The use of artificial intelligence in marketing strategies: Automation, personalization and forecasting

The integration of Artificial Intelligence (AI) in marketing strategies is pivotal in the era of digital transformation, especially in automation, personalization, and forecasting. This research investigates the evolutionary role of AI in transitioning from traditional marketing frameworks to data-driven methodologies, thereby enhancing marketing efficiency and customer engagement. The increasing reliance on AI for strategic decision-making in marketing underscores the significance of this study. Employing a systematic literature review and thematic analysis, this research synthesizes data from an array of studies to thoroughly understand the impact of AI on marketing. The findings reveal that AI significantly streamlines marketing operations, fosters highly personalized marketing strategies, and enhances the accuracy of forecasting market trends and consumer behavior. However, this study also sheds light on the ethical and privacy concerns associated with the use of AI in marketing. Results point towards a significant transformation in marketing practices propelled by AI, marked by improvements in operational efficiency and customer interaction. Nevertheless, the study advocates the importance of addressing ethical considerations and privacy issues, emphasizing responsible AI deployment. The study offers a comprehensive perspective on the integration of AI in marketing and suggests insights into prospective trends. It recommends a balanced approach to leveraging AI’s capabilities while upholding ethical standards. The research’s practical implications aim to guide marketers and researchers towards responsible and effective AI adoption in marketing strategies, paving the way for a future where technology enhances marketing endevaours without compromising ethical integrity

Unveiling AI Aversion: Understanding Antecedents and Task Complexity Effects

Artificial Intelligence (AI) has generated significant interest due to its potential to augment human intelligence. However, user attitudes towards AI are diverse, with some individuals embracing it enthusiastically while others harbor concerns and actively avoid its use. This two essays\u27 dissertation explores the reasons behind user aversion to AI. In the first essay, I develop a concise research model to explain users\u27 AI aversion based on the theory of effective use and the adaptive structuration theory. I then employ an online experiment to test my hypotheses empirically. The multigroup analysis by Structural Equation Modeling shows that users\u27 perceptions of human dissimilarity, AI bias, and social influence strongly drive AI aversion. Moreover, I find a significant difference between the simple and the complex task groups. This study reveals why users avert using AI by systematically examining the factors related to technology, user, task, and environment, thus making a significant contribution to the emerging field of AI aversion research. Next, while trust and distrust have been recognized as influential factors shaping users\u27 attitudes towards IT artifacts, their intricate relationship with task characteristics and their impact on AI aversion remains largely unexplored. In my second essay, I conduct an online randomized controlled experiment on Amazon Mechanical Turk to bridge this critical research gap. My comprehensive analytic approach, including structural equation modeling (SEM), ANOVA, and PROCESS conditional analysis, allowed me to shed light on the intricate web of factors influencing users\u27 AI aversion. I discovered that distrust and trust mediate between task complexity and AI aversion. Moreover, this study unveiled intriguing differences in these mediated relationships between subjective and objective task groups. Specifically, my findings demonstrate that, for objective tasks, task complexity can significantly increase aversion by reducing trust and significantly decrease aversion by reducing distrust. In contrast, for subjective tasks, task complexity only significantly increases aversion by enhancing distrust. By considering various task characteristics and recognizing trust and distrust as vital mediators, my research not only pushes the boundaries of the human-AI literature but also significantly contributes to the field of AI aversion

