dr. Sam Thomas. The report includes sections on the declaration, acknowledgements, table of contents, executive summary, literature review, research methodology, data analysis, findings, conclusions, and references.

1. INDEPENDENT RESEARCH STUDY REPORT ON
“To Analyze the Effectiveness of Recruitment
Apps among Potential Job Aspirants: A Study
among Students of Selected Colleges in Kerala”
Submitted in Partial Fulfillment of the Requirements for the
Award of the Degree of
Master of Business Administration
Of
Cochin University of Science and Technology, Kochi
By
Ms. Gheethu Maria Joy
Under the Guidance of
Dr. Sam Thomas
Assistant Professor
SCHOOL OF MANAGEMENT STUDIES
COCHIN UNIVERSITY OF SCIENCE AND TECHNOLOGY
MARCH 2016

2. DECLARATION
I, Gheethu Maria Joy, do hereby declare that this report is a bonafide
record of the Independent Research Study done by me during the academic
year 2015- 2016, in partial fulfillment of the requirements for the award of the
Degree of Master of Business Administration of the Cochin University of
Science and Technology, Kochi, India.
This work has not been under taken or submitted elsewhere in
connection with any other academic course.
Place: Kochi - 22
Date:
Gheethu Maria Joy

3. ACKNOWLEDGEMENT
First of all I thank Almighty God for his mercy and love which kept me
in good health and sound mind and helped me to complete the independent
research study successfully.
I extend my sincere gratitude to Dr. Sam Thomas, Professor School of
Management Studies - CUSAT, for his expert guidance and assistance.
I express my gratitude to Prof. (Dr.) Moli P. Koshy, Director School of
Management Studies CUSAT, for her guidance and leadership which
motivated and fueled my ambition towards completing this project.
I express my sincere gratitude to all of my respondents. Without them
this research would not have been completed.
I sincerely thank my relatives and friends for their encouragement and
support during the study.
Last but not the least I express my gratitude to everyone who directly
and indirectly helped me for completing the research study in time and thereby
making it a success.
Gheethu Maria Joy

4. CERTIFICATE
This is to certify that the project titled “To Analyze the Effectiveness of
Recruitment Apps among Potential Job Aspirants: A Study among Students
of Selected Colleges in Kerala” has been prepared by Ms. Gheethu Maria Joy,
School of Management Studies, CUSAT, Kochi – India, in partial fulfillment of
the award of the degree of MBA from Cochin University of Science and
Technology, under my guidance and supervision.
Prof. (Dr.) Moli P. Koshy Dr. Sam Thomas
Director Project Guide
Place: Kochi-22
Date: 22 - 04 - 2016

5. Table of Contents
Ch.
No.
Title Page
No.
Executive Summary 1
1 Introduction
Introduction to the Study
Problem Statement
Objective of the Study
2
4
4
2 Literature Review
Technology Acceptance Model (TAM)
Perceived Ease of Use (PEOU)
Perceived Usefulness (PU)
Relationship between PEOU and PU
Relationship between Attitude and Intention
Influence of external Variables
Mobile Apply Is Sinking the Recruitment Process
Best Way to Create a Great Mobile Recruiting Experience
6
7
8
9
10
11
12
13
3 Research Methodology and Design
Research Model and Hypotheses
Participants
Sampling Technique
Data Collection Methodology
Data Analysis Methods
15
16
16
16
17
4 Data Analysis and Interpretation
Respondent Demographics
App Usage Rate
Response of those who have used recruitment apps
Response of those who have not used recruitment apps
Reliability and Factor Analysis
Correlation Analysis
Structural Equation Modelling (SEM)
Hypotheses Testing
Influence of gender in Behavioral intension to use of recruitment
apps
Influence of age group in Behavioral intension to use of
recruitment apps
18
21
22
25
31
32
33
38
40
41
5 Limitations 42
6 Findings and Suggestions 43
7 Conclusions 46
8 References 47
9 Appendix 48

6. 1
Executive Summary
Rapid advancements of internet and smart phone has caused made recruitment
process easy. A click may enable professionals to apply to jobs and take part in recruitment
process they were waiting for. Instant notifications could also be received via use of apps
related to vacancies and recent job openings. Since people often carry their phones with them
and a very few professionals are without smartphones nowadays, it has been easy for
recruiters to find a best fit candidate for their job as well as professionals to take part in the
recruitment process of their dream company. This study examines how effective users feel
about recruitment through apps and the extent to which this has been made a reality.
This study also gets into the problems users face with regard to recruitment through
apps and invites suggestion’s from users to improve the company’s recruitment app. User’s
acceptance of the technology of recruitment through apps has been analysed with the help of
TAM (Technology Acceptance Model). The study also enquires the reasons as to why there
has been reluctance towards recruitment through apps and how the non - user group be
attracted more towards recruitment process via apps.

7. 2
Chapter 1: Introduction
1.1 Introduction to the Study
Apps are evolving the recruitment process, and along with it, the job apply.
Today, job seekers expect to view jobs seamlessly on their mobile devices and apply
to them quickly and easily. So have all companies' career sites jumped on the mobile-
friendly bandwagon? Not by a long shot. “There are all kinds of excuses why
companies aren’t doing mobile recruiting, but it’s a must to remain competitive,” says
Michael Marlatt, executive recruiting partner at Cognizant Technology Solutions and
founder of MREC, an annual mobile recruiting conference.
Job seekers are voting with their mobiles. According to a Pew Internet study,
43 percent of smartphone owners used their mobile to look up information about jobs.
The study points to an even more urgent message for employers that have taken it
slow with mobile: 18 percent of smartphone users submitted a job apply via mobile.
Adding to the urgency, Google announced that, beginning in late April 2015,
they will penalize web sites that fail the search giant's mobile responsive test. The
result will downgrade these sites' placement in search results served to mobile
devices. “Some estimates say 81 percent of company sites will be affected by
Google’s new algorithm,” says Marlatt, “If you care about your ranking, and want to
be seen as innovative, you want to be mobile-ready."
So if your company hasn't revamped its mobile recruiting process recently, it's time.
Here are some pointers.
Complexity kills candidate interest. Pare down your career site to bare
essentials. “Many job seekers fail to complete the job apply because of its
complexity,” says Amy Hughes, director of client adoption at Monster. “An average
of 60 to 70 percent of seekers applying to Monster jobs through company career sites
will never complete the apply process.”
Assess candidates’ priorities. Ask recent hires which elements of your career
site attracted them and which didn't. “Think about what’s going to be important to
users,”

