considering advertisements for related products), if the primary task (engaging with Facebook)
induces high arousal, it can lead to cognitive depletion that impairs their performance on the
secondary task (processing a mobile ad) (Eysenck 1982; Fedorikhin and Patrick 2010). In terms
of both brand recall and attitudinal measures, the impact of a mobile advertisement can suffer
when the prospective consumer is engaged in performing a task that triggers more arousal. Prior
research mostly treats arousal as a generalized antecedent to attention (MacInnis and Jaworski
1989; Petty and Cacioppo 1986), but separating and experimentally identifying the interaction of
these two constructs could provide an opportunity for studying the persuasiveness of mobile
display ads. For example, literature that reveals behavioral consequences of affect (Andrade
2005; Rook and Gardner 1993) identifies conditions that may account for the weak impact of
display advertising in dual-task settings (Pham 1992), such as when high involvement with the
advertising channel lowers subsequent recognition of the advertised brand.
The example of a consumer who browses a Facebook feed and gets exposed to
advertisements at the same time also suggests the need to consider network effects. Some
consumers may be influenced by information about others’ decisions, and word-of-mouth
(WOM) communications tend to exert more powerful influences on consumer decisions than
firm-initiated communication (Herr, Kardes, and Kim 1991; Mahajan, Muller, and Wind 2000).
The emergence of sophisticated customer interaction databases that feature mobile
communication, instant messaging, and social network data allow marketers to study WOM
processes at the micro level (Nitzan and Libai 2011). For example, Nielsen is adding Twitter-
and mobile-linked measures of popularity to its traditional television ratings (Sharma and
Vranica 2013). Related network literature (Aral, Muchnik, and Sundararajan 2009; Katona,
Zubcsek, and Sarvary 2011) seeks to quantify interpersonal influences using social network data.