Spot the differences in traditional and social entrepreneurial intention : highlighting the role of human and social capital

There is general agreement in the scientific community that entrepreneurship plays a central role in the growth and development of an economy in rapidly changing environments (Acs & Virgill 2010). In particular, when business activities are regarded as a vehicle for sustainable growth at large, that goes beyond mere economic returns of singular entities, encompassing also social problems and heavily relying on collaborative actions, then we more precisely fall into the domain of ‘social entrepreneurship’(Robinson et al. 2009). In the entrepreneurship literature, prior studies demonstrated the role of intentionality as the best predictor of planned behavior (Ajzen 1991), and assumed that the intention to start a business derives from the perception of desirability and feasibility and from a propensity to act upon an opportunity (Fishbein & Ajzen 1975). Recognizing that starting a business is an intentional act (Krueger et al. 2000) and entrepreneurship is a planned behaviour (Katz & Gartner 1988), models of entrepreneurial intentions have substantial implications for intentionality research in entrepreneurship. The purpose of this paper is to explore the emerging practice of social entrepreneurship by comparing the determinants of entrepreneurial intention in general versus those leading to startups with a social mission. Social entrepreneurial intentions clearly merit to be investigated given that the opportunity identification process is an intentional process not only typical of for profit start-ups, and yet there is a lack of research examining opportunity recognition in social entrepreneurship (Haugh 2005). The key argument is that intentionality in both traditional and social entrepreneurs during the decision-making process of new venture creation is influenced by an individual's perceptions toward opportunities (Fishbein & Ajzen 1975). Besides opportunity recognition, at least two other aspects can substantially influence intentionality: human and social capital (Davidsson, 2003). This paper is set to establish if and to what extent the social intentions of potential entrepreneurs, at the cognitive level, are influenced by opportunities recognition, human capital, and social capital. By applying established theoretical constructs, the paper draws comparisons between ‘for-profit’ and ‘social’ intentionality using two samples of students enrolled in Economy and Business Administration at the University G. d’Annunzio in Pescara, Italy. A questionnaire was submitted to 310 potential entrepreneurs to test the robustness of the model. The collected data were used to measure the theoretical constructs of the paper. Reliability of the multi-item scale for each dimension was measured using Cronbach alpha, and for all the dimensions measures of reliability are above 0.70. We empirically tested the model using structural equation modeling with AMOS. The results allow us to empirically contribute to the argument regarding the influence of human and social cognitive capital on social and non-social entrepreneurial intentions. Moreover, we highlight the importance for further researchers to look deeper into the determinants of traditional and social entrepreneurial intention so that governments can one day define better polices and regulations that promote sustainable businesses with a social imprint, rather than inhibit their formation and growth.

Data mining traditional Chinese medicine (TCM) lessons learnt from mining in law and allopathic medicine

Key decisions at the collection, pre-processing, transformation, mining and interpretation phase of any knowledge discovery from database (KDD) process depend heavily on assumptions and theorectical perspectives relating to the type of task to be performed and characteristics of data sourced. In this article, we compare and contrast theoretical perspectives and assumptions taken in data mining exercises in the legal domain with those adopted in data mining in TCM and allopathic medicine. The juxtaposition results in insights for the application of KDD for Traditional Chinese Medicine.

Spatial and temporal analysis of Barmah Forest virus disease in Queensland, Australia

