The creating mobile marketing campaigns targeting millennials. Keywords: location-based


The Millenial Perception
of Mobile Location-based Advertising

Emma Patrone

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MSc of Marketing 
at Montpellier Business School

 [email protected]



Over the
last several years, there has been a considerable increase in the literature
concerning mobile marketing and location-based advertising (LBA). However,
little scholarly attention has been paid to the perception and acceptance of
LBA by the millennial generation. Therefore, the aim of this research is to
contribute by filling the gap in literature concerning the link between mobile
LBA and the millennial generation. Quantitative data will be collected thanks
to web-based questionnaires complemented by in-depth interviews of the
homogeneous sampling that represent the millennial generation who grew up in
the digital era.

The aim of
this research is to gain a deeper understanding of the millennial consumers and
how to reach them effectively thanks to location-based advertising, which is on
a growing verge due to the increase of mobile advertising and smarthone users.
The results will serve as marketing input for marketers creating mobile
marketing campaigns targeting millennials.


Keywords:  location-based advertising (LBA), mobile advertising,
location-targeted content, millennial, generation Y.


1.     Introduction

The rapid
growth during the past few years of mobile users, due to de decrease of mobile
prices, and more specifically the augmentation of martphone users has led to the
developpement of mobile marketing spending (Jose Bautista Garcia 2016). This combined with the  increase in navigation or geolocation
applications and devices such as Global Positioning System (GPS) and Cell identity
(ID)  have led to location-based
advertising. Consumers can be reached in any given time and place, especially as
most people always carry their mobile phone with them, (Bruner
and Kumar, 2007; Bauer and Strauss, 2016) based on their proximity of a place
close by or relevant to them
(Unni, Ramaprasad and Harmon, 2007; Limpf and Voorveld, 2015). Location-based advertising
therefore can be defined as mobile advertising using the geographic location of
the customer (Bruner
and Kumar, 2007; Unni and Harmon, 2007; Limpf and Voorveld, 2015).  Companies such as Google or Facebook are
investing massively into developing mobile advertising (Jose Bautista Garcia 2016) and customizable ads. Companies can
customise their ads to their target likes thanks to cookies, keyword searches
and other analytical tools that reveals personal characteristics,  purchasing pattern and preferences making the
ads more relevant to the customer targeted. LBA allows customers to be
reached  individually and in real time
based on their current location (Bruner
and Kumar, 2007; Bauer and Strauss, 2016) with a tailored message. The goal is
to “provide the right format to the right person at the right time” according
to (Tam
and Ho, 2006). Mobile users not only tend to
brownse more now on their mobile than on computers but they purchase more than
ever before on their  mobile phone. Geospatial
data and technologies have revolutionised mobile marketing and direct marketing.

 Currently two main types of LBA exist, the the
push and pull techniques (Limpf
and Voorveld, 2015). The push technique referes to a
marketing approach where the marketer sends an advertisement to a customer without
the customer explicit request (e.g. an italian pizza restarant sends an
advertiment at 7pm to its customer who are in the neighbourhood of its
restaurant) whereas for the pull techniques the marketer waits for the customer
to look for the information before presenting an ad relevant to the customer
request (e.g. a customer looks on google for a hostel in montpellier and the ad
of a relevant nearby hostel business appears at the top of the page). Therefore
the difference between the push and pull technique when it comes to mobile LBA
lies in who takes the initiative to trigger the advertisement (Barnes,
2002; Unni and Harmon, 2007; Xu et al.,
2009; Okazaki, Molina and Hirose, 2012; Limpf and Voorveld, 2015).

phones however are viewed as very personal and intimate devices (Park,
Shenoy and Salvendy, 2008; Okazaki, Li and Hirose, 2009; Limpf and Voorveld,
2015) which means that if the
advertisement is seen as intrusive it will not be accepted and received well by
the customers. Privacy concerns may arise from mobile LBA usage as it tracks
and profiles customers’ geographic positioning (Cleff,
2007; Unni and Harmon, 2007; Xu et al.,
2009; Limpf and Voorveld, 2015). The change of attitude of
consumers and their acceptance and trust of mobile LBA will emmerge from the
respect of their privacy (Wu
and Wang, 2005; Merisavo et al.,
2007; Vatanparast and Asil, 2007; Limpf and Voorveld, 2015).

