1,721,427 research outputs found
I don’t care why you do it, just don’t! Reactions to negative and positive organizational deviance partly depend on the desire for tightness of prevention-focused employees
Full text linkTightness–Looseness (T-L) at the individual level has only begun to receive attention from researchers. Specifically in the organizational context, this is a so far unexplored construct. The study offers first insights into the mechanisms that can trigger individuals’ desire for tightness and the consequences it can have on organizational behaviors. We, therefore, investigated the mediating role of the desire for tightness on the relationship between work regulatory prevention focus and emotional responses to both negative and positive (i.e., pro-social) deviant organizational behaviors. We tested our prediction through a mediational model with a sample of 788 Italian employees (58.6% females, Mage = 35.09). Our findings supported the hypothesized model showing that regardless of the motivation underpinning the norm-violating behavior, employees with a prevention focus are more desirous of tightness and exhibit hostile reactions toward deviance. Given its importance in understanding employees’ behaviors and intentions, which inevitably reflect on the organization’s functionality, the impact of the T-L individual-level dimension in organizations is undoubtedly worthy of deeper investigation
Motivated prejudice: The effect of need for closure on anti-immigrant attitudes in the United States and Italy and the mediating role of binding moral foundations
No full textWhy do people have anti-immigrant attitudes? We proposed that individuals’ need for cognitive closure—an epistemic motivation associated with an aversion to change in established environments—is predictive of a dislike of immigrants through increased binding to powerful groups. In four studies, collected in both Italy (Study 1) and the United States (Studies 2–4), we found that there were effects of need for cognitive closure on anti-immigrant attitudes, as well as indirect effects through binding. These results were significant controlling for participants’ political orientation (Studies 2–4), when either dispositional measure (Studies 1–3) or an experimental induction (Study 4) of need for cognitive closure was used, and when both general attitudes toward immigrants (Studies 1, 2, 4) and attitudes toward immigrants’ economic impact (Studies 3 and 4) were assessed
Examining Employee Desire for Tightness in Organizations: The Role of Work Regulatory Prevention Focus, Need for Leadership, and Emotional Reactions to Organizational Deviance
No full text
Contributo allo sviluppo e alla validazione di una scala per misurare il work locus of control
No full textCharismatic leadership and organisational outcomes: the mediating role of work-group identification
No full textThis research examines the degree of employees' identification with the work‐group as a function of charismatic leadership (e.g., Conger & Kanungo, Citation1998) and the mediating role of work‐group identification (Van Knippenberg & Van Shie, Citation2000) in the relationship between charismatic style and different work outcomes. Thus, the general aim was to analyse leadership and work outcomes as they are associated to social identification processes, referring both to recent developments of charismatic leadership models and to the recent developments of the social identity analysis applied to the workplace (see Abrams & Hogg, Citation2001). Two field surveys were conducted using 200 Italian public and private sector employees (two different working organizations). Two questionnaires were designed in order to collect data. They included different measures of charismatic leadership derived by the literature (e.g., the Conger‐Kanungo Charismatic Leadership Questionnaire; Conger & Kanungo, Citation1994, Citation1998, for Study 2), a scale to assess the degree of identification with the work‐group (Van Knippenberg & Van Shie, Citation2000), and some scales to measure the different outcomes considered (e.g., Brown and Leigh's effort measure, Citation1996; Mobley's turnover intention measure, Citation1977). As predicted, results of Study 1 revealed that charismatic leadership was positively related to work‐group identification, and employees' work effort was positively related to work‐group identification. Work‐group identification also mediates relationship between charismatic leadership and work effort. Results of Study 2 replicated the positive association between charismatic leadership and employees' work‐group identification; work‐group identification is also associated with their job involvement, job satisfaction, performance, and turnover intention. The same mediating role of work‐group identification between charismatic leadership and the criteria mentioned above was found. Underlying mechanisms as well as implications are discussed
Quantum computers,Computer quantistici
No full textA cosa serve un computer quantistico e come si puo' realizzare fisicament
Quantum Techniques in Machine Learning
No full textIn the last few years, we have witnessed an increasing interest in bridging two impor-
tant research areas that fundamentally changed our way and abilities of processing
information, namely Machine Learning and Quantum Computation.
In the Summer 2017, we had the idea of inviting major experts in Quantum Compu-
tation and Information on the one hand, and in Machine Learning and Optimisation
on the other hand, for a meeting at the University of Verona to discuss the latest
advances in the newly born field of Quantum Machine Learning.
The idea developed in a very successful workshop, bringing together more than hun-
dred scientists to attend and/or contribute their results on the two-way interaction
between Machine Learning and Quantum Computation, aimed at demonstrating
how the intersection of the two fields can offer great potential for both.
This special issue is dedicated to this event, which was held in Verona on
6-8 November 2017 under the name of QTML 2017 - 1st Workshop on Quantum
Techniques in Machine Learning. It represents the first of a series of workshop that
are now held yearly in diverse places worldwide.
The volume collects original contributions focused on the following topics and not
limited to the works presented at QTML 2017:
- Quantum computing for enhancing machine learning algorithms
- Machine learning techniques for the analysis of interacting quantum systems
- Quantum entanglement and topology for the efficient representation of quan-
tum systems
- Approaches to machine learning based on Topological Quantum Computation
- Algorithmic techniques for quantum optimisation (e.g. quantum annealing).
We wish to thank all the people who contributed to bringing this special issue
to completion. In particular, we are very grateful to the reviewers who provided a
valuable help to ensure the high quality of the papers, to the authors for the scrupu-
lous work in improving their papers following the reviewers' suggestions, and to all
the participants in QTML 2017 whose lively discussions and critical interventions
greatly inspired the authors
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