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Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Gray Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Gray Wolf Optimization, inspired by the social hierarchy and hunting behavior of gray wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.

The integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.

The performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.

Experimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.

We attempt to construct a grey three-way conflict analysis model with constraints to deal with correlated conflict problems with uncertain information.

In order to address these correlated conflict problems with uncertain information, considering the interactive influence and mutual restraints among agents and portraying their attitudes toward the conflict issues, we utilize grey numbers and three-way decisions to propose a grey three-way conflict analysis model with constraints. Firstly, based on the collected information, we introduced grey theory, calculated the degree of conflict between agents and then analyzed the conflict alliance based on the three-way decision theory. Finally, we designed a feedback mechanism to identify key agents and key conflict issues. A case verifies the effectiveness and practicability of the proposed model.

The results show that the proposed model can portray their attitudes toward conflict issues and effectively extract conflict-related information.

By employing this approach, we can provide the answers to Deja’s fundamental questions regarding Pawlak’s conflict analysis: “what are the underlying causes of conflict?” and “how can a viable consensus strategy be identified?”

Analyses of cultural landscapes need to combine natural and social-cultural components to promote discussions on landscape planning and heritage management. This qualitative research explores the integrated case study of ten municipalities in the “Vineyard Landscape of Piedmont: Langhe-Roero and Monferrato”, Italy, a UNESCO World Heritage cultural landscape. The research aims to raise awareness of its aesthetic-perceptive features, the importance of effective identification of visual impacts and to promote mitigation strategies/actions for updating the current Management Plan.

Two rounds of interviews and focus groups with mayors were performed in 2015 and 2020 to identify trends and drivers of change affecting the territories. Potential mitigation strategies and actions were voted on and selected in response to five critical themes that emerged from the survey, mainly related to real estate and its supplies.

The results suggest tools and policies in the fields of landscape architecture and landscape design that could benefit planning and management at different levels. They support the design of sustainable scenarios, improving mayors' understanding of the significance of cultural landscapes and promoting them as heritage managers. Furthermore, they intend to preserve the authenticity of the landscape by supporting its attributes for long-term conservation.

The research makes an original contribution on the visual implications of anthropogenic landscape transformations in ten municipalities constituting this serial property, six years after its UNESCO nomination (2014).

This study aims at proposing and testing a model delineating how and when the quality of a special B2B professional service, investment relations (IR), would drive corporate intangible value.

This study employs a proprietary dataset on voting records of an annual investment relations (IR) awards event and the corresponding company-level archival data for analysis. Regression analysis is used to test hypotheses.

IR service quality not only directly enhances corporate intangible value, but also indirectly boosts it via information transparency. While competitive intensity does not moderate the relationship between IR service quality and corporate intangible value, its moderating effect on the relationship between information transparency and this value is negative.

The findings advance academic understanding of the mechanism and boundary conditions underlying the complex and dynamic relationships among IR service quality, information transparency, corporate intangible value and competitive intensity. Future research endeavors to verify the present findings in other service and/or geographic settings would help establish their external validity.

The findings advise companies to expand the traditional role of IR by taking it as a powerful communication and relationship marketing tool to improve their visibility and attract investors.

The findings suggest that superior IR service would strengthen the company’s social bonding with institutional investors and effectively signal to them its commitment to good corporate governance practices.

Matching a proprietary dataset on IR voting records with the corresponding company-level archival data over a five-year period to investigate the performance implications of IR service quality within the Hong Kong context rectifies methodological limitation and geographic confinement of prior IR research.

This paper aims to illustrate, by means of a content analysis of 278 weekly School Meeting minutes, the ways in which student voice is actualized in one democratic free school in Germany.

This paper uses a qualitative content analysis methodology of 278 weekly School Meetings minutes.

This paper uses Fielding’s (2012) patterns of partnership typology to illustrate what counts as student voice and participation in a democratic free school.

Limitations included being reliant on translations of German texts, some missing minutes from the entire set, the lack of a single author for the minutes (and thus degree of detail differs) and the fact that the School Meeting minutes make reference to other meetings for various sub-committees for which no minutes exist, and thus, findings on the degree of student voice may be limited. And because this is a study of one school, generalizability may be difficult. Future research into these sub-committee meetings would prove helpful as well as content analyses of other democratic free schools’ meeting minutes.

This study can help people more deeply understand what goes on in democratic free schools and what student voice and participation can mean within this context.

The field of supply chain management (SCM) needs to attract and retain workers to solve the current talent shortage. The purpose of this research is to identify and evaluate factors that influence career advancement in SCM and compare male and female supply chain experts' perceptions of the importance of those factors.

