assessing the effect of consumer’s attitudes towards push...

iness, Marketing and Consumer Psychology (GJETeMCP)Bus-e Global Journal of Emerging Trends in An Online International Research Journal (ISSN: 2311-3170)

2014 Vol: 1 Issue 2

58

www.globalbizresearch.org

Assessing the Effect of Consumers’ Profiles and Attitude towards

Push Notifications and Future Shopping Intentions

Dr. Tamer A. Awad,

Associate Professor of Marketing,

German University in Cairo, Egypt.

Email: [email protected]

Dina Ashraf El-Shihy,

Associate Lecturer,

Ahram Canadian University.

___________________________________________________________________________

Abstract

Purpose - Push notifications are a new advertising medium that has not been sufficiently

explored before and for this reason, the researchers aim to investigate the effect of

consumer's profile and attitudes towards push notifications and future shopping intentions

(FI) on push notifications.

Methodology - Data for this study were gathered by the researchers and were obtained

through the distribution of questionnaires to consumers aged 18 to 30 years and own

smartphones.

Findings - The results of this study revealed that factors such as Perceived Usefulness (PU),

and Perceived Ease of Use (PEOU) had a positive effect on the attitudes and the FI of young

consumers. These findings support the results of previous literature that were conducted on

mobile marketing and SMS advertising. Findings also revealed that different VALS segments

were found to respond differently and have different attitudes and FI towards push

notifications. Finally, demographics such as gender, age, income, and education were found

to be insignificant with FI.

Originality – The research investigate the use of push-notification as a new marekting tool

which different type of businesses have been using over the last few years. Businesses used

push notifications as a more personalised mobile marketing communications tool among

adult tools which repersent the market future in the Egyptian market. Using the TAM the

researchers investigate the consumers’ attitude towards the push notifications and its effect

on their future shopping intentions. Futhermore, the VALS was used to profile the

respondents according to their activities, interests and opnion in relation to their attitude

towards push notifications.

Implications - The researchers recommend that the companies that have not yet applied push

notifications should start adopting this advertising medium and put into consideration the

effect of factors such as PU and PEOU on their target consumers’ attitudes and FI towards

push notifications. Furthermore, it is essential to designate and specify the profiles of the

customers who hold more positive attitudes to push notifications so that the organizations

would bring down their costs, and boost their efficiency.

___________________________________________________________________________

Keywords: Mobile Marketing, Digital Marketing, Push Notifications, Technological

Acceptance Model, VALS, Perceived Usefulness, Perceived Ease of Use, Future Intentions.

mailto:[email protected]


2014 Vol: 1 Issue 2

59


1. Introduction

The central concept of marketing revolves around how to target and satisfy the right

consumers in the right way. However, traditional mass media was not able to reach the

consumers with the required frequency especially that the consumers blocked these channels

and were unwilling and uninterested to engage in them (Friman 2010). On the other hand,

new ways have evolved to reach the consumers and to stay up to the challenges faced. Mobile

penetration has reached 100% as every individual has a mobile phone now (Leek &

Christodoulides 2009).

With the increasing penetration of mobile devices, it became easier for the marketers to

reach the consumers as they developed a new mean and marketing opportunity for businesses.

In addition to the ease of reach, mobiles became a medium for the consumer's immediate and

direct response (Leek & Christodoulides 2009). The rapid growth of the mobile devices has

led to the usage of the handheld devices as a marketing communication tool that can be used

to send product and service advertisements to the consumers. Accordingly, their high usage

created new channels to reach the consumers and new channels for Mobile Marketing (Tsang,

Ho & Liang 2004). Mobile phones allowed the advertisers to better target their consumers,

and customize what they offer based on their preferences. They maximized and facilitated the

individual's life through allowing mobility and immediate access to them anywhere they are

and anytime they want (Friman 2010).

Mobile technology has gained more attention from the researchers in the past few years.

This is because it offers the marketers an enormous opportunity and a unique way to reach

consumers in different locations with less cost and complexity than other advertising

techniques (Leek & Christodoulides 2009). Mobile marketing is considered one of the new

methods that can be used to reach the consumers because the mobile phones help the

advertisers offer information to their target consumers. Therefore, they have been considered

to be one of the very important advertising mediums (Punyatoya & Durgesh 2011).

Push notifications emerged as a new mean of mobile marketing. Push notifications allow

an application that is not running on the device to notify the users with the updates or new

information (Local and Push Notification Programming Guide 2011). Marketers can use push

notifications for several reasons. They represent an additional channel of messaging for

smartphones. Push notifications can be used to send alerts on the devices for the new features

and updates about the applications. They provide information about the coupons and the new

offers. They communicate information about certain events, inform the users when it is their

turn if they are playing a multiplayer game, and they also enable the users to implement the

''peer to peer'' messaging (Push notifications 2011; cf. Local and Push Notification

Programming Guide 2011).


2014 Vol: 1 Issue 2

60


Such development was backed up by smartphones; which enable end-users to set the

functionality according to their preferences; for instance, they can enable or disable the push

notifications for a predefined set of applications. Push notifications are configurable on the

devices. The end-user has the ability to choose either to turn off all the notifications or to turn

on certain applications on the device (Push notifications 2011). Push notifications led the

consumers to use an application from 18% to 30% more. The development of push

notifications made it easier for marketers to target their audience (Heussner 2012).

2. Literature Review

Mobile Marketing – The market of the mobile communications has been experiencing

rapid growth since the year 1994 with regards to the number of subscribers and the number of

the global system for mobile communications (GSM) operations (Barutceu 2008). In this

dynamic environment and with the high competition that have taken place because of the

globalization, organizations have to be more flexible and adaptable to these changes. The new

technologies helped organizations to better reach their consumers in terms of the frequency

and the impact of the advertising (Punyatoya & Durgesh 2011). With the increasing

penetration and adoption of mobile phones, which are constantly with the individuals,

marketers became more capable of reaching consumers anytime anywhere. As a new

advertising channel, mobiles became a medium for the consumer's immediate and direct

response (Leek & Christodoulides 2009).

The growth of the wireless and mobile communications that have been taking place

worldwide significantly have affected the way individuals communicate with each other

either verbally and through text messages. Moreover, consumers are now using their mobile

phones beyond any purposes of personal communication. This gave the marketers the

potential of using mobile phones as platforms for companies through which they can utilize

for transactions; relationship building and brand communication purposes (Bamoriya & Singh

2012). Mobile devices are considered a relatively cheaper and an inexpensive opportunity for

organizations to enter the world of the consumer on the spot because they are in the hands of

the consumers at the point of purchase or sales. Any interaction that happens from the side of

the company to the consumer through a mobile device is considered to be mobile marketing.

This interaction can be in the form of offering a service, help, information, promotion,

advertisement or an invitation (Mannari 2011).

Mobile marketing refers to the usage of mobile phones as a way of marketing

communications. The increased usage of mobile phones between the individuals provides a

better opportunity for the organizations and enables them to communicate and transmit the

advertising messages to the consumers. Therefore, different companies started using mobiles

as marketing tools (Punyatoya & Durgesh 2011). Mobile marketing is the usage of the mobile


2014 Vol: 1 Issue 2

61


phones in order to provide the consumers with personalized information, location and times

specifics that promote ideas, goods and services for the consumers (Mannari 2011).

