Global Innovations in Measurement and Evaluation

We researched the latest developments in theory and practice in measurement and evaluation. And we found that new thinking, techniques, and technology are influencing and improving practice. This report highlights 8 developments that we think have the greatest potential to improve evaluation and programme design, and the careful collection and use of data. In it, we seek to inform and inspire—to celebrate what is possible, and encourage wider application of these ideas

Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

More effective social services

In June 2014, the Productivity Commission was asked to look at ways to improve how government agencies commission and purchase social services. The final report was released in mid-September 2015. It makes several recommendations about how to make social services more responsive, client-focused, accountable and innovative. The final inquiry report has two key messages. First, system-wide improvement can be achieved and should be pursued. Second, New Zealand needs better ways to join up services for those with multiple, complex needs. Capable clients should be empowered with more control over the services they receive. Those less capable need close support and a response tailored to their needs, without arbitrary distinctions between services and funds divided into “health”, “education”, etc. These are significant, but extremely worthwhile, changes for New Zealand

Information Processing view of Electricity Demand Response Systems: A Comparative Study Between India and Australia

Background: In recent years, demand response (DR) has gained increased attention from utilities, regulators, and market aggregators to meet the growing demands of electricity. The key aspect of a successful DR program is the effective processing of data and information to gain critical insights. This study aims to identify information processing needs and capacity that interact to improve energy DR effectiveness. To this end, organizational information processing theory (OIPT) is employed to understand the role of Information Systems (IS) resources in achieving desired DR program performance. This study also investigates how information processing for DR systems differ between developing (India) and developed (Australia) countries. Method: This work adopts a case study methodology to propose a theoretical framework using OIPT for information processing in DR systems. The study further employs a comparative case data analyses between Australian and Indian DR initiatives. Results: Our cross case analysis identifies variables of value creation in designing DR programs - pricing structure for demand side participation, renewable integration at supply side, reforms in the regulatory instruments, and emergent technology. This research posits that the degree of information processing capacity mediates the influence of information processing needs on energy DR effectiveness. Further, we develop five propositions on the interaction between task based information processing needs and capacity, and their influence on DR effectiveness. Conclusions: The study generates insights on the role of IS resources that can help stakeholders in the electricity value chain to take informed and intelligent decisions for improved performance of DR programs

AI for the Public Sector: Opportunities and challenges of cross-sector collaboration

Public sector organisations are increasingly interested in using data science and artificial intelligence capabilities to deliver policy and generate efficiencies in high uncertainty environments. The long-term success of data science and AI in the public sector relies on effectively embedding it into delivery solutions for policy implementation. However, governments cannot do this integration of AI into public service delivery on their own. The UK Government Industrial Strategy is clear that delivering on the AI grand challenge requires collaboration between universities and public and private sectors. This cross-sectoral collaborative approach is the norm in applied AI centres of excellence around the world. Despite their popularity, cross-sector collaborations entail serious management challenges that hinder their success. In this article we discuss the opportunities and challenges from AI for public sector. Finally, we propose a series of strategies to successfully manage these cross-sectoral collaborations

DESIGNING NEXT GENERATION SMART CITY INITIATIVES - HARNESSING FINDINGS AND LESSONS FROM A STUDY OF TEN SMART CITY PROGRAMS

The proliferation of Smart Cities initiatives around the world is part of the strategic response by governments to the challenges and opportunities of increasing urbanization and the rise of cities as the nexus of societal development. As a framework for urban transformation, Smart City initiatives aim to harness Information and Communication Technologies and Knowledge Infrastructures for economic regeneration, social cohesion, better city administration and infrastructure management. However, experiences from earlier Smart City initiatives have revealed several technical, management and governance challenges arising from the inherent nature of a Smart City as a complex Socio-technical System of Systems . While these early lessons are informing modest objectives for planned Smart Cities programs, no rigorous developed framework based on careful analysis of existing initiatives is available to guide policymakers, practitioners, and other Smart City stakeholders. In response to this need, this paper presents a Smart City Initiative Design (SCID) Framework grounded in the findings from the analysis of ten major Smart Cities programs from Netherlands, Sweden, Malta, United Arab Emirates, Portugal, Singapore, Brazil, South Korea, China and Japan. The findings provide a design space for the objectives, implementation options, strategies, and the enabling institutional and governance mechanisms for Smart City initiatives

Capability-actor-resource-service : a conceptual modelling approach for value-driven strategic sourcing

This PhD research addresses a problem within strategic sourcing, which is a critical area of strategic management that is centered on decision-making related to procurement. Strategic sourcing is related to two disciplines: (i) procurement and supply management and (ii) strategic management. Sourcing is the strategic part of procurement that refers to tasks like determining cost saving and value-driven opportunities, choosing the most appropriate go-to market strategies, and selecting and evaluating suppliers for building long-term and short-term contractual relationships. Many companies face challenges in obtaining the benefits associated with effective strategic sourcing. Although the concept of strategic sourcing is fairly well recognized, managers are still challenged by many barriers to its implementation. The main problem is the lack of practical instruments (i.e., tools and techniques) to implement the value-driven management approach to strategic sourcing, while at the same time preparing companies for fact-based decision-making by delivering data management and data analytics capabilities. This is the problem which is addressed with this PhD research. To address this problem, the research goal has been defined as “develop a modeling approach that enables companies 1) to drive fact-based decision-making with respect to procurement data management and procurement analytics”; and 2) to implement strategic sourcing toward achieving value-driven targets”. We apply conceptual modeling as our main solution approach to achieve the above research goal. We define three major areas where conceptual modeling can contribute to strategic sourcing decision-making: conceptualization, design and computer support. The proposed conceptual modeling approach is characterized by four different perspectives: (i) a way of thinking (i.e., a conceptual foundation), (ii) a way of modeling (i.e., a modeling language and method to use it), (iii) a way of working (i.e., a model-based analysis approach), and (iv) a way of supporting (i.e., a computer-aided design tool). The scope of PhD research is limited to the first three perspectives, while for the fourth perspective a solution architecture will be proposed as part of future research. This PhD dissertation is a paper-based dissertation consisting of six chapters. Three chapters (chapter 3, 4, 5) of this dissertation have been submitted to international peer-reviewed journals (chapter 4 is published and chapters 3 and 5 are accepted) and one chapter (chapter 2) has been published in the post-conference proceedings of an international workshop