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An issue of managing a business (unit) as a whole successfully is perceived to belong to the fundamental issues within strategic management. This paper proposes that a business unit can be managed successfully in short and longer term in its focal contexts as a set of three recursive, competence-based, and process-based systems. Many elements of Stafford Beer's (1985) viable system model along the key competence-based theoretical bases are applied to this system design task. The outcome is an ideal, recursive template for advancing competence-based business management (CBBM) and its conceptual modeling. It is assumed that it is possible to design a business unit as a viable system that is capable of sustaining a separate existence at only three levels of hierarchy, as part of single or multi-business firms. Business-process models and their redesign processes are chosen as the 2nd-order, focal system which produces a business unit's competitiveness and solves longitudinal CBBM problems. One level of recursion down includes a unit's value creating, capturing, releveraging, and respective processes that enable to solve cross-sectional problems. One level of recursion up includes a unit's existential foresights and their crafting processes that solve existential problems. Recursivity is designed inside each system in terms of three kinds of subsystems for (a) primary value releveraging, process-model redesign, and business-foresight crafting, (b) the management of varieties in releveraging, modeling, and foreseeing, and (c) the monitoring and probing of all three systems. Systemic competences are incorporated inside respective systems. Such competences possess three flexibilities of absorption, attenuation, and amplification. At each level of recursion, a competence-based process is a unit of conceptual modeling of CBBM. A business unit is defined as a set of its purposeful processes. No thing or one is left outside them. Viability is ensured by real-time interaction and the 1st-, 2nd-, and 3rd-order feedback loops between three systems. Overall, the suggested, recursive, 3-system template is intended to serve future, compatible modeling efforts among interested, pioneering firms, professional CBBM modelers, scholars, and alike. Its novelty is produced by choosing and designing the CBBM modeling as the 2nd-order system-in-focus with its two recursions, by designing and using systemic, competence-based processes as the units of conceptualization, and by choosing and drawing the figures to illustrate the 3-system template in the ways that allow also business managers comprehend and apply the suggested template in practice.

Massively parallel desktop computing capabilities now well within the reach of individual academics modify the environment for posterior simulation in fundamental and potentially quite advantageous ways. But to fully exploit these benefits algorithms that conform to parallel computing environments are needed. This paper presents a sequential posterior simulator designed to operate efficiently in this context. The simulator makes fewer analytical and programming demands on investigators, and is faster, more reliable, and more complete than conventional posterior simulators. The paper extends existing sequential Monte Carlo methods and theory to provide a thorough and practical foundation for sequential posterior simulation that is well suited to massively parallel computing environments. It provides detailed recommendations on implementation, yielding an algorithm that requires only code for simulation from the prior and evaluation of prior and data densities and works well in a variety of applications representative of serious empirical work in economics and finance. The algorithm facilitates Bayesian model comparison by producing marginal likelihood approximations of unprecedented accuracy as an incidental by-product, is robust to pathological posterior distributions, and provides estimates of numerical standard error and relative numerical efficiency intrinsically. The paper concludes with an application that illustrates the potential of these simulators for applied Bayesian inference.

The author introduces a strategic responsiveness model that reflects an organization’s ability to sense environmental changes and learn from emergent adaptive responses that attempt to realign organizational activities and gain a better fit with the changing conditions. The author shows in computational simulations how superior strategic adaptation is associated with higher average returns and lower performance risk among firms that compete in the same industry contexts and generate negatively skewed outcome distributions consistent with empirical observations. The model is refined to incorporate an interactive strategy-making process, where experiential insights from decentralized initiatives update forward-looking projections in central planning. The ensuing analysis demonstrates how this adaptive strategy-making approach further enhances the favorable risk-return outcomes. The author discusses these findings and the implications for the study of dynamic adaptive strategy-making processes.

The wide spread application of projects to many areas of society has led us to identify five generic elements that every project will have to address. They are: project management, project task, stakeholders, resources, and project environment. Each element will be further divided into five sub-elements. This five-by-five model will be used to identify the nature of a specific project and to develop appropriate approaches and means. It will also allow a circular planning process that gradually will lead to coherence among the five elements.

To illustrate the broad spectrum of projects that we see in practice, we have identified five types that will be characterized by the five-by-five model.

In view of the complex nature of most projects, the chapter will introduce four complementary perspectives: a technical, a business, an organizational, and a stakeholder perspective. Each perspective will describe key features of a project and is supported by theories, models, and methods. Although the perspectives are complementary, they are strongly interdependent.