Therefore, mobile marketing is the medium, channel, technology or device through which

organizations can communicate with their audience. Consumers have the ability to not only

receive information from the firms, but they can also initiate interactions or actively send the

firms requests or information. Another definition to mobile marketing can be that it is the

multi way of both communication and promotion of a certain offer between the firm and the

consumers through a mobile device or medium (Mannari 2011).

Push Notifications – A push notification can be defined as a short message that is pushed

to a certain application on an individual's smartphone. This message informs the end user that

there is an update or a calendar event available that is related to that specific mobile

application. For example, an individual might be notified about the scores of sports or

movements of stock through push notifications (Push notifications 2011; cf. Local and Push

Notification Programming Guide 2011).

A push notification is a software application that is mainly designed to work on

smartphones or tablet computers (Bamoriya & Singh 2012). Push notification is a way that

allows an application that is not running on the device to let the users know that it has updates

or information. Push notifications can display alert messages or play an alert sound to notify

the users with the new updates (Local and Push Notification Programming Guide 2011).

The push notifications or the remote notifications come from outside the device. They

originate on the application's provider, and are then pushed to the mobile applications on

devices when there is either data to be downloaded or messages to be read (Local and Push

Notification Programming Guide 2011). Mobile applications are meant to be downloaded and

used on smartphones, tablet PC and/ or portable media players. They are considered to be a

''lighter version of computer applications''. The market of mobile applications has been

dramatically growing since 2009 where they have reached 100 million user base (Rishi 2012).

Individuals can use push notifications for several reasons. They represent an additional

channel of messaging for smartphones. Push notifications can be used to send alerts on the

devices for new features and updates about the applications. It provides information about the

coupons and new offers. It communicates information about certain events, informs the users

when it's their turn to play, and it also enables the users of the application to implement the

''peer to peer'' messaging. Push notifications are considered complements to the SMS and the

MMS messaging. Moreover, it has been stated that any business who wants to develop a

smartphone application, must consider the messaging channel of push notifications (Push

notifications 2011; cf. Local and Push Notification Programming Guide 2011).


2014 Vol: 1 Issue 2

62


Push notification platforms are supported by the ''Apple Push Notification Service''

(APNs). The APNs sends notifications to the iPhone, iPad, and iPod devices. They are also

supported by the ''Android Cloud to Device Messaging'' or (AC2DM) for the Android devices

and the tablets that use Android 2.2 or above (Push notifications 2011). Mobile applications

are available through certain application distribution platforms that are operated by the

operating system of the mobile such as Android Market, BlackBerry Application World and

Apple Application Store. Usually, these applications are downloaded to certain target devices.

These devices are Android, Blackberry or Apple phones. Other applications may be

downloaded by the consumers themselves from different mobile software platforms or from

web applications (Bamoriya & Singh 2012).

Smartphones grant control that permits the end users either to enable or disable these

notifications for a certain application. Push notifications are configurable on devices. The end

user has the ability to choose either to turn off all the notifications or turn on certain

applications on the device (Push notifications 2011). Push notifications lead the consumers to

use an application from 18% to 30% more. The development of push notifications gave the

consumers better control over the messages they receive, it also made it easier for marketers

to target their audience (Heussner 2012).

Smartphones – In the fast evolvement of smartphones over the past few years, most of

the definitions of smartphones have become obsolete. This is basically because its definition

changes with the evolution of mobile devices. For example, a mobile device that was a

smartphone 5 years ago is not considered one today. However, a smartphone today is a high

level device that has a higher price, and outstanding features (Pananen 2011).

Smartphone technology offers spatial resolution and high temporal with built-in timing of

display of the stimuli, and the responses are touch screen. Smartphones are portable tools that

can be easily used, is multimedia enabled, has internet access and is exactly the same for all

users anywhere in the world. The real smartphone's revolution starts with its mass

coordination on all smartphones on the worldwide level (Dufau, Dunabeitia, Moret-Tetay,

McGonigal, Peeters, Alario, Balota, Brysbaert, Carreiras, Ferrand, Ktori, Perea, Rastle,

Sasburg, Yap, Ziegler, Grainger 2011).

The increasing demand of smartphones, media tablets, and other smart electronics gave

birth to a new electronic market era. Moreover, this made the supply chain companies change

the way they think about the producers and designers of the mobile communications and other

electronic devices. Smartphones and other media tablets are dramatically changing the

electronic industry. They also started to create new opportunities to suppliers as it focused on

the delivery of more multipurpose solutions to create a unique and a different user experience

in everything related to the tablets and automobile entertainment systems (Kickham 2011).


2014 Vol: 1 Issue 2

63


The Egyptian Telecommunication Industry – The number of mobile phone subscribers

in Egypt has increased to reach 60.2 million subscribers in August 2010 with 20.3% increase

compared to the number of subscribers in August 2009 (Aly 2010).

The penetration of mobile phones has increased from about 23% in the year 2006 to reach

80% in 2010. It is also expected to expand to more than 100% by 2015 (Rao 2011). The

number of owners of mobile phones has been increasing dramatically. It was also found that

around 9.93 million actually own two lines of mobile phones (Aly 2010).

According to eMisr National Broadband Plan (2011), the mobile phone penetration rate

was higher than the developing and global countries. By the second quarter of 2011, the

penetration has increased to reach 95.07%.

Values and Life Styles (VALS) – VALS is an acronym to ''Values and Lifestyles''. VALS

was first introduced in the year 1978 by Arnold Mitchell. It is considered to be the first

theoretical system that incorporates the individuals' social values and principles that drive

these forces that influence their lives. VALS gave marketers a true segmentation system of

the individuals' lifestyles (Winters 1989; cf. Anandan, et al. 2006; cf. Kahle, et al. 1986).

VALS was created to understand the individual’s personality according to their behaviors

and their changing lifestyles and values. Psychology is used in VALS to segment individuals

according to different personality traits. With the usage of psychology to predict and analyze

the preferences and the choices of the consumers, the VALS system makes a link between

both the personality traits and purchase behavior (Anandan, et al. 2006; cf. Kahle, et al.

1986).

The VALS system has the ability to identify the current and future opportunities as it

segments the consumers with regards to their personality traits that then drives the consumer’s

behaviors. The VALS system can be applied to all phases in the marketing process, starting

from the development of the new product and its entry stage to the advertising and

communications strategy (Kahle, et al. 1986). The main principle or theory of VALS is that

individuals have the ability to express their personalities based on their behaviors. VALS

defines the segments of consumers with regards to their personality traits that have an

influence on their behaviors in the marketplace (Anandan, et al. 2006).

Technological Acceptance Model (TAM) – In 1986, Fred Davis derived the Technology

Acceptance model (TAM) from a general model from social psychology the ''Theory of

Reasoned Action'' (TRA) that was developed to explain human behavior and was actually

proven to be successful in the prediction and explanation of the behaviors of individuals in

different domains (Ekebom 2012; cf. Ismail & Razak 2011). TRA is a well-known model that

has been widely used to explain the individual's behaviors in different domains (Wu & Wang

2005; cf. Mak et al. 2009).


