Algorithms refer to the software programs designed to support problem solving in a wide range of decision domains. Given the Artificial Intelligence (AI) revolution, algorithms have become an integral part of our personal, social, and professional lives. As technology rapidly advances, these algorithms are not only becoming more capable but are also finding a growing array of applications in managerial and consumer decision support. Despite their increasing presence, reactions to algorithms are mixed. While some research highlights a preference for algorithms over human judgment ("algorithm appreciation"), other studies reveal a contrary preference ("algorithm aversion"), where people favor human expertise. This research provides a conceptual framework and empirical evidence regarding factors that may influence preference for algorithmic versus human expert recommendations in business decision contexts. We use experimental psychological methods to investigate how algorithm characteristics, decision-maker psy / Doctor of Philosophy / Amid the AI revolution, algorithms have become central to our personal, social, and professional lives, evolving rapidly in both capability and application. Reactions to these algorithms are mixed: some studies show a preference for algorithms over human judgment, known as "algorithm appreciation," while others reveal a preference for human judgment, or "algorithm aversion." Understanding these preferences is essential. Our research helps to clarify this issue by examining the factors that influence whether people prefer algorithms or human experts in business decisions. Using experimental methods, we explore how algorithm features, decision-maker psychology, and situational factors impact these preferences. We focus on scenarios where algorithms and human experts are presented as competing options rather than complementary ones. Our findings, detailed in two empirical essays, aim to advance marketing literature on algorithms and decision-making, identify future research opportunities, and offer insights for

Promotion is one of the four major marketing elements of the marketing mix (others are product, price and place) in implementing marketing strategy. Promotion is dealing with the ways a company communicates with its customers to persuade them to buy the product. Promotion mix covers all the different ways a company choose to communicate with its customers such as advertising, personnel selling, PR, sales promotion and others. Selecting the optimal blend of the promotion mix is a tough and critical issue for marketers and does not have a fix operative formula. The fast pace of improvements in digitization in this era led companies produce digital products. Due to their inherent characteristic of digital products, such as intangibility, promotion mix selection is a more challenging issue. In my thesis study, I proposed a framework in classifying the digital products and then apply a fuzzy linguistic decision model approach with appropriate genetic algorithms to reach an optimum promotion mix set for digital products. Optimization is targeting to justify the objectives of the company, provide a satisfying marketing performance for the companies of digital product producers and utilize their budget effectively. The proposed model is implemented on an empirical case and produced satisfactory results.

For many decisions validation of their outcomes is invariably problematic to objectively assess. Therefore to aid analysis and validation of decision outcomes, approaches which provide improved traceability and more semantically meaningful measurements of the decision process are required. Hence, this research investigates traceability, transparency, interactivity and auditability to improve the decision making process. Approaches and evaluation measures are proposed to facilitate a richer decision making experience. Multi-Criteria Decision Analysis (MCDA) seeks to determine the suitability of alternatives of a goal with respect to multiple criteria. A key component of prominent MCDA methods is the concept of pairwise comparison. For a set of elements, pairwise comparison enables an accurate and transparent extraction and codification of a decision maker’s preferences, though facilitating a separation of concerns. From a set of pairwise comparisons, a ranking of the elements under consideration can be calculated. There are scenarios when a set of pairwise comparisons undergo alteration, both for individual and multiple decision makers. A set of measures of compromise are proposed to quantify the alteration that a set of pairwise comparisons undergo in such scenarios. The measures seek to provide a decision maker with meaningful knowledge regarding how their views have altered. A set of pairwise comparisons may be inconsistent. When inconsistency is present it adversely affects a ranking of the elements derived from the comparisons. Moreover inconsistency within pairwise comparisons used for consideration of more than a handful of elements is almost inevitable. Existing approaches that seek to alter a set of comparisons to reduce inconsistency lack traceability, flexibility, and specific consideration of alteration to the judgments in a way that is meaningful to a decision maker. An approach to inconsistency reduction is proposed that seeks to address these issues. For many decisions the opinions of multiple decision makers are utilized, either to avail of their combined expertise or to incorporate conflicting views. Aggregation of multiple decision makers’ pairwise companions seek to combine the views of the group into a single representation of views. An approach to group aggregation of pairwise comparisons is proposed that models compromise between the decision makers, facilitates decision maker constraints, considers inconsistency reduction during aggregation and dynamically incorporates decision maker weights of importance. With internet access becoming widespread being able to garner the views of a large group of decision makers’ views has become feasible. An approach to the aggregation of a large group of decision makers’ preferences is proposed. The approach facilitates understanding regarding both the agreement and conflict within the group during calculation of an overall group consensus. A Multi-Objective Optimisation Decision Software (MOODS) prototype tool has been developed that implements both the new measures of compromise and the proposed approaches to inconsistency reduction and group aggregation.