Predictive Factors of Attitude Towards Online Disruptive Advertising

Introduction

The rapid development of advertising technology is changing the way brands and consumers interact. The increasingly collaborative economy happens not only in the service and the commerce industry but also in the marketing industry. Over the course of the years, the marketing and advertising industry has become more and more data-driven, and less reliant on static demographic information (Forbes Insights, 2017). Perhaps the impact sharing economy has on the advertising industry can be seen in the rise of data co-op practices (Swant, 2016). It is a practice where brand and business owners aggregate and compare non-transparent data across verticals to reveal cross-industry behaviors and trends, providing marketers with insight to map and formulate marketing objectives (Ismail, 2015). Some of the major examples of these verticals are marketplace such as e-commerce and service aggregators, as well as a content platform such as news publication (Everstring, 2020). These verticals register behavior in response to a content (often in one form of advertising or the other) in real-time. The data is then compiled by a third party co-op provider to be shared between business owners.

This data-sharing practice is a double-edged sword. On one side, consumers’ needs based on their psychographics and demographics dimensions are increasingly recognized, mapped, and analyzed as tech firms take more and more interest in our digital footprints and identities. People’s activities, profiles, interests, and even location are most probably logged in on Google, while their connections, preferences, and personal data are harvested by Facebook and its networks through a ‘pixel’—a code that tracks visitor’s conversion from Facebook Ads network (Newberry, 2017). On the other, while innovative and disruptive, the acquisition and the management of consumer data within the framework of these technologies can be done in unethical ways that might violate users’ privacy (Porter, 2018). One of the biggest concerns in practicing data co-op is the integrity (Ismail, 2015) and carefulness of the provider and practitioners in handling the data to prevent breaches, something which even giant tech firms like Facebook failed to do (Blumberg, 2020).

Some authors identified a number of changing trends brought by disruptive technology in advertising (Cox, 2016; Rezvani, 2017); they are (1) Jobs that manage and draw insights from big data (e.g. programmatic advertisers, digital marketers, data scientist), will be prioritized and increasingly utilized by brands and businesses, (2) Conversation becomes material for digital advertising, and the ability for “open engagement” and “social listening” online are essentials in producing effective digital ads, (3) Rejection of potential customers against disruptive ads (for example by installing AdBlocker on internet browsers) stems from consumer awareness not wanting to experience the “alienated self” when they are being the “object of manipulation” by digital advertisers, (4) Human interpretations and creativity are required in processing the information generated by bot algorithms to produce effective targeted advertising, and (5) The digital marketing field is decentralized, because today—unlike the advertising world of the past—anyone with minimum knowledge of programming, can adopt, build, customize open source, open access, and “plug-and-play” program that puts up ads on the web. Small institutions are increasingly savvy in utilizing native advertising. Bloggers and micro-influencers (rather than an influencer with a tremendous amount of follower base) become increasingly popular for brands to be partners with (Wissman, 2018). The fifth characteristic is closely related to the basic principle of disruptive innovation which states that “a process whereby a small company with fewer resources is able to succeed” (Christensen, Raynor, & McDonald, 2015, para. 6). Finally, the advancement in sharing economy has made marketing: (1) more effective due because of the data accuracy; (2) scalable because multiple marketing campaigns can be executed and tested at the same time across the globe; and (3) cost-effective and time-efficient (Ismail, 2015).