Interactive media usage among millennial consumer

Journal of Consumer Marketing
Interactive media usage among millennial consumers
Marguerite Moore
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Marguerite Moore, (2012),"Interactive media usage among millennial consumers", Journal of Consumer Marketing, Vol. 29 Iss 6
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Interactive media usage among millennial
consumers
Marguerite Moore
College of Textiles, North Carolina State University, Raleigh, North Carolina, USA
Abstract
Purpose – The purpose of this study is to determine a comprehensive model of millennial usage of interactive technologies in the current marketing
environment based upon actual behavior.
Design/methodology/approach – A data mining approach using decision tree analysis (DTA) generates two comparative models (i.e. millennial
versus generation X and millennial versus baby boomers) of interactive media usage across 21 technology applications. A large national sample
(n ¼ 3,289) sourced from the Kantar Retail IQ constitutes the data for the models.
Findings – Millennial respondents indicate significantly higher usage of interactive media compared to both generation X and boomers across 14
applications. Models indicate that millennials use interactive technologies for utilitarian/information gathering purposes as well as for entertainment.
However, they are less likely to purchase online compared to their older counterparts.
Research limitations/implications – Models provide evidence that both supports and extends previous research into interactive media from a uses
and gratifications perspective. Findings suggest theoretical directions for research for economic versus emotional uses of interactive media.
Practical implications – Findings suggest that while millennials are adept at using technology for research and interactive purposes they tend to buy
in stores, presenting opportunities for multiple channel marketers and challenges for those who market online exclusively.
Originality/value – The paper provides a realistic, comprehensive empirical model of interactive consumer behaviors across three prominent US
cohorts within the current generational cycle.
Keywords Millennial generation, Cohort analysis, Interactive marketing, Decision trees, User studies, Individual behaviour, Marketing starategy
Paper type Research paper
An executive summary for managers and executive
readers can be found at the end of this article.
Introduction
The accelerated pace of technological change in markets over
the past 20 years is unprecedented. The generation of
consumers that is coming of age along with the proliferation
of technology into nearly every facet of daily life is commonly
referred to as The Millennial Generation including members
of the US population born between 1982 and 2000. In
addition, technology drives global homogeneity among
worldwide population within the Millennial age group,
generating cross-border cohorts who exhibit similar attitudes
and behaviors. Members of the Millennial Cohort pose
challenges for their Baby Boomer and Generation X
predecessors in terms of education, management and
marketing (Reisenwitz and Iyer, 2009). Though academics
and practitioners recognize the importance of technology in
the daily function of Millennial consumers (e.g. Kavounis,
2008; Tsao and Steffes-Hansen, 2008), and have made
progress into understanding the motivations for using these
technologies (e.g. Grant and O’Donohoe, 2007), empirical
investigation of interactive technology usage in today’s
dynamic marketing environment is lagging behind the rapid
adoption of these technologies.
The purpose of this research is to determine a
comprehensive empirical model of interactive media usage
specifically among US Millennial consumers, to contribute to
an increasingly important knowledge base related to
technology driven marketing media. To achieve this end, an
inductive approach is employed that compares actual
interactive media usage among Millennial consumers to
their older counterparts: Generation X and Baby Boomers.
The research establishes a realistic model of interactive media
usage in the consumer decision process that provides direct
implications for practitioners who seek knowledge specific to
Millennial behaviors. Further, the research provides
important theoretical direction for understanding adoption
behaviors of interactive marketing technologies among the
three prominent cohorts currently driving US markets:
Millennial, Generation X and the Baby Boomers.
Literature
Consumer interactive media usage
A stream of marketing and more specifically, advertising
literature is evolving along with dissemination of the internet
as a tool for communication and commerce. Among this body
of work, a number of researchers focus upon various aspects
of interactive media and the young consumer’s acceptance or
avoidance of these media over the past five to ten years. This
stream of research focuses primarily upon the motivations and
outcomes of interactive media usage with the majority of
empirical investigation performed within the US using survey
designs with high-school and college student convenience
samples. With the exception of a single study (Reisenwitz and
The current issue and full text archive of this journal is available at
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29/6 (2012) 436–444
q Emerald Group Publishing Limited [ISSN 0736-3761]
[DOI 10.1108/07363761211259241]
436

Iyer, 2009) intergenerational research designs are absent from
this literature. The research can be broadly classified into two
areas: application of uses and gratifications theory, and the
role(s) of involvement, persuasion and information search in
motivating interactive behaviors. Across both of these areas,
consumer attitudes, intentions and behaviors in the context of
interactive media or marketing channels are integrated into
research designs, with limited evidence related to actual
behaviors.