However, little
empirical knowledge exists on the perception of the millenials concening mobile
LBA. Such insight would be determinant for marketer on how to address this
specific generation. Millenials have grown up in the digital, technology and
globalisation era. They are a primordial segment for marketers as they will
represent 75% of the workforce and therefore potential consumers, by 2025.
Marketers need to focus on this segment to understand their perception,
attitude and behaviour regarding mobile LBA if they want to target them
effectively. Furthermore there is no clear data whether different types of mobile
LBA such as the pull and push technique, 
influence the milenials attitude towards mobile LBA. The purpose of this
article is to understand how marketers should advertise in order to gain the
Millennials acceptance regarding mobile LBA;




Theoretical framework

Digital & Mobile

advertising has considerably taken over traditional marketing as it will
represent about 50% of global ads expenditure by 2020 or roughly 20% more in
terms of spending than in 2015 (Frent,
2016) . According to Frent
(2016) , advertising growth will be thanks
to mobile channel increase as its compound annual growth rate (CGAR)  will increase by 22.8% by 2020 representing about
34% of global ad spending vs 20% in 2016. In 2016 mobile marketing accounted
for 56% of digital ad budget according to (Frent,

Mobile LBA

Mobile LBA  is essentially displaying an advertising
message in a specific geographic location thanks to tracking and customers
real-time geographic location usage (Bruner
and Kumar, 2007; Unni and Harmon, 2007; Xu et
al., 2009; Limpf and Voorveld, 2015). Location-targeted content is the
result of smartphone users behavior and movement in space combined with
smarthphone’s portability and geographic location tracking devices (Kelsey/
BIA, 2017). Mobile LBA includes different ad
format such as search engine advertising, display advertising, videos, native
social advertising on social apps and SMS (Limpf
and Voorveld, 2015; Kelsey/ BIA, 2017). Currently the biggest mobile LBA
spending goes to Search Engine Advertising(Limpf
and Voorveld, 2015; Kelsey/ BIA, 2017). This is gradually changing as
companies are now investing more and more in native-social formats which are
bound to increase to $24.2 billion by 2021 as those native-social formats
benefit of an increase in customer demand and possess a lot of advantages as
they are easy to create and manage and allows to measure performances easily (Kelsey/
BIA, 2017). In 2016 native-social formats  represented only $10.6 billion (Kelsey/
BIA, 2017).

Mobile LBA
represents a win-win situation as marketer can target the right segment of
customers with a dedicated message anywhere anytime (Limpf
and Voorveld, 2015) while customers can receive a relevant
and tailored message to their needs at the right time in the right place.
Mobile LBA can therefore be perceived as a time efficient and relevant
marketing tool in this proximity context for the consumer who doesn’t want to
waste time sorting out ads (Unni
and Harmon, 2007; Baek and Morimoto, 2012; Limpf and Voorveld, 2015).  This allows to break through the advertising
clutter formed by non tailored and more traditional advertising (Petty,
2003; Cleff, 2007; Limpf and Voorveld, 2015). Furthermore mobile LBA is part of
the new digital techniques that are regarded as more efficient and  budget friendly vs traditional marketing (Petty,
2003; Cleff, 2007; Limpf and Voorveld, 2015). 
Mobile LBA benefits both parties, marketers and customers, this is why
its spending is increasing quickly as it becomes an essential marketing tool. In
2017 location-targeted mobile ad spending represented $16 billion and 38% of
overall mobile ad revenues (Kelsey/
BIA, 2017). According to BIA Kelsey,
location-targeted mobile ad spending will reach by 2021 about $32.3 billion or
45% of overall mobile ad revenues (non-location targeted representing $39.6


 Two Types of mobile LBA : Push & Pull

Two types
of techniques exists when it comes to mobile LBA;  the Push & Pull techniques. The first one
consist at sending information to


Perception, Attitude
and Acceptance

perception of the mobile LBA will determine how the advertisement is going to
be received by the customers. The perception of an advertisement can be
positive or negative, and is primordial for the marketer, as it will be
determinate the level of acceptance by the user of the ad. If the ad was not
requested but forced upon the customer, a negative feeling might develop and be
associated with the brand, as the advertisement will be perceived as intrusive
and undesired and will disminish its effectiveness (Limpf
and Voorveld, 2015). The success of a mobile LBA  campaign thefore depends of the acceptance of
the ads by the customers (Wu
and Wang, 2005; Merisavo et al.,
2007; Vatanparast and Asil, 2007; Limpf and Voorveld, 2015). Two theories have been developed
to explain the level of acceptance of communication and marketing strategies
through technology.

The first
one is the TRA or  Theory of Reasoned
Action (Fishbein
and Ajzen, 1975; Ajzen and Fishbein, 1980). TRA defines attitude as the
leading characteristique to behavioral intention which in turns transforms into
action or behavioral performance. Concerning mobile LBA this means that if a
customer has a positive attitude towards it they will have an easier time
reaching acceptance in contrast to someone who possess a negative attitude towards
mobile LBA (Ajzen
and Fishbein, 1980; Davis, Bagozzi and Warshaw, 1989; Limpf and Voorveld, 2015)

The second
theorie is TAM or Technology Acceptance Model and was invented by (Davis,
1989). TAM also concur that attitude
towards technologies is the origin to behavioral intention and that in turns
defines the usage of communication technology (Limpf
and Voorveld, 2015). If users have a positive attitude
towards mobile LBA then they will have a positive behavior towards it through
acceptance (Limpf
and Voorveld, 2015).