2014 Vol: 1 Issue 2

64


TAM is considered an extension to TRA. It aims to explain the reason behind a user’s

acceptance or rejection to information technology through adapting TRA (Park 2009; cf.

Mannari 2011). According to TRA, the behavior of an individual is basically determined by

his/her intention to actually perform a specific behavior. Behavioral intention is influenced by

the individual's attitude and subjective norms that are related to that behavior (Kwak &

McDaniel 2011; cf. Mak et al. 2009; cf. Ismail & Razak 2011).

TAM mainly focuses on the explanation of the consumer's attitudes and intentions to use

a certain technology or service, as depicted in fig 1. It is considered one of the very important

theoretical frameworks that can be used to understand the acceptance of users to information

technology (Kwak & McDaniel 2011; cf. Carlsson, Hyvönen, Repo & Walden 2005).

The actual use of technology is mainly influenced by an individual's intention to use it

which is influenced by the attitudes he/she develops towards this new technology. The

attitudes the user acquire from this technology are influenced by two main variables that are

the Perceived Usefulness (PU) and the Perceived Ease of Use (PEOU) of a technology. These

two variables can be affected by other external variables (Mak, et al. 2009; cf. Carlsson, et. al.

2005; cf. Park 2009; cf. Bamoriya, Singh 2012; cf. Treeratanapon 2012; cf. Mannari 2011; cf.

Mak et al. 2009; cf. Ismail & Razak 2011). PU is defined as the extent to which an individual

believes that the usage of a specific system would improve his/her performance in the job

(Lules et al. 2012; cf. Bamoriya & Singh 2012; cf. Treeratanapon 2012). PEOU is the extent

to which an individual believes that the usage of a specific system would be effort-free (Lules

et al. 2012; cf. Bamoriya, Singh 2012; cf. Treeratanapon 2012).

TAM was first developed to examine the consumer's acceptance and utilization of

computer technology, adding that it was then considered a valid model when it came to

predicting the perceptions and acceptance of the individuals to different corporate IT systems.

Fig 1: TAM Model, source (Davis, 1989)


2014 Vol: 1 Issue 2

65


Since then, TAM has been used in other types of technologies including email, voice mail,

software and the World Wide Web, many of studies were conducted in the mobile service

context such as mobile banking, mobile credit card, mobile advertising, and the mobile

services that has also utilized TAM (Ismail & Razak 2011; cf. Carlsson et. al. 2005)

The full growth and development of mobile marketing is heavily based on both the

individual’s acceptance and technology improvement. However, given the fact that mobile

devices are entirely personal, the acceptance of the mobile marketing can be different from

the acceptance of any other technology. The advertising message that is delivered to the

consumer via mobile device can only be accessed by him/her as he/she will be the person who

has possession over this device and therefore, have instant and direct access to it. Due to this

personal relationship that exists between the user and his/her mobile device, the mobile data

services and adoption of mobile marketing should be put apart from the adoption of any other

new technology because not all other technological devices are considered as personal as

mobile devices (Mannari 2011).

Therefore, the following hypotheses have been developed to test the consumer’s acceptance

through TAM, as shown in fig. 2:

H1a: PU of push notifications has a positive effect on FI

H1b: PEOU of push notifications has a positive effect on FI

H1c: PU of push notifications has a positive effect on the attitudes towards push notifications

H1d: PEOU of push notifications has a positive effect on the attitudes towards push

notifications

H1e: PEOU of push notifications has a positive effect on the PU of push notifications

H2a: Different VALS segments have different perceptions on the PU of Push notifications

H2b: Different VALS segments have different perceptions on the PEOU of Push notifications

H2c: The attitudes towards push notification differ with respect to different VALS segments

H2d: Different VALS segments have different FI


2014 Vol: 1 Issue 2

66


3. Methodology

3.1 Sampling and Sample Size

An exploratory quantitative research has taken place through the distribution of

questionnaires on the sample under investigation where they were asked questions related to

the topic. The survey was developed through the data that the researchers gathered from

previous literature, surveys and conceptual frameworks that were adopted from academic

sources.

The sample of the study consisted of 385 Egyptian young consumers aged from 18 to 30

years old. The sample under investigation must have owned smartphone devices. The

questionnaire has been distributed allover Giza and Cairo using the convenience sampling.

3.2 Sample Demographic Profile

Results indicated that 96% of the respondents owned smartphones, while the remaining

4% did not. The respondents of the sample consisted of 55.3% males and 44.7% females.

Regarding the respondents age, 63.6% were between 18 and 22 years old, 26.8% were

between 23 and 27 years old, 5.5% were between 28 and 32 years old, 2.6% were less than 17

years old, and the remaining 1.6% were more than 32 years old.

Concerning the respondents household income, 47% stated that their income was less

than 5000 LE, 15.6% were from 6000 to 10,000 LE, 15.3% were between 17,000 to 22,000,

and 8.3% were more than 23,000 LE per month. Concerning the respondent’s level of

education, 51.9% of the respondents’ highest level of education was high school, 33.8% were

bachelor degree holders, and the remaining 14.3% were engaged in postgraduate studies.

With regards to the respondents’ occupation, 63.4% were students, 11.9% were engineers,

10.1% were teachers or lecturers, 4.4% were employees, 3.9% chose others, 2.3% were

bankers, 1.8% were doctors, 1.0% were accountants, 1.0% were businessmen.

Fig 2: Conceptual Model developed by the researchers


2014 Vol: 1 Issue 2

67


The reliability test that is used in this study is Cronbach’s Alpha. This test is used to

determine the reliability and the internal consistency of the data. The reliability of the

questionnaire resulted in 0.854, which indicates consistency and reliability (demographics

were excluded). Reliability analysis results are shown in table 1

Constructs Cronbach's Alpha

VALS .751**

PU .840**

PEOU .846**

Attitudes .817**

FI .793**

** All constructs are reliable; more than 0.7

Table 1: Reliability Analysis

Each variable was then tested separately for reliability and all variables indicated reliability.

3.3 Instrument Validity

Validity refers to whether the research was able to measure what it what supposed to measure

or not, it also determines if the research results were truthful. Researchers determine the

validity of the research by asking several questions that are usually found in previous

researches (Golafshani 2003). The researchers conducted content validity for all constructs of

the study. It was established because most of the questionnaire items were already derived

from existing instruments and relevant literature (Mak & Nickerson 2009). The PU items

generated in this study were based on Gao, et al. (2012), Wells, et al. (2012) and Bamoriya &

Singh’s (2012) scales. The PEOU items were based on Gao, et al. (2012) and Treeratanapon’s

(2012) scales. The attitude items were based on Gao, et al. (2012) and Altuna & Konuk’s

(2009) scales. And finally, the FI items were based on Wells, et al.’s (2012: 11) scales.

4. Data Analysis and Results

Results of this study have been analyzed using Regression and ANOVA statistics. Through

these tests, the researchers were able to test the hypotheses. Regression helps in exploring the

interrelationships that exists between the variables and explains how different variables can

predict an outcome (Pallant 2002).

