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The purpose of this study is to propose to use the economic value added to measure firm performance against information security investments.

The authors develop a conceptual framework to capture non information technology (IT)-related and IT-related security investment factors and propose to study their holistic influences on firm performance.

The authors propose 14 propositions to understand the relationship between security investments and firm performance.

The authors propose a validation process to guide future research to further empirically capture all needed data and analyze the proposed relationships.

Managers can view security investment from a more comprehensive perspective and understand how to potentially contribute each of the non IT-related and IT-related factors to firm performance.

This is one of the early attempts studying information security investment vs firm performance from a comprehensive conceptual angel.

To better understand and assist business managers to deal with green IT adoption, this paper provides a step‐by‐step process management approach.

By drawing on the process management to investigate the green IT adoption, the paper analyzes and discusses four different phases: plan, design, implement, and measure the performance of the process.

The likelihood that companies will successfully adopt green IT initiatives depends on several organizational and environmental factors. The primary factor is the Champion Support. Lack of implementation barriers is another important factor among others.

By comparing behavioral and technological changes derived from green IT initiatives and unveiling possible factors associated with the adoption process, this paper provides an opportunity for academics to conduct applied research based on the issues discussed.

The paper can be an extremely useful and practical source for top‐level managers, particularly IT managers, to bring greener technologies and more environmentally responsible strategies and practices to their organizations.

The paper contends that the green IT adoption process is an ensemble of four phases: plan, design, implement, and measure the performance of the process. This paper serves as a guide and offers practical measures in terms of understanding how green IT initiatives could be more effectively and efficiently adopted by organizations.

Group support systems (GSS) are becoming increasingly popular in corporations that are looking to reduce travel expenses, increase the effectiveness of group decision‐making and foster an environment that brings out ideas or creativity that may be stifled in standard settings. Companies are looking for solutions and technologies to provide GSS that are affordable, flexible, and integrate with their existing information technology (IT) environment. Deciding what solutions to implement can be difficult, however, the decision process should first involve a self‐analysis of the organization’s current IT environment and architecture, as well as its culture. An analysis of currently available solutions can then follow. This article is intended to aid the IT managers in their selection process of GSS technologies and products for their organizations. In particular, the article focuses on Microsoft’s collaboration technologies and products since its current product line offers the IT managers a wide array of choices for implementing a unified GSS environment.

Over several years, there have been intensive discussions about the importance of knowledge management (KM) within the business community. Effectively implementing a sound KM strategy and becoming a knowledge‐based company is seen as a mandatory condition of success for organizations as they enter the era of the knowledge economy. However, standardized metrics are needed to quantify knowledge and to fully convince management and stakeholders as to the value of KM initiatives. Development of KM metrics has begun in recent years and these metrics are being applied by some organizations, but more research is needed to better define these measures and to make them universal. The purpose of this research is to survey and report the current measures of knowledge assets or intellectual capital, as well as the methods that are popularly being followed by organizations to measure the performance of KM strategies. The research findings should: assist organizations in identifying the measures that are appropriate and suitable for them, for improving the quality of metrics they use for measuring KM effectiveness; and assist researchers in identifying future research needs toward the standardization of KM measurement metrics.

This article is directed towards information technology (IT) and marketing managers considering implementation of a customer relationship management (CRM) solution. The goal of this article is not to provide an all‐inclusive tutorial on CRM, but rather to provide a high level insight of the fundamental principles behind CRM and critical aspects of the IT development process. The article begins with an IT manager’s introduction into the basic CRM business and marketing principles. At the heart of the article is a proposed system development lifecycle that highlights the aspects unique or critical to CRM. Finally, it concludes with some final thoughts for long‐term success. After reading this article, the reader will be mindful of the major issues needed for success and be equipped to discuss primary development matters with vendors, staff and management.

The purpose of this survey research is twofold. First, to study and report the process that is commonly used to create and maintain a competitive intelligence (CI) program in organizations. And second, to provide an analysis of several emergent text mining, web mining and visualization‐based CI tools, which are specific to collection and analysis of intelligence.