8. 3
says Marlatt. “Make the things that most people want as easy as possible to find. You
need to minimize fluff.”
In 2015, mobile job search is an expected convenience. Cater to the lifestyles
of your ideal candidates. “If I’m waiting to pick up my daughter from a music lesson
and I see all these jobs on my smartphone, I want to be able to apply from my phone,”
says Barry Wu, a senior product manager at Monster.
Test-drive your career site on your phone. Play the part of the mobile seeker.
“The first thing companies need to do is assess their sites’ mobile friendliness,” says
Marlatt. “Go and navigate the career site from a mobile device, a smartphone or tablet.
How does it render? What does the candidate experience from the first point of entry?
What’s it like to navigate?”
Ensure that your site is mobile-responsive. Put your site through Google's test for
mobile-responsiveness. “If you realize you’re not mobile friendly, you need to
consider a mobile-responsive design,” says Marlatt. “It may be too much for IBM to
rebuild their site from the ground up for mobile – that’s a huge undertaking. The
workaround is to have a redirect to a simplified mobile site.”
The best sites respond to the mobile device in the user's hand. After you've
made your career site at least minimally mobile-responsive, refine it for all the devices
used by your target candidates. “Apply with Monster, a one-button apply solution, is
smart enough to determine what’s the best experience for the particular mobile device
that the user is on," says Wu.
Consider the nothing-but-mobile apply. Can you decide which applicants to screen
with a phone interview, based solely on their input via mobile? Make this your next
goal. “People are doing quick searches on their phones, just to see what jobs are new
since yesterday,” says Wu. “If you put the whole apply on the phone, candidates
won’t be forced to set a task to complete the process on a laptop or desktop."
On the flip side, avoid trying to fully vet the candidate from her mobile
apply. Don't front-load your application workflow with processes that can wait.

9. 4
“Details like background checks often can be covered during the interview process,”
says Hughes.
If you neglect mobile, you may miss out on diversity. Finally, keep in mind
that if your career site turns off mobile users, your workforce demographics may
suffer. “Hispanics, African-Americans and women are the heaviest users of mobile
technology," says Marlatt. "So when you think about diversity recruiting, mobile is
key.”
1.2 Problem Statement
Nowadays there are apps for almost everything. Understanding the benefits of apps
and its usage rate companies have introduced apps in recruitment procedure, in order
to identify potential candidates those can best fit their job profile. This study aims in
finding out user’s perspective about the recruitment process via apps.
1.3 Objective of the Study
1. To determine the effectiveness of recruitment apps in recruitment process.
2. To determine the usage rate of recruitment apps.
3. To determine reasons as to why there are reluctances, if any, among users to
the recruitment process via apps
4. To find out whether investing in apps would be beneficial for the company
with respect to finding out the best fit candidate for their company.

10. 5
Chapter 2: Literature Review
In 1989, Davis (1989) proposed the technology acceptance model (TAM) to
explain and predict user’s behaviour to use a technological innovation, specifically in
user acceptance of an information technology. TAM is originally an extension of
Theory Reasoned Action (TRA) (Fishbein and Ajzen, 1975), a psychological theory
that seeks to explain people’s actions by identifying the causal connections between
various components: beliefs, attitudes, intentions and behaviours. Unlike the TRA,
TAM is built on two primary variables —- independent variables which includes
perceived usefulness (PU) and Perceived ease-of-use (PEOU) and the dependent
variable Attitude towards using (AT). Davis (1989) defined perceived usefulness as
―the degree to which a person believes that using a particular system would enhance
his or her job performance,‖ and defined perceived ease of use as, ―the degree to
which a person believes that using a particular system would be free of effort.‖
Furthermore, Davis (1993) theorized that actual information system usage is
determined by behavioural intention, and the intention is jointly determined by the
users’ attitude toward using the system and perceived usefulness.
In previous studies, TAM has been widely used by information technology
researchers to gain a better understanding of information technology(IT) adoption and
its usage in organisations (Chismar and Wiley-Patton,2002). However, Legris et al.
(2003) argued that it is imperfect as all TAM relationships are not borne out in all
studies - there remains a wide variation in the predicted effects in various studies with
different types of users and systems. This study by using Technology Acceptance
Model (TAM) aims to investigate issues related to perceptions, intentions and
attitudes towards using apps for recruitment purpose.

11. 6
2.1 Technology Acceptance Model (TAM)
Based on established pertinent theoretical foundations and literature, a research
model has been employed to investigate the technology acceptance factors that
influence the adoption of recruitment apps. The TAM (Davis, 1989), with its basis in
the TRA (Ajzen, 1991; Fishbein & Ajzen, 1975) has emerged as a powerful model in
investigating the acceptance and use of information technology; its relationship to
TRA has been discussed extensively in the literature (Davis, 1989; Keil, et al., 1995;
Mitchell & Greatorex, 1993; Roger, 1995) and need not be elaborated here. The
choice for using TAM as a research model to explain respondents recruitment app
adoption is attributed to its consistent capability to explain a substantial portion of
variances between behavioural intention and actual behaviours derived mainly from
research into the purchase of technology related products (Bobbit & Dabholkar, 2001;
Goldsmith, 2002; Grabner-Krauter & Kaluscha, 2003; Hanque, et al., 2006; King &
He, 2006). The TAM postulates that the perceptions or beliefs about the innovation
are instrumental in the development of attitudes that will eventually result in system
utilization behaviour (Davis, 1989). It also posits that the actual system use is
determined by each user’s behavioural intention to use, which is in turn influenced by
each user’s attitudes towards use. Finally, the attitude is directly affected by the
usefulness and ease of use of the system.
Although this model was originally conceived to model the adoption of
information systems in the workplace (Davis, 1989), scholars in the area of consumer
behaviour have identified two specific dimensions which are relevant to use of
recruitment apps: perceived ease of use and perceived usefulness (Gefen, et al., 2003;
Huang, 2008).

12. 7
2.2 Perceived Ease of Use (PEOU)
According to TAM, PEOU is a major determinant that affects acceptance of a
particular technology (Davis, et al., 1989). PEOU is defined as the concentration of
physical and mental efforts that a user expects to receive when considering the use of
technology i.e. the degree to which a particular technological system would be free
from effort (Davis, 1989). According to Buton-Jones and Hubona (2005), the ease of
learning and becoming skilful at using pervasive technologies, including technologies
and interfaces on online shopping sites, were concluded as valid determinants as to
what makes a technology easy to use. The work of Selamat et al. (2009) further added
that a technology which is perceived to be easier to use than another is more likely to
be accepted by users whereas the more complex a technology is perceived to be, the
slower will be its rate of adoption. This is supported by Teo (2001) as the study
concluded that a system which is easy to use often requires less effort on the part of
users and thereby increases the likelihood of adoption and usage of a particular
technology. Other scholars have also found that the perceived ease of use had a
positive influence on consumers’ attitude in using the Internet for recruitment purpose
(Bisdee, 2007; Yulihasri & Daud, 2011). This is consistent with the work of Childers
et al. (2001) which argued that online recruiters which are able to provide recruitment
apps which are clear and understandable, with less mental effort requirement, and
allow professional’s to use recruitment apps the way they want results in ease of use
perceptions in professional’s minds with favourable attitudinal attachment to online
recruiters which are able to do so.
Accordingly, the study proposes that:
―Perceived ease of use positively affects attitudes towards recruitment apps.‖

