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Contents lists available at ScienceDirect
International Journal of Information Management
journal homepage: www.elsevier.com/locate/ijinfomgt
The efficiency of mobile media richness across different stages of online
consumer behavior
Chi-Hsing Tseng
a,⁎
, Li-Fun Wei
b
a
National Pingtung University, Taiwan
b
Skuld Equation Information Service, Taiwan
ARTICLE INFO
Keywords:
AISAS
Model
Mobile advertisement
Media richness
Product characteristics
ABSTRACT
The popularity of mobile devices has ushered in the prosperity of mobile commerce, yet research on mobile
advertising and mobile marketing remains scant. Marketing ads possessing higher media richness generally have
a positive effect on consumer decision-making, because rich media conveys more information, but mobile ads
with richer media imply higher costs for both the marketer and the audience. The limitations of mobile devices
have further highlighted the difficulty of mobile advertising and the issue of advertising costs. Selecting which
media to deliver the appropriate information is the latest research trend, but few studies have applied the media
richness theory to explain mobile ads’ effect on consumer behavior. This research thus explores the impact of
media richness on consumer behavior at different AISAS (attention, interest, search, action, and share) stages,
adopting experimental research, convenient sampling, and online questionnaire to collect data. From a total of
424 valid questionnaires, we find that media richness has a greater influence on the three early stages of AIS
while having a lower impact on the later stages of AS. This research thus suggests that firms employing mobile
ads should choose high richness media for those potential customers who are at the early stage of consumer
behavior (AIS). For those who at the later stage (AS), it is good enough for marketers to utilize medium richness
mobile ads. Following this suggestion, marketers can place mobile ads more precisely, thus improving the
likelihood of a reduction in advertising costs for both the marketer and audience. As mobile ads with high media
richness are more effective for high perceived risk products, firms need to use high richness media when they are
promoting high perceived risk products even when potential consumers are at the later stage of AS. This research
contributes to marketers dedicated to using a mobile advertisement strategy and helps refine both online con-
sumer behavior and the media richness theory when including the context of mobile commerce.
1. Introduction
The invention of mobile phones has led to a new era of information
and communication technology that has also drastically changed peo-
ple’s lives (Schadler, Bernoff, & Ask, 2014). People can now use their
mobile phone at anytime and anywhere, making it easier for marketers
to access potential customers. Thus, recent developments in informa-
tion technology (IT) largely focus on the use of mobile technologies to
improve firms’ marketing capabilities (Maduku, Mpinganjira, & Duh,
2016). The popularity of mobile devices has ushered in the prosperity of
mobile commerce (m-commerce). Retail m-commerce accounted for
roughly one-third of total e-commerce sales in the U.S. in 2017, and this
share is expected to surpass 50% by 2021 (eMarketer, 2017),
1
thus
bringing about the importance of the development of mobile
advertising for m-commerce. Between 2016 and 2019, mobile ad
spending is on pace to nearly double, hitting $195.55 billion, which
would cover 70.1% of digital ads spent as well as over 25% of total
media ad spending globally (eMarketer (Producer) (2015)).
2
Along with this dynamical shift, marketers must consider how their
mobile strategies interact with their overall advertising and marketing
strategies (Grewal, Bart, Spann, & Zubcsek, 2016). Some scholars
highlight the role of mobile technology adoption in consumer purchase
decision processes, as well as some trends in online and mobile ad-
vertising and mobile marketing (Shankar, Venkatesh, Hofacker, & Naik,
2010). Researchers suggest that online consumer behavior is quite
different in the mobile space as compared to the behavior consumers
exhibit when they utilize PC devices (Raphaeli, Goldstein, & Fink,
2017). First, m-commerce makes it easy for consumers to make
https://doi.org/10.1016/j.ijinfomgt.2019.08.010
Received 21 July 2018; Received in revised form 30 August 2019; Accepted 30 August 2019
⁎
Corresponding author.
E-mail address: chihsingtseng@mail.nptu.edu.tw (C.-H. Tseng).
1
Retrieved from https://on.emarketer.com/rs/867-SLG-901/images/eMarketer_Roundup_Mobile_Marketing_Trends_2018_3.pdf.
2
Retrieved from http://www.emarketer.com/Article/Mobile-Ad-Spend-Top-100-Billion-Worldwide-2016-51-of-Digital-Market/1012299.
International Journal of Information Management 50 (2020) 353–364
Available online 28 September 2019
0268-4012/ © 2019 Elsevier Ltd. All rights reserved.