Development of Human-Computer Interaction for Holographic AIs

Virtual humans and embodied conversational agents play diverse roles in real life, including game characters, chatbots, and teachers. In Augmented Reality (AR), such agents are capable of interacting with the real world. To distinguish between both types of virtual agents, AR agents were conceptually redefined as "holographic Artificial Intelligences (AIs)". Holographic AIs are embodied virtual agents interacting with real objects in Augmented Reality (AR), and can respond to events both in virtual and real environments. This thesis provides a comprehensive investigation into holographic AIs, spanning from their design to their user experience. The purpose of this thesis is to investigate the creation and use of holographic AIs, by creating specific holographic AIs, and then examining how users perceive such entities in order to contribute to the improvement of the user experience. As a result, this thesis explores the design space for and methods for creating holographic AIs, proposing the novel PICS model which include the dimensions of persona, intelligence, conviviality, and senses. Following the PICS model, a set of holographic AIs are designed by using a method of semi-automatic reconstruction. An AI that resembles a human being in appearance and behaviour is endowed with multimodal interactions capable of creating the illusion of physicality. The initial proposed model is then refined based on the experience of creation. Basic body language gestures, such as nodding and opening the arms, are insufficient to engage users, particularly when it comes to intelligent tutoring systems. Therefore, this thesis specifically focuses on an open problem, the generation of re-usable standard instructional gestures. In an experiment, key instructional movements that can be employed by holographic AIs were identified and extracted as animations. The hitherto known range of representational gestures is, epistemologically, further expanded by transformational and imitation gestures, which show how humans manipulate spatio-motor information and characterise posture using hand motion. Therefore, the model can be extended to describe the holographic AI’s behaviour. Moreover, in order to assess the empirical validity of holographic AIs, this research explores learners' trustworthiness towards this novel technology - as a key criterion for efficacy of this AI approach. Trust and trustworthiness, in terms of holographic AIs, refers to a mindset that aids users in achieving objectives based on good intentions. Young learners’ perception of trust is largely influenced by affective aspects of trust, determined by how emotionally responsive a holographic AI is. These findings contribute to the design of personal holographic AIs that can perform a series of meaningful gestures that engage the learner’s attention for learning, which in turn fosters a reliable and trustworthy relationship. Both experiments are able to extend elements by adding gestures and holistic perception to this model

Paradigms for the design of multimedia learning environments in engineering

The starting point for this research was the belief that interactive multimedia learning environments represent a significant evolution in computer based learning and therefore their design requires a re-examination of the underlying principles of learning and knowledge representation. Current multimedia learning environments (MLEs) can be seen as descendants of the earlier technologies of computer-aided learning (CAL), intelligent tutoring systems (ITS) and videodisc-based learning systems. As such they can benefit from much of the wisdom which emerged from those technologies. However, multimedia can be distinguished from earlier technologies by its much greater facility in bringing to the learner high levels of interaction with and control over still and moving image, animation, sound and graphics. Our intuition tells us that this facility has the potential to create learning environments which are not merely substitutes for "live" teaching, but which are capable of elucidating complex conceptual knowledge in ways which have not previously been possible. If the potential of interactive multimedia for learning is to be properly exploited then it needs to be better understood. MLEs should not just be regarded as a slicker version of CAL, ITS or videodisc but a new technology requiring a reinterpretation of the existing theories of learning and knowledge representation. The work described in this thesis aims to contribute to a better understanding of the ways in which MLEs can aid learning. A knowledge engineering approach was taken to the design of a MLE for civil engineers. This involved analysing in detail the knowledge content of the learning domain in terms of different paradigms of human learning and knowledge representation. From this basis, a design strategy was developed which matched the nature of the domain knowledge to the most appropriate delivery techniques. The Cognitive Apprenticeship Model (CAM) was shown to be able to support the integration and presentation of the different categories of knowledge in a coherent instructional framework. It is concluded that this approach is helpful in enabling designers of multimedia systems both to capture and to present a rich picture of the domain. The focus of the thesis is concentrated on the domain of Civil Engineering and the learning of concepts and design skills within that domain. However, much of it could be extended to other highly visual domains such as mechanical engineering. Many of the points can also be seen to be much more widely relevant to the design of any MLE.Engineering and Physical Sciences Research Counci

Papers for Task Force Meeting on Future and Impacts of Artificial Intelligence, 15-17 August 1983