13. 8
2.3 Perceived Usefulness (PU)
PU is another major determinant of attitude toward use in the TAM model
(Davis, et al., 1989). PU is defined as the degree to which the user believes that the
technology will enhance the performance of an activity (Davis, 1989). According to
McCloskey (2004), the ability to improve recruitment apps performance, recruitment
apps productivity, and most importantly, accomplishing recruitment apps goals, were
concluded as valid determinants as to what makes professional’s recruitment activity a
success. This is in line with the findings of Barkhi et al. (2008) as their study suggests
that professional’s will develop favourable attitudes toward recruitment apps that they
believe to provide sufficient benefits or attributes toward a solution and negative
attitudes toward those that are inadequate. Given this scenario, Kim et al. (2003)
argued that recruitment apps which provide functions which aid professional’s in
making better decisions will be perceived as useful. The same logic was observed in
the work of Bisdee (2007) as recruitment apps which are able to provide useful
services to professional’s and services which are not available through traditional
recruitment methods will be perceived as useful by consumers, and thus leads to the
development of favourable attitudes toward recruitment apps. This notion is supported
by Childers et al. (2001) whereby their findings suggest that professionals which had
favourable attitudes toward recruitment apps were found to perceive recruiters as
being useful as recruiters were able to enhance their recruitment apps productivity,
effectiveness and ability.
Hence, it is expected that:
―Perceived usefulness positively affects attitudes towards using recruitment apps.‖

14. 9
2.4 Relationship between PEOU and PU
Perceived ease of use has been empirically verified by many studies as a
predictor of perceived usefulness (King & He, 2006). When all other factors are equal,
users are likely to consider a technology useful when they perceive it as easy to use
(Bruner & Kumar, 2005). Nonetheless, the relationship remains contradictory
(Aladwani, 2002). The work of Gefen and Straub (1997) concluded that the
relationship was not significant in predicting e-mail acceptance as a technology, while
more recent researches (e.g. Jantan, et al., 2001; Shyu &
Huang, 2011) proved otherwise. However, in existing studies around the area of
electronic commerce, both are surmised to be closely linked as Ramayah and Ignatius
(2005) argued that consumers who perceive that recruitment apps is effortless should
in turn develop a tendency to perceive it as useful. The rationale behind such a
phenomenon is due to the fact that consumers would inherently try to mould his or her
perception of recruitment apps based on his or her own experiences in engaging in
recruitment process via apps and the ease in which the shopping activity was
executed. This is in line with the work of Heijden (2000) which suggests that the
easier it is for professional’s to use recruitment apps, the more useful recruitment apps
will be perceived by professional’s.
Therefore, this study anticipates that:
―Perceived ease of use positively affects perceived usefulness of recruitment apps.‖

15. 10
2.5 Relationship between Attitude and Intention
Attitude has long been identified as a cause of intention (Suki & Ramayah,
2010). Fishbein and Ajzen (1975) have categorized attitude into two distinctive
constructs: the first refers to attitude toward the object while the second refers to
attitude toward the behaviour. While the former is referred to as a person’s evaluation
of a specified object, the latter points to a person’s evaluation of a specified behaviour.
Against this backdrop, the latter categorization of attitude is adapted as the
conceptualized operationalization of attitude used in the TAM model - in which in the
adapted form refers to the mediating affective response between usefulness and ease
of use beliefs and intentions to use a target system (Suki & Ramayah, 2010). In the
context of recruitment via apps, the apps for a recruiter is the main contact point by
which the recruiter and professional’s interface in the recruitment process. (Ahn, et al.,
2004). Past scholars have emphasised the understanding of user expectations and how
they feel about the websites that they use (Chen, et al., 2002; Suh & Han, 2002). In the
work of Al-Rafee and Cronan (2006), attitude was found to be the most significant
construct in influencing behavioural intention. Notably, the more positive a
professional’s attitude towards recruitment apps, the higher the intention he or she has
to engage in recruitment via apps (Ahn, et al., 2004).
Thus, the study posits that:
―Attitude towards using positively affects behavioural intention to use recruitment
apps‖

16. 11
2.6 Influence of External Variables
External variables also influence TAM model. For the purpose of this study two
external variables were considered – Gender and Age. This was done to determine if gender
and age had an influence on behaviral intension to use of recruitment apps. Based on this two
hypothesis statements were made :
―Gender had an impact on Behavioral intention towards use of recruitment apps‖
―Age group had an impact on Behavioral intention towards use of recruitment apps‖

17. 12
2.7 Mobile Recruitment Application Process
The number one shortcoming for most career sites is the lack of a mobile
experience altogether. In fact, according to a study done by iMomentus, just 10% of
the Fortune 500 have a mobile optimized web site for their career section. Based on
our own tracking of career sites, 25% of the traffic our clients get comes from mobile.
That means most of the Fortune 500 has a substandard, and in many cases,
dysfunctional application process on mobile.
Here’s what a typical application process looks like from the perspective of a
candidate on his mobile phone:
1. The candidate enters your career site on his mobile phone. You might’ve paid
for this traffic from a source like Indeed, or he may have just entered from your
corporate site. The candidate could’ve also entered your site from a job alert he
got by email from you. Guess how most people first read their emails
afterhours? That’s right, on their phone.
2. If you’re like 90% of the Fortune 500, you don’t have a mobile optimized
career site and instead the candidate sees a large, hard to navigate, and
cumbersome job search.
3. Once the candidate finds a job that’s a good match he wants to apply. Your
career site asks the candidate to upload a resume and fill out dozens of fields.
The applicants gets frustrated and leaves.
This is a huge opportunity lost for the company to recruit a potentially qualified
candidate. Worst of all, it’s a problem that’s not addressed on the career sites of many
tech companies.

18. 13
2.8 Stages in Mobile Recruiting Process
The best way to create a great mobile recruiting experience for the candidates is to
look through your entire recruiting process and optimize every single touch point with
the candidate.
There are three stages in recruiting process:
1. Learn:
This is the part where a prospect visits your company and careers page, reads
about your culture, looks at job opportunities. To make this part of the experience
mobile-friendly, you should most likely implement responsive web design for your
website (meaning that the website will adapt to any screen size automatically), use a
lot of visuals, and little text. Make sure that your company looks equally good on any
screen size, regardless of whether it’s desktop, tablet or phone. Avoid using Flash
since it’s not supported on certain mobile devices.
2. Apply:
Here the candidate selects an opportunity and submits their application. Most
people cannot store resumes on their mobile devices nor do they have the patience to
fill out 50 questions on a tiny phone screen. To solve for this, make sure that
candidates can apply with their social profile or just a basic set of information. The
whole processes should take no longer than a minute or two. If you manage to nail this
part of the strategy, your candidates will most likely will be very impressed with how
seamless your recruiting process is.

