ITSM ProcessGuide – a longitudinal and multi-method field study for real-world DSR artifact evaluation

Process guidance supports users to increase their process model understanding, process execution effectiveness as well as efficiency, and process compliance performance. This paper presents a research in progress encompassing our ongoing DSR project on Process Guidance Systems and a field evaluation of the resulting artifact in cooperation with a company. Building on three theory-grounded design principles, a Process Guidance System artifact for the company’s IT service ticketing process is developed, deployed and used. Fol-lowing a multi-method approach, we plan to evaluate the artifact in a longitudinal field study. Thereby, we will not only gather self-reported but also real usage data. This article describes the development of the artifact and discusses an innovative evaluation approach.

Landscape and planning: strengthening discussions in the decision-making process

Since the emergence of the European Landscape Convention (ELC) in 2000, the important link between landscape and planning has greatly intensified. Now, more than ever, the fundamental role of the planning system in delivering the ELC’s requirements is recognised. This has been further substantiated within Ireland’s recently published National Landscape Strategy. However it has continually been suggested that decision-making processes need to adapt better to the holistic, valueladen and multidimensional approaches underpinning the ELC. In light of these milestones for the preservation, management and planning of landscape, this research sets out to establish synergies and disparities in the existing relationship between landscape and planning. It investigates detailed evidence of the presence and manifestations of landscape in key processes of day-to-day planning practice in Ireland, from individual planning appeals and ‘special’ cases, to the major strategic instruments that inform the making of landscape policies within development plans. This is set within wider theoretical and policy contexts where the compatibility of landscape and planning is subjected to critical scrutiny and then explored through these practical case studies. Driving this research is the intention to make a case for the planning domain to be an ideal ‘home’ for landscape – in all its deep, multidimensional meaning – and for enhancing landscape arguments and objectives in the face of conflict, competing values and power-plays in the real world. Emerging out of this research is a set of recommendations for how, at a national level, new approaches for decision making for and about landscape can be more effective and meaningful.

Reasoning with imprecise trade-offs in decision making under certainty and uncertainty

In many real world situations, we make decisions in the presence of multiple, often conflicting and non-commensurate objectives. The process of optimizing systematically and simultaneously over a set of objective functions is known as multi-objective optimization. In multi-objective optimization, we have a (possibly exponentially large) set of decisions and each decision has a set of alternatives. Each alternative depends on the state of the world, and is evaluated with respect to a number of criteria. In this thesis, we consider the decision making problems in two scenarios. In the first scenario, the current state of the world, under which the decisions are to be made, is known in advance. In the second scenario, the current state of the world is unknown at the time of making decisions. For decision making under certainty, we consider the framework of multiobjective constraint optimization and focus on extending the algorithms to solve these models to the case where there are additional trade-offs. We focus especially on branch-and-bound algorithms that use a mini-buckets algorithm for generating the upper bound at each node of the search tree (in the context of maximizing values of objectives). Since the size of the guiding upper bound sets can become very large during the search, we introduce efficient methods for reducing these sets, yet still maintaining the upper bound property. We define a formalism for imprecise trade-offs, which allows the decision maker during the elicitation stage, to specify a preference for one multi-objective utility vector over another, and use such preferences to infer other preferences. The induced preference relation then is used to eliminate the dominated utility vectors during the computation. For testing the dominance between multi-objective utility vectors, we present three different approaches. The first is based on a linear programming approach, the second is by use of distance-based algorithm (which uses a measure of the distance between a point and a convex cone); the third approach makes use of a matrix multiplication, which results in much faster dominance checks with respect to the preference relation induced by the trade-offs. Furthermore, we show that our trade-offs approach, which is based on a preference inference technique, can also be given an alternative semantics based on the well known Multi-Attribute Utility Theory. Our comprehensive experimental results on common multi-objective constraint optimization benchmarks demonstrate that the proposed enhancements allow the algorithms to scale up to much larger problems than before. For decision making problems under uncertainty, we describe multi-objective influence diagrams, based on a set of p objectives, where utility values are vectors in Rp, and are typically only partially ordered. These can be solved by a variable elimination algorithm, leading to a set of maximal values of expected utility. If the Pareto ordering is used this set can often be prohibitively large. We consider approximate representations of the Pareto set based on ϵ-coverings, allowing much larger problems to be solved. In addition, we define a method for incorporating user trade-offs, which also greatly improves the efficiency.

A critical realist examination of the inhibitors of double loop learning in an agile adoption

The original solution to the high failure rate of software development projects was the imposition of an engineering approach to software development, with processes aimed at providing a repeatable structure to maintain a consistency in the ‘production process’. Despite these attempts at addressing the crisis in software development, others have argued that the rigid processes of an engineering approach did not provide the solution. The Agile approach to software development strives to change how software is developed. It does this primarily by relying on empowered teams of developers who are trusted to manage the necessary tasks, and who accept that change is a necessary part of a development project. The use of, and interest in, Agile methods in software development projects has expanded greatly, yet this has been predominantly practitioner driven. There is a paucity of scientific research on Agile methods and how they are adopted and managed. This study aims at addressing this paucity by examining the adoption of Agile through a theoretical lens. The lens used in this research is that of double loop learning theory. The behaviours required in an Agile team are the same behaviours required in double loop learning; therefore, a transition to double loop learning is required for a successful Agile adoption. The theory of triple loop learning highlights that power factors (or power mechanisms in this research) can inhibit the attainment of double loop learning. This study identifies the negative behaviours - potential power mechanisms - that can inhibit the double loop learning inherent in an Agile adoption, to determine how the Agile processes and behaviours can create these power mechanisms, and how these power mechanisms impact on double loop learning and the Agile adoption. This is a critical realist study, which acknowledges that the real world is a complex one, hierarchically structured into layers. An a priori framework is created to represent these layers, which are categorised as: the Agile context, the power mechanisms, and double loop learning. The aim of the framework is to explain how the Agile processes and behaviours, through the teams of developers and project managers, can ultimately impact on the double loop learning behaviours required in an Agile adoption. Four case studies provide further refinement to the framework, with changes required due to observations which were often different to what existing literature would have predicted. The study concludes by explaining how the teams of developers, the individual developers, and the project managers, working with the Agile processes and required behaviours, can inhibit the double loop learning required in an Agile adoption. A solution is then proposed to mitigate these negative impacts. Additionally, two new research processes are introduced to add to the Information Systems research toolkit.