T
transactions at anytime and anywhere; mobile users may even perform
searches and compare products and prices from within physical store-
fronts (Sumita & Yoshii, 2010). This characteristic may result in con-
sumers choosing to shop online rather than offline. Second, there is the
possibility that m-commerce might raise more privacy and security
concerns among users than e-commerce; not only can more information
be collected on customers, but due to the fact that data are transferred
wirelessly, it is easier to intercept such data (Chong, 2013). Potential
customers may therefore be more hesitant to browse mobile ads, thus
increasing the difficulty and complexity of mobile marketing. Third,
mobile devices possess the disadvantages of small screens and low us-
ability (Ipsos, 2015). Recent studies suggest that searching on small
screens requires more difficult maneuvers of scrolling, thus limiting the
type and amount of information that consumers can retrieve (Shankar
et al., 2010). Considering the above three characteristics of mobile
devices, Raphaeli et al. (2017) suggest that sessions conducted through
mobile devices are more likely to consist of task-oriented behavior,
whereas sessions conducted through personal computing devices are
characterized by a more exploration-oriented browsing behavior,
highlighting another issue in how m-commerce differs from e-com-
merce. Issues of mobile promotions and mobile shopper marketing still
need further exploration (Varnali & Toker, 2010), because research on
mobile advertising and mobile marketing remain sparse (Grewal et al.,
2016). To fill this gap in the literature and in response to management’s
dilemma over m-commerce, the study looks into how mobile ads affect
consumer behavior in the m-commerce context.
The media richness theory regards communication channels as
possessing a set of objective characteristics that determine their cap-
ability to carry information, with rich information being more capable
of reducing equivocality versus lean information (Daft & Lengel, 1986).
Previous research on the media richness theory mainly focused on the
determinants of media choice (Daft & Lengel, 1986). McGrath and
Hollingshead (1993) propose the task-media fit hypotheses, suggesting
the fit of media choice according to decision-making tasks. Thus,
scholars began to apply the media richness theory to explore the impact
of the choice of media on consumer decision-making [e.g., Lim &
Benbasat, 2000;Maity & Dass, 2014;Maity, Dass, & Kumar, 2018]. In
general, the richer the media content is, the greater is the advertising
effect, because rich media is able to convey more information (Lim &
Benbasat, 2000;Suh, 1999;Trevino, Webster, & Stein, 2000).
The primary goal of marketers is to design the proper content of
mobile advertising to attract consumers (Shareef, Dwivedi, Kumar, &
Kumar, 2017), but mobile ads possessing richer media incur higher
advertising production costs and users’ traffic costs. The limitations of
mobile devices (Ipsos, 2015;Raphaeli et al., 2017;Shankar et al., 2010)
have further highlighted the issue of advertising costs. Thus, selecting
the appropriate media to help consumers make decisions efficiently and
effectively is crucial in m-commerce (Palvia, Pinjani, Cannoy, & Jacks,
2011). Following this strand of research, we apply the media richness
theory combined with the hypotheses of task-media fit (Maity & Dass,
2014;McGrath & Hollingshead, 1993) to explore appropriate media for
mobile ads.
Drawing from the shift of online shopping behavior and in response
to rapid technological changes, Dentsu
3
proposes the latest purchase
model containing five processes: attention, interest, search, action, and
share (AISAS) (Kono, 2009). The AISAS model especially emphasizes
the importance of communicating via the Internet with online com-
munities (i.e., stages of search, action, and share), thus meeting the
recent online business trend of two-way communications. The AISAS
model more accurately describes online consumer behavior due to the
interactive and personalized nature of the Internet (Wei & Lu, 2013). As
consumers increasingly use mobile devices to access the Internet, it is
important to understand how such use is changing online behavior in
m-commerce. Nevertheless, the literature still lacks research relating to
advertising issues in response to each stage of consumer behavior,
let alone mobile advertising. Grewal et al. (2016) are the first to point
out that the phase of the purchase decision process and the temporal
dynamics of the choice task will affect mobile ad effectiveness. Re-
search relating to both media richness and online consumer behavior
provide very limited suggestions for m-commerce practitioners. To
close the research gap, it is worthwhile to adopt the AISAS model to
explore how marketers choose different mobile media richness across
different stages of consumer behavior. Accordingly, the first research
question is how mobile media richness affects different AISAS stages of
online consumer behavior.
Consumers generally engage in different purchasing decision pro-
cesses among different product categories. For some products, quality
can be determined before the product is actually purchased, while for
other products this is nearly impossible (Lowengart & Tractinsky,
2001). When consumers make a purchase decision they will gather
different levels of information according to the product characteristics.
