The paper begins by reviewing the Meetings literature to explore the existing theoretical guidance on conceptualising meetings as a collective and integrated set of activities, rather than as singular events in isolation of each other. The Systems literature is reviewed to identify concepts which may be adopted to enable a systematised view of meetings. The central focus of the paper is to explore the theoretical ways through which organisations’ meetings could be conceptualised as an integrated ‘system of meetings’, rather than as single events. An outline of the empirical data source is then provided, along with the methodology adopted to record and analyse the data.

]]>researcher wishes to describe the strength and direction of the relationship between two

normally continuous variables. The statistic obtained is Pearson’s product-moment

correlation (r), and SPSS also provides the statistical significance of r. In addition, if the

researcher needs to explore the relationship between two variables while statistically

controlling for a third variable, partial correlation can be used. This is useful when it is

suspected that the relationship between two variables may be influenced, or confounded, byA correlation is a measure of the linear relationship between two variables. It is used when a

researcher wishes to describe the strength and direction of the relationship between two

normally continuous variables. The statistic obtained is Pearson’s product-moment

correlation (r), and SPSS also provides the statistical significance of r. In addition, if the

researcher needs to explore the relationship between two variables while statistically

controlling for a third variable, partial correlation can be used. This is useful when it is

suspected that the relationship between two variables may be influenced, or confounded, by the impact of a third variable. Correlations are a very useful research tool but they do not address the predictive

power of variables. This task is left to regression. Regression is based on the idea that the

researcher must first have some valid reasons for believing that there is a causal relationship

between two or more variables. A well known example is the consumer demand for products

and the level of income of consumers. If income increases then demand for normal goods

such as cars, foreign travel will increase. In regression analysis, a predictive model needs to

fit to both the data and the model. And then we can use the result to predict values of the

dependent variable (DV) from one or more independent variables (IVs). In straight forward 2

terms, simple regression seeks to predict an outcome from a single predictor; whereas2

terms, simple regression seeks to predict an outcome from a single predictor; whereas multiple regression seeks to predict an outcome from several predictors.multiple regression seeks to predict an outcome from several predictors. ]]>