Article Preview
TopIntroduction
Mobile and smart devices are changing the way customers and businesses interact with each other, such as through personalized experiences and advertisements (Arya, Bhadoria, & Chaudhari, 2018; Bhadoria, Chaudhari, Tomar, & Singh, 2017). eMarketer (2015) showed that about 88% of U.S. firms are using at least one social networking site (Twitter, Facebook, Instagram, Pinterest, Snapchat, etc.) for marketing purposes. Internetworldstats (2020) reported that this trend is expected to spread to other countries with a high literacy and Internet-penetration rate, such as Jordan (85.3%). According to a Statista (2019) report, there has been a dramatic increase in the number of social media users, from 0.97 billion in 2010 to 2.82 billion in 2019, worldwide. Facets of mobile technology that have been discussed and debated in the literature include ubiquity, deep learning, privacy issues, and personalized advertisements (Gupta, Agrawal, & Yamaguchi, 2019; Gupta, Yamaguchi, & Agrawal, 2018; Jararweh et al., 2017). Personalized marketing and advertisements via mobile and social media outlets are expected to grow (Tucker, 2014).
Research showed that marketers and advertisers in the United States will spend around $43.9 billion this year on mobile ads (Benes, 2019). Biometric computing and recognition have become prevalent in personalized marketing (Arya & Bhadoria, 2019), which is why mobile devices are highly individualized and are important personal communication tools (Bacile, Ye, & Swilley, 2014). Shawky (2019) argued that less accessible consumer segments can be reached through social media and mobile platforms ads. Existing literature showed that personalized mobile ads are antecedents of high brand loyalty, brand attachment, brand engagement, positive attitude, perceived quality, and intention to forward the ads (e.g., Di Gangi & Wasko, 2016; Grewal et al., 2016; Shanahan, Tran, & Taylor, 2019; Tucker, 2014; Walrave et al., 2019).
In this study, Mobile Ad Wearout represents excessive exposure to the same marketing and advertising campaigns. Mobile and pop-up ads do not only affect the human–mobile interaction experience, but also install negative feelings and, as such, are generally unwanted marketing and advertising channels. Past scholars focused mostly on the positive outcomes of mobile ads (Tucker, 2014; Walrave et al., 2019), which misled marketing managers to think that repetition and high exposure to personalized mobile ads is a way to attract and retain consumers. However, mobile ads can be disturbing due to the nature of their execution (e.g., too loud or too long) or placement (e.g., too many or too frequent), and their execution process may cause unfavorable spillover effects on marketing and advertising efforts (Brechman et al., 2016). Extant theoretical discussions illustrated that Mobile Ad Wearout can cause privacy concerns, for example, intrusiveness and irritation among consumers.
Consequently, the human–device interaction experience can be a barrier for engagement between scientific end-user developers (Segal, & Morris, 2011) and can affect usage behavior among social media users (Amos, Zhang, & Pentina, 2014; Di Gangi & Wasko, 2016). In this study, the human–device interaction experience is users’ experiences with Mobile Ad Wearout. Surprisingly, studies that investigated Ad Wearout were mostly in an offline context and in Western nations (Naik et al., 1998; Pechmann & Stewart, 1990; Peggy, 1999; Royo-Vela & Meyer, 2016). To the best of the author(s) knowledge, there are no studies on Mobile Ad Wearout in the Arabian context. The aim of this paper is to examine the impact of Mobile Ad Wearout on perceived consumer intrusiveness and irritation, and the impact of perceived consumer intrusiveness and irritation on consumer loyalty and engagement. A partial least squares structural equation modeling (PLS-SEM) technique is deployed to examine these relationships. The PLS-SEM technique showcases the variance explained in the outcome variable by the predictor variable (Hair et al., 2017), and its “causal-predictive” power ensures a balance between theoretical construct explanation, causes, and prediction (Abubakar, & Al-zyoud, 2020; Shmueli et al., 2019).
The remainder of this paper is organized as follows. In Section 2, we review the literature on the concepts under investigation and conclude with hypotheses development. In Section 3, we present the research instruments, data collection, and sampling approach. Section 4 presents the research data analyses techniques and results, and Section 5 presents a discussion of the findings, implications, research limitations, and future recommendations. Section 6 presents the concluding comments.