Thus, researchers always include product type as the moderator when
studying the topic of consumer decision-making (Maity & Dass, 2014;
Wei & Lu, 2013). Nevertheless, few studies have explored the moder-
ating effect of product characteristics over all stages of consumer be-
havior, not to mention mobile shopping behavior. Accordingly, the
second research question is how a product type moderates the relation
between mobile media richness across all stages of online consumer
behavior.
Together with the prosperity of mobile commerce, the issue of ef-
fective mobile advertising is turning more and more important.
However, there is still limited research relating to the match between
mobile media choices and online consumer behavior. Our study thus
contributes to both academics and practitioners in three areas.
Primarily, this research looks to find the fit between mobile ads and the
stages of online consumer. Our findings should provide useful sugges-
tions for m-commerce practitioners to place mobile ads more precisely,
thus improving the likelihood of a reduction in advertising costs for
both the marketer and audience. This study also extends the hypotheses
of task-media fit (McGrath & Hollingshead, 1993;Maity & Dass, 2014)
to the mobile advertising area and adds new insights for m-commerce
researchers to address research gaps in mobile advertising. Second,
marketing issues of online shopping at each stage of the consumer de-
cision are different from the physical world (Butler & Peppard, 1998).
Moreover, online consumer behavior with mobile devices is quite dif-
ferent from PC devices (Raphaeli et al., 2017), and media choice has
become a very important decision (Palvia et al., 2011), because small
screens and low usability of mobile devices might hinder the use of the
m-commerce channel (Ipsos, 2015;Shankar et al., 2010). As such, this
study explores the impact of mobile ads with various degrees of media
richness on consumer behavior under different AISAS stages. Thus, our
second contribution is to refine both the media richness theory and the
online consumer behavior model by taking m-commerce into con-
sideration. Third and finally, this research provides better insights into
the impacts of product characteristics on the relation between media
richness and all stages of consumer behavior. The research offers fur-
ther advice to m-commerce practitioners selling different commodities
and helps close the research gap in the moderating effect of product
characteristics over all stages of consumer behavior. Overall, our find-
ings contribute to both academia and real-world mobile marketing si-
tuations.
2. Theoretical background
2.1. Consumer behavior in the m-commerce context
The diffusion of the Internet has changed consumer purchasing
behavior very substantially, with the biggest change being that con-
sumers now find it easier to search for, share, and purchase products
3
Dentsu is the largest advertising agency in Japan.
C.-H. Tseng and L.-F. Wei International Journal of Information Management 50 (2020) 353–364
354
through the Internet (Kono, 2009). In response to rapid technological
changes, Dentsu proposed a new model, AISAS (attention, interest,
search, action and share), which more accurately describes online
consumer behavior due to the interactive and personalized nature of the
Internet (Wei & Lu, 2013). The AISAS model of Dentsu suggests that
after consumers become interested in a product, they will search for
product-related information through the Internet, followed by taking
action to purchase it. Consumers subsequently share their experiences
and assessments over the Internet with other potential consumers,
feeding back to the search stage on the Internet (Kono, 2009). Thus,
marketers are not only posting messages on media for notification, but
they are also trying to assist consumers at obtaining detailed informa-
tion that they are interested in, making comparative studies, and
sharing actual feelings of use after purchasing with other consumers
(Ritsuya, 2008). Marketers even need to motivate customers to parti-
cipate in branding co-creation in social network sites (SNSs) (Kamboj,
Sarmah, Gupta, & Dwivedi, 2018).
Scholars further point out that the credibility of the introducer of
the advertisement (Shareef, Mukerji, Dwivedi, Rana, & Islam, 2019),
consumers’ attitudes toward SNSs advertisements (Shareef, Mukerji,
Alryalat, Wright, & Dwivedi, 2018), and customers’ perceived help-
fulness of online reviews (Ismagilova, Dwivedi, & Slade, 2019) are
crucial factors in persuading consumers to like social media-based
marketing and to make a purchase. As consumers have greater social
interactions with each other, consumers on SNSs are prone to impulse
buying behavior owing to such social interactions (Alalwan, Rana,
Dwivedi, & Algharabat, 2017;Arora, Bansal, Kandpal, Aswani, &
Dwivedi, 2019;Dwivedi, Kapoor, & Chen, 2015;Shiau, Dwivedi, & Lai,
2018;Xiang, Zheng, Lee, & Zhao, 2016); this has led to the increasing
importance of social interactions when researchers are exploring con-
sumer behavior in the m-commerce context. Marketers should first
utilize social media sites or mobile instant messaging to place mobile
ads to attract the audience’s attention and interest. Next, marketers
need to interact with potential customers and assist them in searching
for further information and promote them to make a purchase. Finally,
marketers should encourage customers to share ads or experiences with
their friends. The AISAS model emphasizes the importance of SAS
(search, action, and share) for Internet shopping (Kono, 2009), thus
meeting the recent online business trend of social interactions, and so
the AISAS model is useful for exploring mobile consumer behavior.