Uses and gratifications theory (UGT) represents the
predominant approach to understanding interactive media
usage, particularly among young consumers. UGT assumes
that individuals select and use media in a goal-directed
manner to achieve a level of desired gratification, therefore
different types of media are perceived as unique by individuals
and must compete for their attention (Katz et al., 1974).
UGT is popular among interactive media researchers due to
its guidance in examining goal-directed behaviors which are
inherent in these dynamic technologies (Ruggiero, 2000).
Grant (2005) and Grant and O’Donahoe (2007) investigate
young consumer’s behavior with online marketing practices
and mobile marketing communications, respectively. Both
studies, performed on Scottish student samples, underscore
the potential negative impact of interactive marketing efforts
on the younger consumer’s market perspective. The former
study finds that young consumers most often use the internet
for “mood enhancement” and “experiential learning”.
Qualitative data from the same study indicates that students
find online advertising to be mundane and boring compared
to television advertising. The latter study, also performed on a
sample of 13-17 year olds, indicates further evidence that
young consumers use interactive media (i.e. mobile phones)
for entertainment and social stimulation as opposed to
marketing research to inform their purchases. More recently,
Tsao and Steffes-Hansen (2008) using a UGT approach find
that US high school and middle school also tend to use the
internet for entertainment and technology exploration.
The literature also indicates that consumer involvement and
the role of persuasion impact adoption of interactive
technologies. Leug et al. (2006) find that involvement,
accessibility and time spent in a given channel positively
impact ultimate channel choice in their comparative study of
traditional versus online channels among US teens. Sullivan
and Heitmeyer (2008) also report that retailer preference
positively impacts Generation Y patronage intentions in their
study of traditional (i.e. non-interactive) retail channels. From
a marketing communications perspective, Tsao and Steffes-
Hansen (2008) report that teens who exhibit a preference for
“cyber-communication” spend more time surfing the internet
than those who prefer human communication.
Consistent with the tone of Grant (2005) and Grant and
O’Donohoe (2007), Henrie and Taylor (2009) report that
Millennial consumers (i.e. US college student sample) who
recognize that they are targets of persuasion tend to develop
negative attitudes towards the persuader. Related research
also suggests that “teen internet mavens”, or teens with
extensive internet experience and knowledge tend to influence
family information search using marketing media sourced
from the Internet (Belch et al., 2005).
In summary, the academic literature offers insights into
young consumers’ motivations for using interactive media,
particularly the Internet medium. In addition to UGT driven
research, evidence that consumer involvement plays a positive
role in interactive media usage for information search is also
demonstrated. The research also underscores the skepticism
with which young consumers perceive persuasive marketing
efforts from both traditional and technology driven
environments.
Generational cohort theory
Generational cohort theory (GCT) asserts that populations
can be grouped into generations based upon placement in the
historical cycle which includes specific events that shape the
attitudes and behaviors of members within each cohort.
Though GCT is most commonly applied within the US
context, global homogenization supported by technology
drives the development of cross border cohorts (Schewe and
Meredith, 2004). GCT is commonly applied to market
analysis, given its efficiency and effectiveness in targeting
markets. The cohort analysis methodological approach has
been present in the literature for over thirty years (Reynolds
and Rentz, 1981; Reynolds and Rentz, 1981) and continues
to be strategically relevant in today’s data intensive market
environment. Different terminology and timeframes are used
to define and describe the generational cohorts. The
comprehensive framework of US generations purported by
Howe and Strauss (1991) informs the design for this research.
The Millennial Generation includes population born
between 1982 and 2004 and includes approximately 78
million members. Due to its size and growing market power,
the Millennial Generation is currently the primary focus of
popular media and marketers. The preceding group,
Generation X includes population born between 1961 and
1981 and is proportionately smaller than the Millennial
Generation and the older Baby Boomer Generation. For this
reason, Generation X is characterized as forgotten or ignored
by marketers. Howe and Strauss (1991) refer to Generation X
as the 13th generation. The Baby Boomer generation includes
population born between 1943 and 1960. The Baby Boom
generation includes approximately 80 million members and is
so large that it youngest members are referred to as Generation
Jones. Boomers are known for being values driven and seek
instant gratification. In recent years, Boomers have been
challenged with decreasing real estate values and losses in
portfolio value forcing a new frugality among this group.
The empirical research into interactive media usage
presented in the previous section primarily offers insight into
young consumers’ motivations for adopting these technologies.
Indeed, researchers and marketers tend to assume that young
consumers will aggressively integrate technology into their daily
lives, yet the manner that they use the numerous tools available
to them remains underexplored and potentially misunderstood.
The research reported in this study, directly addresses this
knowledge gap using a comparative cohort analysis on a multi-
generational US sample.