Model Summaryb

Model R R Square Adjusted R Square Std. Error of the Estimate

1 .636a .405 .403 .56442

Table 2: Regression Analysis


2014 Vol: 1 Issue 2

68


ANOVAb

Model Sum of Squares Df Mean Square F Sig.

1 Regression 82.935 1 82.935 260.333 .000a

Residual 122.013 383 .319

Total 204.948 384

Table 3: ANOVA Analysis

When the relationship between PU and FI was tested, R square was equal to .405 which

means that around 40% of the variation in FI is caused by the variation in PU. As seen in the

ANOVA table above, PU was highly significant with FI. The value is considered significant

if it is less than 0.05 which makes these variables highly significant as the P value of the

variable was equal to 0.000. The significance of the results proves that the hypothesis ‘’PU of

Mobile Marketing has a positive effect on FI’’ is supported. Therefore, H1a is accepted.

Model Summaryb


1 .634a .402 .401 .56556


ANOVAb

Model

Sum of

Squares Df Mean Square F Sig.

1 Regression 82.444 1 82.444 257.757 .000a

Residual 122.504 383 .320

Total 204.948 384


When the relationship between PEOU and FI was tested, R square was equal to .402

which means that around 40% of the variation in FI is caused by the variation in PEOU. As

seen in the ANOVA table above, PEOU was found to be highly significant with FI as the P


2014 Vol: 1 Issue 2

69


value was equal to 0.000. The significance of the results proves that the hypothesis ‘’PEOU of

Mobile Marketing has a positive effect on FI’’ is supported. Therefore, H1b is accepted.

Model Summaryb


Dimension 1 .704a .495 .494 .52576


ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression 103.877 1 103.877 375.784 .000a

Residual 105.871 383 .276

Total 209.748 384


When the relationship between PU and Attitudes was tested, R square was equal to .495

which means that around 50% of the variation in Attitudes is caused by the variation in PU.

As seen in the ANOVA table above, PU was highly significant with Attitudes as the P value

was equal to 0.000. The significance of the results proves that the hypothesis ‘’PU of Mobile

Marketing has a positive effect on attitudes towards push notifications’’ is supported.

Therefore, H1c is accepted.

Model Summaryb

Model

R R Square Adjusted R Square

Std. Error of the

Estimate

Dimension 1 .724a .524 .523 .51069


ANOVAb



2014 Vol: 1 Issue 2

70


1 Regression 109.859 1 109.859 421.225 .000a

Residual 99.889 383 .261

Total 209.748 384


When the relationship between PEOU and Attitudes was tested, R square was equal to

.524 which means that around 50% of the variation in Attitudes is caused by the variation in

PEOU. As seen in the ANOVA table above, PEOU was highly significant with FI as the P


Mobile Marketing has a positive effect on Attitudes towards push notifications’’ is supported.

Therefore, H1d is accepted.

Model Summaryb

Model

R R Square Adjusted R Square

Std. Error of the

Estimate

Dimension0 1 .749a .561 .560 .51305


ANOVAb


1 Regression 128.843 1 128.843 489.481 .000a

Residual 100.815 383 .263

Total 229.658 384


When the relationship between PEOU and PU was tested, R square was equal to .561

which means that around 56% of the variation in PU is caused by the variation in PEOU. As

seen in the ANOVA table above, PEOU was found to be highly significant with PU as the P


Mobile Marketing has a positive effect on PU of Push Notifications’’ is supported. Therefore,

H1e is accepted.


2014 Vol: 1 Issue 2

71


Number of Cases in each Cluster

Cluster 1 85.000

2 95.000

3 82.000

4 59.000

5 64.000

Valid 385.000

Missing .000

Table 12: Cluster Membership

K means Cluster was conducted to divide the respondents into segments based on VALS.

Each segment represents the individuals who are similar in terms of their characteristics and

lifestyles and that differentiate them from other segments.

Segment one consisted of 85 respondents and were called the ‘’Experiencers’’. Segment

two consisted of 95 respondents and were called the ‘’Thinkers’’. Segment three consisted of

82 respondents and were called the ‘’Survivors’’. Segment four consisted of 59 respondents

and were called the ‘’Achievers’’. And segment five consisted of 64 respondents and were

called the ‘’Makers’’.

Segments were given these names based on their characteristics; members of each cluster

have common characteristics. Hereunder is an explanation of the characteristics of each

cluster.

The Experiencers

The main characteristics that differentiate this segment from other segments are the extent

to which they are experience chasers. Individuals in this segment like facing and trying new

things, are adventure seekers, pursue change; they crave excitement and are always looking

for thrills. They are also interested in outrageous things.

Model Summaryb,c

Model R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Cluster

Number of

Case = 1

(Selected)

Cluster

Number of

Case ~= 1

(Unselected)


2014 Vol: 1 Issue 2

72


Model Summaryb,c

Dimension0 1 .062a .467 .004 -.009 .75038


ANOVAb,c


1 Regression .163 1 .163 .289 .593a

Residual 42.793 76 .563

Total 42.956 77


The relationship between The Experiencers and PU was tested. R square was equal to

.004 which means that there is almost no relation between the variables. The Experiencers

were highly insignificant with PU as the P value was equal to 0.593.

Model Summaryb,c

Model R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Cluster

Number of

Case = 1

(Selected)

Cluster

Number of

Case ~= 1

(Unselected)

Dimension0 1 .232a .467 .054 .041 .71279


ANOVAb,c


1 Regression 2.192 1 2.192 4.314 .041a

Residual 38.613 76 .508

Total 40.805 77


2014 Vol: 1 Issue 2

73



The relationship between the Experiencers and PEOU was tested. R square was equal to

.054 which means that around 5% of the variation in PEOU is caused by the variation in the

characteristics of the Experiencers segment. Therefore, even though the value is significant,

there is almost no relation between the variables. The Experiencers were significant with

PEOU as the P value of the variable was equal to 0.041.

Model Summaryb,c

Model R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Cluster

Number of

Case = 1

(Selected)

Cluster

Number of

Case ~= 1

(Unselected)

Dimension0 1 .258a .393 .067 .054 .23026


ANOVAb,c


1 Regression .288 1 .288 5.430 .022a

Residual 4.029 76 .053

Total 4.317 77


The relationship between Attitudes and the Experiencers was tested. R square was equal

to .067 which means that around 7% of the variation in the characteristics of the Experiencers

segment is caused by the variation in attitudes. Therefore, there is a very weak relation

between the variables. Attitudes were found to be significant with the Experiencers as the P

value was equal to 0.022.

Model Summaryb,c

Model R R Square Adjusted Std. Error of


2014 Vol: 1 Issue 2

74


Model Summaryb,c

Cluster Number

of Case = 1

(Selected)

Cluster Number

of Case ~= 1

(Unselected)

R Square the Estimate

Dimension0 1 .171

a .451 .029 .017 .72165


ANOVAb,c


1 Regression 1.194 1 1.194 2.294 .134a

Residual 39.579 76 .521

Total 40.773 77


The relationship between the Experiencers and FI was tested. R square was equal to .029

which means that around 3% of the variation in FI is caused by the variation in the

characteristics of the Experiencers segment. Therefore, there is a very weak relation between

the variables. The Experiencers were insignificant with FI as the P value was equal to 0.134.