19. 14
3. Engage:
This is how you communicate with the candidate once they are in your system.
Typically the bulk of the communication is through email, so make sure your
messages are easy to read on a small screen. Use text sparingly and make sure
everything you say has a clear call to action. You can infuse mobile into the candidate
interview experience as well. At LinkedIn we text them with directions and traffic
alerts and them with an iPad pre-loaded their interview schedule for the day. Think
about what might make sense for your company.

20. 15
Chapter 3: Research Methodology and Design
Data was collected from two different groups: users and non – users of
recruitment apps. Research model was applied to user group. Those in the user group
are examined based on their current use of recruitment apps or their previous
experience of usage. TAM has successfully predicted and explained the behavioural
intention to adopt the technology. The following research model was adopted.
The following hypothesis’s with reference to TAM model was tested:
(i) Perceived ease of use positively affects perceived usefulness of recruitment
apps.
(ii) Perceived ease of use positively affects attitudes towards recruitment apps.
(iii) Perceived usefulness positively affects attitudes towards using recruitment
apps.
3.1 Research Model and Hypotheses
Perceived
Usefulness
Perceived
Ease of Usage
Attitude
towards Use
Behavioural
Intension to
Use
-- Research Model

21. 16
(iv) Attitude towards using positively affects behavioural intention to use
recruitment apps
(v) Perceived ease of use positively affects intention to use mobile recruitment
apps.
(vi) Perceived usefulness positively affects intention to use recruitment apps.
Participants of this study consisted of:
(i) Job aspirants of Kerala who have completed or is in the verge of completion
of degree and PG courses (Btech/ BCA/ BCom/ BBA/ MBA/ Mtech) from
universities of CUSAT, Mahatma Gandhi and Kerala (both Private and
Government colleges),
(ii) Currently working Professionals who were in search for a job in their dream
company.
While it is difficult to get responses from a whole population, sampling is an
attempt to draw a conclusion based on a small representation in a given population.
The sampling technique used in the present study is non-probability convenience
sampling. Convenience sampling is found used in many studies investigating
technology acceptance. Further, the technique is used to ensure a better response rate
in a short amount of time.
The data collection was done by survey research method using questionnaire
distributed online via google forms. Responses were collected from 100 users and 200
non – users of recruitment apps. Telephonic interview was also done to get reliable
responses
3.2 Population
3.4 Data Collection Methodology
3.3 Sampling Technique

22. 17
Factor analysis and reliability testing were performed to ensure that all research
constructs are reliable. Factor analysis was executed in order to reduce the complexity
of research constructs, as well as ensuring that minimal latent constructs were
employed to explain the shared variance of measured constructs use in the
questionnaire. Reliability analysis was conducted in order to check the internal
validity and consistency of the items. Hypothesis testing is performed to determine
whether hypotheses proposed based upon a review from existing literature are
supported or not supported. Independent sample t-Test was conducted to determine
whether gender had an influence in behavioural intension to use of recruitment apps.
One way Anova was performed to determine whether age group had an influence in
behavioural intension to use of recruitment apps. Statistical Package for Social
Sciences (SPSS) software was used to perform all the above analysis. Structural
Equation Modelling (SEM) was also performed to determine whether the TAM model
is valid in this study. Warppls was the software used in performing SEM.
3.5 Data Analysis Methods

23. 18
Chapter 4: Data Analysis and Interpretation
4.1 Respondent Demographics
This questionnaire was administered to 300 respondents via google forms.
Respondent consisted of 55% female and 45% male; age in the range of below 20
years (3.3%), 20 to 24 years (69.2%), 25 to 30 years (9.2%) And above 30 years
(0.8%) ; with bachelor’s degree in technology (68.3%), in computer applications
(12%), in science with computer (5%), in science with maths/ physics / chemistry
(6%), in commerce (5%) and in others like multimedia (3%).
Gender Distribution
[CATEGORY
NAME]
[PERCENTAG
E]
[CATEGORY
NAME]
[PERCENTAG
E]
Male
Female

24. 19
Age Distribution
Bachelor’s Degree
0.8
3.3
9.2
69.2
0
10
20
30
40
50
60
70
80
Above 30 years Below 20 years 25 to 30 years 20 to 24 years
Percentage
Age
68.3
12
11
5
3
0 10 20 30 40 50 60 70 80
Btech
BCA
BSc
Bcom
Others
Percentage
Degree

25. 20
The table below summarizes the demographic information of the respondents.
Sl.No. Socio-demographic
variables
Description Percentage
(%)
1. Gender Male
Female
45
55
2. Age Below 20 years
20 to 24 years
25 to 30 years
Above 30 years
3.3
69.2
9.2
0.8
3. Under Graduation Btech
BCA
BSC.
Bcom
Others
68.3
12
11
5
3

26. 21
4.2 App Usage Rate
Out of the total respondents 33% of them had used recruitment apps and 66%
of them had not used recruitment apps. This means that only a few of the respondents
have adopted to the technology of recruitment apps and there is a high level of
reluctances with respect to use of recruitment apps.
Used
33%
Not Used
66%
Used recruitment apps
Not used recruitment apps

27. 22
4.3 Response of those who have used recruitment apps
This portion of the questionnaire aimed at identifying the problems respondents
faced while using recruitment apps. Suggestions to improve apps were also recorded.
a. How they had come to know about the company’s recruitment app
Majority of the respondents (58.8 %) had come to know about recruitment apps
via friends. 23.5% became aware of recruitment apps while simply searching through
app store, 11.8 % became aware through many other sources like google search for
websites. 5.9% of them became aware about apps from their family / relatives.
b. Reasons for downloading the company’s recruitment app?
82% of the students were interested in recruitment app mainly because they
were able to know about the latest job opening through pop ups and notifications
quickly and this way they wouldn’t miss out an opportunity. Among this 82%, 70% of
the students were unemployed, 11 % wanted to be part of their dream company. 8%
who used apps had jobs for themselves but were trying to get updated with job
openings to help their friends know about it.11% of the students felt they could easily
58.8
23.5
11.8
5.9
0
10
20
30
40
50
60
70
Friends While simply searching
through app store
Other Family / Relatives