Barmah Forest virus (BFV) disease is one of the most widespread mosquito-borne diseases in Australia. The number of outbreaks and the incidence rate of BFV in Australia have attracted growing concerns about the spatio-temporal complexity and underlying risk factors of BFV disease. A large number of notifications has been recorded continuously in Queensland since 1992. Yet, little is known about the spatial and temporal characteristics of the disease. I aim to use notification data to better understand the effects of climatic, demographic, socio-economic and ecological risk factors on the spatial epidemiology of BFV disease transmission, develop predictive risk models and forecast future disease risks under climate change scenarios. Computerised data files of daily notifications of BFV disease and climatic variables in Queensland during 1992-2008 were obtained from Queensland Health and Australian Bureau of Meteorology, respectively. Projections on climate data for years 2025, 2050 and 2100 were obtained from Council of Scientific Industrial Research Organisation. Data on socio-economic, demographic and ecological factors were also obtained from relevant government departments as follows: 1) socio-economic and demographic data from Australian Bureau of Statistics; 2) wetlands data from Department of Environment and Resource Management and 3) tidal readings from Queensland Department of Transport and Main roads. Disease notifications were geocoded and spatial and temporal patterns of disease were investigated using geostatistics. Visualisation of BFV disease incidence rates through mapping reveals the presence of substantial spatio-temporal variation at statistical local areas (SLA) over time. Results reveal high incidence rates of BFV disease along coastal areas compared to the whole area of Queensland. A Mantel-Haenszel Chi-square analysis for trend reveals a statistically significant relationship between BFV disease incidence rates and age groups (ƒÓ2 = 7587, p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state. A cluster analysis was used to detect the hot spots/clusters of BFV disease at a SLA level. Most likely spatial and space-time clusters are detected at the same locations across coastal Queensland (p<0.05). The study demonstrates heterogeneity of disease risk at a SLA level and reveals the spatial and temporal clustering of BFV disease in Queensland. Discriminant analysis was employed to establish a link between wetland classes, climate zones and BFV disease. This is because the importance of wetlands in the transmission of BFV disease remains unclear. The multivariable discriminant modelling analyses demonstrate that wetland types of saline 1, riverine and saline tidal influence were the most significant risk factors for BFV disease in all climate and buffer zones, while lacustrine, palustrine, estuarine and saline 2 and saline 3 wetlands were less important. The model accuracies were 76%, 98% and 100% for BFV risk in subtropical, tropical and temperate climate zones, respectively. This study demonstrates that BFV disease risk varied with wetland class and climate zone. The study suggests that wetlands may act as potential breeding habitats for BFV vectors. Multivariable spatial regression models were applied to assess the impact of spatial climatic, socio-economic and tidal factors on the BFV disease in Queensland. Spatial regression models were developed to account for spatial effects. Spatial regression models generated superior estimates over a traditional regression model. In the spatial regression models, BFV disease incidence shows an inverse relationship with minimum temperature, low tide and distance to coast, and positive relationship with rainfall in coastal areas whereas in whole Queensland the disease shows an inverse relationship with minimum temperature and high tide and positive relationship with rainfall. This study determines the most significant spatial risk factors for BFV disease across Queensland. Empirical models were developed to forecast the future risk of BFV disease outbreaks in coastal Queensland using existing climatic, socio-economic and tidal conditions under climate change scenarios. Logistic regression models were developed using BFV disease outbreak data for the existing period (2000-2008). The most parsimonious model had high sensitivity, specificity and accuracy and this model was used to estimate and forecast BFV disease outbreaks for years 2025, 2050 and 2100 under climate change scenarios for Australia. Important contributions arising from this research are that: (i) it is innovative to identify high-risk coastal areas by creating buffers based on grid-centroid and the use of fine-grained spatial units, i.e., mesh blocks; (ii) a spatial regression method was used to account for spatial dependence and heterogeneity of data in the study area; (iii) it determined a range of potential spatial risk factors for BFV disease; and (iv) it predicted the future risk of BFV disease outbreaks under climate change scenarios in Queensland, Australia. In conclusion, the thesis demonstrates that the distribution of BFV disease exhibits a distinct spatial and temporal variation. Such variation is influenced by a range of spatial risk factors including climatic, demographic, socio-economic, ecological and tidal variables. The thesis demonstrates that spatial regression method can be applied to better understand the transmission dynamics of BFV disease and its risk factors. The research findings show that disease notification data can be integrated with multi-factorial risk factor data to develop build-up models and forecast future potential disease risks under climate change scenarios. This thesis may have implications in BFV disease control and prevention programs in Queensland.

Eigenvoice modeling for cross likelihood ratio based speaker clustering : a Bayesian approach

This paper proposes the use of Bayesian approaches with the cross likelihood ratio (CLR) as a criterion for speaker clustering within a speaker diarization system, using eigenvoice modeling techniques. The CLR has previously been shown to be an effective decision criterion for speaker clustering using Gaussian mixture models. Recently, eigenvoice modeling has become an increasingly popular technique, due to its ability to adequately represent a speaker based on sparse training data, as well as to provide an improved capture of differences in speaker characteristics. The integration of eigenvoice modeling into the CLR framework to capitalize on the advantage of both techniques has also been shown to be beneficial for the speaker clustering task. Building on that success, this paper proposes the use of Bayesian methods to compute the conditional probabilities in computing the CLR, thus effectively combining the eigenvoice-CLR framework with the advantages of a Bayesian approach to the diarization problem. Results obtained on the 2002 Rich Transcription (RT-02) Evaluation dataset show an improved clustering performance, resulting in a 33.5% relative improvement in the overall Diarization Error Rate (DER) compared to the baseline system.