The Thinkers

The main characteristics that constitute this segment are their intelligences,

understandings and interests. They consider themselves intellectuals and believe that they

have more ability than most individuals. Individuals in this segment like to understand more

about how the universe works. They are interested in mechanical things, hardware and

automotive stores. They also love to make things with their hands.

Model Summaryb,c

Model R R Square Adjusted R Std. Error of


2014 Vol: 1 Issue 2

75


Model Summaryb,c

Cluster

Number of

Case = 2

(Selected)

Cluster

Number of

Case ~= 2

(Unselected)

Square the Estimate

Dimension0 1 .304a .295 .092 .083 .56744


ANOVAb,c


1 Regression 3.016 1 3.016 9.366 .003a

Residual 29.623 92 .322

Total 32.638 93


The relationship between the Thinkers and PU was tested. R square was equal to .092

which means that around 10% of the variation in PU is caused by the variation in the

characteristics of this segment. Therefore, even though the value is significant, there is a weak

relation between the variables. The Thinkers were significant with PU as the P value was

equal to 0.03.

Model Summaryb,c

Model R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Cluster

Number of

Case = 2

(Selected)

Cluster

Number of

Case ~= 2

(Unselected)

Dimension0 1 .325a .344 .105 .096 .56090



2014 Vol: 1 Issue 2

76


ANOVAb,c


1 Regression 3.407 1 3.407 10.831 .001a

Residual 28.944 92 .315

Total 32.351 93


The researchers tested the strength of the relationship between the Thinkers and PEOU. R

square was equal to .105 which means that around 10% of the variation in PEOU is caused by

the variation in the characteristics of this segment. Therefore, even though the value is

significant, there is a weak relation between the variables. The Thinkers were highly

significant with PEOU as the P value was equal to 0.001.

Model Summaryb,c

Model R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Cluster

Number of

Case = 2

(Selected)

Cluster

Number of

Case ~= 2

(Unselected)

Dimension0 1 .331a .238 .109 .100 .22879


ANOVAb,c


1 Regression .591 1 .591 11.293 .001a

Residual 4.816 92 .052

Total 5.407 93



2014 Vol: 1 Issue 2

77


The relationship between Attitudes and the Thinkers was tested. R square was equal to

.109 which means that around 11% of the variation in attitudes is caused by the variation in

the characteristics of the Thinkers. Therefore, even though the value is significant, there is a

weak relation between the variables. Attitudes were found to be highly significant with the

Thinkers as the P value was equal to 0.001.

Model Summaryb,c

Model R

R

Square

Adjusted R

Square

Std. Error of

the Estimate

Cluster Number

of Case = 2

(Selected)

Cluster Number of

Case ~= 2

(Unselected)

Dimension0 1 .412a .270 .169 .160 .48662


ANOVAb,c


1 Regression 4.443 1 4.443 18.763 .000a

Residual 21.785 92 .237

Total 26.228 93


The relationship between the Thinkers and FI was tested. R square was equal to .169

which means that around 17% of the variation in the FI is caused by the variation in this

segment’s characteristics. Therefore, there is a fair relation between the variables. Thinkers

were highly significant with FI as the P value was equal to 0.000.

The Survivors

Individuals in this segment are risk averse individuals who are mainly concerned about

the stability and peacefulness of their and their family’s lives. They like sticking to a certain

unchangeable routine and do not like trying new things. They neither like varieties in their

lives nor challenges. Moreover, they do not like learning about the things that may not be

useful to them. Individuals in this segment are survivors who mainly care about their basic

needs and wants. They believe that a woman’s concern should mainly be taking care of her


2014 Vol: 1 Issue 2

78


family and that she will not be satisfied unless she can provide a happy and a safe

environment for her family.

Model Summaryb,c

Model R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Cluster

Number of

Case = 3

(Selected)

Cluster

Number of

Case ~= 3

(Unselected)

Dimension0 1 .483a .203 .233 .224 .66992


ANOVAb,c


1 Regression 10.795 1 10.795 24.054 .000a

Residual 35.455 79 .449

Total 46.250 80


The relationship between the Survivors and PU was tested. R square was equal to .233

which means that around 23% of the variation in PU is caused by the variation in this

segment’s characteristics. Therefore, there is a fair relation between the variables. The

Survivors were found to be highly significant with PU as the P value was equal to 0.000.

Model Summaryb,c

Model R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Cluster

Number of

Case = 3

(Selected)

Cluster

Number of

Case ~= 3

(Unselected)


2014 Vol: 1 Issue 2

79


Model Summaryb,c

Dimension0 1 .492a .288 .242 .232 .64931


ANOVAb,c


1 Regression 10.635 1 10.635 25.225 .000a

Residual 33.307 79 .422

Total 43.942 80


The relationship between the Survivors and PEOU was tested. R square was equal to .242

which means that around 24% of the variation in PEOU is caused by the variation in this

segment’s characteristics. Therefore, there is a fair relation between the variables. The

Survivors were highly significant with PEOU as the P value was equal to 0.000.

Model Summaryb,c

Model R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Cluster

Number of

Case = 3

(Selected)

Cluster

Number of

Case ~= 3

(Unselected)

Dimension0 1 .424a .273 .180 .170 .22610


ANOVAb,c


1 Regression .887 1 .887 17.342 .000a

Residual 4.039 79 .051


2014 Vol: 1 Issue 2

80


ANOVAb,c

Total 4.925 80


The relationship between Attitudes and the Survivors was tested. R square was equal to

.180 which means that around 18% of the variation in the characteristics of the Survivors

segment is caused by the variations in attitudes. Therefore, there is a fair relation between the

variables. Attitudes were found to be highly significant with the Survivors as the P value was

equal to 0.000.

Model Summaryb,c

Model R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Cluster

Number of

Case = 3

(Selected)

Cluster

Number of

Case ~= 3

(Unselected)

Dimension0 1 .446a .329 .199 .189 .64514


ANOVAb,c


1 Regression 8.168 1 8.168 19.626 .000a

Residual 32.880 79 .416

Total 41.049 80


The relationship between the Survivors and FI was tested. R square was equal to .199

which means that around 20% of the variation in the FI is caused by the variation in this

segment’s characteristics. Therefore, there is a fair relation between the variables. Survivors

were highly significant with FI as the P value of the variable was equal to 0.000.


2014 Vol: 1 Issue 2

81


The Achievers

The sense of achievement is what differentiates this segment from other segments.

Individuals in this segment are mainly concerned with their successes, advancements and are

hard workers. They like being in charge of groups, like to lead others, and enjoy showing

off. They are interested in art, culture and history; they find hardware or automotive stores

interesting and are interested in learning about them, they are into traveling and would want

to spend a couple of years abroad if given that chance.

Model Summaryb,c

Model R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Cluster

Number of

Case = 4

(Selected)

Cluster

Number of

Case ~= 4

(Unselected)

Dimension0 1 .174a .431 .030 .017 .82917


ANOVAb,c


1 Regression 1.540 1 1.540 2.240 .139a

Residual 49.501 72 .688

Total 51.041 73


The relationship between the Achievers and PU was tested. R square was equal to .030

which means that around 3% of the variation in PU is caused by the variation in this

segment’s characteristics. Therefore, there is almost no relation between the variables.