The chapter will give an overview of the book.

In this chapter we discuss model selection and predictive accuracy tests in the context of parameter and model uncertainty under recursive and rolling estimation schemes. We begin by summarizing some recent theoretical findings, with particular emphasis on the construction of valid bootstrap procedures for calculating the impact of parameter estimation error. We then discuss the Corradi and Swanson (2002) (CS) test of (non)linear out-of-sample Granger causality. Thereafter, we carry out a series of Monte Carlo experiments examining the properties of the CS and a variety of other related predictive accuracy and model selection type tests. Finally, we present the results of an empirical investigation of the marginal predictive content of money for income, in the spirit of Stock and Watson (1989), Swanson (1998) and Amato and Swanson (2001).

The strategic management literature emphasizes the concept of business intelligence (BI) as an essential competitive tool. Yet the sustainability of the firms’ competitive advantage provided by BI capability is not well researched. To fill this gap, this study attempts to develop a model for successful BI deployment and empirically examines the association between BI deployment and sustainable competitive advantage. Taking the telecommunications industry in Malaysia as a case example, the research particularly focuses on the influencing perceptions held by telecommunications decision makers and executives on factors that impact successful BI deployment. The research further investigates the relationship between successful BI deployment and sustainable competitive advantage of the telecommunications organizations. Another important aim of this study is to determine the effect of moderating factors such as organization culture, business strategy, and use of BI tools on BI deployment and the sustainability of firm’s competitive advantage.

This research uses combination of resource-based theory and diffusion of innovation (DOI) theory to examine BI success and its relationship with firm’s sustainability. The research adopts the positivist paradigm and a two-phase sequential mixed method consisting of qualitative and quantitative approaches are employed. A tentative research model is developed first based on extensive literature review. The chapter presents a qualitative field study to fine tune the initial research model. Findings from the qualitative method are also used to develop measures and instruments for the next phase of quantitative method. The study includes a survey study with sample of business analysts and decision makers in telecommunications firms and is analyzed by partial least square-based structural equation modeling.

The findings reveal that some internal resources of the organizations such as BI governance and the perceptions of BI’s characteristics influence the successful deployment of BI. Organizations that practice good BI governance with strong moral and financial support from upper management have an opportunity to realize the dream of having successful BI initiatives in place. The scope of BI governance includes providing sufficient support and commitment in BI funding and implementation, laying out proper BI infrastructure and staffing and establishing a corporate-wide policy and procedures regarding BI. The perceptions about the characteristics of BI such as its relative advantage, complexity, compatibility, and observability are also significant in ensuring BI success. The most important results of this study indicated that with BI successfully deployed, executives would use the knowledge provided for their necessary actions in sustaining the organizations’ competitive advantage in terms of economics, social, and environmental issues.

This study contributes significantly to the existing literature that will assist future BI researchers especially in achieving sustainable competitive advantage. In particular, the model will help practitioners to consider the resources that they are likely to consider when deploying BI. Finally, the applications of this study can be extended through further adaptation in other industries and various geographic contexts.

There is continual evidence of ineffective e-Learning programmes that are set amid emerging information and communication technology (ICT) tools by higher education (HE) providers. While many of the existing accounts outline the potential of integrating such educational technology into their teaching and learning practice, other studies point out the adoption challenges of such programmes. This chapter tackles this dilemma in two respects. Firstly, through an examination of the limitations surrounding the instructional systems design (ISD) models while urging the need for empirical evidence and ratification processes to substantiate these models as they relate to online instructional environments. Secondly, through the investigation of the effectiveness offered by ICT tools under different instructional environments in order to facilitate the effective application of e-Learning. Field evaluation in the form of a series of 2×3 factorial quasi-experiments was conducted at four higher education institutions in Saudi Arabia. The empirical results confirm the validity of the ISD model and reliably captured its effects in improving learners’ performance under three instructional delivery modes. The empirical evidence reveals the extent of effectiveness of the proposed prescriptive ISD model enabling an improved design of ICT-based HE instructional strategies. On a managerial level, the findings facilitate the delivery mode decision making by HE providers in terms of the congruence of technology integration under each of the three learning experiences. The calibrated assessment measures provide a discussion to extend the practical implication of the current e-Pedagogical practice in the e-Learning industry.