Exploring Uncertainty in Geodemographics with Interactive Graphics

Geodemographic classifiers characterise populations by categorising geographical areas according to the demographic and lifestyle characteristics of those who live within them. The dimension-reducing quality of such classifiers provides a simple and effective means of characterising population through a manageable set of categories, but inevitably hides heterogeneity, which varies within and between the demographic categories and geographical areas, sometimes systematically. This may have implications for their use, which is widespread in government and commerce for planning, marketing and related activities. We use novel interactive graphics to delve into OAC – a free and open geodemographic classifier that classifies the UK population in over 200,000 small geographical areas into 7 super-groups, 21 groups and 52 sub-groups. Our graphics provide access to the original 41 demographic variables used in the classification and the uncertainty associated with the classification of each geographical area on-demand. It also supports comparison geographically and by category. This serves the dual purpose of helping understand the classifier itself leading to its more informed use and providing a more comprehensive view of population in a comprehensible manner. We assess the impact of these interactive graphics on experienced OAC users who explored the details of the classification, its uncertainty and the nature of between – and within – class variation and then reflect on their experiences. Visualization of the complexities and subtleties of the classification proved to be a thought-provoking exercise both confirming and challenging users’ understanding of population, the OAC classifier and the way it is used in their organisations. Users identified three contexts for which the techniques were deemed useful in the context of local government, confirming the validity of the proposed methods

The Use of Trademarks in Empirical Research: Towards an Integrated Framework

This paper represents an early attempt to develop an integrated framework linking empirical studies that make use of trademark statistics. Despite its youth, this field of scholarly activity has already accumulated a critical mass of papers that allow us to draw first general conclusions about the trademark lifecycle and its impact on organisational functioning. Based on a systematic review of 64 articles with some elements of empirical trademark analysis, five broad research areas have been identified, namely: the determinants of trademark deposits; the relationship between trademarks and innovation processes; the role of trademarks in differentiating product offerings; the strategic use of trademarks; and the impact of trademarks on firm performance. Within each category, a more detailed aggregation of articles has also been proposed. Overall, the analysis has shown that the performance-based perspective currently dominates the research landscape, with studies on trademark deposits and the trademark-innovation link to follow. At the same time, there is still little known about micro-foundations of a company's trademarking behaviour; the use of trademarks and other intellectual property rights in a complementary way and its effect on value transference; as well as the performance implications of differentiation strategy. This paper considers these and other findings to outline directions for future research

Freshwater ecosystem services in mining regions : modelling options for policy development support

The ecosystem services (ES) approach offers an integrated perspective of social-ecological systems, suitable for holistic assessments of mining impacts. Yet for ES models to be policy-relevant, methodological consensus in mining contexts is needed. We review articles assessing ES in mining areas focusing on freshwater components and policy support potential. Twenty-six articles were analysed concerning (i) methodological complexity (data types, number of parameters, processes and ecosystem-human integration level) and (ii) potential applicability for policy development (communication of uncertainties, scenario simulation, stakeholder participation and management recommendations). Articles illustrate mining impacts on ES through valuation exercises mostly. However, the lack of ground-and surface-water measurements, as well as insufficient representation of the connectivity among soil, water and humans, leave room for improvements. Inclusion of mining-specific environmental stressors models, increasing resolution of topographies, determination of baseline ES patterns and inclusion of multi-stakeholder perspectives are advantageous for policy support. We argue that achieving more holistic assessments exhorts practitioners to aim for high social-ecological connectivity using mechanistic models where possible and using inductive methods only where necessary. Due to data constraints, cause-effect networks might be the most feasible and best solution. Thus, a policy-oriented framework is proposed, in which data science is directed to environmental modelling for analysis of mining impacts on water ES

Improved resource efficiency and cascading utilisation of renewable materials

In light of various environmental problems and challenges concerning resource allocation, the utilisation of renewable resources is increasingly important for the efficient use of raw materials. Therefore, cascading utilisation (i.e., the multiple material utilisations of renewable resources prior to their conversion into energy) and approaches that aim to further increase resource efficiency (e.g., the utilisation of by-products) can be considered guiding principles. This paper therefore introduces the Special Volume “Improved Resource Efficiency and Cascading Utilisation of Renewable Materials”. Because both research aspects, resource efficiency and cascading utilisation, belong to several disciplines, the Special Volume adopts an interdisciplinary perspective and presents 16 articles, which can be divided into four subjects: Innovative Materials based on Renewable Resources and their Impact on Sustainability and Resource Efficiency, Quantitative Models for the Integrated Optimisation of Production and Distribution in Networks for Renewable Resources, Information Technology-based Collaboration in Value Generating Networks for Renewable Resources, and Consumer Behaviour towards Eco-friendly Products. The interdisciplinary perspective allows a comprehensive overview of current research on resource efficiency, which is supplemented with 15 book reviews showing the extent to which textbooks of selected disciplines already refer to resource efficiency. This introductory article highlights the relevance of the four subjects, presents summaries of all papers, and discusses future research directions. The overall contribution of the Special Volume is that it bridges the resource efficiency research of selected disciplines and that it presents several approaches for more environmentally sound production and consumption

Understanding new venture market application search processes: A propositional model.

Technology-based ventures are confronted with complex decisions on how to apply their technology platform in highly uncertain and ambiguous market environments. Based on four case studies, a dynamic decision model is developed in which we highlight the similarities between the search and learning processes in venture development contexts and in new product development contexts. This entrepreneurial search and learning process is understood as consisting of sequences of episodes – characterized by uncertainty and ambiguity - and scripts – i.e. approaches to market application search. The model implies that a venture's adaptability - i.e. its ability to move efficiently and effectively between these episodes and their related scripts - influences its survival.Case studies; Decision; Decisions; Learning; Market; Model; Processes; Product; Product development; Research; Sequences; Similarity; Studies; Technology; Uncertainty;

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

Scenario of the organic food market in Europe

Scenario analysis is a qualitative tool for strategic policy analysis that enables researchers and policymakers to support decision making, and a systemic analysis of the main determinants of a business or sector. In this study, a scenario analysis is developed regarding the future development of the market of organic food products in Europe. The scenario follows a participatory approach, exploiting potential interactions among the relevant driving forces, as selected by experts. Network analysis is used to identify the roles of driving forces in the different scenarios, and the results are discussed in comparison with the main findings from existing scenarios on the future development of the organic sector

A conceptual model of virtual product development process

In today’s dynamic marketplace, companies are under strong pressure to introduce new products for long-term survival with their competitors. Besides, every company cannot cope up progressively or immediately with the market requirements due to knowledge dynamics being experienced in competitive milieu. Increased competition and reduced product life cycles put force upon companies to develop new products faster. In response to this pressing need there should be some new approach compatible in flexible circumstances. This paper presents a solution based on the Stage-Gate system, which is closely linked with virtual team approach. Virtual teams can provide a platform to advance the knowledge-base in a company and thus to reduce time-to-market. This article introduces conceptual product development architecture under a virtual-team umbrella. The paper describes all the major aspects of new product development (NPD), NPD process and its relationship with virtual team, Stage-Gate system and finally presents a modified Stage-Gate system. It also provides the guidelines for the successful implementation of virtual team in new products development.Modified Stage-Gate System, Virtual Product Development, Conceptual Model

"There are too many, but never enough": qualitative case study investigating routine coding of clinical information in depression.

We sought to understand how clinical information relating to the management of depression is routinely coded in different clinical settings and the perspectives of and implications for different stakeholders with a view to understanding how these may be aligned