Mobile valuations : the Internet's impact on traditional valuation services

Preparing valuations is a time consuming process involving site inspections, research and report formulation. The ease of access to the internet has changed how and where valuations may be undertaken. No longer is it necessary to return to the office to finalise reports, or leave your desk in order to undertake research. This enables more streamlined service delivery and is viewed as a positive. However, it is not without negative impacts. This paper seeks to inform practitioners of the work environment changes flowing from increased access to the internet. It identifies how increased accessibility to, and use of, technology and the internet has, and will continue to, impact upon valuation service provision into the future.

The process and patterns of combining the use of traditional Chinese medicine and Western medicine in Taiwanese people with cancer

Objective: The nature of contemporary cancer therapy means that patients are faced with difficult treatment decisions about surgery, chemotherapy and radiotherapy. For some, this process may also involve consideration of therapies that sit outside the biomedical approach to cancer treatment, in our research, traditional Chinese medicine (TCM). Thus, it is important to explore how cancer patients in Taiwan incorporate TCM into their cancer treatment journey. This paper aims to explore of the patterns of combining the use of TCM and Western medicine into cancer treatment journey in Taiwanese people with cancer. Methods: The sampling was purposive and the data collected through in-depth interviews. Data collection occurred over an eleven month. The research was grounded in the premises of symbolic interactionism and adopted the methods of grounded theory. Twenty four participants who were patients receiving cancer treatment were recruited from two health care settings in Taiwan. Results: The study findings suggest that perceptions of health and illness are mediated through ongoing interactions with different forms of therapy. The participants in this study had a clear focus on “process and patterns of using TCM and Western medicine”. Further, ‘different importance in Western medicine and TCM’, ‘taken for granted to use TCM’, ‘each has specialized skills in Western medicine and TCM’ and ‘different symptoms use different approaches (Western medicine or TCM)’ may explicit how the participants in this study see CAM and Western medicine. Conclusions/Implications for practice: The descriptive frame of the study suggests that TCM and Western medicine occupy quite distinct domains in terms of decision making over their use. People used TCM based on interpretations of the present and against a background of an enduring cultural legacy grounded in Chinese philosophical beliefs about health and healthcare. The increasingly popular term of 'integrative medicine' obscures the complex contexts of the patterns of use of both therapeutic modalities. It is this latter point that is worthy of further exploration.

Application of clustering technique to generating capacity state model for reliability evaluation

Capacity probability models of generating units are commonly used in many power system reliability studies, at hierarchical level one (HLI). Analytical modelling of a generating system with many units or generating units with many derated states in a system, can result in an extensive number of states in the capacity model. Limitations on available memory and computational time of present computer facilities can pose difficulties for assessment of such systems in many studies. A cluster procedure using the nearest centroid sorting method was used for IEEE-RTS load model. The application proved to be very effective in producing a highly similar model with substantially fewer states. This paper presents an extended application of the clustering method to include capacity probability representation. A series of sensitivity studies are illustrated using IEEE-RTS generating system and load models. The loss of load and energy expectations (LOLE, LOEE), are used as indicators to evaluate the application

Speaker linking using complete-linkage clustering

Speaker diarization determines instances of the same speaker within a recording. Extending this task to a collection of recordings for linking together segments spoken by a unique speaker requires speaker linking. In this paper we propose a speaker linking system using linkage clustering and state-of-the-art speaker recognition techniques. We evaluate our approach against two baseline linking systems using agglomerative cluster merging (AC) and agglomerative clustering with model retraining (ACR). We demonstrate that our linking method, using complete-linkage clustering, provides a relative improvement of 20% and 29% in attribution error rate (AER), over the AC and ACR systems, respectively.

Grouping people in social networks using a weighted multi-constraints clustering method

Grouping users in social networks is an important process that improves matching and recommendation activities in social networks. The data mining methods of clustering can be used in grouping the users in social networks. However, the existing general purpose clustering algorithms perform poorly on the social network data due to the special nature of users' data in social networks. One main reason is the constraints that need to be considered in grouping users in social networks. Another reason is the need of capturing large amount of information about users which imposes computational complexity to an algorithm. In this paper, we propose a scalable and effective constraint-based clustering algorithm based on a global similarity measure that takes into consideration the users' constraints and their importance in social networks. Each constraint's importance is calculated based on the occurrence of this constraint in the dataset. Performance of the algorithm is demonstrated on a dataset obtained from an online dating website using internal and external evaluation measures. Results show that the proposed algorithm is able to increases the accuracy of matching users in social networks by 10% in comparison to other algorithms.

