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The purpose of the paper is to analyze the development needs and opportunities in the distribution system operators’ (DSO) tariff structures in the smart-grid environment.

The impacts of the distribution pricing schemes for the stakeholders and their requirements for the tariff structures are evaluated by qualitative analyses. Furthermore, there is a case analysis concerning the practical development possibilities of the DSO tariff structures in Finland.

Major finding of the paper is that the demand-based power band tariff is the optimal solution for the DSO pricing structure, when taking into account the real-life limitations and the requirements of the stakeholders.

Outcomes of the paper can be applied in practice in design of the pricing schemes in the electricity distribution. Incentive provision impacts and cost reflectivity of the DSO tariffs can be improved by implementing the suggested pricing structure.

The paper provides a novel viewpoint on the study of the DSO tariff design by considering the impacts of the pricing for the stakeholders and their requirements for tariff structure. Furthermore, the real-life limitations in the tariff design have been taken into account by analyzing the development options in Finland. Results are useful, especially for the DSOs, regulators and academics, who are working with the tariff development issues.

In healthcare, there is limited knowledge of and experience with patient choice management. The purpose of this paper is to focus on patient choice, apply and test demand-supply-based operating (DSO) logic integrated with clinical setting in clarifying choice contexts, investigate patient’s choice-making at different contexts and suggest context-based choice architectures to manage and develop patient choice.

Prenatal screening and testing in the Helsinki and Uusimaa Hospital District (HUS), Finland, was taken as an example. Choice points were contextualized by using the DSO framework. Women’s reflections, behaviors and experience at different choice contexts were studied by interviewing women participating in prenatal screening and testing. Semi-structured interview data were processed by thematic analysis.

By applying DSO logic, four choice contexts (prevention, cure, electives and continuous care) were relevant in the prenatal screening and testing episode. Women had different choice-making in prevention and cure mode contexts regarding choice activeness, information needs, social influence, preferences, emotion status and choice-making difficulty. Default choice was widely accepted by women in prevention mode and individual counseling can help women make informed choice in cure mode.

The authors apply the DSO model to contextualize the patient choice in one care episode and compare patient choice-making at different contexts. The authors also suggest the possible context-based choice architectures to manage and promote patient choice

This paper examines how manufacturing firms impacted by the nitrogen oxides (NO_x) Budget Trading Program (NBP) strategically managed working capital to release funds for increased costs and mitigate the negative impact on firm performance.

The study uses a panel data set including 11,302 manufacturing firm-year observations listed on the US exchanges during the period 2000–2008. The authors use Tobin's Q to proxy for firm performance, and cash holding, cash conversion cycle (CCC), days sales outstanding (DSO), days sales inventory (DSI) and days payable outstanding (DPO) for working capital management (WCM). The empirical analysis is conducted using both ordinary least squares (OLS) and propensity score matching (PSM) regressions.

The authors find that firms respond to the higher utility costs imposed by the NBP by decreasing CCC, DSO and DSI. This active WCM response partially mitigated the impact of increased compliance costs on performance for firms affected by the NBP. Results are robust in PSM regressions.

Climate change is a global issue that has attracted increasing attention in recent years. This study shows how firms can adjust short-term financing strategies to address the costs of compliance with climate change regulation.

The paper contributes to the emerging literature on corporate finance and climate policy actions. The authors use the unique experimental setting of the NBP to examine the regulatory impact on corporate financial management. The authors demonstrate how firms used active WCM to mitigate the negative performance impact of regulatory compliance with the NBP, providing novel insight on the implication of compliance with climate change legislation.

The purpose of this paper is to examine whether the level of financial credit available in a country influences the level of trade credit provided to customers.

The authors examine the association between the supply of trade credit and the availability of country-level private financial credit using multivariate regression models that account for country-level heterogeneity, macroeconomic conditions and firm-specific characteristics. The data set is a pooled sample of publicly traded firms incorporated in 66 countries.

Supporting the re-distributional view of trade credit, robust results suggest that suppliers incorporated in countries with increased access to financial credit provide increased trade credit to their customers. Further results indicate significant differences in trade credit usage across geographical regions. Consistent with existing research using samples of US firms, the use of trade credit is correlated with firm-level measures of financial constraints and product market dynamics.

