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Testing business processes is crucial to assess the compliance of business process models with requirements. Automating this task optimizes testing efforts and reduces human error while also providing improvement insights for the business process modeling activity. The primary purposes of this paper are to conduct a literature review of Business Process Model and Notation (BPMN) testing and formal verification and to propose the Business Process Evaluation and Research Framework for Enhancement and Continuous Testing (bPERFECT) framework, which aims to guide business process testing (BPT) research and implementation. Secondary objectives include (1) eliciting the existing types of testing, (2) evaluating their impact on efficiency and (3) assessing the formal verification techniques that complement testing.

The methodology used is based on Kitchenham's (2004) original procedures for conducting systematic literature reviews.

Results of this study indicate that three distinct business process model testing types can be found in the literature: black/gray-box, regression and integration. Testing and verification approaches differ in aspects such as awareness of test data, coverage criteria and auxiliary representations used. However, most solutions pose notable hindrances, such as BPMN element limitations, that lead to limited practicality.

The databases selected in the review protocol may have excluded relevant studies on this topic. More databases and gray literature could also be considered for inclusion in this review.

Three main originality aspects are identified in this study as follows: (1) the classification of process model testing types, (2) the future trends foreseen for BPMN model testing and verification and (3) the bPERFECT framework for testing business processes.

This study aims to analyze the imbalances in the public finance structure of Pakistan’s economy and highlight the need for comprehensive reforms. Specifically, it aims to contribute to the empirical literature by analyzing the relationship between fiscal vulnerability, financial stress and macroeconomic policies in Pakistan’s economy between 1971 and 2020.

The study develops an index of fiscal vulnerability, an index of financial stress and an index of macroeconomic policies. The fiscal vulnerability index is based on the patterns of fiscal indicators resulting from past trends of the selected variables in Pakistan’s economy. The financial stress in Pakistan is caused from the financial disorders that are acknowledged in the composite index, which is based on variables with the potential to indicate periods of stress stemming from the foreign exchange market, the securities market and the monetary policy components. The macroeconomic policies index is developed to analyze the mechanism through which fiscal vulnerability and financial stress have influenced macroeconomic policies in Pakistan. The causal association between fiscal vulnerability, financial stress and macroeconomic policies is analyzed using the auto-regressive distributive lags approach.

There exists a long-run relationship between the three indices, and a bi-directional causality between fiscal vulnerability and macroeconomic policies.

This study contributes to the development of a fiscal monitoring mechanism, which has the basic purpose of analyzing the refinancing risk of public liabilities. Moreover, it focuses on fiscal vulnerability from a macroeconomic perspective. The study tries to develop a framework to assess fiscal vulnerability in light of “The Risk Octagon” theory, which focuses on three risk components: fiscal variables, macroeconomic-disruption-associated shocks and non-fiscal country-specific variables. The initial contribution of this work to the literature is to develop a framework (a fiscal vulnerability index, financial stress index and macroeconomic policies index) for effective and result-oriented macro-fiscal surveillance. Moreover, empirical literature emphasized and advised developing countries to develop their own capacity mechanisms to assess their fiscal vulnerability in light of the IMF guidelines regarding vulnerability assessments. This study thus attempts to fulfill the said gap identified in literature.

The purpose of this paper is to develop a web-based interactive learning object (ILO) of introductory Computer Science (CS) concept on recursion and compare two feedback methods in the learning assessment part.

Test driven development (TDD) approach was used to develop ILO. The authors adapted Multimedia Educational Resource for Learning and Online Teaching (MERLOT) standard instrument to evaluate ILO’s effectiveness as an e-learning tool. Three respondents, from a list of pre-identified prospective evaluators, were randomly chosen and served as raters for MERLOT, while 32 student-respondents coming from first-year Math and CS undergraduate majors were randomly assigned to each ILO version implementing either one of the two feedback methods.

ILO obtained mean ratings above 4 (in scale 1-5) in three MERLOT criteria, namely, potential effectiveness as teaching tool, ease of use, and quality of content, which is rated highest (mean=4.40, SD=0.53). The study also revealed that immediate feedback increases retention while delayed feedback improves generating new knowledge. Respondents who viewed the ILO implementing immediate feedback in their first session had statistically significantly higher scores (mean=8.25, SD=0.80) than those who viewed with delayed feedback (mean=7.63, SD=0.89). In their second session, the same observation was noted although with higher mean scores. These results give evidence that the developed ILO met standards in e-learning material and showed evidence of its effectiveness with preferably implementing immediate feedback.

Although the developed ILO can now be used in school as supplementary learning material in teaching the concept of recursion in an introductory CS subject, a pilot testing of the web-based ILO using a larger sample of respondents to validate its effectiveness for online distance learning educational material can be pursued. Furthermore, in designing and creating an ILO, the provision of feedback during the assessment stage is necessary for effecting learning.

The study was a first to develop ILO for CS topic on recursion. The paper also compared which of two known feedback methods is best to implement in an ILO.