Achievers were found to be highly insignificant with PU as the P value was equal to 0.139.

Model Summaryb,c



2014 Vol: 1 Issue 2

82


Model Summaryb,c

Cluster

Number of

Case = 4

(Selected)

Cluster

Number of

Case ~= 4

(Unselected)

Square the Estimate

Dimension0 1 .227a .461 .051 .038 .71409


ANOVAb,c


1 Regression 1.990 1 1.990 3.903 .052a

Residual 36.715 72 .510

Total 38.705 73


The strength of the relationship between the Achievers and PEOU was tested. R square

was equal to .051 which means that around 5% of the variation in PEOU is caused by the

variation in this segment’s characteristics. Therefore, there is a very weak relation between

the variables. The Achievers were insignificant with PEOU as the P value was equal to 0.052.

Model Summaryb,c

Model R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Cluster

Number of

Case = 4

(Selected)

Cluster

Number of

Case ~= 4

(Unselected)

Dimension0 1 .042a .432 .002 -.012 .22541


ANOVAb,c


2014 Vol: 1 Issue 2

83


ANOVAb,c


1 Regression .006 1 .006 .125 .725a

Residual 3.658 72 .051

Total 3.665 73


The relationship between Attitudes and the Achievers was tested. R square was equal to

.002 which means that there is almost no relation between the variables. Attitudes were highly

insignificant with the Achievers as the P value was equal to 0.725.

Model Summaryb,c

Model R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Cluster

Number of

Case = 4

(Selected)

Cluster

Number of

Case ~= 4

(Unselected)

Dimension0 1 .338a .437 .114 .102 .77895


ANOVAb,c


1 Regression 5.628 1 5.628 9.276 .003a

Residual 43.687 72 .607

Total 49.315 73


The strength of the relationship between the Achievers and FI was tested. R square was

equal to .114 which means that around 11% of the variation in FI is caused by the variation in

this segment’s characteristics. Therefore, even though the value is significant, there is a weak


2014 Vol: 1 Issue 2

84


relation between the variables. The Achievers were significant with FI as the P value was

equal to 0.03.

The Makers

The last segment is the Makers. The Makers are very interested in making things with

their hands to the extent that they would prefer making things than buying them. Therefore,

they are interested in wood metal or other such material, they are very interested in how

mechanical things work as well, and like to look through hardware or automotive stores.

Model Summaryb,c

Model R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Cluster

Number of

Case = 5

(Selected)

Cluster

Number of

Case ~= 5

(Unselected)

Dimension0 1 .054a .416 .003 -.015 .67184


ANOVAb,c

Model Sum of

Squares Df Mean Square F Sig.

1 Regression .073 1 .073 .163 .688a

Residual 25.277 56 .451

Total 25.350 57


The strength of the relationship between the Makers and PU was tested. R square was

equal to .003 which means that there is almost no relation between the variables. The

Survivors were highly insignificant with PU as the P value was equal to 0.688.

Model Summaryb,c



2014 Vol: 1 Issue 2

85


Model Summaryb,c

Cluster

Number of

Case = 5

(Selected)

Cluster

Number of

Case ~= 5

(Unselected)

Square the Estimate

dimension0 1 .013a .448 .000 -.018 .59892


ANOVAb,c


1 Regression .003 1 .003 .010 .922a

Residual 20.088 56 .359

Total 20.091 57


The strength of the relationship between the Makers and PEOU was tested. R square was

equal to .000 which means that there is no relation between the variables. The Makers were

highly insignificant with PEOU as the P value was equal to 0.922.

Model Summaryb,c

Model R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Cluster

Number of

Case = 5

(Selected)

Cluster

Number of

Case ~= 5

(Unselected)

Dimension0 1 .018a . .000 -.018 .20983


ANOVAb,c



2014 Vol: 1 Issue 2

86


ANOVAb,c

1 Regression .001 1 .001 .018 .893a

Residual 2.466 56 .044

Total 2.466 57


The strength of the relationship between Attitudes and the Makers was tested. R square was

equal to .000 which means that there is no relation between the variables. Attitudes were

highly insignificant with the Makers as the P value was equal to 0.893.

Model Summaryb,c

Model R

R Square

Adjusted R

Square

Std. Error of

the Estimate

Cluster

Number of

Case = 5

(Selected)

Cluster

Number of

Case ~= 5

(Unselected)

Dimension0 1 .029a . .001 -.017 .65897


ANOVAb,c


1 Regression .020 1 .020 .046 .831a

Residual 24.317 56 .434

Total 24.337 57


The strength of the relationship between the Makers and FI was tested. R square was

equal to .001 which means that there is no relation between the variables. The Makers were

highly insignificant with FI as the P value was equal to 0.831.


2014 Vol: 1 Issue 2

87


Therefore, from the ANOVA tables presented previously, measuring the significance of each

of the 5 segments of VALS on PU, the Experiencers were insignificant with 0.593, the

Thinkers were significant with 0.003, the Survivors were significant with 0.00, the Achievers

were insignificant with 0.139 and the Makers were insignificant with 0.688. This makes

hypothesis ‘’Different VALS segments have different perceptions on the PU of push

notifications’’ supported. Therefore, H2a is accepted.

The ANOVA tables presented previously, measuring the significance of each segment of

the 5 segments on PEOU, the Experiencers were insignificant with 0.41, the Thinkers were

significant with 0.001, the Survivors were significant with 0.00, the Achievers were

insignificant with 0.052 and the Makers were insignificant with 0.922. This makes hypothesis

‘’Different VALS segments have different perceptions on the PEOU of push notifications’’

supported. Therefore, H2b is accepted.

The ANOVA tables presented earlier, measuring the significance of each the 5 VALS

segments on Attitudes, attitudes were insignificant with the Experiencers with 0.22, were

significant with the Thinkers with 0.001, were significant with the Survivors with almost

about 0.000, were insignificant with the Achievers with 0.725 and were insignificant with the

Makers with 0.893. This makes hypothesis ‘’the attitudes towards push notifications differ

with respect to different VALS segments’’ supported. Therefore, H2c is accepted.

The ANOVA tables presented earlier, measuring the significance of each segment of the

5 VALS segments on FI, the Experiencers were insignificant with 0.134, the Thinkers and the

Survivors were significant with 0.00, the Achievers were significant with 0.03 and the Makers

were insignificant with 0.831. This makes hypothesis ‘’Different VALS segments have

different FI’’ supported. Therefore, H2d is accepted.

5. Discussion and Conclusions

This paper serves as a foundation and basis for an enhanced understanding of the

consumer's acceptance and adoption of push notifications. As stated earlier, push notifications

are a new marketing communication tool. Therefore, it was essential to identify the most

important factors that affect the consumer’s willingness to adopt it. The aim of this study was

to explore the effect of young consumer’s attitudes toward push notifications and FI in Egypt.

The research findings were an extension to the marketing communications and mobile

marketing literature. The literature tackled the consumer’s acceptance to new technologies

and the most noticeable and reliable theory was TRA that was then extended and developed to

TAM (Gao, et al. 2012). Investigating the effect of TAM variables such as PU, PEOU, and

attitudes helped in finding how young consumers responded to this new mean of

communication.