Attitude is
therefore a key factor in both TRA and TAM theories towards acceptance and
positive behavioral intention.  Attitude
towads  a brand and acceptance of the
advertisement can therefore be linked to whether or not a company respects of
information privacy (Limpf
and Voorveld, 2015) and respect of the customer’s
desires to be contacted or not by the company.



and their Characteristics

 Millenials are the generations who grew up in
the digital technology era and globalisation era(Chhateja
and Jain, 2010). Those individuals where born
between the 1980-2000 and are also called generation Y and Z.  They charcterise by being multi-taskers with
a good education background (Chhateja
and Jain, 2010). They are very mobile, flexible and
adapt very quickly to new  technology which
they accord a lot of importance to as well as innovative products (Chhateja
and Jain, 2010). Equally they are very social medias
orientated and display loyalty towards digital medias (Chhateja
and Jain, 2010). They tend to spend a lot of their
free time on social devices and possess a digital life. They feel globally
connected and are ethnically very diverse (Chhateja
and Jain, 2010). They have a high sensibility and
are not afraid to speak out or rebel especially online and on social medias.
They are impatient and get bored quickly due to short attention span (Olsen,
2005; Chhateja and Jain, 2010). The Millennials use internet and
their technological devices greatly whether for entertainement, educational
purposes or to look for information online (Oblinger,
2003; Chhateja and Jain, 2010). In 2009 it was found out through a
nationwide survey that in the USA, 75% of the Generation Y are constantly
online, engaging on social medias (Internet,
2009; Chhateja and Jain, 2010) and interacting on their
smartphones and other devices whether to check their e-mails, watch some
videos, check their feed, send an SMS or chat online.

By 2025
they will represent 75% of the whole active population. This is the reason we
are focusing on this generation who are the new consumers to which companies
have to adapt their marketing strategy in order to reach this primordial
segment effectively (Chhateja
and Jain, 2010; Jurisic and Azevedo, 2011). They have already led to the
decrease of traditional medias while digital marketing and digital products are
increasing exponetionally (Chhateja
and Jain, 2010; Miranda and Tappe, 2012).


and time agenda/plan

3.1 Data

We Sampled publications
that were most relevant from trusted scientific sources such as EBSCO and
Xerfi. All of the articles from EBSCO were selected thanks to the following
keywords ” location-based advertising”, “location-based marketing” and
“geolocation” with the addition of the complementary word “mobile” while
answering specific crtiterias. The articles used for this research were
reviewed by their peers and fall under the category of scholarly journals.
Furthermore the articles chosen to back up this research were published no
earlier than 2013 in order to contribute with meaningful and up-to-date
information. We used Xerfi to complement the study with recent data concerning
the mobile advertising industry and the digital industry.


3.2 Survey

The research
study will be based on quantitative data which will be collected thanks to
web-based questionnaires complemented by in-depth interviews of the homogeneous
purposive sampling that represent the millennial
according to Saunders, Lewis and Thornhill’s ( 2012) non-probability sampling technique
selection. The participants will receive the questionnaires by mail and through
social medias while the in-depth interviews will be conducted with a volunteers
from a pool of students of the Montpellier Business School. The questionnaires
will be sent from February to Juillet. In this case and according to Saunder
(2012) and Saunders, Lewis and Thornhill (2012) for a non-probability sample size we
will need between 4 and 12 participants as the minimum sample size to gather a
relevant number of questionnaires for the a homogeneous purposing sampling as
for a semi-strcucture interview they recommend a minimum sample size of 5 to 25



Ajzen, I. and Fishbein, M. (1980) Understanding Attitudes and
Predicting Social Behavior, Englewood Cliffs, NJ: Prentice-Hall.

Baek, T. H. and Morimoto, M. (2012)
‘Stay Away from Me: Examining the Determinants of Consumer Avoidance of
Personalized Advertising’, Journal of Advertising, 41(1), pp. 59–76.

Barnes, S. (2002) ‘Wireless Digital
Advertising: Nature and Implications’, International Journal of Advertising,
21(3), pp. 399–420.

Bauer, C. and Strauss, C. (2016)
‘Location-based advertising on mobile devices: A literature review and
analysis’, Management Review Quarterly. Springer Berlin Heidelberg,
66(3), pp. 159–194. doi: 10.1007/s11301-015-0118-z.