This paper aims to present a tool-supported approach for visualising personas as social goal models, which can subsequently be used to identify security tensions.

The authors devised an approach to partially automate the construction of social goal models from personas. The authors provide two examples of how this approach can identify previously hidden implicit vulnerabilities and validate ethical hazards faced by penetration testers and their safeguards.

Visualising personas as goal models makes it easier for stakeholders to see implications of their goals being satisfied or denied and designers to incorporate the creation and analysis of such models into the broader requirements engineering (RE) tool-chain.

The approach can be used with minimal changes to existing user experience and goal modelling approaches and security RE tools.

This study aims to investigate the organisational structure to exploit data analytics in the educational sector. The paper proposes three different organisational configurations, which describe the connections among educational actors in a national system. The ultimate goal is to provide insights about alternative organisational settings for the adoption of data analytics in education.

The paper is based on a participant observation approach applied in the Italian educational system. The study is based on four research projects that involved teachers, school principals and governmental organisations over the period 2017–2020.

As a result, the centralised, the decentralised and the network-based configurations are presented and discussed according to three organisational dimensions of analysis (organisational layers, roles and data management). The network-based configuration suggests the presence of a network educational data scientist that may represent a concrete solution to foster more efficient and effective use of educational data analytics.

The value of this study relies on its systemic approach to educational data analytics from an organisational perspective, which unfolds the roles of schools and central administration. The analysis of the alternative organisational configuration allows moving a step forward towards a structured, effective and efficient system for the use of data in the educational sector.

This work aims at proposing a novel Internet of Things (IoT)-based and cloud-assisted monitoring architecture for smart manufacturing systems able to evaluate their overall status and detect eventual anomalies occurring into the production. A novel artificial intelligence (AI) based technique, able to identify the specific anomalous event and the related risk classification for possible intervention, is hence proposed.

The proposed solution is a five-layer scalable and modular platform in Industry 5.0 perspective, where the crucial layer is the Cloud Cyber one. This embeds a novel anomaly detection solution, designed by leveraging control charts, autoencoders (AE) long short-term memory (LSTM) and Fuzzy Inference System (FIS). The proper combination of these methods allows, not only detecting the products defects, but also recognizing their causalities.

The proposed architecture, experimentally validated on a manufacturing system involved into the production of a solar thermal high-vacuum flat panel, provides to human operators information about anomalous events, where they occur, and crucial information about their risk levels.

Thanks to the abnormal risk panel; human operators and business managers are able, not only of remotely visualizing the real-time status of each production parameter, but also to properly face with the eventual anomalous events, only when necessary. This is especially relevant in an emergency situation, such as the COVID-19 pandemic.

The monitoring platform is one of the first attempts in leading modern manufacturing systems toward the Industry 5.0 concept. Indeed, it combines human strengths, IoT technology on machines, cloud-based solutions with AI and zero detect manufacturing strategies in a unified framework so to detect causalities in complex dynamic systems by enabling the possibility of products’ waste avoidance.

In this study, the authors examine artificial knowledge as a fundamental stream of knowledge management for sustainable and resilient business models in supply chain management (SCM). The study aims to provide a comprehensive overview of artificial knowledge and digitalization as key enablers of the improvement of SCM accountability and sustainable performance towards the UN 2030 Agenda.

Using the SCOPUS database and Google Scholar, the authors analyzed 135 English-language publications from 1990 to 2022 to chart the pattern of knowledge production and dissemination in the literature. The data were collected, reviewed and peer-reviewed before conducting bibliometric analysis and a systematic literature review to support future research agenda.

The results highlight that artificial knowledge and digitalization are linked to the UN 2030 Agenda. The analysis further identifies the main issues in achieving sustainable and resilient SCM business models. Based on the results, the authors develop a conceptual framework for artificial knowledge and digitalization in SCM to increase accountability and sustainable performance, especially in times of sudden crises when business resilience is imperative.

The study results add to the extant literature by examining artificial knowledge and digitalization from the resilience theory perspective. The authors suggest that different strategic perspectives significantly promote resilience for SCM digitization and sustainable development. Notably, fostering diverse peer exchange relationships can help stimulate peer knowledge and act as a palliative mechanism that builds digital knowledge to strengthen and drive future possibilities.

This research offers valuable guidance to supply chain practitioners, managers and policymakers in re-thinking, re-formulating and re-shaping organizational processes to meet the UN 2030 Agenda, mainly by introducing artificial knowledge in digital transformation training and education programs. In doing so, firms should focus not simply on digital transformation but also on cultural transformation to enhance SCM accountability and sustainable performance in resilient business models.

This study is, to the authors' best knowledge, among the first to conceptualize artificial knowledge and digitalization issues in SCM. It further integrates resilience theory with institutional theory, legitimacy theory and stakeholder theory as the theoretical foundations of artificial knowledge in SCM, based on firms' responsibility to fulfill the sustainable development goals under the UN's 2030 Agenda.