2014 Vol: 1 Issue 2

88


The findings of the research revealed that PU and PEOU were strong determinants of the

consumer’s attitudes and intentions to receive push notifications. PEOU was found to have a

strong effect on PU as well. The positive relationship confirmed the findings of previous

literature that employed TAM to test the consumer’s acceptance in the business to consumer’s

adoption contexts to mobile marketing and new technologies.

The first hypothesis of this study ‘’PU of Mobile Marketing has a positive effect on FI’’

was accepted as PU was highly significant with FI. That was in support with Bamoriya &

Singh (2012) that mentioned that PU is a strong determinant of the consumer’s intentions to

receive SMS advertising. Furthermore, Wu & Wang (2005) and Du (2012) also mentioned

that PU has a strong effect on consumer's FI and is positively related to the intentions and

willingness to use the mobile data services.

PEOU was also found to be highly significant with FI which makes the second hypothesis

of this study ‘’PEOU of Mobile Marketing has a positive effect on FI’’ accepted. These

results in support with previous literature on SMS advertising as Bamoriya & Singh (2012)

explained that PEOU was a strong determinant in the explanation of the consumer's intentions

to receive SMS advertising.

However, Wu & Wang (2005) refuted these findings stating that even though previous

literature and research stated that the PEOU has a direct effect on the consumer's FI, it is not a

very strong factor in determining the consumer's FI like other TAM variables. They also

stated that PEOU has an indirect effect on the consumer's intentions through PU.

Moreover, PU was highly significant with attitudes. The significance of the results proves

that the hypothesis ‘’PU of Mobile Marketing has a positive effect on attitudes towards push

notifications’’ is accepted. PU is a strong predictor of the attitudes of the consumers towards

SMS advertising (Bamoriya & Singh 2012). This was in support with Gao, et al. (2012) and

Du (2012) who mentioned that PU is the main and the most important determinant of the

young consumers attitudes towards mobile marketing. Besides, PU of using the mobile device

has a strong influence on the consumer’s attitude.

Furthermore, PEOU was also highly significant with attitudes. Therefore, the hypothesis

‘’PEOU of Mobile Marketing has a positive effect on Attitudes towards push notifications’’ is

accepted. However, this was inconsistent with previous findings, Gao, et al. (2012) stated that

PEOU was insignificant with attitudes on mobile marketing. Bamoriya & Singh (2012) also

mentioned that PEOU was a weaker predictor of the consumer's attitudes towards SMS

advertising.

Bamoriya & Singh (2012) also argued that PU was a stronger and more influential

predictor of attitudes towards mobile marketing than PEOU because users are more familiar


2014 Vol: 1 Issue 2

89


and experienced with mobile marketing. Therefore, PEOU is considered a weaker predictor

with regards to the attitudes of the consumers to mobile marketing.

PEOU was highly significant with PU which makes the hypothesis ‘’PEOU of Mobile

Marketing has a positive effect on PU of Push Notifications’’ accepted. These findings were

in support with Gao, et al. (2012) who mentioned that PEOU has a significant positive effect

on PU and the adoption of mobile marketing.

VALS was used to divide the respondents into segments according to their similarities

and their lifestyles. The VALS survey was divided into 5 segments, each segment represents

the individuals who are similar in terms of their characteristics and lifestyles and that

differentiate them from other segments.

For example, the Experiencers, Thinkers and Achievers share high motivation and

interest in trying new things. These three segments are adventure seekers who crave

excitement and always look for thrills. They enjoy trying new things that they have not done

before. Therefore, these segments develop positive attitudes towards push notifications.

The Makers are very much interested in making things with their own hands and would

rather make things than buy them; they are interested in wood, metal and making things in

such materials. However, the Makers are only interested in a few things and their interests are

somewhat narrow and limited in comparison to the Experiencers, Thinkers and Achievers.

The Survivors are risk averse individuals who are mainly concerned with the stability and

peacefulness of their lives. Individuals in this segment do not prefer trying new things. They

only care about their basic needs and wants and that is why they have low motivation. This

segment has the least motivation to try something that is not basic or essential to them and

they would be resistant to try something they have not done before because they are the

survivors.

According to the above mentioned differences among segments, the attitudes and

responses of the consumers to push notifications differed. Therefore, different VALS

segments had different FI because not all segments were proven to be significant with FI.

Different VALS segments were found to have different perceptions to PU and PEOU of push

notifications. Also, the attitudes towards push notifications differed with respect to different

VALS segments.

6. Implications

Mobile communications have been experiencing a rapid growth in the past years. This is

especially the case after the spread of smartphones such as the iPhone, iPod, Android and

others. This growth creates other new opportunities for the advertisers and marketers to reach

their target consumers. It is important that marketers recognize and understand the various


2014 Vol: 1 Issue 2

90


obstacles and drivers that affect the acceptance of the consumers to mobile marketing

practices (Gao, et al. 2012).

Business sectors that want to develop a smartphone application, must consider the

communication channel of push notifications (Push notifications 2011; cf. Local and Push

Notification Programming Guide 2011). This is basically because developing mobile

applications represent significant marketing opportunities for businesses (Chen, et al. 2012).

Therefore, the researchers recommend the businesses that have not yet applied this

advertising medium to start considering applying it.

Push notifications are relatively a new topic that has not been adequately researched

before by academics especially in the Egyptian context. Therefore, this paper is considered a

new extension to the mobile marketing literature which provides good theoretical implications

to this study.

VALS has not been used in previous researches that have tackled mobile marketing.

Moreover, VALS has not been sufficiently applied in the Egyptian context. However, in this

study, the researchers were able to formulate profiles to the users among different groups in

Egypt and findings were able to reveal that different segments had an effect on the adoption

of the consumers to push notifications.

Furthermore, VALS and TAM have not been used together in literature before.

Nevertheless, this research tackled the effect of different VALS segments on TAM variables

(PU and PEOU). Therefore, another contribution is that different VALS segments responded

differently to the PU and PEOU of push notifications.

Moreover, the findings of this study propose various managerial implications to the

organizations that are involved in the strategies of mobile marketing and the development of

such programs. The study also suggests that marketers should know the effect of factors such

as PU and PEOU on the attitudes and FI towards push notifications among young consumers.

In order to increase the consumer's acceptance to mobile marketing, marketers should

focus on enhancing the PU and PEOU of the advertising messages. This is to ensure that the

target consumers develop positive attitudes to mobile marketing which would accordingly

influence their behaviors and FI over time. Marketers can increase PU and PEOU of mobile

marketing by offering incentives, personalized messages, and informative content (Bamoriya

& Singh 2012).

As PU and PEOU were found to be significant variables with regards to push

notifications, they should be taken seriously by marketers in their advertising messages. Also,

as PU has a positive effect on PEOU, this should provide good managerial implications for

them to better reach and satisfy their customers. Consequently, increase their FI and adoption

rates.


2014 Vol: 1 Issue 2

91


7. Limitations and Prospects for Future Research

The researchers were exposed to some limitations through the conduction of the research.