28. 23
know more about the company from apps.10% used apps because they found it easy
and quick to track their application status.7% of the sample were found to be app
developers and there were another 12% who were also interested to know about the
various features the apps offered. 23% of the students wanted to see how apps could
be useful and they were just in the verge of trying it and had not much experience to
share. 17% of the student were using apps because they felt apps were simple, easy to
use and user friendly. There were 10% of students who used apps just because their
friends found them useful.
c. Percentage of students still using the app?
59% of the 33% respondents who used recruitment apps had discontinued its
usage whereas about 41% still used apps.
d. Name of the apps being used for recruitment purposes
Students were found using the following apps:
* 35% of the students used TCSNextStep app,
* 60% each used the app of naukri and indeed jobs,
* 5% each used apps Jobscience, Linkedin Recruiter, Monster. Com,
freshersworld, shine, Pocket aptitude, M4maths, Reliance.
[CATEGOR
Y NAME]
[PERCENT
AGE]
[CATEGOR
Y NAME]
[PERCENT
AGE]
Continued using apps
Apps
Discontinued usage of Apps

29. 24
e. Reason as to why professionals have not applied to a job via mobile
Device (User’s point of view)
Majority of the respondents (41.2%) felt complexity and inability to customize
resume as the main reason as to why professionals have not applied to a job via
mobile device. 29.4 % of them had not been looking for it. 17.6 % felt the main
reluctance towards apps could be the inability of apps to store resume and the amount
of time consumption which was often too long than other means. 5.29% of them
needed more time to adjust with the use of apps. 5.90% couldn’t find any mobile
application availability. None of them felt uninteresting job could be reason for
reluctance for using recruitment apps.
0 5.90% 5.29%
17.6 17.6
29.4
41.2 41.2
0
5
10
15
20
25
30
35
40
45
No
interesting
job
No mobile
apply
option
Needed
more time
to adjust
with the
use of
apps
Tried but
took too
long
No resume
stored
Haven’t
been
looking
Tried but
too
complex
Couldn’t
customize
resume

30. 25
4.4 Response of those who have not used recruitment apps
This portion of the questionnaire aimed at identifying the reasons as to why
there were reluctance in the sample population from using recruitment apps and how
many of them could be made to use these apps.
a. Possess Smartphones
Among the 66% of the respondents who did not use recruitment apps, 96.1% of
them possessed smartphones.
b. Frequency of using internet in phones
[VALUE]
0
20
40
60
80
100
120
YES

31. 26
Majority of the respondents (79.6 %) who had never used recruitment apps
where using internet in smartphones several times a day.
c. Purpose of Using apps
0 0
1
3.9 3.9
11.7
0
2
4
6
8
10
12
14
Only 1 to 2
days a week
Only 1 to 3
times a month
No, I have
never used
internet in my
phone
3 to 5 days a
week
Only when I
feel its
absolutely
necessary
1 to 2 times a
day
77.5
42.2
88.2
81.4
89.2
5.9
0
10
20
30
40
50
60
70
80
90
100
Reading News Job Search Entertainment Social media Watsapp Other

32. 27
89.2% of the respondents used watsapp. 81.4% were using apps for social
media engagement and 88.2 were using it for other entertainment purposes. 77.5%
were using apps to read news. 42.2 % used apps for job search and 5.9 % used apps
for other purposes like playing games.
d. Apps Role in Job search
80% of respondents who never used recruitment apps believed that apps could
definitely help in job search while 20% of them felt apps could never be helpful with
regard to search of job opportunities.
[CATEGOR
Y NAME]
[PERCENT
AGE]
[CATEGOR
Y NAME]
[PERCENT
AGE]
ROLE IN JOB SEARCHES
YES
No

33. 28
e. Response to statement:
Statement 1:
“I have stopped applying if their mobile technology isn’t up to par. That
alone tells you something about the company’s priorities and whether
they are savvy.”
7.8
15.5
53.4
18.4
4.9
0
10
20
30
40
50
60
Strongly
Disagree
Disagree Neutral Agree Strongly Agree

34. 29
4.9% of the respondents strongly believed that a company should be able to
show its mettle in mobile applications. 18.4 % just agreed to the statement. 53.4 %
were in a neutral opinion meaning its fine both ways. 7.8% strongly disagreed to the
statement and 15.5 % could not support the statement.
Statement 2:
“I haven’t downloaded my dream company’s career app on my mobile
device. However, I have visited my dream company’s career site. Save
time and money and invest in your site instead of building a complicated
app”
8.7
23.3
33
30.1
4.9
0
5
10
15
20
25
30
35
Strongly Disagree Disagree Neutral Agree Strongly Agree

35. 30
4.9% of the respondents strongly believed that a company should not waste its
time building complicated apps. 30.1 % just supported the statement. 33 % were in a
neutral opinion meaning it is fine both ways. 23.3% could not support the statement
and 8.7 % totally disagreed believing apps were not waste of time and money.
f. Expectations from a company’s recruitment app
88.2 % of respondents wanted recruitment apps to give them job alerts. 80.4 %
felt they should be able to apply for jobs via recruitment apps. 76.5 % wanted to these
apps to enable them to track their application status. 58.8 % felt they should be able to
share information via email also. 36.3 % felt recruitment apps should enable them to
share information via social networks as well. 2% wanted other features such as
history of the company and recent developments in the company as well.

36. 31
4.5 Reliability and Factor Analysis
Factor analysis and reliability testing were performed to ensure that all research
constructs are reliable. Factor analysis is a statistical method used to
describe variability among observed, correlated variables in terms of a potentially
lower number of unobserved variables called factors. The relationship of each variable
to the underlying factor is expressed by factor loadings. Reliability analysis was
conducted in order to check the internal validity and consistency of the items used for
each factors. Cronbach’s alpha value from reliability test was utilized. A common rule
88.2
76.5
80.4
58.8
36.3
2
0
10
20
30
40
50
60
70
80
90
100
Should be able
to receive job
alerts
Should be able
to track
application
status
Should be able
to apply for
jobs
Should be able
to share
information via
email
Should be able
to share
information
through social
networks
Other

37. 32
of thumb is that indicators should have a Cronbach’s alpha of at least 0.7 to judge the
set of items as reliable. The analysis was done using SPSS software and the result of
the analysis is summarized in the table below.
Four factors were: perceived usefulness, perceived ease of use, attitude toward
use of recruitment apps, and Behavioral intention to use recruitment apps
Factors loading ranged from 0.754 to 0.980, Cronbach’s alpha was calculated
for each factor (0.755, 0.866, 0.767, and 0.980) and each was found reliable.
Dimension Item Factor
Loadings
Cronbach’s
alpha
Perceived ease of use PEU1
PEU2
PEU3
PEU4
.832
.926
.887
.754
.755
Perceived usefulness PU1
PU2
.884
.884
.866
Attitude toward use ATU1
ATU2
.812
.812
.767
Behavioural Intention to use BIU1
BIU2
.980
.980
.980
4.6 Correlation Analysis
Correlation analysis was performed to examine the relationship between
perceived ease of use, perceived usefulness, attitude toward recruitment apps, and
intention to use recruitment apps. The bivariate relationships indicated that all of the
variables were significantly correlated with each other at the 0.01 level. The
correlations among the latent constructs ranged from .191 to .885. The results support
the non-existence of multi-collinearity as all correlation values are below 0.7, so no
multi-collinearity was found among the latent variables.