IIASA's Clearinghouse activity is oriented towards issues of interest among our National Member Organizations. Here, in the forefront, are the issues concerning the promise and impact of science and technology on society and economy in general, and some selected branches in particular. Artificial Intelligence (AI) is one of the most promising research areas. There are many indications that the long predicted upswing of this discipline is finally in the making. A recent survey had Nobel-laureates predict that the most influence in the next century will be made by computers, AI, and robotics. Already, at present, "expert" systems are emerging and applied; natural language understanding systems developed; AI principles are used in robots, flexible automation, computer aided-design, etc. All this will have an, as yet, unspecified social and economic impact on the activity of human beings, both at work and leisure. It certainly takes interdisciplinary and cross-culturally based studies to enhance the understanding of this complex phenomenon. This is the aim of our endeavors in the field which is in excess of our duty to pass useful knowledge to our constituency. We think that IIASA, cooperating in this respect with the Austrian Society for Cybernetic Studies (ASCS), can develop some comparative advantage here. This publication contains papers written by leading personalities, both East and West, in the field of artificial intelligence on the future and impact of this emerging discipline. We hope that the meeting, where the papers will be discussed, will not only identify important areas where the impact of artificial intelligence will be felt most directly, but also find the most rewarding issues for further research

Artificial intelligence as writing: knowledge-based hypertext systems as a medium for communication

This thesis is an exploration of a new metaphor for artificial intelligence (AI). Traditionally, the computer within AI has been viewed as an agent, one with which the user engages in a conversation. More recently certain researchers have proposed the notion that artificial intelligence (and indeed computing in general) can be more appropriately seen as a form of writing. Initially this thesis reviews the literature in this area, and aspects of AI which support the approach. Features of writing are then described which show parallels with AI. This then allows us to take lessons from the history and development of both traditional writing and the new computer-based writing systems to inform the design of a new type of artificial intelligence system. A design based on these features, called Running Texts is presented through a number of small examples. Issues that arise from these and possible future developments, based on the implementation are then discussed. A rationale for users choosing to learn a system such as Running Texts is proposed, as benefits from the psychological and social implications of writing can be applied to AI systems, when they are seen as writing. The same parallels point out potential problems, and suggest new ways to see the relation between AI and thought

Negotiated Tutoring: An Approach to Interaction in Intelligent Tutoring Systems

This thesis describes a general approach to tutorial interaction in Intelligent Tutoring Systems, called "Negotiated Tutoring". Some aspects of the approach have been implemented as a computer program in the 'KANT' (Kritical Argument Negotiated Tutoring) system. Negotiated Tutoring synthesises some recent trends in Intelligent Tutoring Systems research, including interaction symmetry, use of explicit negotiation in dialogue, multiple interaction styles, and an emphasis on cognitive and metacognitive skill acquisition in domains characterised by justified belief. This combination of features has not been previously incorporated into models for intelligent tutoring dialogues. Our approach depends on modelling the high-level decision-making processes and memory representations used by a participant in dialogue. Dialogue generation is controlled by reasoning mechanisms which operate on a 'dialogue state', consisting of conversants' beliefs, a set of possible dialogue moves, and a restricted representation of the recent utterances generated by both conversants. The representation for conversants' beliefs is based on Anderson's (1983) model for semantic memory, and includes a model for dialogue focus based on spreading activation. Decisions in dialogue are based on preconditions with respect to the dialogue state, higher level educational preferences which choose between relevant alternative dialogue moves, and negotiation mechanisms designed to ensure cooperativity. The domain model for KANT was based on a cognitive model for perception of musical structures in tonal melodies, which extends the theory of Lerdahl and Jackendoff (1983). Our model ('GRAF' - GRouping Analysis with Frames) addresses a number of problems with Lerdahl and Jackendoff's theory, notably in describing how a number of unconscious processes in music cognition interact, including elements of top-down and bottom-up processing. GRAF includes a parser for musical chord functions, a mechanism for performing musical reductions, low-level feature detectors and a frame-system (Minsky 1977) for musical phrase structures