The authors provide one of the first studies to examine differences in trade credit extension across a large number of countries.

The aim of this paper is to introduce a newly developed multi-criteria analysis for the comparison of two grid expansion alternatives, conventional and voltage-regulated distribution transformer. The case study comprises environmental, economic, technical and social aspects.

The newly developed method decision condition Preference Ranking Organization METHod for Enrichment Evaluation (DC-PROMETHEE) combines scenario planning with the multi-attribute decision-making method PROMETHEE. DC-PROMETHEE supports the decision-maker to evaluate the total potential of an alternative considering a large number of decision conditions. The calculated performance indicator supports the decision-maker to select the best alternative.

The voltage-regulated distribution transformer shows a high overall potential in the present case study. This leads to the recommendation to the investigated distribution system operator to include the voltage-regulated distribution transformers as a grid expansion measure.

The DC-PROMETHEE can be applied to other distribution system operators by considering their individual grid topology and preferences. Other fields of application are infrastructure investments in the service area, in which expansion alternatives are evaluated in a large number of decision conditions. Examples include telecommunication, gas supply, water supply, sewage and rail networks.

This paper develops the DC-PROMETHEE approach. The DC-PROMETHEE enables the multi-criteria evaluation of a few alternatives in a large number of decision conditions.

The enabling role of accounting in supporting classical contractual exchange has been extensively analysed in agency theory. In contrast, analyses the role of accounting in enabling empirically important and welfare‐enhancing long‐term relations which rely on trust and co‐operation rather than legal remedies. Under what circumstances does accounting strengthen, weaken or even destroy the trust which underpins relations both within and between organizations? What are the implications for accountability? Explores these general questions in the contrasting contexts of compulsory contracting policies in UK local government and the transition from socialism in Eastern Europe.

Examines, through in‐depth case studies, the process of strategic decision making arising from the management of compulsory competitive tendering (CCT) for catering and cleaning services following the 1988 Local Government Act. Examines in particular the contrasting approaches to the management of employee relations and discusses the extent to which a coherent management strategy in this area has been followed. Draws conclusions about the implications of decisions relating to choice of organizational structure and control systems for the implementation of strategic policy.

With the advancements in photo editing software, it is possible to generate fake images, degrading the trust in digital images. Forged images, which appear like authentic images, can be created without leaving any visual clues about the alteration in the image. Image forensic field has introduced several forgery detection techniques, which effectively distinguish fake images from the original ones, to restore the trust in digital images. Among several forgery images, spliced images involving human faces are more unsafe. Hence, there is a need for a forgery detection approach to detect the spliced images.

This paper proposes a Taylor-rider optimization algorithm-based deep convolutional neural network (Taylor-ROA-based DeepCNN) for detecting spliced images. Initially, the human faces in the spliced images are detected using the Viola–Jones algorithm, from which the 3-dimensional (3D) shape of the face is established using landmark-based 3D morphable model (L3DMM), which estimates the light coefficients. Then, the distance measures, such as Bhattacharya, Seuclidean, Euclidean, Hamming, Chebyshev and correlation coefficients are determined from the light coefficients of the faces. These form the feature vector to the proposed Taylor-ROA-based DeepCNN, which determines the spliced images.

Experimental analysis using DSO-1, DSI-1, real dataset and hybrid dataset reveal that the proposed approach acquired the maximal accuracy, true positive rate (TPR) and true negative rate (TNR) of 99%, 98.88% and 96.03%, respectively, for DSO-1 dataset. The proposed method reached the performance improvement of 24.49%, 8.92%, 6.72%, 4.17%, 0.25%, 0.13%, 0.06%, and 0.06% in comparison to the existing methods, such as Kee and Farid's, shape from shading (SFS), random guess, Bo Peng et al., neural network, FOA-SVNN, CNN-based MBK, and Manoj Kumar et al., respectively, in terms of accuracy.

The Taylor-ROA is developed by integrating the Taylor series in rider optimization algorithm (ROA) for optimally tuning the DeepCNN.