38. 33
Factor PU PEU ATU BIU
PU Pearson
Correlation
1 .660** .502** .492**
PEU Pearson
Correlation
.660** 1 .519** .637**
ATU Pearson
Correlation
.502** .519** 1 .507**
BIU Pearson
Correlation
.492** .637** .507** 1
**. Correlation is significant at the 0.01 level (2-tailed).
4.7 Structural Equation Modelling (SEM)
Structural equation modeling (SEM) refers to a diverse set of mathematical
models, computer algorithms, and statistical methods that fit networks of constructs to
data. SEM includes confirmatory factor analysis, path analysis, partial least squares
path analysis, LISREL and latent growth modeling. The term should not be confused
with Structural Modeling in economics. Structural equation models are often used to
assess unobservable 'latent' constructs. They often invoke a measurement model that
defines latent variables using one or more observed variables, and a structural model

39. 34
that imputes relationships between latent variables. The links between constructs of a
structural equation model may be estimated with independent regression equations or
through more involved approaches such as those employed in LISREL.
Use of SEM is commonly justified in the social sciences because of its ability
to impute relationships between unobserved constructs (latent variables) from
observable variables. To provide a simple example, the concept of human
intelligence cannot be measured directly as one could measure height or weight.
Instead, psychologists develop theories of intelligence and write measurement
instruments with items (questions) designed to measure intelligence according to their
theory.[6] They would then use SEM to test their theory using data gathered from
people who took their intelligence test. With SEM, "intelligence" would be the latent
variable and the test items would be the observed variables.
In SEM diagrams, latent variables are commonly shown as ovals and observed
variables as rectangles. The below diagram shows how error (e) influences each
intelligence question and the SAT, ACT, and GPA scores, but does not influence the
latent variables. SEM provides numerical estimates for each of the parameters
(arrows) in the model to indicate the strength of the relationships. Thus, in addition to
testing the overall theory, SEM therefore allows the researcher to diagnose which
observed variables are good indicators of the latent variables.
Various methods in structural equation modeling have been used in the
sciences, business, education, and other fields. Use of SEM methods in analysis is
controversial because SEM methods generally lack widely accepted goodness-of-fit
statistics and
Most SEM software offers little latitude for error analysis. This puts SEM at a
disadvantage with respect to systems of regression equation methods, though the latter
are limited in their ability to fit unobserved 'latent' constructs.

40. 35
WarpPLS is a powerful PLS-based structural equation modeling (SEM)
software. Since its first release in 2009, its user base has grown steadily, now
comprising more than 5,000 users in over 33 countries.
Some of its most distinguishing features are the following:
- It is very easy to use, with a step-by-step user interface guide.
- It identifies nonlinear relationships, and estimates path coefficients accordingly.
- It also models linear relationships, using standard PLS algorithms.
- It models reflective and formative variables, as well as moderating effects.
- It calculates P values, model fit indices, and collinearity estimates.
This study was intended to simultaneously examine the direct and indirect
relationships among the constructs of the proposed model and to test the fit between
the proposed model and the obtained data. For its ease and wide applicability in
modelling multivariate relations, SEM with WarpPls was chosen to do the analyses.
From the results of SEM it is clear that Attitude towards use of recruitment apps is
more influenced by users perceived usefulness (β = 0.41) than their Perceived ease of
usage ( β = 0.24). Behavioural Intension to use of recruitment apps is most influenced

41. 36
by Attitude towards use (β = 0.40), followed by perceived ease of usage (β = 0.30)
and Perceived usefulness (β = 0.24).
Model fit and quality indices in SEM
Parameter Test
value
Standard Conclusions
Average path coefficient
(APC)
0.497 P<0.001 Significant
Average R-squared (ARS) 0.422 P<0.001 Significant
Average adjusted R-squared
(AARS)
0.414 P<0.001 Significant
Average block VIF (AVIF) 2.044 acceptable
if <= 5,
ideally <= 3.3
Ideal
Average full collinearity VIF
(AFVIF)
1.925 acceptable
if <= 5,
ideally <= 3.3
Ideal
Tenenhaus GoF (GoF) 0.586 small >= 0.1,
medium>=0.25,
large >= 0.36
large
Sympson's paradox ratio
(SPR)
1.000 acceptable
if >= 0.7,
ideally = 1
Ideal
R-squared contribution ratio
(RSCR)
1.000 Acceptable
if >= 0.9,
ideally = 1
Ideal
Statistical suppression ratio
(SSR)
1.000 acceptable
if >= 0.7
Acceptable
Nonlinear bivariate causality 1.000 acceptable Acceptable

42. 37
direction ratio (NLBCDR) if >= 0.7
Regarding the meaning and calculation of fit indices, their names are
descriptive and were calculated as averages of absolute-value path coefficients (APC),
R-squared values (ARS), average adjusted (i.e., divided by degrees of freedom) R-
squared (AARS), and average variance inflation factors (AVIF). The Tennenhaus GoF
index (Tenenhaus et al. 2005) is a measure of a model’s explanatory power, defined as
the square root of the product between the average communality index and ARS. The
communality index for a latent variable is defined as the sum of squared, un-rotated
loadings with each loading associated with an indicator, divided by the number of
indicators. The average communality index for a model is defined similarly, and
considers all latent variables in the calculation. The SPR index is a measure of the
extent to which a model is free from Simpson’s paradox instances. A Simpson’s
paradox indicates a causality problem, suggesting that a hypothesized path is either
implausible or reversed (Pearl 2009; Wagner 1982). The RSCR index is a measure of
the extent to which a model is free from negative R-squared contributions, which
occur with Simpson’s paradox instances (Pearl 2009; Wagner 1982). The SSR index
is a measure of the extent to which a model is free from statistical suppression
instances (MacKinnon et al. 2000). A statistical suppression instance indicates a
causality problem, suggesting a hypothesized path is either implausible or reversed.
The NLBCDR index is a measure
of the extent to which bivariate, non-linear coefficients of association support the
hypothesized directions of causal links in a model.

43. 38
4.8 Hypotheses Testing
Hypothesis testing is performed to determine whether hypotheses proposed
based upon a review from existing literature are supported or not supported. The
following hypothesises were tested based on the research framework.
H1: Perceived ease of use positively affects perceived usefulness of recruitment apps.