Firstly, convenience sampling is a non-probability sampling technique that limits the

generalization of the findings. Therefore, a probability sampling that would make the findings

of the study more generalizable is recommended in future research.

Secondly and lastly, literature stated that there are other factors that have an effect on the

attitudes and future behavioral intentions of the consumers towards mobile marketing that

have not been tested in this study. These factors include personal attachment, privacy

concerns, innovativeness, risk avoidance (Gao, et al. 2012), perceived enjoyment, perceived

entertainment, image, output quality, job relevance and result demonstrability (Mannari

2011), perceived trust (Bamoriya & Singh 2012). Therefore, the study suggests examining

these factors, their roles and effect with regards to push notifications in future research.

References

Altuna, O., & Konuk, F. (2009). Understanding Consumer Attitudes toward Mobile

Advertising and its Impact on Consumer’s Behavioral Intentions: A Cross-Market

Comparison of United States and Turkish consumers. International Journal of Mobile

Marketing, 4 (2), 43-51.

Aly, H. (2010). Mobile Phone Surveys: Challenges and Obstacles. The Egyptian Cabinet

Information and Decision Support Center. Public Opinion Poll Center. 1-22.

Anandan, C., Mohanraj, M., & Madhu, S. (2006). A Study of the Impact of Values and

Lifestyles (VALS) on Brand Loyalty with Special Reference to English Newspapers. Journal

of Management. 97- 112.

Bamoriya, H., & Singh, R. (2012). SMS Advertising in India: is TAM a Robust Model for

Explaining intention? Organizations and Markets in Emerging Economies, 3, 1 (5), 89-101

Barautceu, S. (2008). Consumer’s Attitudes towards Mobile Marketing and Mobile

Commerce in Consumer Markets. Ege Academic Review 8 (1), 15-32.

Carlsson, C., Hyvoenen, K., Repo, P. & Walden, P. (2005). Adoption of Mobile Services

across Different Technologies. 1-13.

Chen, K., Hsu, Y., Wu, C. (2012). Mobile Phone Applications as Innovative Marketing Tools

for Hotels. International Journal of Organizational Innovation. 5 (2), 116-140.

Du, P. (2012). Factors Influencing Consumer's Acceptance of Mobile Marketing: An

Empirical Study of the Chinese Youth Market. International Journal of China Marketing,

2(2), 24-37.

Dufau, S., Dunabeitia, J., Moret-Tatay, C., McGonigal A., Peeters D., Alario, F., Balota, D.,

Brysbaert, M., Carreiras, M., Ferrand, L., Ktori, M., Perea, M., Rastle, K., Sasburg, O., Yap,

M., Ziegler, J., Grainger, J. (2011). Smart Phone, Smart Science: How the Use of


2014 Vol: 1 Issue 2

92


Smartphones Can Revolutionize Research in Cognitive Science. PLoS ONE 6 (9), 1-4

Ekebom, E. (2012). Adoption of Smartphones: iPhone. Research of Adopting a Mobile Phone

Innovation from Private Consumer's Viewpoint. 1-77.

eMisr National Broadband Plan- Phase 1: A Framework for Broadband Development. (2011).

NTRA (National Telecom Regulatory Authprity). 2- 140.

Friman, J. (2010). Consumer Attitudes toward Mobile Advertising. Aalto University School of

Economics, 1-73.

Gao, T., Rohm, A. J., Sultan, F., & Huang, S. (2012). Antecedents of Consumer Attitudes

toward Mobile Marketing: A Comparative Study of Youth Markets in the United States and

China. Thunderbird International Business Review, 54(2), 211-224.

Golafshani, N. (2003). Understanding Reliability and Validity in Qualitative Research. The

Quantitative Report. 8(4), 597-606.

Heussner, K. (2012). Push Notifications: Not So Annoying Anymore. Adweek, 53(15), 21.

Ismail, M., & Razak, R. (2011). The Determinant Factors Influencing Young Consumer's

Acceptance of Mobile Marketing in Malaysia. African Journal of Business Management, 5

(32), 12531-12542.

Kahle, L., Beatty, S., & Homer, P. (1986). Alternative Measurement Approaches to

Consumer Values: The List of Values (LOV) and Values and Life Styles (VALS). The

Journal of Consumer Research. 13 (3), 405-409.

Kickham, V. (2011). Media Tablets and Smart Phones drive the Industry. 54.

Kwak, D., & McDaniel, S. (2011). Using an Extended Technology Acceptance Model in

Exploring Antecedents to Adopting Fantasy Sports League Websites. International Journal of

Sports Marketing & Sponsorship, 240-253.

Lederer, A., Maupin, D., Sena, M., & Zhuang, Y. (2000). The Technology Acceptance Model

and the World Wide Web. Decision Support Systems 29, 269–282.

Leek, S., & Christodoulides, G. (2009). Next-Generation Mobile Marketing: How Young

Consumers React to Bluetooth-Enabled Advertising. Journal of advertising research, 44-53.

Local and Push Notification Programming Guide. (2011). Apple Inc. 1-55.

Lules, I., Omwansa, K., & Waema, M. (2012). Application of Technology Acceptance Model

(TAM) in M-Banking Adoption in Kenya. International Journal of Computing and ICT

Research, 6 (1), 31-43.

Mak, B., & Nickerson, R., Isaac, H. (2009). A Model of Attitudes towards the Acceptance of

Mobile Phone Use in Public Places. International Journal of Innovation and Technology

Management, 6 (3), 305-326.


2014 Vol: 1 Issue 2

93


Mannari, C. (2011). Role of Relevance in Acceptance of Mobile Advertising: A Consumer

Approach, 1-99. Paananen, T. (2011). Smartphone Cross-Platform Frameworks. A Case

Study. 1-108.

Pallant, J. (2002). SPSS Survival Manual: A Step by Step Guide to Data Analysis using SPSS

for Windows. 1-318.

Park, S. (2009). An Analysis of the Technology Acceptance Model in Understanding

University Students' Behavioral Intention to Use e-Learning. Educational Technology &

Society, 12 (3), 150–162.

Punyatoya, P., & Durgesh, P. (2011). Attitude towards Mobile Advertising: A Study of Indian

Consumers. International Conference on Management, Economics and Social Sciences, 614-

617.

Push Notifications (2011). OpenMarket. 1-20.

Rao, M. (2011). Mobile Africa Report. Regional Hubs of Excellence and Innovation.4-67.

Rishi, M., (2012). Innovations Around Mobile Applications: Scope for Indian Developers.

Journal of Technology Management for Growing Economies. 3(2), 119-136.

Treeratanapon, T. (2012). Design of the usability measurement framework for mobile

applications. International conference on computer and information technology. 71-75.

Tsang, M., Ho, S., & Liang, T. (2004). Consumer Attitudes toward Mobile Advertising: An

Empirical Study. International Journal of Electronic Commerce, 8 (3), 65–78.

Winters, L. (1989). SRI Announces VALS 2. Marketing Research, 1(2), 67-69.

Wu, J., & Wang, S. (2005). What Drives Mobile Commerce? An Empirical Evaluation of the

Revised Technology Acceptance Model. Information & Management 42, 719–729.

assessing the effect of consumer’s attitudes towards push...

Documents