Methods
The research employs a reductionist data mining approach to
construct realistic models of interactive media usage among
Millennial consumers using a large national sample. Decision
tree analysis (DTA) is used to generate comparative models
between Millennials and Generation X and Baby Boomers,
respectively, to determine behaviors unique to the
generational cohorts. Data for the study (n ¼ 3,289) are
provided by the Kantar Retail IQ database.
Marguerite Moore
Volume 29 · Number 6 · 2012 · 436–444
437

Decision tree analysis
Decision tree analysis provides a powerful tool for
investigating numerous relationships among quantitative
data in a simultaneous manner. DTA produces a
hierarchical model of effects that predict a single dependent
variable referred to as the target variable. Compared to
traditional statistical linear and discrete approaches, DTA is
useful for exploring relationships among diverse data due to
fewer operational constraints and thus greater modeling
flexibility. The primary requirement of DTA is the availability
of very large datasets.
Chi-square automatic interaction detector (CHAID) is
used to model interactive marketing usage among Millennial
consumers. The CHAID algorithm is required to model the
effects of binomial predictor variables on a single binomial
target variable. CHAID uses chi-squares to systematically
compare variables and split them in groups, referred to as
nodes, based on significance testing. Tree configuration
indicates hierarchical effects, with more significant effects
commonly occurring in earlier tree splits. Further, CHAID
allows variable to split in multiple nodes within the tree
configuration. A priori model settings include: a maximum of
five levels for each tree, a minimum 25 cases per parent note
and ten cases per child node. Pearson chi-squares are used to
detect differences in the cases with alpha designated at 0.01
for both splitting and merging. A Bonferroni adjustment is
also used in the model to further reduce the likelihood of
Type I error occurrence.
CHAID facilitates cross-validation by performing both
training and testing models. The training model is first
generated to establish tree structure followed by the testing
model which is generated to ensure that the training model
structure holds among the sample data. In cases that sample
sizes are limited, a larger portion of the data is used to
generate the initial training model and a smaller portion is
used to generate the subsequent testing model (SPSS, 2002).
For both models, 70 percent of the respective samples are
used to generate the training models while the remaining 30
percent of respondents are used to generate the testing
models. Given adequate structure consistency between the
initial and testing models, effects from the training model are
interpreted.
Measures
For both the Millennial versus Generation X model and the
Millennial versus Baby Boomer model the predictor variables
include 21 items which probe interactive marketing usage in
the context of clothing, shoes and accessories purchases and
recent interactive marketing usage not related to a specific
purchasing context (i.e. over the past six months) (Appendix).
All predictor variables are measured binomially, indicating
engagement or no engagement in the 21 focal interactive
marketing activities. The binomial target (i.e. dependent)
variable for each model is depicted by generational cohort
membership. Therefore, the 21 interactive marketing
behaviors are used to predict the target variable for cohort
membership, thereby profiling the unique behaviors of these
groups in terms of the measured independent variables.
Sample data
Total sample size consists of 3,289 respondents including:
616 Millennial cases, 1,552 Generation X cases and 1,121
Baby Boomer cases. Data are accessed from the Kantar Retail
IQ database which uses a nationwide online consumer panel
as the sample frame. The Kantar panel includes
approximately one million households across the US,
recruited through different channels including web portals,
web communities, web aggregators and Internet advertising
firms. All respondents designate themselves as the primary
household shopper. The data for this study were collected
during June 2010 and focused upon consumers’ use of
interactive technologies in their everyday shopping behaviors
for clothing, shoes and accessories as well as their specific
behaviors over the past six months (Appendix).
Findings
Sample characteristics
Sample characteristics are evaluated for each cohort
including: gender, income, marital status, ethnicity,
household size, and home ownership. Frequencies are used
to analyze sample demographics. In cases that response rates
vary for the observed demographic variables, actual
percentages based upon the full sub-sample sizes are used
to calculate population proportions.
Millennial respondents are predominantly female (85
percent) and report the highest frequency of annual
household incomes between $35K-$49,999K (16 percent)
and $50K-$79,999K (20 percent). The majority of
Millennials report that they are single (58 percent), followed
by married (39 percent). Though the Millennial sub-sample is
predominantly Caucasian (75 percent), the cohort sample is
the most ethnically diverse among the study with Hispanic
and Asian/Pacific Islander representing 11 percent each and
African Americans representing 6 percent of the group. The
majority household sizes for Millennial respondents include
one to two members (54 percent) with most Millennials
reporting that they rent (46 percent) or own (34 percent) their
home.