A range of recently published research literature on CI processes, applications, tools and technologies to collect and analyze competitive information within organizations is reviewed to explore their current state, issues and challenges learned from their practice.

The paper provides executive decision makers and strategic managers a better understanding of what methods are available and appropriate to the decisions they must make and the steps involved in CI undertaking.

The findings of this research provide the managers of CI programs a context for understanding which tools and techniques are better suited to their specific types of problems; and help them develop and evaluate a usable set of tools and best practices to apply to their industry.

Advanced analytics‐driven data analyses allow enterprises to have a complete or “360 degrees” view of their operations and customers. The insight that they gain from such analyses is then used to direct, optimize, and automate their decision making to successfully achieve their organizational goals. Data, text, and web mining technologies are some of the key contributors to making advanced analytics possible. This paper aims to investigate these three mining technologies in terms of how they are used and the issues that are related to their effective implementation and management within the broader context of predictive or advanced analytics.

A range of recently published research literature on business intelligence (BI); predictive analytics; and data, text and web mining is reviewed to explore their current state, issues and challenges learned from their practice.

The findings are reported in two parts. The first part discusses a framework for BI using the data, text, and web mining technologies for advanced analytics; and the second part identifies and discusses the opportunities and challenges the business managers dealing with these technologies face for gaining competitive advantages for their businesses.

The study findings are intended to assist business managers to effectively understand the issues and emerging technologies behind advanced analytics implementation.

Managing enterprise performance is an important, yet a difficult process due to its complexity. The process involves monitoring the strategic focus of an enterprise, whose performance is measured from the analysis of data generated from a wide range of interrelated business activities performed at different levels within the enterprise. This study aims to investigate management data systems technologies in terms of how they are used and the issues that are related to their effective management within the broader context of enterprise performance management (EPM).

A range of recently published research literature on data warehousing, online analytic processing and EPM is reviewed to explore their current state, issues and challenges learned from their practice.

The findings of the study are reported in two parts. The first part discusses the current business practices of these technologies, and the second part identifies and discusses the issues and challenges the business managers dealing with these technologies face for gaining competitive advantage for their businesses.

The study findings are intended to assist the business managers to effectively understand the issues and technologies behind EPM implementation.

There is a tremendous explosion in the amount of data that organizations generate, collect and store. Managers are beginning to recognize the value of this asset, and are increasingly relying on intelligent systems to access, analyze, summarize, and interpret information from large and multiple data sources. These systems help them make critical business decisions faster or with a greater degree of confidence. Data mining is a promising new technology that helps bring business intelligence into these systems. While there is a plethora of data mining techniques and tools available, they present inherent problems for end‐users such as complexity, required technical expertise, lack of flexibility and interoperability, etc. These problems can be mitigated by deploying software agents to assist end‐users in their problem solving endeavors. This paper presents the design and development of an intelligent software agent based data analysis and mining environment called IDM, which is utilized in decision making activities.

This paper examines how the altering nature of planning decisions affects operational efficiency in seaport container terminals. The uncertainty and the role of the planner were investigated considering the dynamic integrated planning function of the quay to yard interface.

A system dynamics model has been built to illustrate the integrated dynamic environment. Data collection was conducted at a leading container terminal at a hub port. The model was simulated for different scenarios to derive findings.

The planner has been identified as the agent who makes alterations between the initial operational plan and the actual plan. The initial plan remains uncertain even when there is no impact from crane breakdowns, requiring a significant number of alterations to be made. The planner who had worked on the yard plan had altered (approximately 45%) the initial plan than the alterations done by the planner who had worked on the vessel plan. As a result, the feedback loop that is created by the remaining moves at each hourly operation influences the upcoming operation as much as crane breakdowns influence.

The uncertainty and the role of the planner were investigated considering the dynamic integrated planning function of the quay to yard interface. The findings of this study are significant since terminal efficiency is examined considering the quayside and landside as an integrated system.