Magdalena Kowalczyk and Josette Caruana
This paper compares governmental accounting and budgeting in two European Union (EU) member states, analysing the developments in each country as to how they “fit” into the EU's…
Abstract
Purpose
This paper compares governmental accounting and budgeting in two European Union (EU) member states, analysing the developments in each country as to how they “fit” into the EU's harmonization project and the push towards the implementation of accrual accounting compliant with the International Public Sector Accounting Standards (IPSAS).
Design/methodology/approach
An explanatory multiple case study is used to describe the structures and changes in governmental systems (accounting and budgetary) in Malta and Poland. The methodology takes a qualitative interpretative approach, examining the underlying legislations and related ministerial publications as secondary sources.
Findings
Focusing on the output from financial accounting and budgetary systems, the results illustrate how organizations respond in a different manner to similar institutional pressures. In particular, Poland shows no inclination to adopt the IPSAS, but emerges with a sophisticated budgeting system while Malta is more focused on developing its financial reporting in line with the IPSAS. The theoretical lens highlights that while both countries tend towards pragmatic legitimacy, Poland appears more inclined towards exchange legitimacy, and Malta is more subject to influence legitimacy.
Research limitations/implications
At a practical level, this study should be read by public sector accounting standard setters. It illustrates how EU member states are engaging with the IPSAS, emphasizing the ambitious nature of the EU's harmonization project, in spite of the structural legitimacy that the EU institution emanates.
Originality/value
Previous comparative international governmental accounting studies have examined accounting reform processes and developed or applied various theoretical models to try to understand the process. This study looks at the output from such reform processes. The two countries are seemingly experiencing the same type of pressures exerted by the demands of EU membership. However, the translation of the same external macro-forces at macro-level to micro (organizational)-level results in different compliance with the desired harmonization of governmental accounting systems.
Details
Keywords
Robert Pawlusiński and Magdalena Kubal
The growing importance of Krakow as the tourist destination in Eastern Europe has inspired changes in its hospitality industry as early as in the mid-nineteenth century. This…
Abstract
The growing importance of Krakow as the tourist destination in Eastern Europe has inspired changes in its hospitality industry as early as in the mid-nineteenth century. This chapter addresses the following questions – how has the hospitality industry developed during this period? Where did it concentrate? How did the hospitality offer expanded, and was the nature of the competition between owners? Due to the limited availability of historical statistical information on the service industry, the data for this study was derived from guide books, diaries, calendars, and newspapers (“Chronicle of Cracow”) throughout 1848–1939. The authors have examined about 30,500 volumes from which a selection of relevant information and press advertisements was made. Through the examination of historical press announcements for more than 90 years, the authors were able to reproduce the direct location of the hospitality industry objects, their changes of location, the identity of owners, the profile of provided services, and the economic and spatial transformations of the hospitality industry in Krakow.
Details
Keywords
Ahmad Mozaffari, Nasser Lashgarian Azad and Alireza Fathi
The purpose of this paper is to demonstrate the applicability of swarm and evolutionary techniques for regularized machine learning. Generally, by defining a proper penalty…
Abstract
Purpose
The purpose of this paper is to demonstrate the applicability of swarm and evolutionary techniques for regularized machine learning. Generally, by defining a proper penalty function, regularization laws are embedded into the structure of common least square solutions to increase the numerical stability, sparsity, accuracy and robustness of regression weights. Several regularization techniques have been proposed so far which have their own advantages and disadvantages. Several efforts have been made to find fast and accurate deterministic solvers to handle those regularization techniques. However, the proposed numerical and deterministic approaches need certain knowledge of mathematical programming, and also do not guarantee the global optimality of the obtained solution. In this research, the authors propose the use of constraint swarm and evolutionary techniques to cope with demanding requirements of regularized extreme learning machine (ELM).
Design/methodology/approach
To implement the required tools for comparative numerical study, three steps are taken. The considered algorithms contain both classical and swarm and evolutionary approaches. For the classical regularization techniques, Lasso regularization, Tikhonov regularization, cascade Lasso-Tikhonov regularization, and elastic net are considered. For swarm and evolutionary-based regularization, an efficient constraint handling technique known as self-adaptive penalty function constraint handling is considered, and its algorithmic structure is modified so that it can efficiently perform the regularized learning. Several well-known metaheuristics are considered to check the generalization capability of the proposed scheme. To test the efficacy of the proposed constraint evolutionary-based regularization technique, a wide range of regression problems are used. Besides, the proposed framework is applied to a real-life identification problem, i.e. identifying the dominant factors affecting the hydrocarbon emissions of an automotive engine, for further assurance on the performance of the proposed scheme.
Findings
Through extensive numerical study, it is observed that the proposed scheme can be easily used for regularized machine learning. It is indicated that by defining a proper objective function and considering an appropriate penalty function, near global optimum values of regressors can be easily obtained. The results attest the high potentials of swarm and evolutionary techniques for fast, accurate and robust regularized machine learning.
Originality/value
The originality of the research paper lies behind the use of a novel constraint metaheuristic computing scheme which can be used for effective regularized optimally pruned extreme learning machine (OP-ELM). The self-adaption of the proposed method alleviates the user from the knowledge of the underlying system, and also increases the degree of the automation of OP-ELM. Besides, by using different types of metaheuristics, it is demonstrated that the proposed methodology is a general flexible scheme, and can be combined with different types of swarm and evolutionary-based optimization techniques to form a regularized machine learning approach.