Bay Arinze

Generation Y Adoption of Instant Messaging: An Examination of the Impact of Social Usefulness and Media Richness on Use Richness

By integrating Media Richness Theory, Channel Expansion Theory, and the Technology Acceptance Model (TAM), we study the postadoption use behavior of instant messaging. We developed the construct “use richness” as a measure of the extent to which users use the media communication capacity after adoption and proposed a conceptual model of the antecedents of use richness. Through a field survey with 272 valid responses and structural equation modeling, we empirically tested our model and found that use richness is positively affected by perceived media richness, perceived usefulness, and perceived social usefulness.

A framework for using OO mapping methods to rapidly configure ERP systems

The Enterprise Object Model captures and transforms user requirements into detailed configuration settings for ERP software, reducing ERP system configuration effort and maintenance costs.

Factors that Determine the Adoption of Cloud Computing: A Global Perspective

Cloud computing has spread within enterprise faster than many other IT innovations. In cloud computing, computer services are accessed over the Internet in a scalable fashion, where the user is abstracted in varying degrees from the actual hardware and software and pays only for resources used. This paper examines the adoption of cloud computing in various regions of the world, as well as the potential of cloud computing to impact computing in developing countries. The authors propose that cloud computing offers varying benefits and appears differently in regions across the world, enabling many users to obtain sophisticated computing architectures and applications that are cost-prohibitive to acquire locally. The authors examine issues of privacy, security, and reliability of cloud computing and discuss the outlook for firms and individuals in both developing and developed countries seeking to utilize cloud computing for their computing needs.

A simulation model for industrial marketing

Developing an effective marketing mix is an important task for product planners seeking to gain competitive advantage in industrial markets. In these markets, product planning is made complex due to inadequate data sources, stochastic behavior, and the peculiar response of industrial markets to marketing instruments. By employing a simulation model as the heart of a marketing decision support system (MKDSS) it is possible to model the stochastic elements of the marketing mix, the interactions between marketing instruments, and competitive effects, to support marketing decision-making processes. This paper describes such a simulation model for aiding product planners in developing the marketing mix. The model utilizes Monte Carlo simulation in representing market dynamics, comparative marketing efforts, and competitive actions, in order to assess the effectiveness of combinations of marketing actions, that is, marketing policies. It is therefore viewed as a tool for improving decision-making effectiveness, and the basis of a marketing decision support system (MKDSS) for marketing managers. The methodology for applying the decision model is outlined, together with an illustrative example of its use, based on data from case study in a British firm.

The predictive accuracy of artificial neural networks and multiple regression in the case of skewed data: exploration of some issues

Abstract Business organizations can be viewed as information-processing units making decisions under varying conditions of uncertainty, complexity, and fuzziness in the causal links between performance and various organizational and environmental factors. The development and use of appropriate decision-making tools has, therefore, been an important activity of management researchers and practitioners. Artificial neural networks (ANNs) are turning out to be an important addition to an organizations decision-making tool kit. A host of studies has compared the efficacy of ANNs to that of multivariate statistical methods. Our paper contributes to this stream of research by comparing the relative performance of ANN and multiple regression when the data contain skewed variables. We report results for two separate data sets; one related to individual performance and the second to firm performance. The results are used to highlight some salient issues related to the use of ANN and multiple regression models in organizational decision-making.

A knowledge based decision support system for computer performance management

Abstract Computer systems managers make decisions about hardware and software selection, performance evaluation, capacity planning, and other resource variables on the basis of factual data, accounting data, subjective judgements, and assumptions about the resource consumption of the jobs being run. The importance of computer resource planning calls for effective support methods. A Knowledge-Based DSS (KBDSS) will be able to assist managers in making these policy decisions by utilizing knowledge of the existing configuration and its capabilities, the organizational computing environment, available external resources, and their suppliers. Combining procedural and declarative methods, such a KBDSS may provide early warning of possible bottlenecks, forecast growth of hardware usage, and employ knowledge based inferencing to suggest suitable remedial actions to the systems manager. This paper presents a KBDSS for supporting computer resource planning decisions using a procedural/declarative framework, and illustrates the systems usage aspects.

Market planning with computer models: A case study in the software industry

Abstract Product planners spend a great deal of time collecting and analyzing data in order to engage effectively in marketing activity. In increasingly competitive environments, this task is more critical due to the multiplicity of options and factors the planner must consider. For business or industrial markets in particular, an added difficulty is often the lack of reliable data pertaining to market statistics and market dynamics. This paper describes a computer-based marketing decision support system (MKDSS) that was developed for use in support of the marketing function at an expert system company. It was used to support the product planners strategy for marketing the companys central product, an expert system shell, by aiding in the selection of a suitable marketing mix. The case examines MKDSS development issues and discusses several considerations and implications for the design and use of MKDSS in business markets.

Matching client/server processing architectures with information processing requirements: a contingency study

Abstract The 1990s are witnessing the rapid growth of client/server (C/S) computing, but for an organization to benefit from a C/S model, it should ensure that the processing architecture matches its information needs. Researchers have suggested that organizations moving to this model should identify their information requirements, and then determine the appropriate architectures to support them. This study utilizes information processing theory to examine the match between an organizations information processing requirements and its C/S architectures. The independent variables in this study are task characteristics, and the processing architectures. The dependent variable is effectiveness. The data for this study was obtained from C/S managers and users in a variety of industries, through a combination of archival data, telephone interviews, and a mailed survey. It was analyzed using hierarchical regression. The results indicate that an appropriate match between task characteristics and C/S processing architectures is an important determinant of system effectiveness.

Legal determinants of the global spread of e-commerce

This paper examines the effects of quality of legal rules and enforcement, creditor rights, shareholder rights and the level of technology integration in the market on the global spread of e-commerce. We report three primary results. First, consistent with our hypotheses, quality legal rules and enforcement and creditor rights in each country are significantly and positively related to 1998 global e-commerce revenues. The relationships between shareholder rights, technology integration and e-commerce revenues are weak. Second, consistent with 1998 results, quality legal rules and enforcement and creditor rights are significantly and positively associated with 1999 global e-commerce revenues. Finally, when 1998 and 1999 data are pooled together, the results are stronger and consistent with the individual year findings. All our results are robust to alternative model specifications, time periods and scaling or non-scaling of the dependent variable. Taken together, the results underscore the importance of quality legal rules and creditor protection in global spread of e-commerce.

Combining and selecting forecasting models using rule based induction

Abstract As inaccurate forecasts can lead to lost business and inefficient operations, it is imperative that forecasts be as accurate as possible. A major problem however, is that no single forecasting method is the most accurate for every data time series. Thus, generating a forecast is often an uncertain affair, involving the use of heuristics by human experts and/or the consistent use of forecasting models whose accuracy may or may not be the most accurate for that time series. To compound matters, the best forecasts are often produced by combining forecasting models. This research describes the use of an Artificial Intelligence (AI)-based technique, rule-based induction, to improve forecasting accuracy. By using training sets of time series (and their features), induced rules were created to predict the most appropriate forecasting method or combination of methods for new time series. 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 an expert ‘assistant’ for specialist forecasters; and increases in forecasting accuracy.