Prospering in a transition economy through information technology-supported organizational learning

Abstract.  This paper presents the findings of a longitudinal study of the Slovenian company Sava during its 1995–2004 transition period when it adapted to and prospered in a free market economy. The company is particularly interesting because of its successful transition from a socialist company operating in a protected market to a privatized company operating in a capitalist global market, as well as the pivotal role of information technology (IT)-supported organizational learning that brought about radical change and successful transition. Our investigation of Sava's experiences demonstrates how the company's increasing attention to organizational learning, integration of working and learning, and its constant innovation of products and processes created new needs for IT support that motivated the adoption of new IT systems (such as Lotus Notes, document management systems, SAP), which in turn increased Sava's capacity to learn. Furthermore, our study reveals how the role of IT systems in organizational learning depends on the nature of learning (single-loop, double-loop or triple-loop learning) and the organizational level at which learning takes place (individual, group/department or organization). By providing insight into the emergence of distinct types of IT-supported learning and their vital role in Sava's successful transition, the paper contributes to a deeper understanding of the relationship between IT and organizational learning that is relevant and inspiring to other companies, especially those operating in transition economies.     

Artificial intelligence applications in the telecommunications industry

Abstract: Artificial intelligence (AI) has been applied to the telecommunications industry for more than a decade. The purpose of this paper is to examine the application of AI in the telecommunications industry sector. Our research finds that AI's first main application in telecommunications is in the network management area. Expert systems and machine learning are the two AI techniques that have been widely used in telecommunications, while machine learning and distributed artificial intelligence are the two AI techniques which are most promising for the future. The research also finds that different AI techniques have their unique applications in the telecommunications industry.     

Multi-metaphor method: organizational metaphors in information systems development

Abstract.  Conventional methods have been criticized for their positivist philosophy and for either ignoring the organizational context of information systems development (ISD) or using only a simplistic, machine-based conceptual model of organizations. We have developed an approach to enable systems developers to use a richer view of organizations and a more interpretive approach. Multi-Metaphor Method (MMM) supports developers via a range of metaphors as cognitive structuring devices to understand an organization so that developers can learn to move between different ways of 'reading' the social context in which ISD occurs. We explain the rationale and theoretical underpinnings of MMM and then discuss our action research to investigate the viability and relevance of MMM during ISD practice. We show how the developers' use of organizational metaphors via MMM did not just enable rich conceptualizations of the client organizations but also influenced the ISD process and final product. We review the learning outcomes and discuss the implications for systems development practice of our research.     

Evaluating Information Technology investments: research findings and reappraisal

Abstract. Investments in Information Technology are large and rising, but frequently the subject of disappointed expectations. Some explanations can be found in how they are evaluated and controlled. The paper reports findings from case study work and a 50 organization research project carried out in 1990-91. It first highlights 10 common problem areas that can be addressed. It then focuses on evaluation at the critical feasibility stage of projects and identifies from the research a number of ways in which evaluation practice can be improved. It finds a fall-off in evaluation at subsequent project stages, little attempt to link evaluation across the lifetime of systems, and a fragmented approach to learning from the evaluation experience for future investments. A number of modern alternatives to traditional financially based investment appraisal techniques that dominate the sample are then critically assessed. Tailored to specific organizational circumstances and projects these offer ways forward. Broader thinking, in terms of Information Systems (IS) investments, and a deeper understanding of IS as human activity systems lead on to multiple effectiveness criteria and related measures. However, major improvements may lie in allying more appropriate techniques and measures to developing evaluation as an organizational and social process, with the deeper organizational learning about IT this would entail.     

How Can Organizational Memory Theories Contribute to Organizational Memory Systems?

Organizational memory systems (OMS) support organizations to ensure organizational learning, flexibility and efficiency and the management of change. The paper describes the term organizational memory and reviews a set of theories contributing to this field. As organizational memory is considered interdisciplinary, theories are drawn from research fields as different as organization science, psychology, sociology and artificial intelligence. The consequence is that the design of information systems supporting organizational memory has to be seen in this very specific context which leads to different perspectives contrasting traditional software development. These different perspectives are integrated into our view on organizational memory systems. The paper is concluded by an outlook on the main research questions which we intend to address in our research.     

Linking soft systems methodology with data-focused information systems development

Abstract. Where the soft systems methodology (SSM) is used in the development of organizational information systems a clear division exists between the use of SSM to identify what information systems are required and conventional development activities in which it is decided how those information systems will be supplied. Discussion of how SSM might be more closely linked to conventional information systems development methodologies has been concentrated upon process-focused approaches to information systems development. This has been partly due to a perceived mismatch between the underlying philosophies of SSM and the alternative data-focused development methodologies. This paper argues that this perception may be mistaken; not only do the existing forms of data analysis have a large though unacknowledged subjective component but the SSM concept of appreciation may provide a model of human sense-making that the data-focused approaches currently lack and from which they may benefit. The idea of appreciation also allows that an alternative, interpretative form of data analysis might be used within SSM. It is therefore the conclusion of this paper that some closer integration of SSM with data-focused approaches to information systems development is theoretically feasible and may be practically desirable. A number of possible advantages of such integration are described.     

Dialectical hermeneutics: a theoretical framework for the implementation of information systems

Abstract. Despite the proliferation of information technology, the implementation of information systems remains problematic. Many systems fail to live up to expectations and some end up as disasters. Research on implementation has been conducted for the past two decades, but no theoretical framework for IS implementation has been widely accepted. This paper proposes dialectical hermeneutics as a theoretical framework for IS implementation research. Dialectical hermeneutics, as an integrative theoretical framework, combines both interpretive and critical elements, and addresses those social and organizational issues which are critical to the successful implementation of information systems. The case study research method was used to examine the implementation of systems in three organizations.     

无人系统的视觉感知安全研究

随着现代科技的不断革新,以机器学习尤其是深度学习为代表的人工智能技术正在改变无人系统的发展,推动无人作战等作战形态快速演变,对未来战争带来颠覆性影响。然而由于深度学习的不可解释性、脆弱性等问题,人工智能技术在现实应用中产生了诸多不确定性和安全风险。本文聚焦人工智能技术在军事无人系统中的安全问题,从视觉感知的角度出发,重点分析了安全风险来源、对抗样本理论和视觉感知对抗攻击方法和防御对策,最后对无人系统领域人工智能应用的安全问题进行了总结。     

Machine learning: a survey of current techniques

Machine learning is the essence of machine intelligence. When we have systems that learn, we will have true artificial intelligence. Many machine-learning strategies exist, this paper reviews the state of the art in machine learning and provides a glimpse of the pioneers of present machine-learning systems and strategies. Learning in noisy domains, the evolutionary learning, learning by analogy and explanation-based learning are just some of the methods covered. Emphasis is placed on the algorithms employed by many of the systems, and the merits and disadvantages of various approaches. Finally an examination of VanLehn's theory of impasse-driven learning is made.     

Learning in multi-agent systems: a case study of construction claims negotiation

The ability of agents to learn is of growing importance in multi-agent systems. It is considered essential to improve the quality of peer to peer negotiation in these systems. This paper reviews various aspects of agent learning, and presents the particular learning approach—Bayesian learning—adopted in the MASCOT system (multi-agent system for construction claims negotiation). The core objective of the MASCOT system is to facilitate construction claims negotiation among different project participants. Agent learning is an integral part of the negotiation mechanism. The paper demonstrates that the ability to learn greatly enhances agents' negotiation power, and speeds up the rate of convergence between agents. In this case, learning is essential for the success of peer to peer agent negotiation systems.