44. 39
H2: Perceived ease of use positively affects attitudes towards recruitment apps.
H3: Perceived usefulness positively affects attitudes towards using recruitment apps.
H4: Attitude towards using positively affects behavioural intention to use recruitment
apps
H5: Perceived ease of use positively affects intention to use mobile recruitment apps.
H6: Perceived usefulness positively affects intention to use recruitment apps.
Hypothesis P value
Beta
value
Supported
or
Not
supported
H1: Perceived ease of use
positively affects perceived
usefulness of recruitment apps.
0.00 0.67 Supported
H2: Perceived ease of use
positively affects attitudes towards
recruitment apps.
0.00 0.24 Supported
H3: Perceived usefulness positively
affects attitudes towards using
recruitment apps.
0.00 0.41 Supported
H4: Attitude towards using
positively affects behavioural
intention to use recruitment apps
0.00 0.40 Supported
H5: Perceived ease of use
positively affects intention to use
mobile recruitment apps.
0.00 0.30 Supported
H6: Perceived usefulness positively
affects intention to use recruitment
apps.
0.00 0.24 Supported

45. 40
H1 to H6 was verified using SEM. From the results of SEM it is clear that Attitude
towards use of recruitment apps is more influenced by users perceived usefulness (β =
0.41) than their Perceived ease of usage ( β = 0.24). Behavioural Intension to use of
recruitment apps is most influenced by Attitude towards use (β = 0.40), followed by
perceived ease of usage (β = 0.30) and Perceived usefulness (β = 0.24).
.
4.9 Influence of gender in Behavioral intension to use of
recruitment
apps
The variables were grouped based on gender. The aim of this test was to determine
how much impact gender had on Behavioural intension to use of recruitment apps.
The results were as follows.
Group Statistics
Gender N Mean Std. Deviation Std. Error Mean
BI Male 46 3.32 .805 .119

46. 41
Female 55 3.05 .762 .103
Independent Samples Test
Levene's Test
for Equality of
Variances
t-test for Equality of Means
F Sig. t df Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
95% Confidence
Interval of the
Difference
Lower Upper
BI
Equal
variances
assumed
1.853 .177 1.669 99 .098 .261 .156 -.049 .571
Equal
variances
not assumed
1.660 93.788 .100 .261 .157 -.051 .572
Since Significance value is greater than 0.01, this implied gender had no effect
on Behavioural intention to use of recruitment apps.
Thus the hypothesis based on literature review which stated “ Gender had an
impact on Behavioral intention towards use of recruitment apps” is not supported.
4.10 Influence of Age group in Behavioral intension to use of
recruitment apps
One way anova was the test performed to determine if age group had impact on
Behavioral intention to use of recruitment apps.
The age groups considered were
(i) Below 20 years, (ii) 20 to 24 years, (iii) 25 to 30 years, (iv) Above 30 years.

47. 42
The following results were obtained:
ANOVA
BI
Sum of Squares df Mean Square F Sig.
Between Groups .445 2 .222 .354 .702
Within Groups 65.914 105 .628
Total 66.359 107
Since the value of significance was greater the 0.01, this implied Age group did
not have an impact on Behavioural intension to use of recruitment apps. This could be
because majority of the sample population consisted of people in the age group 20 to
24 (69.2%).
Thus the hypothesis based on literature review which stated “Age group had an
impact on Behavioral intention towards use of recruitment apps” is not supported.

48. 42
Chapter 5: Limitations of the Study
The following are the limitations for the study:
1. Data collection was confined to some colleges in Kerala.
2. The size of the sample is small
3. The time frame for the project is less
4. All variables of TAM model has not been taken into consideration.

49. 42
Chapter 6: Findings and Suggestions
Out of the total respondents 33% of them had used recruitment apps and 66%
of them had not used recruitment apps. This means that only a few of the respondents
have adopted to the technology of recruitment apps and there is a high level of
reluctances with respect to use of recruitment apps.
Majority of the respondents (58.8 %) had come to know about recruitment apps
via friends. 23.5% became aware of recruitment apps while simply searching through
app store, 11.8 % became aware through many other sources like google search for
websites. 5.9% of them became aware about apps from their family / relatives.
59% of the 33% respondents who used recruitment apps had discontinued its
usage whereas about 41% still used apps.
Majority of the respondents (41.2%) felt complexity and inability to customize
resume as the main reason as to why professionals have not applied to a job via
mobile device. 29.4 % of them had not been looking for it. 17.6 % felt the main
reluctance towards apps could be the inability of apps to store resume and the amount
of time consumption which was often too long than other means. 5.29% of them
needed more time to adjust with the use of apps. 5.90% couldn’t find any mobile
application availability. None of them felt uninteresting job could be reason for
reluctance for using recruitment apps.
Majority of the respondents (79.6 %) who had never used recruitment apps
where using internet in smartphones several times a day.
89.2% of the respondents used watsapp. 81.4% were using apps for social
media engagement and 88.2 were using it for other entertainment purposes. 77.5%
were using apps to read news. 42.2 % used apps for job search and 5.9 % used apps
for other purposes like playing games.
80% of respondents who never used recruitment apps believed that apps could
definitely help in job search while 20% of them felt apps could never be helpful with
regard to search of job opportunities.

50. 43
4.9% of the respondents strongly believed that a company should be able to
show its mettle in mobile applications. 18.4 % just agreed to the statement. 53.4 %
were in a neutral opinion meaning its fine both ways. 7.8% strongly disagreed to the
statement and 15.5 % could not support the statement.
4.9% of the respondents strongly believed that a company should not waste its
time building complicated apps. 30.1 % just supported the statement. 33 % were in a
neutral opinion meaning it is fine both ways. 23.3% could not support the statement
and 8.7 % totally disagreed believing apps were not waste of time and money.
88.2 % of respondents wanted recruitment apps to give them job alerts. 80.4 %
felt they should be able to apply for jobs via recruitment apps. 76.5 % wanted to these
apps to enable them to track their application status. 58.8 % felt they should be able to
share information via email also. 36.3 % felt recruitment apps should enable them to
share information via social networks as well. 2% wanted other features such as
history of the company and recent developments in the company as well.
There is a significant level of correlation among the variables.
From the results of SEM it is clear that Attitude towards use of recruitment apps is
more influenced by users perceived usefulness (β = 0.41) than their Perceived ease of
usage ( β = 0.24). Behavioural Intension to use of recruitment apps is most influenced
by Attitude towards use (β = 0.40), followed by perceived ease of usage (β = 0.30)
and Perceived usefulness (β = 0.24).
Since Significance value is greater than 0.01 for the influence of gender aspect,
this implied gender had no effect on Behavioural intention to use of recruitment apps.
Since Significance value is greater than 0.01 for the influence of age group
aspect, this implied age group had no effect on Behavioural intention to use of
recruitment apps.
Respondents also had the following suggestions:
1. Awareness level of recruitment via apps is less. So people should be made
more aware by use of advertisements or posts in company website and social
media.
2. Apps must not be complex and should be made more user-friendly and ease to
use.