Silver Gelatin Print: A group exhibition curated by Karen Milder that investigates and captures the traditional processes using photograms, liquid emulsion, toning, hand-colouring and film. Part of the Queensland Festival of Photography 4 (QFP4).

Pillar of salt: (3 hand-applied silver gelatin photographs) Statement: For women moving into new experiences and spaces, loss and hardship is often a price to be paid. These courageous women look back to things they have overcome in order to continue to grow.

Experimental and modelling investigation of monolayer development with clustering

Standard differential equation–based models of collective cell behaviour, such as the logistic growth model, invoke a mean–field assumption which is equivalent to assuming that individuals within the population interact with each other in proportion to the average population density. Implementing such assumptions implies that the dynamics of the system are unaffected by spatial structure, such as the formation of patches or clusters within the population. Recent theoretical developments have introduced a class of models, known as moment dynamics models, which aim to account for the dynamics of individuals, pairs of individuals, triplets of individuals and so on. Such models enable us to describe the dynamics of populations with clustering, however, little progress has been made with regard to applying moment dynamics models to experimental data. Here, we report new experimental results describing the formation of a monolayer of cells using two different cell types: 3T3 fibroblast cells and MDA MB 231 breast cancer cells. Our analysis indicates that the 3T3 fibroblast cells are relatively motile and we observe that the 3T3 fibroblast monolayer forms without clustering. Alternatively, the MDA MB 231 cells are less motile and we observe that the MDA MB 231 monolayer formation is associated with significant clustering. We calibrate a moment dynamics model and a standard mean–field model to both data sets. Our results indicate that the mean–field and moment dynamics models provide similar descriptions of the 3T3 fibroblast monolayer formation whereas these two models give very different predictions for the MDA MD 231 monolayer formation. These outcomes indicate that standard mean–field models of collective cell behaviour are not always appropriate and that care ought to be exercised when implementing such a model.

Collaborative design learning for traditional crafts industry

Traditional craft industries need assistance with being transformed into creative industries; as such a transformation will support them to face the future competitive global market. Assistance such as advisory programs should serve long-term benefit for crafts industries as well as optimize self-help potential. Advisory programs using participatory methods will enable craftspeople and stakeholders to reveal resources and potencies, such as socio-cultural value, tradition and other kind of heritages, to generate new innovative ideas of craft design in a sustainable way.

The potential contributions of traditional Chinese medicine to emergency medicine

BACKGROUND: Despite the fact that traditional Chinese medicine (TCM) has been developed and used to treat acute and urgent illness for many thousands of years. TCM has been widely perceived in western societies that TCM may only be effective to treat chronic diseases. The aim of this article is to provide some scientific evidence regarding the application of TCM in emergency medicine and its future potential. METHODS: Multiple databases (PubMed, ProQuest, Academic Search Elite and Science Direct) were searched using the terms: Traditional Chinese Medicine/ Chinese Medicine, Emergency Medicine, China. In addition, three leading TCM Journals in China were searched via Oriprobe Information Services for relevant articles (published from 1990—2012). Particular attention was paid to those articles that are related to TCM treatments or combined medicine in dealing with intensive and critical care. RESULTS: TCM is a systematic traditional macro medicine. The clinical practice of TCM is guided by the TCM theoretical framework – a methodology founded thousands of years ago. As the methodologies between TCM and Biomedicine are significantly different, it provides an opportunity to combine two medicines, in order to achieve clinical efficacy. Nowadays, combined medicine has become a common clinical model particular in TCM hospitals in China. CONCLUSIONS: It is evident that TCM can provide some assistance in emergency although to combine them in practice is stillits infant form and is mainly at TCM hospitals in China. The future effort could be put into TCM research, both in laboratories and clinics, with high quality designs, so that TCM could be better understood and then applied in emergency medicine.

Student's experiences with traditional bullying and cyberbullying : findings from a Romanian sample

The field of cyberbullying is relatively new and there is no universal consensus on its definition, measurement and intervention. Authors agree that bullying has entered into the digital domain and professionals require the skills to help identify and prevent these behaviours. Ninety two students were surveyed to determine their experience with different types of bullying behaviors (face-to-face, cyberbullying or both), as bully, victim or witness. Our objective was to explore the association between those types of bullying and anxiety. The results suggest a significant association between face-to-face bullying and anxiety. Similarly, there was significant association between experiencing both types of bullying and anxiety. Further studies are required with larger and more diverse samples in order to verify current findings and to test for additional associations.