Generation X respondents are also predominantly female
(82 percent) and report the highest incomes among the
cohorts: $50K-$79,999K (20 percent) and above $100K (23
percent). The Generation X subsample is also predominantly
Caucasian (84 percent) followed by African-American (6
percent), Hispanic (5 percent) and Asian (4 percent).
Generation X respondents report the highest incidence of
marriage among the cohorts (63 percent). The majority
household size among Generation X is the largest among the
cohorts with 47 percent reporting households with two or
three members. Generation X respondents report the second
highest level of home ownership (65 percent) behind
Boomers.
Again, female respondents (81 percent) represent the
majority of the Baby Boomer sub-sample. Baby Boomers
report the second highest incomes, slightly behind Generation
X with 19 percent reporting household incomes between
$50K-$79,999K and 19 percent reporting incomes above
$100K. The Boomer sub-sample is the least ethnically diverse
among the sample with 89 percent of respondents reporting
that they are Caucasian, followed by African American (5
percent), Hispanic (4 percent) and Asian (1 percent).
Boomers report household sizes larger than Millennials, but
smaller than Generation X with 67 percent including two or
less members. Among the cohorts, Boomers indicate the
highest level of home ownership with 77 percent.
Marguerite Moore
Volume 29 · Number 6 · 2012 · 436–444
438

Decision trees
CHAID generated two distinct models that compare and
classify interactive marketing usage among Millennial
consumers with Generation X and Baby Boomers. Both
models indicate identical structure and similar risk estimates
between respective training and testing versions which
suggests the models generalize well across the data. Based
upon the consistency between the training and testing models,
interpretation of the decision trees proceeded.
Millennials versus Generation X
The CHAID model comparing Millennial behaviors to those of
Generation X indicated a four level structure with twelve total
nodes and six significant variable splits (Figure 1 and Table I).
The initial split is indicated by the variable “accessed social
network from my mobile device in the past six months”
(x2
¼ 17.35, p , 0.000), followed by the second most
significant split “download retailer widget” (x2
¼ 13.00,
p , 0.000) and the third most significant split “follow retailer
or brand on Twitter” (x2
¼ 7.85, p , 0.005). The remaining
three splits in order of significance include: “purchased a
product online in the past six months” (x2
¼ 7.71, p , 0.005),
“like a retailer or brand on Facebook” (x2
¼ 7.19, p , 0.007)
and “spent time on a social networking over the past six
months” (x2 ¼ 6.92, p , 0.008).
Interpretation of the chi-square statistics indicates that
Millennials were proportionately more likely to have accessed
a social networking site from their mobile device in the past
six months compared to Generation X. Among the sample
Figure 1 Comparative model of interactive marketing usage: Millennial versus Generation X
Table I Decision tree effects Millennial versus Generation X marketing usage
Split Variable Chi-square p-value Effect order
1 Accessed social networking site from mobile device in past six months 17.3579 0.0000 1
2 Follow retailer or brand on Twitter 7.8354 0.0051 3
3 Download retailer widget 13.0095 0.0003 2
4 Purchased a product online in past six months 7.7190 0.0055 4
5 “Like” a retailer/brand on Facebook 7.1914 0.0073 5
6 Spent time on a social networking site (e.g. Facebook, Twitter, blog, etc.) in past six months 6.9257 0.0085 6
Note: *Effects presented for training model (n ¼ 2,168)
Marguerite Moore
Volume 29 · Number 6 · 2012 · 436–444
439

respondents who accessed a social network in the past six
months, Millennials were also more likely to report that they
regularly follow brands on Twitter. However, out of both
Millennial and Generation X respondents who follow Twitter,
Millennial consumers were less likely to have purchased a
product online in the past six months. Interestingly,
Millennial consumers who report that they did not purchase
online also indicate that they did not spend time on social
networking sites over the past six months.
Among respondents who indicate (i.e. split one) that they
do not use a mobile device to engage in social networking, the
Millennials reported that they were more likely to download a
retailer widget compared to Generation X. Among the
respondents who downloaded a widget to connect to retailers,
Millennials were more likely to have “liked a retailer or brand
on Facebook”.
Millennials versus Baby Boomers
The decision tree that compares Millennials’ interactive
marketing behaviors with those of Baby Boomers indicated a
five level structure, with 16 total nodes and eight significant
predictors (Figure 2 and Table II). Overall model effects
indicate more pronounced differences between the Millennial
Generation and the Baby Boomers compared to the differences
indicated in the Millennial versus Generation X model. The
initial split for the model is indicated by “downloaded utility
‘app’ in the past six months” (x2
¼ 60.12, p , 0.000) followed
by “sign-up or receive e-mail or text” (x2
¼ 40.30, p , 0.000).
