Strategic value of data analytics in interorganizational relationships

Researchers suggest that data analytics (DA) enhance decisions related to interorganizational relationships (IOR) and lead to reduced risk and improved performance. However, and despite this potential, firms face challenges regarding effective use of their DA capabilities to enhance their IORs. The massive investment in DA, as well as the need for an efficient use of DA in IOR settings, create the potential opportunities for two streams of research: a deeper understanding of business value of DA in IOR; and a systematic examination of DA’s strategy for an enhanced alignment with IORs. Despite the published scholarly works in these two research streams, the complexity, diversity, and newness associated with DA technologies make our understanding of the business value of DA in IOR and DA strategy for IOR incomplete. First, our understanding of why and how DA impact IOR performance is inadequate and fragmented. Second, the focus of the preponderance of published empirical papers in understanding the value of DA is at the operational level, and the strategic implications of DA capabilities in IOR are not addressed. Third, the literature fails to consider the inherent heterogeneity among the user base of DA systems, and consequently, the findings are not generalizable. Finally, the literature fails to address the impact of external factors, such as complexity and volatility on DA strategy. In this dissertation, I attempt to contribute to the literature by focusing on these research gaps and investigating them in three studies. In the first study, a holistic value-view of a firm’s supply chain enabled by DA for improved business performance, is presented based on two complementary views of market-oriented coordination and strategic supplier partnership. The study discusses how DA capabilities impact the constituents of this complementary view of supply chain to amplify business performance. I propose a theoretical model of the effect of DA capabilities on a firm’s co-creation of value, with its partners for business performance. Then, I test the model empirically based on a survey of 198 practitioners. My findings show that DA capabilities improve upstream and downstream integration and leverage the co-creation of value. The second study provides a better understanding of the impact of DA on interorganizational collaborations by answering two fundamental research questions: “How does a firm use its DA capabilities to improve collaboration and enhance performance?” and “What is the impact of DA capabilities on a firm’s collaboration and performance?” To answer these questions and to provide a deeper insight from multiple perspectives, I utilized a mixed method research by conducting a thorough content analysis of 34 published case studies, followed by a confirmatory research based on a survey of 210 practitioners to empirically test the insights generated from my content analysis. My findings identify several paths to improved performance using DA capabilities. My analysis suggests that DA capabilities, used appropriately in an interorganizational collaborative environment, lead to reduced costs and the need for required working capital and ultimately better performance through improved collaborative relationships such as planning and scheduling. In the third study, I expand the results of the two prior studies by analyzing the DA strategic focus. I employ an agent-based simulation to test different DA strategies in various business environments that are identified by levels of complexity and dynamism. My findings indicate that optimum DA strategy has a quadratic relationship with the levels of complexity and dynamism, which explains the prior contradictory findings of the IS literature. These three studies contribute to the business value of IT and IS strategy literatures by investigating the business value of DA in IOR settings, identifying impacts of DA on value co-creation in IORs and determining a suitable DA strategy based on various environmental factors

A review of the identification of market power in the liberalized electricity markets

The liberalization of the electricity market aimed to promote competition, innovation, and fair pricing for consumers. However, as with any imperfect system, certain loopholes exist. Some major players in the electricity market have taken advantage of these loopholes to benefit from their market power. This research examines various methods for detecting market power in the liberalized electricity market and proposes a combination of detection methods that effectively address the issue of market power abuse. Two approaches to market power detection were identified and analyzed. The first approach involves the use of structural indices and analysis, including Concentration Ratio (Crn), Herfindahl-Hirschman Index (HHI), Pivotal Supplier Indicator(PSI), Residual Supply Index(RSI), Structure Conduct Performance Model, and Residual Demand Analysis. The second approach utilizes simulation models such as Linear Optimization, Supply Function Equilibrium, Cournot- Nash Framework, Agent-Based Model, and New Empirical Industrial Organization. The research findings indicate that combining market simulation approaches, such as the linear optimization model, with other methods like residual demand analysis, concentration ratios, and agent-based models, provides a comprehensive approach to market power detection. The linear optimization model can identify potential discrepancies by comparing marginal costs and prices, thereby indicating possible market power abuse. By incorporating residual demand analysis, a deeper understanding of the demand side of the market can be gained. Additionally, considering concentration ratios and employing agent-based models to capture strategic choices and behaviors of market participants can enhance the accuracy of market power detectio

