8 resultados para Law|Psychology, Social

em CentAUR: Central Archive University of Reading - UK


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The study of intuition is an emerging area of research in psychology, social sciences, and business studies. It is increasingly of interest to the study of management, for example in decision-making as a counterpoint to structured approaches. Recently work has been undertaken to conceptualize a construct for the intuitive nature of technology. However to-date there is no common under-standing of the term intuition in information systems (IS) research. This paper extends the study of intuition in IS research by using exploratory research to cate-gorize the use of the word “intuition” and related terms in papers published in two prominent IS journals over a ten year period. The entire text of MIS Quarterly and Information Systems Research was reviewed for the years 1999 through 2008 using searchable PDF versions of these publications. As far as could be deter-mined, this is the first application of this approach in the analysis of the text of IS academic journals. The use of the word “intuition” and related terms was catego-rized using coding consistent with Grounded Theory. The focus of this research was on the first two stages of Grounded Theory analysis - the development of codes and constructs. Saturation of coding was not reached: an extended review of these publications would be required to enable theory development. Over 400 incidents of the use of “intuition”, and related terms were found in the articles reviewed. The most prominent use of the term of “intuition” was coded as “Intui-tion as Authority” in which intuition was used to validate a research objective or finding; representing approximately 37 per cent of codes assigned. The second most common coding occurred in research articles with mathematical analysis, representing about 19 per cent of the codes assigned, for example where a ma-thematical formulation or result was “intuitive”. The possibly most impactful use of the term “intuition” was “Intuition as Outcome”, representing approximately 7 per cent of all coding, which characterized research results as adding to the intui-tive understanding of a research topic or phenomena. This research contributes to a greater theoretical understanding of intuition enabling insight into the use of intuition, and the eventual development of a theory on the use of intuition in academic IS research publications. It also provides potential benefits to practi-tioners by providing insight into and validation of the use of intuition in IS man-agement. Research directions include the creation of reflective and/or formative constructs for intuition in information systems research.

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We are social beings. What we do and don’t do, what we think, the decisions we take are all influenced by those around us. Sometimes we are conscious of those influences, often we are not. Those who influence us are not just our close family and friends, our own social and professional networks, but the wider societies and cultures to which we belong. The goals we espouse, the values we hold, the image we have of ourselves are all molded to a large extent by our interactions and relationships with other people. The social sciences offer a range of concepts and tools for exploring these influences. In this paper, I introduce some of these and illustrate them with recent research I and my colleagues have been doing at the University of Reading among livestock farmers in the UK, with a view to providing insights that can then be used to plan and implement more effective interventions.

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This article provides a critical examination of the way that property rules are applied judicially in the context of social security law concerned with the assessment of capital, and especially in connection with the determination of ownership and the valuation of assets, which can have a critical bearing on entitlement to various means-tested benefits.

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BACKGROUND: Social networks are common in digital health. A new stream of research is beginning to investigate the mechanisms of digital health social networks (DHSNs), how they are structured, how they function, and how their growth can be nurtured and managed. DHSNs increase in value when additional content is added, and the structure of networks may resemble the characteristics of power laws. Power laws are contrary to traditional Gaussian averages in that they demonstrate correlated phenomena. OBJECTIVES: The objective of this study is to investigate whether the distribution frequency in four DHSNs can be characterized as following a power law. A second objective is to describe the method used to determine the comparison. METHODS: Data from four DHSNs—Alcohol Help Center (AHC), Depression Center (DC), Panic Center (PC), and Stop Smoking Center (SSC)—were compared to power law distributions. To assist future researchers and managers, the 5-step methodology used to analyze and compare datasets is described. RESULTS: All four DHSNs were found to have right-skewed distributions, indicating the data were not normally distributed. When power trend lines were added to each frequency distribution, R(2) values indicated that, to a very high degree, the variance in post frequencies can be explained by actor rank (AHC .962, DC .975, PC .969, SSC .95). Spearman correlations provided further indication of the strength and statistical significance of the relationship (AHC .987. DC .967, PC .983, SSC .993, P<.001). CONCLUSIONS: This is the first study to investigate power distributions across multiple DHSNs, each addressing a unique condition. Results indicate that despite vast differences in theme, content, and length of existence, DHSNs follow properties of power laws. The structure of DHSNs is important as it gives insight to researchers and managers into the nature and mechanisms of network functionality. The 5-step process undertaken to compare actor contribution patterns can be replicated in networks that are managed by other organizations, and we conjecture that patterns observed in this study could be found in other DHSNs. Future research should analyze network growth over time and examine the characteristics and survival rates of superusers.