Under the development of the Internet, consumers tend to search
information via it and share their experiences with others, and so online
advertisements and users’ comments on social media have become the
main factor affecting consumers’ purchase decision (Kapoor et al.,
2017;Tseng, Kuo, & Chen, 2014). Grewal et al. (2016) first explore the
relationship between the consumer behavior process and the effec-
tiveness of mobile ads. Mobile ads can stimulate consumers’ recognition
of an unmet need and push them to go further for a purchase in their
immediate vicinity. In the post-purchase phase, consumers often review
their purchases on social media, where the advertiser in turn might
place related mobile ads for the consumers’ friends to see alongside the
review (Grewal et al., 2016). Thus, marketers need to put different
mobile ads according to consumers’ stage in the shopping process.
The limitations of mobile devices have made the influence of mobile
ads on consumer behavior more complex. Raphaeli et al. (2017) con-
clude there are three important characteristics of m-commerce. First,
mobile devices help facilitate transactions anytime and anywhere for
users and provide timely information (Sumita & Yoshii, 2010). Second,
m-commerce raises greater privacy and security concerns among users
(Chong, 2013). Third, the small screens and low usability of mobile
devices may hamper long and complex use of an m-commerce channel
(Ipsos, 2015). The first characteristic encourages marketers to apply
mobile advertising, while the second and third characteristics increase
the burden of mobile device users because of higher information ac-
quisition and search costs. As consumers increasingly use mobile de-
vices to access the Internet, it is important to understand how such use
is changing online behavior in m-commerce. Mobile advertising should
be more precise and have a better fit with the different statuses of
consumers.
2.2. Media richness theory
2.2.1. The concept of media richness
The media richness theory derives from the information processing
theory and refers to communication efficiency among individuals af-
fected by media fitness (Daft & Lengel, 1983,1984,1986). Media
richness denotes a medium’s ability to convey a variety of information
(Daft, Lengel, & Trevino, 1987;Vazquez, Dennis, & Zhang, 2017).
Communication media vary in the richness of information processed,
based on feedback capability, communication channels used, source,
and language (Daft & Lengel, 1983,1984). Richer media allow users to
communicate in a more immediate way, improving their understanding
of ambiguous messages (Dennis & Kinney, 1998).
From the perspective of the media richness theory, high performing
managers should match media having the appropriate amount of in-
formation richness with the characteristics of a specific task (Daft &
Lengel, 1986). Many researchers applied the media richness theory and
suggest that the content of mobile commercials determines their success
(Shareef et al., 2017). Researchers further note that media richness can
predict customers’ choice of communication media (Lee, Kozar, &
Larsen, 2009), decision quality (Kahai & Cooper, 2003), user satisfac-
tion, and usage of instant messaging applications (Anandarajan, Zaman,
Dai, & Arinze, 2010;Deng, Lu, Wei, & Zhang, 2010;Ogara, Koh, &
Prybutok, 2014). Tseng, Cheng, Li, and Teng (2017)) first explore how
media richness contributes to customer loyalty towards mobile instant
messaging (MIM). In general, the richer the media content is, the
greater is the advertising effect, because rich media is able to convey
more information (Lim & Benbasat, 2000;Suh, 1999;Trevino et al.,
2000).
In the m-commerce context, marketers should especially design
mobile ads with adequate information, because mobile devices have
small screens and possess low usability, which may hamper long and
complex use of an m-commerce channel (Ipsos, 2015;Raphaeli et al.,
2017). The task-media fit hypotheses, as extensions of the media rich-
ness theory, suggest the fit of media choice according to decision-
making tasks (McGrath & Hollingshead, 1993). Drawing from these
hypotheses, our research infers that mobile ads might require varying
degrees of media richness depending on different situation. It is
worthwhile to apply the media richness theory to explore the influence
of media richness on different stages of consumer behavior in the m-
commerce context.
2.2.2. Mobile advertising and media choice
The Mobile Marketing Association defines mobile marketing as “a
set of practices that enable organizations to communicate and engage
with their audience in an interactive and relevant manner through any
mobile device or network”.
