Breaking free from the industrial age paradigm of branding

The paper aims to inform readers of the themes that emerged at the 2007 Thought Leaders International Conference on Brand Management and challenges academics and practitioners to rethink the basics of branding. The paper encourages academics and practitioners to escape from the continued confines of industrial age branding and the ‘influencing’ mindset and embrace the age of openness and co-creation. It is argued that we need to evolve from the industrial age paradigm of branding that informed brand management for decades and adjust practice and research accordingly.

Ontology modeling for generation of clinical pathways

Purpose: Increasing costs of health care, fuelled by demand for high quality, cost-effective healthcare has drove hospitals to streamline their patient care delivery systems. One such systematic approach is the adaptation of Clinical Pathways (CP) as a tool to increase the quality of healthcare delivery. However, most organizations still rely on are paper-based pathway guidelines or specifications, which have limitations in process management and as a result can influence patient safety outcomes. In this paper, we present a method for generating clinical pathways based on organizational semiotics by capturing knowledge from syntactic, semantic and pragmatic to social level. Design/methodology/approach: The proposed modeling approach to generation of CPs adopts organizational semiotics and enables the generation of semantically rich representation of CP knowledge. Semantic Analysis Method (SAM) is applied to explicitly represent the semantics of the concepts, their relationships and patterns of behavior in terms of an ontology chart. Norm Analysis Method (NAM) is adopted to identify and formally specify patterns of behavior and rules that govern the actions identified on the ontology chart. Information collected during semantic and norm analysis is integrated to guide the generation of CPs using best practice represented in BPMN thus enabling the automation of CP. Findings: This research confirms the necessity of taking into consideration social aspects in designing information systems and automating CP. The complexity of healthcare processes can be best tackled by analyzing stakeholders, which we treat as social agents, their goals and patterns of action within the agent network. Originality/value: The current modeling methods describe CPs from a structural aspect comprising activities, properties and interrelationships. However, these methods lack a mechanism to describe possible patterns of human behavior and the conditions under which the behavior will occur. To overcome this weakness, a semiotic approach to generation of clinical pathway is introduced. The CP generated from SAM together with norms will enrich the knowledge representation of the domain through ontology modeling, which allows the recognition of human responsibilities and obligations and more importantly, the ultimate power of decision making in exceptional circumstances.

Multi-agent based modeling and simulation of complex system in hospital

This study puts forward a method to model and simulate the complex system of hospital on the basis of multi-agent technology. The formation of the agents of hospitals with intelligent and coordinative characteristics was designed, the message object was defined, and the model operating mechanism of autonomous activities and coordination mechanism was also designed. In addition, the Ontology library and Norm library etc. were introduced using semiotic method and theory, to enlarge the method of system modelling. Swarm was used to develop the multi-agent based simulation system, which is favorable for making guidelines for hospital's improving it's organization and management, optimizing the working procedure, improving the quality of medical care as well as reducing medical charge costs.

The role of soil microbes in the global carbon cycle: tracking the below-ground microbial processing of plant-derived carbon for manipulating carbon dynamics in agricultural systems.

It is well known that atmospheric concentrations of carbon dioxide (CO2) (and other greenhouse gases) have increased markedly as a result of human activity since the industrial revolution. It is perhaps less appreciated that natural and managed soils are an important source and sink for atmospheric CO2 and that, primarily as a result of the activities of soil microorganisms, there is a soil-derived respiratory flux of CO2 to the atmosphere that overshadows by tenfold the annual CO2 flux from fossil fuel emissions. Therefore small changes in the soil carbon cycle could have large impacts on atmospheric CO2 concentrations. Here we discuss the role of soil microbes in the global carbon cycle and review the main methods that have been used to identify the microorganisms responsible for the processing of plant photosynthetic carbon inputs to soil. We discuss whether application of these techniques can provide the information required to underpin the management of agro-ecosystems for carbon sequestration and increased agricultural sustainability. We conclude that, although crucial in enabling the identification of plant-derived carbon-utilising microbes, current technologies lack the high-throughput ability to quantitatively apportion carbon use by phylogentic groups and its use efficiency and destination within the microbial metabolome. It is this information that is required to inform rational manipulation of the plant–soil system to favour organisms or physiologies most important for promoting soil carbon storage in agricultural soil.

The history of entrepreneurship: Medieval origins of a modern phenomenon

The origins of enterprise are often associated with the Industrial Revolution, but this article presents evidence of entrepreneurial activities from a much earlier date – the medieval period. Between 1250 and 1500 the church, merchants and members of the royal court all engaged in activities that demonstrated the entrepreneurial characteristics of innovation, risk-taking and judgement. The activities of the prior of Tynemouth and the career of the wool merchant William de la Pole illustrate these developments. By focusing on individuals rather than firms, it is possible to push back the study of entrepreneurship beyond the Industrial Revolution and early-modern trade to a period that witnessed the origins of the modern state.

Geoadditive models for regional count data: an application to industrial location

We propose a geoadditive negative binomial model (Geo-NB-GAM) for regional count data that allows us to address simultaneously some important methodological issues, such as spatial clustering, nonlinearities, and overdispersion. This model is applied to the study of location determinants of inward greenfield investments that occurred during 2003–2007 in 249 European regions. After presenting the data set and showing the presence of overdispersion and spatial clustering, we review the theoretical framework that motivates the choice of the location determinants included in the empirical model, and we highlight some reasons why the relationship between some of the covariates and the dependent variable might be nonlinear. The subsequent section first describes the solutions proposed by previous literature to tackle spatial clustering, nonlinearities, and overdispersion, and then presents the Geo-NB-GAM. The empirical analysis shows the good performance of Geo-NB-GAM. Notably, the inclusion of a geoadditive component (a smooth spatial trend surface) permits us to control for spatial unobserved heterogeneity that induces spatial clustering. Allowing for nonlinearities reveals, in keeping with theoretical predictions, that the positive effect of agglomeration economies fades as the density of economic activities reaches some threshold value. However, no matter how dense the economic activity becomes, our results suggest that congestion costs never overcome positive agglomeration externalities.