Outsourcing has become a ubiquitous phenomenon, but IT outsourcing success has been elusive. Over half of the outsourcing contracts are ended prematurely and some organizations are beginning to backsource. This research employs a unique lens to understand outsourcing. Although most IT outsourcing studies employ absolute success measure, this research utilizes expectations and disconfirmations to predict success. Specifically, the Expectation Disconfirmation Theory is used to understand the role of various types of expectations on IT outsourcing success. A Delphi study of IT outsourcing experts in addition to a survey on success is utilized to present a triangulation of data to support the value of understanding how a clients expectations impact that elusive goal of IT outsourcing success.

In this study we empirically examine a new global phenomenon - online sourcing with a quantitative research method. Online sourcing is the newest development in outsourcing recently that uses Internet as the primary sourcing platform to approach the global sourcing of services. We believe that the emergence of online sourcing will fundamentally change the way work is done. Thus, gaining a deep understanding of the adoption of online sourcing becomes particularly important. Drawing upon theories of firms including transaction cost theory, we propose an integrative theoretical framework for the understanding of online sourcing decisions from the service clients perspective. The research model is examined with the data collected from an online sourcing platform. Our findings suggest the integrative theoretical framework rather than single perspective in understanding online sourcing decisions. Moreover, the study discloses how outsourcing decision attributes grounded in theories are interrelated within an integrative theoretical framework, as well as the relative importance of each theoretical perspective. Finally implications to theory, practice, business and society are discussed.

Technology makes buying faster, smarter, and more convenient for consumers, and supplies endless opportunities for impulse buying. Research of this topic examines influencers for impulsive online shopping, and marketing techniques used to encourage such actions. After a thorough literature review to identify important factors relating to impulsive purchases and to develop hypotheses, a survey is conducted to provide data on which the hypotheses can be tested. The survey is divided into four sections examining the effectiveness of online marketing techniques, shopping habits, Internet habits, and questions relating to a recent online impulse purchase. Theories to test include ones relating to customer service, customer impulsiveness, website personalization, time pressure created by websites, comparison shopping, and the flow of an Internet session. After the running of several regression analyses, the researcher finds the most significant factors for online impulse purchases are those that are important in offline retail outlets as well. Technology does not create impulse buyers, it provides impulsive consumers more resources to shop and the ability to complete transactions in a short amount of time, making those purchases truly a reaction to an impulse.

The objective of this study is to improve our understanding of IT Outsourcing Governance, which has become an increasingly significant topic in recent years. In order to gain an understanding of this topic, an extensive literature review was performed and analyzed with argument mapping. The data yielded from the argument maps was used to create a comprehensive framework for IT Outsourcing Governance. What resulted was a new conceptualization of IT Outsourcing Governance, focused not only on governance structures and decision rights, but also on relationship management. This conceptualization not only provides a better understanding, but also presents a sharp contrast to the traditional view of IT Governance in which relationship management is not a major factor. In addition to the argument maps, a social representations survey was performed in order to elicit differences in the conceptualization of IT Outsourcing Governance between academics and practitioners. The results of the survey were used to perform a core/periphery analysis, which identified core and peripheral concepts used by academics and practitioners when discussing IT Outsourcing Governance. The core topics identified show a high degree of overlap with the dimensions of the framework previously developed. Finally, a review of existing technologies developed specifically to manage outsourcing arrangements (Outsourcing Relationship Management Tools) was performed in order to better understand the alignment between technology and management practices. This analysis showed that the features of Outsourcing Relationship Management tools seem to be misaligned with the duties and responsibilities of people in charge of the day-to-day management of the client-vendor relationship. The contribution of this research lies in advancing our understanding of IT Outsourcing Governance by providing a framework and new definition for this concept, along with a new theoretical lens to understand the evolution of relationships into partnerships. Additionally, this study augments the tools available to researchers by introducing argument mapping, a seldom-used technique for discourse analysis that proved to be very effective for eliciting relevant dimensions related to IT Outsourcing Governance from a literature review.

Commercial sugarcane farming requires large quantities of water to be delivered to the fields. Ideal irrigation schedules are produced indicating how much water to be supplied to fields considering multiple objectives in the farming process. Software packages do not fully account for the fact that the ideal irrigation schedule may not be met due to limitations in the water distribution network. This dissertation proposes the use of mathematical modelling to better understand water supply and demand management on a commercial sugarcane farm. Due to the complex nature of water stress on sugarcane, non-linearities occur in the model. A piecewise linear approximation is used to handle the non-linearity in the water allocation model and is solved in a commercial optimisation software package. A test data set is first used to exercise and evaluate the model performance, then to illustrate the practical applicability of the model, a commercial sized data set is used and analysed.

