The exact distribution of the maximum,minimum and the range of Multinomial/Dirichlet and Multivariate Hypergeometric frequencies

The exact distribution of the maximum and minimum frequencies of Multinomial/Dirichlet and Multivariate Hypergeometric distributions of n balls in m urns is compactly represented as a product of stochastic matrices. This representation does not require equal urn probabilities, is invariant to urn order, and permits rapid calculation of exact probabilities. The exact distribution of the range is also obtained. These algorithms satisfy a long-standing need for routines to compute exact Multinomial/Dirichlet and Multivariate Hypergeometric maximum, minimum, and range probabilities in statistical computation libraries and software packages.     

On the clustering term in ecological analysis: how do different prior specifications affect results?

We study how different prior assumptions on the spatially structured heterogeneity term of the convolution hierarchical Bayesian model for spatial disease data could affect the results of an ecological analysis when response and exposure exhibit a strong spatial pattern. We show that in this case the estimate of the regression parameter could be strongly biased, both by analyzing the association between lung cancer mortality and education level on a real dataset and by a simulation experiment. The analysis is based on a hierarchical Bayesian model with a time dependent covariate in which we allow for a latency period between exposure and mortality, with time and space random terms and misaligned exposure-disease data.     

A conceptual framework of the adoption of innovations in organizations: a meta-analytical review of the literature

Journal of Management and Governance - Studies on the adoption of innovations in organizations are abundant and have introduced many different factors that are likely to influence adoption...     

Identifying Environmental and Social Factors Predisposing to Pathological Gambling Combining Standard Logistic Regression and Logic Learning Machine

Identifying potential risk factors for problem gambling (PG) is of primary importance for planning preventive and therapeutic interventions. We illustrate a new approach based on the combination of standard logistic regression and an innovative method of supervised data mining (Logic Learning Machine or LLM). Data were taken from a pilot cross-sectional study to identify subjects with PG behaviour, assessed by two internationally validated scales (SOGS and Lie/Bet). Information was obtained from 251 gamblers recruited in six betting establishments. Data on socio-demographic characteristics, lifestyle and cognitive-related factors, and type, place and frequency of preferred gambling were obtained by a self-administered questionnaire. The following variables associated with PG were identified: instant gratification games, alcohol abuse, cognitive distortion, illegal behaviours and having started gambling with a relative or a friend. Furthermore, the combination of LLM and LR indicated the presence of two different types of PG, namely: (a) daily gamblers, more prone to illegal behaviour, with poor money management skills and who started gambling at an early age, and (b) non-daily gamblers, characterised by superstitious beliefs and a higher preference for immediate reward games. Finally, instant gratification games were strongly associated with the number of games usually played. Studies on gamblers habitually frequently betting shops are rare. The finding of different types of PG by habitual gamblers deserves further analysis in larger studies. Advanced data mining algorithms, like LLM, are powerful tools and potentially useful in identifying risk factors for PG.     

Effective strategizing practices in pluralistic settings: the case of Academic Medical Centers

Pluralism in organizations is dramatically and progressively increasing today, which makes it difficult for managers to capture a clear picture of what is really going on at the moment and to make strategic decisions. How strategy is formed and implemented in pluralistic contexts has been substantially underestimated for many years. This article investigates how specific strategy processes are constructed and which practices can be successfully adopted in pluralistic contexts, such as in Academic Medical Centers. We drew from a strategy as practice approach and adopted an ethnographic methodology to conduct the study. This paper demonstrates that some practices (strategy workshops, brutal facts and graphic charts) can reduce the risks of setting the strategy agenda in pluralistic organizations. Implications for managers and scholars are discussed.     

Change and stability of migration intentions. Evidence from Italy

Due to a lack of relevant data, very few empirical studies have examined the changes in and stability of secondary migration intentions. We aim to fill this gap by analysing return migration intentions among international migrants in Italy. Data are drawn from the cross-sectional SCIF survey conducted by ISTAT in 2011–2012. Our findings reveal that migration intentions at the beginning of the migratory experience tend to differ from those measured at more advanced migration stages (i.e. at the time of the survey). In particular, intentions to return seem less stable than intentions to stay. When confirming intentions to return or remain, critical factors include financial stability, family situation and ties with the country of origin and destination. Additionally, having an Italian partner, a partner living in Italy, and a positive self-assessed family financial condition are positively associated with transitioning from a temporary plan to a permanent settlement intention.     

Organizational Learning under Organizational Impermanence: Collaborative Ties in Film Project Firms

In the last two decades a lot of research has been devoted to unveiling the processes through which organizations learn and store knowledge. This research is typically concerned with organizations lastingly engaged in the provision of goods or services. Permanency is usually presumed in order for the encoding of inferences from history to take place. But what if organizational permanency cannot be assumed ex-ante? Project firms represent an interesting case in point. A project firm is a transient form of organization that ceases to exist as soon as its single target is achieved, as such it does not exhibit stable structures nor does it exhibit ostensible history-based paths upon which to build its choices and nurture its organizational knowledge. This apparent paradox can be resolved, in part, by extending the view from the isolated project to the relational context in which project firms operate. Using longitudinal data from the U.S. feature film industry, we show that the process of organizational formation and dissolution that characterizes this context is underpinned by patterns of enduring collaborations among interdependent industry participants. We build on these findings to speculate on processes of learning and remembering that interpenetrate project firms’ boundaries, by being embedded within a texture of stable interpersonal ties.