1,721,125 research outputs found
Creativity and artificial intelligence: a multilevel perspective
Full text linkArtificial intelligence is likely to revolutionize multiple aspects of organizational creativity. Through a multilevel theoretical lens, the present paper reviews the extant body of knowledge on creativity at individual, team and organizational levels, and draws a series of propositions on how the implementation of artificial intelligence may affect each level. Spanning cognitive, behavioural and psychological domains, our propositions aim at directing future research efforts on important creativity-related areas likely to be affected by artificial intelligence, including the trade-off between convergent and divergent thinking, the distribution of skills within groups, and the absorptive capacity of organizations
Likelihood inference for a semi-parametric causal model addressing partial compliance by continuous principal strata
No full textWeighted estimation in multilevel ordinal and binary models in the presence of informative sampling designs
No full textMultilevel models are often fitted to survey data gathered with a complex multistage sampling design. However, if such a design is informative, in the sense that the inclusion probabilities depend on the response variable even after conditioning on the covariates, the standard maximum likelihood estimators are biased. In this paper, following the Pseudo Maximum Likelihood approach of Skinner (1989), we propose a probability-weighted estimation procedure for multilevel ordinal and binary models which eliminates the bias generated by the informativeness of the design. The reciprocals of the inclusion probabilities at each sampling stage are used toweight the log-likelihood function and the weighted estimators obtained in this way are tested by means of a simulation study for the simple case of a binary random intercept model with and without covariates. The variance estimators are obtained by a boostrap procedure. The maximization of the weighted log-likelihood of the model is domne by the NLMIXED procedure of the SA, which is based on adaptive Gaussian quadrature. Also the bootstrap estimation of variances is implemented in the SAS environment
The effects of citation-based research evaluation schemes on self-citation behavior
No full textWe investigate the changes in the self-citation behavior of Italian professors following the introduction of a citation-based incentive scheme, for national accreditation to academic appointments. Previous contributions on self-citation behavior have either focused on small samples or relied on simple models, not controlling for enough confounding factors. The present work adopts a complex statistics model implemented on bibliometric individual data for over 15,000 Italian professors. Controlling for a number of covariates (number of citable papers published by the author; presence of international authors; number of co-authors; degree of the professor's specialization), the average increase in the number of self-citations per paper following introduction of the national scientific accreditation (ASN) is of 9.5%. The increase is common to all disciplines and academic ranks, albeit with diverse magnitude. Moreover, the increase is sensitive to the relative incentive, depending on the status of the scholar with respect to the ASN. A further analysis shows that there is much heterogeneity in the individual patterns of self-citing behavior, albeit with very few outliers
Latent growth models with multiple indicators: a longitudinal analysis of student ratings
No full textFrom rankings to funnel plots: The question of accounting for uncertainty when assessing university research performance
No full textThe work applies the funnel plot methodology to measure and visualize uncertainty in the research performance of Italian universities in the science disciplines. The performance assessment is carried out at both discipline and overall university level. The findings reveal that for most universities the citation-based indicator used gives insufficient statistical evidence to infer that their research productivity is inferior or superior to the average. This general observation is one that we could indeed expect in a higher education system that is essentially non-competitive. The question is whether the introduction of uncertainty in performance reporting, while technically sound, could weaken institutional motivation to work towards continuous improvement
INSTITUTIONAL DETERMINANTS OF VENTURE CAPITAL ACTIVITY: AN EMPIRICALLY DRIVEN LITERATURE REVIEW AND A RESEARCH AGENDA
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Inverse probability weighting to estimate causal effects of sequential treatments: a latent class extension to deal with unobserved confounding
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