Abstract

Research into permission-based mobile marketing is increasingly common due to the widespread adoption of mobile technology and its use as a communication channel. Yet few studies have attempted to analyze the factors that determine attitudes toward mobile advertising while simultaneously considering: the links among them and consumers' intentions, behavior, and/or cognitive and affective variables simultaneously. The present research therefore sought to deepen understanding of the antecedents and consequences of attitudes toward permission-based mobile advertising. More specifically, it sought to identify the antecedents of attitudes toward mobile advertising and the bridges between these attitudes and consumers' intentions upon receiving advertising on their mobile devices. To this end, a causal model was proposed and tested with a sample of 612 mobile phone users that was collected from a panel of Spanish adults who receive advertising on their mobile phones in the form of SMS text messages. The structural model used was validated using the partial least squares (PLS) regression technique. The results show that the greatest influence was that exerted by positive emotions on feelings, suggesting that positive emotions have an indirect effect on attitude toward mobile advertising. This influence was even greater than their direct effect. Another important, though less powerful, effect was the influence of attitude on behavioral intentions to receive mobile advertising. In contrast, the influence of cognitive variables on attitude was less relevant.

Highlights

  • The high potential that mobile devices offer as a medium for delivering advertising to consumers is based on several key factors

  • partial least squares (PLS) path modeling is frequently used in researches referred to Theory of Reasoned Action (TRA), Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB) (Henseler et al, 2016)

  • There is a debate about which technique is more appropriate, if PLS (e.g., Wold, 1982; Lohmöller, 1989), or covariance based structural equation modeling (SEM) (e.g., Jöreskog and Wold, 1982), we decided to use PLS due to the higher benefits it provides in this kind

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Summary

Introduction

The high potential that mobile devices offer as a medium for delivering advertising to consumers is based on several key factors. Companies tend to believe that mobile advertising has a high capacity to reach almost anyone anywhere at any time (Haghirian and Madlberger, 2005; Richard and Meuli, 2013) This belief is supported, among other things, by the high penetration rate of mobile phones among end users, which became quite significant in the 1990s (e.g., Khalifa and Shen, 2008; Zhang and Mao, 2008). Today, it continues to increase worldwide, making it possible to understand the large amounts of time consumers spend on their devices (e.g., Bart et al, 2014). These concerns illustrate the need for permission-based mobile advertising (PBMA), that is, advertising requiring individuals to give their permission before they can receive any type of advertising message (Godin, 1999; Varnali and Toker, 2010)

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