Faktorer som kan påverka företags val mellan K2 och K3 : En jämförelsestudie mellan två branscher

Titel: Faktorer som kan påverka företags val mellan K2 och K3 Författare: Angelica Höglund och Julia Wiman Handledare: Klas Sundberg Bakgrund och problem: Det nya K-regelverket som infördes 1 januari 2014 innebär att mindre onoterade företag idag har valmöjligheten att välja mellan regelverken K2 och K3. För dessa mindre företag är valet av regelverk viktigt då det kommer att påverka företagets räkenskaper. Syfte: Syftet med studien är att studera fördelningen av K2 och K3 för mindre onoterade företag inom två olika branscher, dessa är byggbranschen och juridik,- ekonomi- och konsultbranschen, samt vilka faktorer som kan påverka valet av regelverk. Teori: Studien bygger på två teorier, dessa är positive accounting theory och institutionell teori. Metod: I studien används en kvantitativ forskningsmetod där årsredovisningar har granskats och sammanställts, samt en kvalitativ metod som bygger på fyra semistrukturerade intervjuer där de identifierade faktorerna analyserats utifrån en analysmodell. Resultat och slutsats: Utifrån det empiriska materialet identifierades sju faktorer, dessa var leasing, företagets storlek, rättvisande bild, revisorns påverkan, komponentavskrivning, kostnad/nytta och koncernredovisning. Ovanstående faktorer har kategoriserats och analyserats utifrån PAT och institutionell teori. Förslag till vidare forskning: Ett förslag vore att forska vidare på de identifierade faktorerna för att undersöka i hur stor utsträckning de påverkar företagens beslut. En intressant studie hade även varit att genomföra en fallstudie som utgår från att upprätta en årsredovisning utifrån både K2 och K3 för att studera de skillnader som kan uppkomma.

Towards a single crystal Raman spectrum of kaolinite at 77 K

The Raman spectra at 77 K of the hydroxyl stretching of kaolinite were obtained along the three axes perpendicular to the crystal faces. Raman bands were observed at 3616, 3658 and 3677 cm−1 together with a distinct band observed at 3691 cm−1 and a broad profile between 3695 and 3715 cm−1. The band at 3616 cm−1 is assigned to the inner hydroxyl. The bands at 3658 and 3677 cm−1 are attributed to the out-of-phase vibrations of the inner surface hydroxyls. The Raman spectra of the in-phase vibrations of the inner-surface hydroxyl-stretching region are described in terms of transverse and longitudinal optic splitting. The band at 3691 cm−1 is assigned to the transverse optic and the broad profile to the longitudinal optic mode. This splitting remained even at liquid nitrogen temperature. The transverse optic vibration may be curve resolved into two or three bands, which are attributed to different types of hydroxyl groups in the kaolinite.

K-tree : large scale document clustering

We introduce K-tree in an information retrieval context. It is an efficient approximation of the k-means clustering algorithm. Unlike k-means it forms a hierarchy of clusters. It has been extended to address issues with sparse representations. We compare performance and quality to CLUTO using document collections. The K-tree has a low time complexity that is suitable for large document collections. This tree structure allows for efficient disk based implementations where space requirements exceed that of main memory.

The knowledge advantage (K-Adv): unleashing creativity and innovation

The K-Adv has been developed around the concept that it comprises an ICT enabling infrastructure that encompasses ICT hardware and software infrastructure facilities together with an enabling ICT support system; a leadership infrastructure support system that provides the vision for its implementation and the realisation capacity for the vision to be realised; and the necessary people infrastructure that includes the people capabilities and capacities supported by organisational processes that facilitates this resource to be mobilised.

Document clustering with K-tree

This paper describes the approach taken to the XML Mining track at INEX 2008 by a group at the Queensland University of Technology. We introduce the K-tree clustering algorithm in an Information Retrieval context by adapting it for document clustering. Many large scale problems exist in document clustering. K-tree scales well with large inputs due to its low complexity. It offers promising results both in terms of efficiency and quality. Document classification was completed using Support Vector Machines.

The role of timber plantations in U.K. investment portfolios

Although timber plantations and forests are classified as forms of agricultural production, the ownership of this land classification is not limited to rural producers. Timber plantations and forests are now regarded as a long-term investment with both institutional and absentee owners. While the NCREIF property indices have been the benchmarks for the measurement of the performance of the commercial property market in the UK, for many years the IPD timberland index has recently emerged as the U.K. forest and timberland performance indicator. The IPD Forest index incorporates 126 properties over five regions in the U.K. This paper will utilise the IPD Forestry Index to examine the performance of U.K. timber plantations and forests over the period 1981-2004. In particular, issues to be critically assessed include plantation and forest performance analysis, comparative investment analysis, and the role of plantations and forests in investment portfolios, the risk reduction and portfolio benefits of plantations and forests in mixed-asset portfolios and the strategic investment significance of U.K. timberlands.

Random Indexing K-tree

Random Indexing K-tree is the combination of two algorithms suited for large scale document clustering.

Clustering with random indexing K-tree and XML structure

This paper describes the approach taken to the clustering task at INEX 2009 by a group at the Queensland University of Technology. The Random Indexing (RI) K-tree has been used with a representation that is based on the semantic markup available in the INEX 2009 Wikipedia collection. The RI K-tree is a scalable approach to clustering large document collections. This approach has produced quality clustering when evaluated using two different methodologies.