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More and more organizations make parts of their information systems available to their business partners and often face integration and interoperability issues. To counter these problems, web services appeared as a promising technology to bridge the gap between organizations and their partners. While web services generally focus on the implementation of functional concerns (FC) such as ordering of goods, the reservation of flights, etc. also non‐functional concerns (NFC) such as security, reliable messaging, performance, and availability have to be addressed appropriately. The purpose of this paper is to identify web services' requirements.

In this paper the authors provide a survey on works in the area of NFCs in web services. After presenting a common terminology, the most important requirements in that context are presented. Further, the authors assess these works against the requirements.

The evaluation reveals that there is no approach that supports the requirements to a satisfying degree. Based on that, the authors motivate the need for a novel holistic approach to NFCs in web services.

The paper presents an extended version of one of the papers presented at iiWAS2010.

Business process models, while primarily intended for process documentation, communication, and improvement, are often also used as input for developing process-oriented software systems (Ouyang et al., 2009). Ensuring correctness, handling complexity, and improving reusability and maintainability of business process models are important for all these goals. The purpose of this paper is to propose an aspect-oriented business process modeling and correctness controlling method based on Petri nets to satisfy these goals.

The aspect-oriented paradigm provides a proper mechanism to modularization, and thus reduces the complexity of models, and also improves reusability and maintainability. However, weaving aspects into base processes may bring in mistakes or errors. To ensure correctness of modeling, this paper presents a formal approach to modeling aspect-oriented business processes and a method to ensure modeling correctness. Petri net is used as the process modeling language and its analysis techniques are applied to analyze the correctness of modeling. Two types of correctness, specifically, aspect-aspect correctness and base-aspect correctness are analyzed. A real banking process model is studied systematically in the case study to evaluate the approach and the performance assessments are conducted to show the cost and effect of the approach.

Designing aspect-oriented business process models help organizations reusing the model elements to reduce redundancy of their model repository, improving their maintainability, and supporting them to adapt to the changes of business requirements with flexible modeling. It is important to stress that the correctness of business process modeling is important in ensuring the quality of the models, especially in the safety-critical business domains, such as financial business domain.

In this paper, separation of concerns is used to separate the cross-cutting activities and core activities in accordance with the different functions of these activities, and an approach to modeling aspect-oriented business processes is proposed. First, the cross-cutting activities are encapsulated as aspects, while core business activities are modeled as base processes. Then, according to the correctness requirements of business process models, based on the weaving mechanisms of aspect-oriented approach, weaving correctness is defined. Weaving correctness controlling methods between multi-aspects and between aspects and base processes are designed. Errors or mistakes of aspect-oriented business process modeling are prevented during the procedure of modeling to ensure error-free business process modeling.

The purpose of this paper is to analyze how monetary fundamentals affect exchange rate movements.

To develop this paper, a Bayesian Network modeling is applied to explore the causal interactions between monetary fundamentals and exchange rate fluctuations. Subsequently, a sensitivity analysis is performed to asses and estimate exchange rate behavior with uncertain monetary fundamentals. Furthermore, a Granger Causality test is used as suggested in the Econometric literature to determine the causality direction among factors.

The empirical findings show that money supply and interest rate have a significant positive effect on exchange rate, whereas inflation rate has a considerable negative effect on exchange rate. In addition, the authors deduce that real income has an indirect impact on exchange rate and a direct impact on inflation rate, interest rate and money supply. Furthermore, the sensitivity analysis shows that monetary uncertainty has a considerable effect on exchange rate fluctuations. Moreover, the Granger Causality test reveals that there is a unique unidirectional causality running from money supply to exchange rate.

The model can be considered as a vital management tool for international investors and financial analysts to explore the effect of monetary fundamentals on exchange rate behavior. It allows estimating exchange rate fluctuations with uncertain monetary factors.

This study is the first one which applied a Bayesian Network modeling to examine the exchange rate determination problem. Results of this research are presented under a clear graphical representation that can be easily useful by monetary policymakers and international traders to determine the influential monetary factors on exchange rate behavior. Also, the model will help them in estimating the effect of monetary uncertainty on exchange rate fluctuations.

The purpose of this research is to study the relationship between exchange rate fluctuations and stock market returns of the seven highest economic performing emerging countries (E7).

The study is conducted using the daily data for exchange rates and stock market returns in each of the E7 countries from January 1, 2019, to January 1, 2022. The study employs the ordinary least squares, autoregressive distributed lag error correction regression and generalized autoregressive conditional heteroskedasticity (GARCH (1,1)) regression models to fully investigate the impact of exchange rate on stock markets. For further investigation, the GARCH (1,1) model is run twice for each country with and without the inclusion of exchange rate to determine its effect on the volatility of stock returns.

The findings support the presence of cointegration relationship between the variables for all countries. The results reveal significant positive long-run relationship between exchange rates and stock market returns in all countries except for Indonesia, which evidenced a significant negative impact. The results of the GARCH (1,1) add that the inclusion of exchange rate in the model accounts for a slight change in the volatility of stock returns.

The research provides empirical evidence that appreciating currencies are perceived positively by investors leading to better performing capital markets. The outcomes of this study may assist policy makers in understanding to what degree changes in exchange rates can influence capital markets, as well as narrow the gap in literature regarding which theory is more relevant in explaining how exchange rate fluctuations impact market values.