Economies of the world invest in ICT with the uniform purpose of obtaining socio-economic benefits. Determination of the most appropriate path of success linking ICT to socio-economic benefits is a complex multidimensional problem that is not amenable to methodological approaches based on a single perspective. Policy analysts and researchers are, therefore, in need of adequate tools for modelling and analysing the complex ICT4D investment problem. In this paper, we present a consistent solution-oriented theoretical and methodological approach that focuses on discovering pitfalls and best practices that the experience of travelling “ICT → outcomes” path may offer. Specifically, we ask: “What are some lessons that ‘newer entrants’ (represented by the economies of Sub-Saharan Africa (SSA)) may learn from ‘earlier travelers’ (represented by Transition Economies (TE))?” Among many findings is the difference in the sources of growth in productivity between the SSA- and TE-dominated groups of economies. Besides practical insights, this study offers several contributions to policy-makers and researchers including: a theoretical framework that allows for conceptually complex inquiries into the matters of ICT4D and a multi-method methodology that can be used to identify (a) important areas suitable for benchmarking and (b) differences requiring a customised context-specific approach.

Much of formal human activities in the contemporary world has been overly influenced by the world-views of the countries characterised as Western, Educated, Industrial, Rich, and Democratic (WEIRD)...

This study explores the factors that can impact individual performance when using enterprise resource planning (ERP) systems. Starting from the proposition that organizational performance depends on individuals' task accomplishments, we test a structural model of task–technology fit, ERP user satisfaction, and individual performance in ERP environments. This research utilizes a survey method to examine the perceptions of ERP users. We performed factor and reliability analyses to assess the validity of the survey instrument. Six factors were identified as having an impact on individual performance: System Quality, Documentation, Ease of use, Reliability, Authorization, and Utilization. To explore the relationships among these factors, we conducted regression and multivariate adaptive regression splines analysis, and compared the findings from these two analytical techniques. The study provides evidence that System Quality, Utilization, and Ease of Use are the most important factors bearing on individual performance in ERP environments. Our findings also provide IT managers and researchers with knowledge of how these factors can be manipulated to improve individual performance when using ERP systems.

Decision tree (DT) induction is among the more popular of the data mining techniques. An important component of DT induction algorithms is the splitting method, with the most commonly used method being based on the Conditional Entropy family. However, it is well known that there is no single splitting method that will give the best performance for all problem instances. In this paper, we develop and explore hybrid splitting methods from two entropy-based families: the Conditional Entropy family and another family that is based on the Class-Attribute Mutual Information (CAMI). We compare conventional splitting methods based on single measures with hybrid splitting methods based on multiple measures. The results suggest that the hybrid methods could be competitive in terms of classification accuracy and are thus worthy of future research.

Many decision models that are based on Dempster-Shafer belief functions involve the elicitation of subjective belief data from a group of experts based on qualitative preferences. Given that a major reason for using a group is the assumption that the combined group judgment is likely superior to individual judgment, then the issues of synthesis and consensus assessment and consensus building become important. In this article we present an integrated structured, noninvasive action learning knowledge elicitation process for eliciting from a group of experts the basic probability assignments that are required by Dempster-Shafer theory (DST)–based expert systems. This process accommodates the expert's uncertainty, identifies inconsistencies in the expert's opinion, takes advantage of the potential benefits of group work while mitigating against the negative effects, and addresses the issues of consensus assessment and consensus building. © 2003 Wiley Periodicals, Inc.

The authors present an approach that will be useful in knowledge acquisition from experts on the degree of belief in, or the probability of, the truthfulness of various propositions. Its advantages include exploring the given problem situation using linguistic quantifiers; avoiding the premature use of numeric measures; and identifying input data that is inconsistent with the theory of belief functions.