Bruner, G. and Kumar, A. (2007)
‘Attitude toward Location-based Advertising’, J Interact Advert, 7(2),
pp. 3–15. doi: 10. 1080/15252019.2007.10722127.

Chhateja, J. and Jain, V. (2010)
‘Understanding Generation Y and their Perspective on Proximity and Permission
based SMS Marketing’, Romanian Journal of Marketing.

Cleff, E. (2007) ‘Privacy Issues in
Mobile Advertising’, Journal of Avertising, 21(3), pp. 89–97.

Davis, F. D. (1989) ‘Perceived
Usefulness, Perceived Ease of Use, and User Acceptance of Information
Technology’, MIS Quarterly, 13(3), pp. 319–40.

Davis, F. D., Bagozzi, R. P. and
Warshaw, P. R. (1989) ‘User Acceptance of Computer Technology: A Comparison of
Two Theoretical Models’, Management Science, 35(8), pp. 982–1003.

Fishbein, M. and Ajzen, I. (1975) Belief,
Attitude, Intention, and Behavior: An Introduction to Theory and Research.
Reading, MA: Addison- Wesley.

Frent, P. (2016) The Global
Advertising Industry?: the market.

Internet, P. (2009) ‘Portrait of a
Twitter user: status update demographics’, Pew Research Center.

Jurisic, B. and Azevedo, A. (2011)
‘Building customer brand relationships in the mobile comunications market: The
role of brand tribalism and brand reputation’, Brand Management,
18(4/5), pp. 349–366.

Kelsey/ BIA (2017) Location-Targeted
Mobile Ad Spend to Reach over $32 Billion in 2021. chantilly, Va. Available

Location-Targeted Mobile Ad Spend to Reach over $32 Billion in 2021

(Accessed: 22 January 2018).

Limpf, N. and Voorveld, H. A. M.
(2015) ‘Mobile Location-Based Advertising: How Information Privacy Concerns
Influence Consumers’ Attitude and Acceptance’, Journal of Interactive
Advertising, 15(2), pp. 111–123. doi: 10.1080/15252019.2015.1064795.

Merisavo, M. et al. (2007) ‘An
Empirical Study of the Drivers of Consumer Acceptance of Mobile Advertising’, Journal
of Interactive Advertising, 7(2), pp. 41–50.

Miranda, A. and Tappe, A. (2012)
‘Demographic Group: Generation Y. AF-Knowledge at hand’, Ware.

Oblinger, D. (2003) ‘Boomers,
Gen-Xers, and millennials: Understanding the new students’, Educause Review,
pp. 36–47.

Okazaki, S., Li, H. and Hirose, M.
(2009) ‘Consumer Privacy Concerns and Preference for Degree of Regulatory
Control’, Journal of Advertising, 38(4), pp. 63–77.

Okazaki, S., Molina, F. J. and
Hirose, M. (2012) ‘Mobile Advertising Avoidance: Exploring the Role of
Ubiquity’, Electronic Markets, 22(3), pp. 169–83.

Olsen, S. (2005) ‘The millennials
usher in a new era’, CNET News.

Park, T., Shenoy, R. and Salvendy, G.
(2008) ‘Effective Advertising on Mobile Phones: A Literature Review and
Presentation of Results from 53 Case Studies’, Behaviour and Information
Technology, 27(5), pp. 355–73.

Petty, R. D. (2003) ‘Wireless
Advertising Messaging: Legal Analysis and Public Policy Issues’, Journal of
Public Policy and Marketing, 22(1), pp. 71–82.

Saunder, M. N. K. (2012) ‘Choosing
Research Participants’, in Symons, G. and Cassell, C. (eds) The practice of
qualitative organizational research: core methods and current challenges.
London, pp. 37–55.

Saunders, M., Lewis, P. and
Thornhill, A. (2012) Research Methods for Business Students. sixth edit.
Pearson Education Limited.

Tam, K. and Ho, S. (2006)
‘Understanding the impact of web personalization on user information processing
and decision outcomes’, MIS Quarterly, 30(4), pp. 865–890.

Unni, R. and Harmon, R. (2007)
‘Perceived Effectiveness of Push vs. Pull Mobile Location-Based Advertising’, Journal
of Interactive Advertising, 7(2), pp. 28–40.

Vatanparast, R. and Asil, M. (2007)
‘Factors Affecting the Use of Mobile Advertising’, International Journal of
Mobile Marketing, 2(2), pp. 21–34.

Wu, J.-H. and Wang, S.-C. (2005)
‘What Drives Mobile Commerce? An Empirical Evaluation of the Revised Technology
Acceptance Model’, Information and Management, 42(5), pp. 719–29.

Xu, H. et al. (2009) ‘The Role
of Push-Pull Technology in Privacy Calculus: The Case of Location- Based
Services’, 26(3), pp. 135–73.