Hur påverkar ägarbilden hos svenska företag den rörliga ersättningen till den verkställande direktören?

Bakgrund Det diskuteras mycket idag om ersättningar till verkställande direktörer (Vd) och storleken på dessa. Många har synpunkter på den enligt dem väldigt höga ersättningen och framförallt är många kritiska till de stora bonusar som tilldelas till företagsledare. Samtidigt är det ägarna som genom representation i styrelse och på bolagstämma bestämmer ersättningen. Syfte Syftet med studien är att undersöka om det föreligger ett samband mellan ägarbilden och andel rörlig lön till den verkställande direktören. Metod I studien tillämpas en deduktiv forskningsansats där vi utgått från existerande teorier och tidigare empirisk forskning för att skapa en hypotes som sedan kan bekräftas eller förkastas. Hypotesen testas genom en kvantitativ metod där data samlats in över 56 stycken svenska bolag för att sedan testas med statistisk regressionsanalys. Slutsats Undersökningen visar att det finns ett negativt samband mellan ett större ägande och andel rörlig ersättning till verkställande direktör.

Testing Seasonal Unit Roots in Data at Any Frequency, an HEGY approach

This paper generalizes the HEGY-type test to detect seasonal unit roots in data at any frequency, based on the seasonal unit root tests in univariate time series by Hylleberg, Engle, Granger and Yoo (1990). We introduce the seasonal unit roots at first, and then derive the mechanism of the HEGY-type test for data with any frequency. Thereafter we provide the asymptotic distributions of our test statistics when different test regressions are employed. We find that the F-statistics for testing conjugation unit roots have the same asymptotic distributions. Then we compute the finite-sample and asymptotic critical values for daily and hourly data by a Monte Carlo method. The power and size properties of our test for hourly data is investigated, and we find that including lag augmentations in auxiliary regression without lag elimination have the smallest size distortion and tests with seasonal dummies included in auxiliary regression have more power than the tests without seasonal dummies. At last we apply the our test to hourly wind power production data in Sweden and shows there are no seasonal unit roots in the series.

Managerial leadership for research use in nursing and allied health care professions : a narrative synthesis protocol

BACKGROUND: Nurses and allied health care professionals (physiotherapists, occupational therapists, speech and language pathologists, dietitians) form more than half of the clinical health care workforce and play a central role in health service delivery. There is a potential to improve the quality of health care if these professionals routinely use research evidence to guide their clinical practice. However, the use of research evidence remains unpredictable and inconsistent. Leadership is consistently described in implementation research as critical to enhancing research use by health care professionals. However, this important literature has not yet been synthesized and there is a lack of clarity on what constitutes effective leadership for research use, or what kinds of intervention effectively develop leadership for the purpose of enabling and enhancing research use in clinical practice. We propose to synthesize the evidence on leadership behaviours amongst front line and senior managers that are associated with research evidence by nurses and allied health care professionals, and then determine the effectiveness of interventions that promote these behaviours.Methods/design: Using an integrated knowledge translation approach that supports a partnership between researchers and knowledge users throughout the research process, we will follow principles of knowledge synthesis using a systematic method to synthesize different types of evidence involving: searching the literature, study selection, data extraction and quality assessment, and analysis. A narrative synthesis will be conducted to explore relationships within and across studies and meta-analysis will be performed if sufficient homogeneity exists across studies employing experimental randomized control trial designs. DISCUSSION: With the engagement of knowledge users in leadership and practice, we will synthesize the research from a broad range of disciplines to understand the key elements of leadership that supports and enables research use by health care practitioners, and how to develop leadership for the purpose of enhancing research use in clinical practice.

A data driven approach for automating vehicle activated signs

Vehicle activated signs (VAS) display a warning message when drivers exceed a particular threshold. VAS are often installed on local roads to display a warning message depending on the speed of the approaching vehicles. VAS are usually powered by electricity; however, battery and solar powered VAS are also commonplace. This thesis investigated devel-opment of an automatic trigger speed of vehicle activated signs in order to influence driver behaviour, the effect of which has been measured in terms of reduced mean speed and low standard deviation. A comprehen-sive understanding of the effectiveness of the trigger speed of the VAS on driver behaviour was established by systematically collecting data. Specif-ically, data on time of day, speed, length and direction of the vehicle have been collected for the purpose, using Doppler radar installed at the road. A data driven calibration method for the radar used in the experiment has also been developed and evaluated. Results indicate that trigger speed of the VAS had variable effect on driv-ers’ speed at different sites and at different times of the day. It is evident that the optimal trigger speed should be set near the 85th percentile speed, to be able to lower the standard deviation. In the case of battery and solar powered VAS, trigger speeds between the 50th and 85th per-centile offered the best compromise between safety and power consump-tion. Results also indicate that different classes of vehicles report differ-ences in mean speed and standard deviation; on a highway, the mean speed of cars differs slightly from the mean speed of trucks, whereas a significant difference was observed between the classes of vehicles on lo-cal roads. A differential trigger speed was therefore investigated for the sake of completion. A data driven approach using Random forest was found to be appropriate in predicting trigger speeds respective to types of vehicles and traffic conditions. The fact that the predicted trigger speed was found to be consistently around the 85th percentile speed justifies the choice of the automatic model.