The third most significant predictor for the model is
“downloaded game or entertainment app to mobile device in
past six months” (x2
¼ 17.56, p , 0.000), followed by “read or
post on retailer blog” (x2
¼ 15.98, p , 0.000) and “purchased
a product online in past six months” (x2
¼ 15.64, p , 0.000).
The last three significant predictors include: “accessed retailer
website from mobile device in past six months” (x2
¼ 10.35,
p , 0.001), “downloaded and used coupons for an online
purchase in the past six months” (x2
¼ 8.36, p , 0.003) and
“purchased a product online in past six months” (x2
¼ 8.23,
p , 0.004).
The initial split that differentiates Millennial and Boomer
interactive behaviors is whether respondents downloaded a
utility application to their mobile device over the past six
months. Millennials were highly more likely to have
downloaded a utility application compared to the older
Boomers. Among all respondents who downloaded a utility
Figure 2 Comparative model of interactive marketing usage: Millennial versus Boomers
Marguerite Moore
Volume 29 · Number 6 · 2012 · 436–444
440

application, Millennials were also more likely to have signed
up for retailer e-mail over the past six months. Among those
who signed up for retailer e-mail, the Millennial respondents
were less likely to purchase a product online compared to the
Boomers. Further, among the respondents who did not
purchase online, Millennials were far more likely to have read
or posted on a retailer blog in the past six months.
Respondents who indicate that they did not sign-up for
retailer e-mail in the past six months, further split into three
additional levels. The Millennials among this group indicate
that they downloaded gaming or entertainment applications at
a significantly higher rate than Boomers. Among those who did
not download gaming applications, the Boomers were more
likely to have purchased a product online in the past six months
(i.e. split seven). However, among those who purchased online,
the Millennial respondents were more likely to have used an
online coupon for their purchases (i.e. split eight).
Respondents who indicate that they did not download a
utility application in the initial model split indicated a second
significant level (i.e. split three). Among this group,
Millennials indicated a higher incidence of accessing retailer
websites from their mobile device in the past six months. This
variable represented the terminal node for this portion of the
decision tree.
Conclusions and discussion
The DTA suggests several patterns of behavior among
Millennial consumer use of interactive media that
underscores the cohorts’ distinct manner with which they
appear to integrate these tools into their daily lives. Not
unexpectedly, the model that contrasts usage between
Millennials and Boomers indicates more marked differences
in both the variety of significant variables as well as in the
magnitude of effect sizes. The differences between Millennial
behaviors and Generation X were less pronounced compared
to the former contrast.
Compared to Generation X, Millennials are more active at
integrating technologies into their daily lives for marketing
purposes. Millennials use their mobile device and traditional
internet means to connect to retailers or brands. They engage
in social networking using their mobile device, but do not
report regular social networking to the degree that Generation
X respondents report. The model also suggests a clear
indication that Millennials do not use the Internet to purchase
products compared to Generation X, despite their noted
frequent use of interactive technologies to connect with
retailers and brands.
According to the DTA model that contrasts usage between
Millennials and Boomers, the differences are more distinct
(i.e. stronger effects) and more extensive (i.e. more significant
variables or tree depth). The two groups are significantly
different across seven different variables, with the “purchased
product online in past six months” variable splitting twice
within the DTA hierarchy. For all significant variables, with
the exception of purchasing online, Millennial respondents
demonstrate significantly higher integration of interactive
media. Using both mobile devices and traditional internet
tools, Millennials accessed online resources for both
utilitarian and entertainment purposes. They also indicate
significantly higher interactive connections with retailers or
brands through their blogging activities and couponing
behaviors. Again, Millennials were less likely to purchase a
product online compared to the Boomer respondents.
From a general perspective, both models indicate significant
usage of interactive media among Millennials for a variety of
reasons: information seeking, entertainment focused, increased
utility and deal focused. However, despite this activity
Millennial consumers do not purchase products online to a
comparable degree of the older generations considered in the
study. The findings suggest implications for both practice and
future research (for practical implications see managerial
implications and applications). The study’s design is grounded
in actual behavioral data and is exploratory in this sense.
However, interpretation of the findings through the uses and
gratifications perspective indicates partial agreement with past
research using this approach. Consistent with Grant (2005)
and Tsao and Steffes-Hansen (2008), the models suggest that
US Millennials actively use interactive technology for
entertainment as well as technology exploration (i.e.
downloading applications). Contrary to Grant’s study of
young Scots (Grant, 2005) the results suggest that US
Millennials actively engage in marketing research over using a
variety of means: blogs, e-mail, mobile connections, utilities
and various applications.
