Data as Infrastructure

Publisher PD

The Organization of Software Development: A Cybernetic Perspective

This paper considers an organizational perspective upon software development. It proposes the use of a cybernetic approach is complementary to the more conventional software process improvement paradigm. The findings of three case studies are briefly presented

Structured Methodology Usage in ERP Implementation Projects: An Empirical Investigation

This paper reports on the findings of two case studies of ERP implementation. The cases have addressed the questions of whether structured menthods are used in ERP implementations, and how they are used. The evidence presented shows that in practice structured methods appear to be expendable in implementation projects. However, they are valued for political reasons (e.g. gaining the trust of senior managers). The authors propose that these findings may be ascribed to the nature of ERP systems themselves: i.e. that they require the organization to adapt to the technology rather than the technology to adapt to the organization

Data As Infrastructure

The National Infrastructure Commission appointed Dr Peter Kawalek (Director of the Centre for Information Management at Loughborough University) and Ali Bayat (Alliance Manchester Business School at the University of Manchester) to explore the case for considering data as Infrastructure. This was one of four pieces of external research commissioned to support the Commission’s study, Data for the Public Good

A Normative Model for Assessing SME IT Effectiveness

Information technology (IT) is a key enabler of modern small businesses, yet fostering reliably effective IT systems remains a significant challenge. This paper presents a light weight IT effectiveness model for small businesses to assess their IT and formulate strategies for improvement. Employing an action research approach we investigate a mixed method analysis of 120 survey responses from small family businesses and user participation in 10 semi-structured interviews. We then conduct critical reflection to identify refinements which are validated using 72 survey responses from university students. The results present compelling evidence that employees’ normative patterns (norms) are a significant driver of IT effectiveness in a second order PLS predictive model able to explain 26% of observed variance. A norms-based approach to IT effectiveness helps fill a significant research and managerial gap for organizations unable or unwilling to adopt IT best practice frameworks used by large organizations. Our findings imply that comparing norms to IT best practices may offer a less technical approach to assessing IT operations, which may be well suited to small businesses. Although further investigation cycles are needed to systematically test this model, we encourage small business managers to: 1) anticipate IT risks and mitigate them; 2) identify measures of IT performance, and monitor them, and 3) review/synchronize business and IT goals

Cultural foundations of corporate control: an empirical enquiry [Abstract]

Cultural foundations of corporate control: an empirical enquir

Artificial Intelligence Adoption in Criminal Incestigations: Challenges and Opportunities for Research

Artificial Intelligence (AI) offers the potential to transform organisational decision-making and knowledge-sharing processes that support criminal investigations. Yet, there is still limited evidence-based knowledge concerning the successful use of AI for criminal investigations in literature. This paper identifies the main areas and current dynamics of the adoption of AI in criminal investigations using bibliometric analysis. We synthesise existing research by identifying key themes researchers have delved into on AI in criminal investigations. The themes include crime prediction and human-centred issues relating to AI use in criminal investigations. Finally, the paper elaborates on the challenges that may influence AI adoption in criminal investigations by police professionals. These challenges include possible laggard effects with AI adoption, implementation challenges, lack of government oversight, and a skills gap