Analysis and modeling a distributed co-operative multi agent system for scaling-up business intelligence

Modeling A Distributed Co-Operative Multi Agent System in the area of Business Intelligence is the newer topic. During the work carried out a software Integrated Intelligent Advisory Model (IIAM) has been develop, which is a personal finance portfolio ma

Creating an adaptive asset allocation fund to outperform inflation in the South African financial market

Includes abstract.Includes bibliographical references (leaves 112-113).In this dissertation, I detail the process I went through to create a new asset allocation product, with the intention of beating inflation over the long term, in the South African flnancial market space. This process has been a contributor to the creation of my model for new product development in the financial market space. Simulation is at the core of this process. At the outset, I cover a brief history and contextualise absolute return funds, looking at the difference between an absolute return fund, a balanced fund and a hedge fund. The move from defined benefit to defined contribution pension funds and the impact this has had on consulting actuaries risk appetites is visited. My concern in this regard is that capital preservation is being maximised, at the expense of capital growth, without taking into account the devastating effects of inflation

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

From metaheuristics to learnheuristics: Applications to logistics, finance, and computing

Un gran nombre de processos de presa de decisions en sectors estratègics com el transport i la producció representen problemes NP-difícils. Sovint, aquests processos es caracteritzen per alts nivells d'incertesa i dinamisme. Les metaheurístiques són mètodes populars per a resoldre problemes d'optimització difícils en temps de càlcul raonables. No obstant això, sovint assumeixen que els inputs, les funcions objectiu, i les restriccions són deterministes i conegudes. Aquests constitueixen supòsits forts que obliguen a treballar amb problemes simplificats. Com a conseqüència, les solucions poden conduir a resultats pobres. Les simheurístiques integren la simulació a les metaheurístiques per resoldre problemes estocàstics d'una manera natural. Anàlogament, les learnheurístiques combinen l'estadística amb les metaheurístiques per fer front a problemes en entorns dinàmics, en què els inputs poden dependre de l'estructura de la solució. En aquest context, les principals contribucions d'aquesta tesi són: el disseny de les learnheurístiques, una classificació dels treballs que combinen l'estadística / l'aprenentatge automàtic i les metaheurístiques, i diverses aplicacions en transport, producció, finances i computació.Un gran número de procesos de toma de decisiones en sectores estratégicos como el transporte y la producción representan problemas NP-difíciles. Frecuentemente, estos problemas se caracterizan por altos niveles de incertidumbre y dinamismo. Las metaheurísticas son métodos populares para resolver problemas difíciles de optimización de manera rápida. Sin embargo, suelen asumir que los inputs, las funciones objetivo y las restricciones son deterministas y se conocen de antemano. Estas fuertes suposiciones conducen a trabajar con problemas simplificados. Como consecuencia, las soluciones obtenidas pueden tener un pobre rendimiento. Las simheurísticas integran simulación en metaheurísticas para resolver problemas estocásticos de una manera natural. De manera similar, las learnheurísticas combinan aprendizaje estadístico y metaheurísticas para abordar problemas en entornos dinámicos, donde los inputs pueden depender de la estructura de la solución. En este contexto, las principales aportaciones de esta tesis son: el diseño de las learnheurísticas, una clasificación de trabajos que combinan estadística / aprendizaje automático y metaheurísticas, y varias aplicaciones en transporte, producción, finanzas y computación.A large number of decision-making processes in strategic sectors such as transport and production involve NP-hard problems, which are frequently characterized by high levels of uncertainty and dynamism. Metaheuristics have become the predominant method for solving challenging optimization problems in reasonable computing times. However, they frequently assume that inputs, objective functions and constraints are deterministic and known in advance. These strong assumptions lead to work on oversimplified problems, and the solutions may demonstrate poor performance when implemented. Simheuristics, in turn, integrate simulation into metaheuristics as a way to naturally solve stochastic problems, and, in a similar fashion, learnheuristics combine statistical learning and metaheuristics to tackle problems in dynamic environments, where inputs may depend on the structure of the solution. The main contributions of this thesis include (i) a design for learnheuristics; (ii) a classification of works that hybridize statistical and machine learning and metaheuristics; and (iii) several applications for the fields of transport, production, finance and computing