4
A mobile device provides a gateway to the
relationship between consumers and the retailer, making it an ideal
supplementary channel for distance selling and physical retailing
(Shankar et al., 2010). For digital marketers or advertisers, mobile
media are important channels due to their potential to support one-to-
one and one-to-many communications both cheaply and effectively
(Watson, McCarthy, & Rowley, 2013).
Among many mobile service technologies (Barnes, 2002), SMS
(short message service) and MMS (multimedia message service) are
important mobile advertising tools that enable firms to deliver mes-
sages to potential customers. Gavilan, Avello, and Abril (2014)) find a
greater impact of MMS mobile ads on vividness and elaboration, while
SMS mobile ads have a greater influence on the quantity dimension.
4
http://mmaglobal.com/wiki/mobile-marketing (2018-3-4).
C.-H. Tseng and L.-F. Wei International Journal of Information Management 50 (2020) 353–364
355
Several mobile marketing researchers also have identified that phone-
based SMS content is the key driving force for the success of launching
this promotional channel (Shareef et al., 2017). Although electronic
media is not as rich as face-to-face communications, because of no voice
tone with a natural language, mobile devices still possess richness
through video clips, fast feedback, and natural language usage (Vickery,
Droge, Stank, Goldsby, & Markland, 2004). Vazquez et al. (2017) sug-
gest that MIM (mobile instant messaging) channels such as WeChat can
add to richness by incorporating photo sharing, voice messages, video
calls, and “Moments” sharing. MIM can convey not only text messages,
but also multimedia messages like photos and videos. Thus, our re-
search utilizes MIM as the form of mobile ad media and applies the
media richness theory to examine the impact of media richness.
McGrath and Hollingshead (1993) classify media along a continuum
of “increasing potential richness of information” (Suh, 1999), with re-
searchers identifying four types of media along this richness continuum:
text, audio, video, and face-to-face communications. Face-to-face is the
richest medium as it allows mutual feedback and simultaneously con-
veys a variety of cues. Text-based interaction (e.g. texting through
mobile devices or browsing information through text-only cell phone
browsers) is less “rich” than audio, video, or face-to-face interaction
(Maity & Dass, 2014). As interactivity is one of the most critical aspects
associated with the online arena, many researchers have treated it as an
interaction and communication process between people, while another
group of researchers has focused on the technology aspect, whereby
people interact with technical devices (Alalwan, 2018). No matter what
researchers have emphasized, interactivity is an essential factor for
mobile advertising. Combining McGrath and Hollingshead (1993)’s and
Vazquez et al. (2017)’s classification and Alalwan (2018)’s interactivity
concepts, this research separates the media richness of MIM, from
lowest to highest richness, into text messages (texting through mobile
devices), interactive photos (able to click through to get further in-
formation), and interactive video ads (also with the click-through
3. Hypothesis development
3.1. The influence of mobile media richness on consumer behavior
The literature generally agrees that the richer the media are, the
greater the advertising effect will be, because rich media are able to
convey more information (Lim & Benbasat, 2000;Suh, 1999;Trevino
et al., 2000). An improvement in media richness reduces the cost of
information search and increases the number of options that consumers
consider when making choices (Maity et al., 2018). Nevertheless, the
amount and accuracy of information cause customers to perceive that
the information has high quality in the e-commerce context (Song,
Baker, Lee, & Wetherbe, 2012). McGrath and Hollingshead (1993)
propose task-media fit hypotheses and suggest that communication
media aligns along the “increasing potential richness of information”
continuum [Suh (1999), p.297]. Task-media fit hypothesizes (McGrath
& Hollingshead, 1993) that media at the two ends of the continuum are
ineffective for carrying out communication tasks, as they cause dis-
traction (too rich) or are incapable of transmitting the necessary in-
formation (too lean) (Suh, 1999). Thus, it is better to use media that
offer medium richness for the case of choice and negotiating tasks.
Maity and Dass (2014) further explore this literature and investigate
task-media fit in the context of choice tasks, finding that consumers
prefer channels with medium and high media richness for carrying out
complex decision-making tasks. Consumers are likely to undertake
simple decision-making tasks in channels that incorporate low levels of
media richness. Therefore, this research infers that the influence of
mobile media richness might be different due to various task com-
plexities across different stages of consumer behavior.
Grewal et al. (2016) suggest that the phase of the purchase decision
process and the temporal dynamics of the choice task affect mobile ad
effectiveness. Mobile ads can stimulate consumers’ recognition of
unmet needs and help draw them into the store and away from com-
C.-H. Tseng and L.-F. Wei International Journal of Information Management 50 (2020) 353–364