The findings also suggest that as expected Millennials are
superiorly adept at using these technologies in their daily life
compared to the older generations. The comparison between
Millennials and Generation X indicates that members of the
older cohort are more likely to engage in social networking
from a non-mobile connection while Milllennials are more
likely to access social networks from their mobile phone.
Given the variety and speed with which Millennials use the
potential portfolio of interactive tools, this generation appears
to control the technology in a manner that is integrated into
their daily lives from both utilitarian and entertainment
Table II Decision tree effects Millennial versus Boomers interactive marketing usage
Split Variable Chi-square p-value Effect order
1 Downloaded utility “app” in past six months 60.1298 0.0000 1
2 Sign-up or receive e-mail from retailer in past six months 40.3015 0.0000 2
3 Accessed retailer website from mobile device in past six months 10.3520 0.0013 6
5 Downloaded game or entertainment app to mobile device in past six months 17.5625 0.0000 3
6 Read or post on retailer blog 15.9833 0.0001 4
8 Downloaded and used coupons for an online purchase in past six months 8.3660 0.0038 7
Note: *Effects presented for training model (n ¼ 1,737)
Marguerite Moore
Volume 29 · Number 6 · 2012 · 436–444
441

perspectives. However, a substantial question remains
regarding the Millennial consumer’s resistance to online
purchasing. Potential limitations related to this finding are
noted in the Limitations and Future Research section.
Managerial implications and applications
Results suggest that older Millennials (i.e. born between 1982
and 1992) use interactive technologies for multiple purposes: to
connect with retailers or brands online, to download coupons
online, to source entertainment and utility applications, etc.
Further, Millennial consumers also report active usage of
mobile devices to carry out these behaviors. However, these
consumers do not buy products online at rates comparable to
the older generations. The lack of willingness to purchase online
does not appear to have arisen from accessibility or income
limitations given the sample data characteristics.
A major distinction among Millennials is the propensity to
download applications particularly “utility apps” to connect
to retailers or brands. Marketers including retailers and
manufacturers should incorporate practical uses of technology
into their interactive marketing media when targeting
Millennials. Firms who undertake these efforts must also
ensure that they deliver value in terms of information and
time to the dubious Millennial consumer. This approach may
not prove as useful for the older cohorts, particularly the
Boomer Generation who does not use these technologies as
efficiently as the younger cohort.
Millennial consumers in the study do not purchase
products online compared to the older cohorts. They use
interactive media to gather information on the go (i.e. using
their mobile device) including promotional media. This
finding suggests an opportunity for brands who market in
multiple channels to connect with Millennials and stimulate
retail patronage using interactive promotional tactics. Based
on previous research, marketers should be cognizant of the
potential to irritate the Millennial consumer with too much
contact or potentially inflated promotional promises (Henrie
and Taylor, 2009). The same principles apply to firms who
market online exclusively, however this model is further
challenged to convert Millennials to online purchasers.
Analysts and academics commonly assume that Millennials
inter-mix function and entertainment using interactive
technology. The models suggest that Millennials use their
technology in directed, brief sessions. The research indicates a
higher incidence of social networking on a home computer
among Generation X respondents, with Millennials preferring
to use their mobile device for this purpose. Therefore, efforts
to integrate business with pleasure may actually irritate rather
than attract the Millennial consumer. When approaching this
consumer, marketers must have clear messages, effective
technology and follow through on their claims.
Limitations and future research
The purpose of this study is to determine the actual
interactive behaviors of Millennial consumers in a
comparative multi-generational format grounded in robust
national data. Research tradeoffs were consciously undertaken
to pursue the study’s purpose. Limitations are primarily
related the study’s design including use of secondary data and
the DTA method. Though the sample data are accessed from
a reputable and reliable source, the questionnaire design was
not determined by the researcher and as such covered only
those questions important to the firm. Fortunately, the
interactive technology questions were extensive in the scope of
applications that they covered. Further, the six items that
evaluated general interactive behaviors (Appendix), as
opposed to actions taken in the past six weeks, were
anchored to the context of clothing, shoes and accessories
purchasing. Findings could be markedly different for different
product categories such as consumer electronics or services.
The age range of the Millennial Cohort within the sample
includes respondents born between 1982 and 1992 due to a
minimum age requirement of eighteen for participating in the
survey. Therefore, younger Millennials are not included in the
analysis and may use interactive technologies differently from
older Millennials. Previous generational analyses note
intergenerational differences between younger and older
portions of large cohorts such as the Boomers with
Generation Jones. As rapid diffusion of these technologies
continues, marketing research will be critical to understand
the nuances with which this large consumer group adopts and
uses these tools.
