Orakwue Bay Arinze

Cognitive computing is a form of problem-solving that incorporates machine learning, big data, data mining, natural language processing, machine vision, robotics, and other strands of artificial intelligence. Cognitive computing solutions can be used as sole or partial solutions to augment decision-making. The financial services industry is in a state of transformation, driven by the convergence of rapid changes in financial service technologies (fintech) - including cognitive computing, the digitization of the consumer, the emergence of younger investors (millennials), increased regulatory scrutiny (DOL regulation), and continued fee compression for products and services. Cognitive computing offers a disruptive opportunity in the financial services industry by not only empowering the financial intermediary but also by delivering increased engagement and value to the consumer. This study examines how the use of cognitive computing to improve financial advice can provide value for the financial intermediary and the end consumer. For the intermediary, the study will assess how cognitive computing can augment and supercharge the expertise of the financial advisor, enabling the advisor to deliver improved advice. For the consumer, the study will assess how cognitive computing can deliver high-quality, accurate advice comparable to that of a human advisor.

Cloud computing is a form of computing that enables using organizations to deploy and access applications over the Internet on demand in a dynamically scalable and virtualized form. This paper discusses organizational requirements for cloud computing and different forms of cloud computing. It examines ways in which cloud computing service models can be matched to a subscriber's information processing requirements and lays the foundation for a testable model.

When Internet technology is used within the bounds of an organization, the network is referred to as an intranet. This study examined organizational, technical, and contextual variables expected to be associated with intranet infusion in organizations. A survey was mailed to 1,000 senior level computer executives in the United States. The organization was the unit of analysis. Seven independent variables were examined with intranet infusion. Results indicate that top management support, IT infrastructure, and competition are positively related to intranet infusion. Organizational size is negatively related to intranet infusion. Implications and areas for further research are discussed.

The past decade has seen a rapid growth in the deployment of intranets. However, there is little, if any empirical evidence to date that explains what contributes to intranet implementation success. This work-in-progress examines organizational, technical, and contextual factors that may be related to the deployment and use of intranets in organizations in the United States. Specifically, factors that may affect the diffusion and infusion of intranet technology within organizations are explored.

Forecasting is an important and necessary activity for all types of organizations. Selecting a forecasting model is a complex and time-consuming task. This research describes the use of two Artificial Neural Network topologies namely, Multilayer Perceptron and Simple Recurrent Networks to predict the most accurate forecasting model for any given time series. Econometric time series datasets were used to train and test the ANNs. The results of this experiment, which appear promising are presented together with guidelines for its practical application. Potential benefits include dramatic reductions in the effort and cost of forecasting; the provision of assistance to specialist forecasters; and increases in forecasting accuracy.

The 1990s are witnessing the rapid growth of the service industry. Service organizations are attempting to empower their workers with use client/server (C/S) systems. For an organization to benefit from the C/S model, however, it should ensure that its C/S processing architecture matches its information needs. This study examines the information requirements of a service organization in terms of the customer-contact dimension. This dimension can range from high contact to low contact. These two environments differ in terms of task uncertainty. This study proposes that, for an organization's C/S system to be effective, the task uncertainty which it is designed for should fit the C/S processing architectures. The results indicate that an appropriate fit between task uncertainty and C/S processing architectures is indeed an important determinant of C/S effectiveness.

The issue of data capture for DSS databases is an important one both for DSS developers and DSS users facing ill-structured problems in noisy and difficult environments. While the available database technologies (e.g. specialized hardware, DBMSs, and query languages) and other DSS-building tools are well advanced, methods for DSS data capture in less tractable environments are lacking. This often serves to limit the effectiveness of the above technologies in DSS development and use. The paper addresses the issue of data collection for DSS and presents a framework and an approach for detecting, preventing, and correcting errors in data collected. The framework employs analogies from the field of data communications to this end, and illustrates the approach using an example from the industrial marketing area.< >

The current status of the knowledge-based system which is the most promising and desirable enhancement to computer-aided-design technology is reviewed. An approach to incorporating a knowledge-based system into a computer-aided-design system for the design of an injection molded part is proposed. To demonstrate the procedure involved and the information revealed, a Prolog knowledge-based system is applied to designing new molds for the injection molding of plastic balls. Two new mold designs are proposed. The associated computer-aided-design system is described in some detail. Areas in which further work is needed are identified.< >