Quality Measures for Agent-Oriented Software

The popularity of software agents and multiagent systems has increased, and this is now one of the most active areas in informatics research and development. Agent-oriented software engineering is a new paradigm. But no paradigm would be complete without a means for measuring its quality. This work is the result of an investigation in order to evaluate characteristics of a software agent, measuring its most relevant characteristics. For this, we consider the three most important characteristics of a software agent: its social ability, autonomy and proactivity. Measurable attributes are defined for each feature and then are defined measures to evaluate each attribute, and from these values obtain the value of the characteristics considered are proposed. To validate these measures are applied to a case study

Gender Differences in Entrepreneurial Orientation: Evidence from Ghana

This study explores the differences in the entrepreneurial orientation between men and women using data from a survey of 300 small and medium-scale (SMEs) enterprises from Accra, Ghana, using the Covin and Slevin entrepreneurial orientation scale. The findings of the study indicate that there are significant differences between the levels of entrepreneurial orientation among the two genders. These differences are in risk-taking, innovativeness and proactiveness. Men are found to be more entrepreneurial oriented than women. This is mainly attributable to the different socio-economic conditions that men and women are exposed to. The study contributes to the literature on entrepreneurial and gender theory. The findings imply that policies aimed at developing entrepreneurs should be gender sensitive. Keywords: Entrepreneurial Orientation, Gender, Small Business, Enterprise, Ghana

Is the red dragon green? An examination of the antecedents and consequences of environmental proactivity in China

China is the world’s second largest economy and the largest emitter of carbon dioxide, yet we know little about environmental proactivity in the most populated country in the world. We address this gap through a survey of 161 Chinese companies with two respondents per firm (N = 322) where we seek to identify the antecedents and consequences of environmental proactivity. We identify two categorizations of environmental proactivity: Environmental operational improvements and environmental reporting. We find that ecological motivations and regulatory stakeholder pressure are positively related to both types of environmental proactivity, and external stakeholder pressure is negatively related to environmental reporting. Furthermore, we find that (1) if a firm is environmentally proactive (as it relates to either measure) and they are ecologically motivated, there is a positive and significant cost advantage, and (2) if a firm makes use of environmental operational improvement and they are competitively motivated, there is a positive and significant reputation advantage. Implications for researchers, managers, and policy-makers in China are discussed

Spyware detection technique based on reinforcement learning

Analysis of the antivirus technologies, showed that they are not able to detect new spyware with high efficiency, which significantly reduces the reliability and efficiency of its identification. Techniques based on heuristic analysis have a high rate of false positives. The paper presents a new technique for the spyware detection method in computer systems that provides a principle of proactivity and is based on mechanisms machine learning with the reinforce-mentlearning. The suggested method of spyware detection is based on software behavior analysis in computer systems. The suggested method involves the computer systems monitoring concerning the software, operates with the behavior

A context-aware intentional service prediction mechanism in PIS

International audiencePervasive Information System (PIS) represents a new generation of Information Systems (IS) available anytime, anywhere in a pervasive environment. In this paper, we propose to enhance PIS transparency and efficiency through a context-aware intentional service prediction approach. This approach allows anticipating user's future needs, offering and recommending him the most suitable service in a transparent and discrete way. We detail in this paper our service prediction mechanism and present encouraging experimental results demonstrating our proposition