The sample data are heavier in female and Caucasian
respondents. Potential gender differences may affect use of
interactive technologies and thus the outcomes of the study’s
usage models. This sample characteristic should be
considered when interpreting the results, particularly from a
practical perspective. Brands that market exclusively to males
should carefully interpret the study’s results for their
respective business contexts. Additionally gender differences
may also impact future theoretical work in this area.
When using decision tree analysis (DTA), replication of tree
structure over multiple samples bolster the ability to
generalize findings. Though cross-validation analyses suggest
stable model structure for both comparisons among the
sample data, replication of the tree structure using new
datasets would be ideal. Likewise, though the sample size is
considerably larger than those used for traditional statistical
models, extremely large sample sizes provide additional power
for CHAID which considers each case in its algorithm.
Though the data are recent (i.e. June 2010), given the rapid
pace of change in interactive capabilities, the applicability of
the research directly to markets is potentially short-lived.
Therefore, research should continue to evolve along with this
phenomenon from practical and theoretical standpoints.
Consumer integration of interactive technology will
continue to be an important focus for academic and
marketing researchers in the foreseeable future. From an
academic perspective, the study suggests further work from
the uses and gratifications perspective by examining media
use through economic/utilitarian versus social/entertainment
purposes. From a practical perspective, actionable research in
this particular area is promising due to the availability of rich
secondary data sets that reflect a range of actual behavior.
Therefore, replication studies for interactive technology and
usage are needed to build direct practical knowledge which
can also lead to theoretical knowledge through identification
of behavioral patterns.
References
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making”, Journal of Business Research, Vol. 58, pp. 569-75.
Marguerite Moore
Volume 29 · Number 6 · 2012 · 436–444
442

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pp. 3-37.
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shopping preferences and intentions: exploring the role of
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Appendix
Measure items *
Thinking specifically about specifically about clothing, shoes
and accessories retailers which of the following ways do you
shop?
.
Sign-up for e-mail or text.
.
Follow a retailer or brand on Twitter.
.
Like a retailer or brand on Facebook.
.
Read or post on retailer blog.
.
Download retailer widget.
.
Searched for coupons or promotional codes online.
Which of the following activities, if any, have you engaged in
the past six months?
.
Downloaded a retailer or shopping “app” to my mobile
device.
.
Downloaded a game or entertainment “app” to my mobile
device.
.
Downloaded a utility “app” to by mobile device.
.
Downloaded a social networking “app” to my mobile
device.
.
Downloaded coupons to my mobile device.
.
Researched product from my mobile device while
shopping in store.
.
Researched product online before making a purchase in
store.
.
Signed-up to receive e-mails from a retailer.
.
Purchased a product online.
.
Downloaded and used coupons for an in store purchase.
.
Downloaded and used coupons for an online purchase.
.
Spent time on a social networking site (Facebook, Twitter,
blog, etc.).
.
Engaged in social networking conversations online
(posting, chatting, etc.).
.
Accessed social networking site on my mobile device.
.
Accessed retailer website on my mobile device.
*Response categories for all items: yes/no.
Corresponding author
Marguerite Moore can be contacted at: Marguerite_moore@
ncsu.edu
Executive summary and implications for
managers and executives
This summary has been provided to allow managers and executives
a rapid appreciation of the content of this article. Those with a
particular interest in the topic covered may then read the article in
toto to take advantage of the more comprehensive description of the
research undertaken and its results to get the full benefits of the
material present.
Over the last two decades or so, technology has continued to
advance at an unparalleled rate. This means that consumers
in the US born between 1982 and 2000 are surrounded by
technology in most aspects of their everyday lives.
Furthermore, the ubiquitous nature of technology has
produced “global homogeneity” for this so-called Millenial
Generation. One significant consequence is the emergence of
cross-national cohorts of individuals whose attitudes and
behaviors are comparable.
The pervasive role of technology in the lives of Millenial
consumers is widely acknowledged in academic circles. Some
understanding of what factors motivate usage of these
technologies has also occurred. However, empirical research
into the use of interactive technologies among this consumer
cohort remains limited considering the rate of adoption.
Interactive media and its significance among younger
consumers has attracted the attention of researchers
exploring the internet’s use for communication and business
purposes. But the approaches adopted thus far have
concentrated more on attitudes and intentions rather than
actual usage behavior.
Marguerite Moore
Volume 29 · Number 6 · 2012 · 436–444
443

It is the opinion of several scholars that users and gratification
theory (UGT) provides an established framework for
understanding how young consumers use interactive media.
That selection and usage of media is goal-driven is a key
premise of UGT, which also purports that a level of gratification
is the desired end-result. Individuals regard different media
types as unique and thus rivals for their attention.