51. 44
3. Job seekers expect career websites to be optimized for mobile. So career
websites should also be taken care.
4. Post open positions on sites with mobile apps
5. Enable creating an account for the first time also via apps, because many
company apps just help you login through apps only after an account has been
created in their website
6. Provide more information in apps
7. Enable tracking of application status easy.
8. Provide push notifications about latest job openings.
9. Add all features needed in recruitment process in company’s recruitment apps.
10.Provide all the necessary information about the recruitment procedure in apps.

52. 46
Chapter 7: Conclusion
In general, this study modified the original TAM in order to measure
behavioural intention of recruitment apps. The study has adopted the core constructs
used in TAM. Specifically, it validates the relationship between perceived ease of use,
perceived usefulness, attitude towards usage, and overall impact on behavioural
intention to use of recruitment apps. The study revealed that neither age nor gender
would influence behavioural intension to use of recruitment apps. Since the study was
conducted among users as well as non – users of recruitment apps, so the study could
identify what are the problems that users faced as well as addition of what all features
would enable non – users to become users. Since the study was confined to Kerala
state alone this study just brings out the Keralites intention to use of recruitment apps
alone.

53. 47
Chapter 8: References
1. W. H. Dutton and B. D. Loader, Digital academe: new media in higher education
and learning: Routledge, 2004.
2. D. Radcliffe, "Technological and pedagogical convergence between work-based
and campus-based learning," Educational Technology & Society, vol. 5, pp. p54-
59, 2002.
3. S. Naidu, E-learning: A guidebook of principles, procedures and practices:
Commonwealth Educational Media Centre for Asia (CEMCA), 2003.
4. K. A. Al-Busaidi and H. Al-Shihi, "Instructors' Acceptance of Learning
Management Systems: A Theoretical Framework," Communications of the
IBIMA, vol. 2010, p. 2010, 2010.
5. M. F. Paulsen, "Experiences with Learning Management Systems in 113
European Institutions," Educational Technology & Society, vol. 6, pp. 134-148,
2003.
6. M. F. Paulsen, "Online Education Systems: Discussion and definition of terms,"
NKI Distance Education, 2002.
7. N. Cavus and A. a. M. Momani, "Computer aided evaluation of learning
management systems," Procedia - Social and Behavioral Sciences, vol. 1, pp.
426-430, 2009.
8. P. Arroway, E. Davenport, G. Xu, and D. Updegrove, "EDUCAUSE core data
service fiscal year 2009 summary report," Boulder, CO: EDUCAUSE, 2010.
9. T. Browne, M. Jenkins, and R. Walker, "A longitudinal perspective regarding the
use of VLEs by higher education institutions in the United Kingdom," Interactive
Learning Environments, vol. 14, pp. 177-192, 2006.
10.M. Robinson and M. Ally, "Transition to e-Learning in a Gulf Arab Country," in
The 2nd Annual Forum on e-Learning Excellence in the Middle East, Dubai,
UAE, 2009.

54. 48
Appendix
Questionnaire
Three sets of questionnaires were used for the study. The first set enquired
about the basic demographic details of all the respondents. The second set was for non
– users of recruitment apps. The third set was for users of recruitment apps.
Questionnaire Section – I
Gender
1. Male
2. Female
Age
1. Below 20 years
2. 20 to 24 years
3. 25 to 30 years
4. Above 30 years
Undergraduation
1. Btech
2. BCA
3. BSC
4. Others
Questionnaire Section – II – for Non – users
Do you have a smart phone?
1. Yes
2. No
How often do you use internet in your phone?
1. Several times a day
2. 1 to 2 times a day
3. 3 to 5 days a week

55. 49
4. 1 to 2 days a week
5. 1 to 3 times a month
6. Only when I feel it’s absolutely necessary
7. Never
For what purposes do you use apps?
1. Reading News
2. Job search
3. Entertainment
4. Social media
5. Watsapp
6. Other
Do you feel apps can help you in Job search?
1. Yes 1.
2. No 2.
“I have stopped applying if their mobile technology isn’t up to par. That alone tells
you something about the company’s priorities and whether they are savvy.” [Put
tick on the option of your choice]
1. Strongly Disagree
2. Disagree 1.
3. Neutral 2.
4. Strongly Agree 3.
5. Agree 4.

56. 50
“I haven’t downloaded my dream company’s career app on my mobile device.
However, I have visited my dream company’s career site. Save time and money and
invest in your site instead of building a complicated app”. [Put tick on the option of
your choice]
1. Strongly Disagree
2. Disagree
3. Neutral
4. Strongly Agree
5. Agree
What are your expectations from your dream company’s recruitment app?
1. Should be able to receive job alerts
2. Should be able to track application status
3. Should be able to apply for jobs
4. Should be able to share information via email
5. Should be able to share information through social networks
6. Other
Questionnaire Section – II – for Users of recruitment apps
How did you come to know about the company’s recruitment app?
1. Friends
2. Family / Relatives
3. While simply searching through app store
4. Other

57. 51
Are you still using the app?
1. Yes 1.
2. No 2.
What according to you could be the reason as to why professionals have not applied
to a job via mobile device?
1. No resume stored
2. Needed more time to adjust with the use of apps
3. Haven’t been looking
4. No interesting job
5. Couldn’t customize resume
6. No mobile apply option
7. Tried but took too long
8. Tried but too complex
9. Other
Questions with respect to TAM Model
Answer the following Questions based on a recruitment app of your choice
Name of the recruitment app Chosen :
Perceived Usefulness
1. Rate your favourite company recruitment app (from 1 : Worst to 5 : Best)
based on its Content
2. Rate your favourite company recruitment app (from 1 : Worst to 5 : Best)
based on its Performance.
Perceived Ease of Usage
1. Rate your favourite company recruitment app (from 1 : Worst to 5 : Best)
based on its Content
2. Rate your favourite company recruitment app (from 1 : Worst to 5 : Best)
based on its Performance.

58. 52
3. Rate your favourite company recruitment app (from 1 : Worst to 5 : Best)
based on its capability to navigate from one content to another.
4. Rate your favourite company recruitment app (from 1 : Worst to 5 : Best)
based on its Ease of use.
Attitude towards Use of recruitment apps
1. I like using the app
a) Strongly Disagree
b) Disagree
c) Neutral
d) Strongly Agree
e) Agree
2. In my Opinion it is desirable to use recruitment apps
a) Strongly Disagree
b) Disagree
c) Neutral
d) Strongly Agree
e) Agree
Behavioural Intension to use of recruitment apps
1. I Plan to use recruitment apps in future
a) Strongly Disagree
b) Disagree
c) Neutral
d) Strongly Agree
e) Agree
2. Assuming that I have access to it, I intend to use it
a) Strongly Disagree

59. 53
b) Disagree
c) Neutral
d) Strongly Agree
e) Agree