The e-retail sector in South Africa has a significant opportunity to capture a large portion of the country's retail industry. Central to seizing this opportunity is leveraging the advantages that the online setting affords. In particular, the e-retailer can offer an extremely large catalogue of products; far beyond what a traditional retailer is capable of supporting. However, as the catalogue grows, it becomes increasingly difficult for a customer to efficiently discover desirable products. As a consequence, it is important for the e-retailer to develop tools that automatically explore the catalogue for the customer. In this dissertation, we develop a recommender system (RS), whose purpose is to provide suggestions for products that are most likely of interest to a particular customer. There are two primary contributions of this dissertation. First, we describe a set of six characteristics that all effective RS's should possess, namely; accuracy, responsiveness, durability, scalability, model management, and extensibility. Second, we develop an RS that is capable of serving recommendations in an actual e-retail environment. The design of the RS is an attempt to embody the characteristics mentioned above. In addition, to show how the RS supports model selection, we present a proof-of-concept experiment comparing two popular methods for generating recommendations that we implement for this dissertation, namely, implicit matrix factorisation (IMF) and Bayesian personalised ranking (BPR).

Since the establishment of Markowitz's theory, numerous studies have been carried out over the past six decades or so that cover the benefits, limitations, modifications and enhancements of Mean Variance (MV) optimisation. This study endeavours to extend on this, by means of adding factors to the minimum variance framework, which would increase the likelihood of outperforming both the market and the minimum variance portfolio (MVP). An analysis of the impact of these factor tilts on the MVP is carried out in the South African environment, represented by the FTSE-JSE Shareholder weighted Index as the benchmark portfolio. The main objective is to examine if the systematic and robust methods employed, which involve the incorporation of factor tilts into the multicriteria problem, together with covariance shrinkage – improve the performance of the MVP. The factor tilts examined include Active Distance, Concentration and Volume. Additionally, the constant correlation model is employed in the estimation of the shrinkage intensity, structured covariance target and shrinkage estimator. The results of this study showed that with specific levels of factor tilting, one can generally improve both absolute and risk-adjusted performance and lower concentration levels in comparison to both the MVP and benchmark. Additionally, lower turnover levels were observed across all tilted portfolios, relative to the MVP. Furthermore, covariance shrinkage enhanced all portfolio statistics examined, but significant improvement was noted on drawdown levels, capture ratios and risk. This is in contrast to the results obtained when the standard sample covariance matrix was employed.

This mixed-methods study focused on the internal human factors responsible for data breaches that could cause adverse impacts on organizations. Based on the Swiss cheese theory, the study was designed to examine preventative measures that managers could implement to minimize potential data breaches resulting from internal employees' behaviors. The purpose of this study was to provide insight to managers about developing strategies that could prevent data breaches from cyber-threats by focusing on the specific internal human factors responsible for data breaches, the root causes, and the preventive measures that could minimize threats from internal employees. Data were collected from 10 managers and 12 employees from the business sector, and 5 government managers in Ivory Coast, Africa. The mixed methodology focused on the why and who using the phenomenological approach, consisting of a survey, face-to-face interviews using open-ended questions, and a questionnaire to extract the experiences and perceptions of the participants about preventing the adverse consequences from cyber-threats. The results indicated the importance of top managers to be committed to a coordinated, continuous effort throughout the organization to ensure cyber security awareness, training, and compliance of security policies and procedures, as well as implementing and upgrading software designed to detect and prevent data breaches both internally and externally. The findings of this study could contribute to social change by educating managers about preventing data breaches who in turn may implement information accessibility without retribution. Protecting confidential data is a major concern because one data breach could impact many people as well as jeopardize the viability of the entire organization.

Providing automated support for model selection is a significant research challenge in model management. Organizations maintain vast growing repositories of analytical models, typically in the form of spreadsheets. Effective reuse of these models could result in significant cost savings and improvements in productivity. However, in practice, model reuse is severely limited by two main challenges: (1) lack of relevant information about the models maintained in the repository, and (2) lack of end user knowledge that prevents them from selecting appropriate models for a given problem solving task. This study built on the existing model management literature to address these research challenges. First, this research captured the relevant meta-information about the models. Next, it identified the features based on which models are selected. Finally, it used Analytic Hierarchy Process (AHP) to select the most appropriate model for any specified problem. AHP is an established method for multi-criteria decision-making that is suitable for the model selection task. To evaluate the proposed method for automated model selection, this study developed a simulated prototype system that implemented this method and tested it in two realistic end-user model selection scenarios based on previously benchmarked test problems.

Efforts in the marketing sciences can be distinguished between the analysis of individual customers and the examination of portfolios of customers, giving scarce theoretical guidance concerning the strategic allocation of promotional investments. Yet, strategic asset allocation is considered in financial economics theory to be the most important set of investment decisions. The problem addressed in this study was the application of strategic asset allocation theory from financial economics to marketing science with the aim of improving the financial results of investment in direct marketing promotions. This research investigated the components of efficient marketing portfolio construction which include multiattribute numerical optimization, stochastic Brownian motion, peer index tracking schemes, and data mining methods to formulate unique investable asset classes. Three outcomes resulted from this study on optimal diversification: (a) reduced saturative promotional activities balancing inefficient advertising cost and enterprise revenue objectives to achieve an investment equilibrium state; (b) the use of utility theory to assist in the lexicographic ordering of goal priorities; and (c) the solution approach to a multiperiod linear goal program with stochastic extensions. A performance test using a large archival set of customer data illustrated the benefits of efficient portfolio construction. The test asset allocation resulted in significantly more reward than that of the benchmark case. The results of this grounded theory study may be of interest to marketing researchers, operations research practitioners, and functional marketing executives. The social change implication is increased efficiency in allocation of large advertising budgets resulting in improved corporate performance.