Previous work involving Scottish students indicated that
“entertainment and social stimulation” motivated their use of
mobile phones. By comparison, information seeking to inform
buying decisions inspired minimal interest. A conclusion of
this and other studies was that marketing appeals using
interactive media may not prove effective.
Adoption of interactive technologies is also influenced by
consumer involvement and persuasion, the literature reports.
For instance, evidence shows that internet surfing occurs more
frequently among adolescents who prefer “cyber
communication” than those who mainly interact face-face-face.
There is also evidence that internet-savvy teens strongly
influence family use of marketing media accessed online. Such
consumers are also seemingly unresponsive to persuasion
attempts and are negative towards those responsible for them.
A typical marketing strategy is to group consumers into
different age cohorts. It is widely accepted that the attitudes
and behaviors of members within each generation are shaped
by significant events which they experience as they grow up.
Generational cohort theory (GCT) thus incorporates
knowledge of these unique characteristics to target
consumer groups more effectively.
The population born between around 1982 and 2004 are
labeled as Millenials, a generation which interests marketers
due to its size and growing market influence. Generation X
consumers represent the preceding era and this cohort is
much smaller then Millenials or Baby Boomers, identified as
those born between 1943 and 1960. This generation is
renowned for being “values driven” and enticed by instant
gratification. But harsh economic realities have necessitated a
more frugal attitude among these consumers in recent years.
In the present study, Moore aims to acquire knowledge of
ways in which younger consumers use the interactive
technologies available to them. Her analysis involves
comparing Millenial consumers with both Generation X and
Baby Boomers. Various factors are considered to explore
interactive marketing usage in relation to the purchase of
clothing, shoes and accessories. Recent usage not linked to a
particular marketing context is likewise addressed.
A nationwide consumer panel was used to obtain secondary
data pertaining to 3,289 of US consumers, all identifying
themselves as the main shopper for their household. The total
sample consisted of 616 Millenials, 1,552 Generation X and
1,121 Baby Boomers. Demographic information was
recorded for each cohort and data examination showed that:
.
females accounted for at least 80 percent in all three
cohorts;
.
ethnic diversity was greatest in the Millenial sample and
least among Baby Boomers;
.
Generation X consumers generally reported the highest
incomes, with Baby Boomers second largest; and
.
home ownership was most evident among Baby Boomers,
followed by Generation X then Millenials.
Moore uses decision tree analysis (DTA) as it is a proven
means of scrutinizing relationships in diverse data. It reveals
distinct ways in which Millenial consumers routinely engage
with interactive media. When measured against the other
generational cohorts, analysis exposed more pronounced
differences to Baby Boomers than with Generation X
consumers.
In comparison with the Generation X population,
Millenials are more inclined to:
.
habitually use interactive technologies for marketing
purposes;
.
connect with retailers and brands using both mobile
devices and conventional internet methods; and
. use their mobile device for social networking but engage in
the activity less.
Millenials indicate a markedly greater integration of
interactive media than Baby Boomers in all but one aspect:
.
deploying mobile device and traditional online tools;
.
using internet resources for functional and entertainment
reasons; and
.
interactive links with brands or retailers via blogs and
couponing activities.
Despite their considerably greater engagement with
interactive media, Millenials indicated a much lower
inclination to make online purchases than either or the two
older cohorts.
The author believes that scope thus exists for brands to use
“interactive promotional tactics” as a means to gain their
custom. Marketing which cover multiple channels will have a
greater chance of success since the current work indicates that
Millenial consumers prefer to access interactive media via
their mobiles phones. In contrast, non-mobile connections to
social networking sites are more favored by older consumers.
Evidence shows that both functional and entertainment
reasons prompt Millenials to use of interactive technology. It
is crucial that marketers who target this cohort recognize this
and integrate “practical uses of technology” into the
interactive media used. Providing value in respect of
information and time could help lessen the skepticism that
currently prevails. Moore additionally advises against overkill,
pointing to other research showing how too much contact and
exaggerated promises can alienate the Millenial consumer.
Clear communication, efficient technology and delivering on
promises are therefore imperative.
Minimum age requirements meant that Millenials aged below
18 were not included in this study. Future research might
therefore investigate potential differences in usage between such
consumers and older members of their generational cohort.
The author also acknowledges the sample bias towards female
and Caucasian subjects and warns against generalizing results to
a wider population without further study. And although data
used was recent, marketers and practitioners are made aware
that the applicability of findings could be transient due to the
ongoing rapid evolvement of interactive media.
(A prećis of the article “Interactive media usage among millennial
consumers”. Supplied by Marketing Consultants for Emerald.)
Marguerite Moore
Volume 29 · Number 6 · 2012 · 436–444
444
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