In recent decades, digital advertising and artificial intelligence (AI) have deeply influenced the overall business landscape. This dissertation is divided into two distinctive phases. Phase 1 discusses how the current literature on digital advertising lacks a holistic picture, especially from the industry’s perspective. Managers and stakeholders need clear, actionable information regarding the various digital advertising domains or tools and their effective deployment methods to optimally market their products and services. Furthermore, the current literature related to digital advertising is also indifferent to mobile and desktop distinctions. For effective utilization of digital advertising budgets, it is important to understand the uniqueness of mobile advertising as compared to desktop advertising. Phase 2 studies AI, highlighting its important implications and discussing factors that can hinder its effective utilization in any project. This dissertation also elaborates on how th
e AI-based chatbot represents a crucial tool in the AI marketing and advertising domains. However, the current literature lacks a conceptual foundation regarding the efficient use of the chatbot and its effective adoption in a company’s existing marketing operations. Furthermore, while the industry considers AI a revolutionary technology that has the ability to improve existing business functions to a new level, which will eventually result in higher profits and efficiency, not all AI projects will have the desired outcome. The current literature themes required further investigation to uncover various factors that can impact the effective utilization of AI in different projects, especially those related to marketing or advertising functions. This dissertation presents that herding behavior in AI technology, issues related to data management practices, and new legal privacy frameworks, such as the General Data Protection Regulation (GDPR), are the most pressing issues related to AI that require further investigation. No existing literature has tried to measure the effects of herding behavior in AI, and the related knowledge streams are limited in terms of a firm’s preparedness regarding data collection and management practices and related new legal privacy frameworks, such as the GDPR.
Therefore, the objectives of this dissertation are as follows: (1) developing a framework for understanding the digital advertising ecosystem, methods, and tools, with the sub-objective of developing a separate framework to highlight the difference between digital advertising in mobiles and desktops, and (2) determining the most important implications of AI in marketing and highlighting factors that can impact the effective utilization of AI in different projects. The motivation behind this dissertation is to identify the research gaps in the current literature in a manner that has practical relevance. For phase 1, the dissertation employs a literature review to drive suitable answers by collecting and analyzing secondary forms of data. In phase 2, the dissertation employs the semi-structured qualitative interview method. In other words, the objective of the first phase of this dissertation, deals with the solid comprehension of digital advertising. Overall, our objective was simply to develop a well-rounded literature manual, which might give complete details about digital advertising and its effective practical implementation, in order to market any product or service in the best way possible. For more acute understanding, it was also necessary to understand the difference of digital advertising on mobiles as compared to desktop. The objective was to clearly distinguish mobile digital advertising from desktop digital advertising, so that marketing budgets can be allocated to these devices systematically, which will leads to better optimization of digital marketing budgets. The objectives related to second phase of this dissertation was mainly to uncover and address important implication of AI in marketing and uncovering factors, which might directly or indirectly affect the successful implementation of AI in marketing related projects. The objective was to explain important direct implication of AI and give details about the diverse factors that might directly impact the successful integration of AI in various companies or projects.
The results of phase 1 reveal that most digital advertising is organized in the form of Internet advertising paid spots and spaces (IAPS). There are three main advertising domains that operate under the IAPS: search engine advertising, social media advertising, and display advertising. Furthermore, the dissertation finds that location and context are the unique elements of mobile advertising that distinguish between mobiles and desktops. This dissertation also identifies advertising domains that can only be deployed on mobiles: short messaging service (SMS) , in-application advertising, location-based advertising, mobile social media, and search engine advertising.
Phase 2 identifies the chatbot as a primary tool of AI in marketing and offers details about conceptual models of the front and back ends of a chatbot, which represent how chatbots can replace human agents and help chatbot adoption in the marketing function. The results in phase 2 support the claim that there is a herding trend in terms of investing in AI technology, which suggests that companies are currently following what others are doing. This is because AI is a complicated yet promising technology, and the industry currently lacks a complete understanding of it. The confusion that has followed has led to the herding trend. However, herding should not be considered a negative investment signal but rather an opportunity to understand AI technology in more detail to reach more informed decisions. The results also show that companies are currently lacking in the needed structures and systems for the collection and management of data that are required to support AI projects. New privacy frameworks, such as the GDPR, will support the industry in cultivating more responsible use of AI technology.
There are many theoretical implications in this dissertation. Phase 1 synthesizes the previous literature and develops a theoretical model that covers and explains most of the digital advertising landscape. The dissertation also contributes unique insights by explaining that the most distinguishable factor of mobile advertising versus desktop advertising is that mobile advertising messages can be tailored according to location and context. Phase 1 also includes a theoretical model that incorporates the simultaneous working of the mobile advertising domain, context, and location to improve its effectiveness without breaching any privacy fences.
Phase 2 shows that the theoretical model presented in this dissertation can be considered one of the earliest efforts to examine how chatbots can be integrated into a firm’s existing marketing and customer service areas; hence, it represents a novel contribution to the existing literature. Phase 2 also enriches the existing literature by offering two theoretical models: one that identifies why there is herding behavior in the AI space, and one that discusses important data-related problems that have the potential to negatively affect the successful implementation of AI in various projects.
This dissertation has vital practical implications. Phase 1 provides managers and stakeholders with a complete manual that will give them a solid understanding of existing digital advertising tools and how these domains can be deployed effectively to reach their marketing goals. Phase 1 also clarifies that digital advertising on mobiles can differ from that on desktops, which can help with the creation of a separate strategy for mobiles to yield better results. Phase 2 presents details about the most important implications of AI by creating theoretical conceptual models that display the suitable working of the front and back ends of the chatbot interface. The dissertation also builds unique theoretical frameworks for important issues related to AI and explains how investors, stakeholders, CEOs, and managers can best utilize AI by developing the right mental model and taking appropriate actions to build a strong foundation for successful AI implementation.
The results of this dissertation are limited due to a lack of related data in both the digital advertising and AI fields. Future research opportunities exist in terms of more specific contemporary trends in a related field (e.g., programmatic buying), which will promote automation of the marketing field.
Keywords: digital advertising, digital marketing, artificial intelligence, chatbots, herding behavior in AI, data issues in AI, digital privacy, GDPR
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