First, 32 factors perceived as affecting career advancement in SCM were identified by conducting a literature review and consulting 36 experts. Those factors were grouped into four categories: “environmental and structural”, “human capital”, “individual” and “interpersonal”. Those factors were validated via the Delphi method, and ten factors were retained for further study. Second, the voting analytical hierarchy process was used to determine the priority weights experts assigned to these factors. The weights assigned by male and female experts were compared to determine if there were differences between the women's and men's perceptions of the factors' importance.

The findings reveal that the category of human capital factors is the most important, followed by individual factors and the least important is interpersonal factors. The experts consulted for this research emphasized “skills”, “a good fit between an individual and an organization” and “self-confidence” as important factors for career advancement. There were two unexpected results. First, the experts rejected all the environmental and structural factors. Second, no significant difference was found between the male and female groups' evaluations.

Prior to this study, no integrated approach to identify and evaluate the factors perceived which affect career advancement in SCM had been developed. This research is a single empirical and integrative study in France that provides valuable insights for academics and practitioners.

The current study investigates the impact of directors' attributes on the extent of compliance with International Financial Reporting Standards (IFRS) fair value disclosure requirements. The attributes investigated include directors' human capital (accounting qualification) and social capital (political association), directors' share ownership and the power distance between the chief executive officer (CEO) and the rest of the board members.

The study uses disclosure analysis to measure the extent of compliance with the fair value disclosure requirements of IFRS. Ordinary least squares (OLS) regression is used to test the relationship between the disclosure score and directors' attributes. Data were collected from the annual reports and websites of the sample companies.

Contrary to conventional belief, this study's findings suggest that directors' social capital and the power distance between the CEO and the rest of the board act as more powerful factors than directors' human capital in explaining corporate mandatory disclosure. Specifically, the results indicate that powerful actors form a dominant coalition and co-opt influential constituents from the institutional domain to neutralize the effect of legal coercion and the accounting expertise of board members and Big Four audit firms on the extent of compliance with institutional (fair value) rules.

This study utilizes Oliver's (1991) framework of strategic response to institutional processes in the Bangladeshi context. Although the study provides new insights into corporate disclosure practices, findings are not generalizable due to different institutional settings in different countries. Therefore, future studies could replicate the approach in different institutional settings.

The findings of this study will be of interest to the International Accounting Standards Board (IASB) as it focuses on a developing country that has adopted IFRS 13 and other fair value-related standards relatively recently.

The disclosure analysis contained in this study represents the first comprehensive analysis of the extent of compliance with the fair value disclosure requirements of IFRS. Furthermore, this study considers the impact of directors' social capital and finds that it is a more powerful determinant of the extent of compliance with IFRS as compared to human capital.

This study aims to give a glimpse of the existing blockchain applications across industries and add to a complete knowledge of the blockchain’s properties.

Systematic literature review is used as the research strategy for this investigation and other aspects of the preferred reporting items for systematic reviews and meta-analyses framework have been incorporated to create a scholarly publications evaluation of the blockchain-based application in the financial arena and its future. The research looks at 86 studies published between 2018 and 2022.

There has been a steady but noticeable increase in the study of blockchain’s potential in many application domains over the past few of years. This rising tendency illustrates the newness and potential of blockchain technology, as well as the increasing attention from academics. According to the findings, blockchain is an appropriate solution for processing transactions using cryptocurrencies; nevertheless, it still has significant technical issues and limits that require to be exploring and solving before it can be considered a viable option. It is therefore, necessary to have a high level of reliability for payments and confidentiality, in addition to maintaining the anonymity of nodes, to stop assaults and efforts to disrupt transactions in the blockchain.

This study has several important theoretical and practical implications. First, it adds to the body of knowledge on blockchain and Fintech, focusing on the transaction side. While much blockchain research has focused on how the technology may affect strategic choices, this study has shed light on its potential from the perspective of financial reporting. Second, by highlighting the importance of the demand for the prompt identification of losses, this work adds to the body of knowledge on the factors that influence transaction frauds involving paper money. Additionally, by establishing the link between transparency and virtual transactions, the author backs up the asymmetric responses of investors to different investment possibilities. It looks at the evolution of financial technology (Fintech) and shows how it can be used to take the advantage of unique opportunities.

The study is different and novel from the previously published literature on this topic mainly because of its comprehensiveness, as it revolves around all industrial and commercial areas. The three main lines of research have been outlined, namely, classifying the many blockchain-based innovations that will alter the financial landscape in many industries; identifying whether these industries are a good fit for blockchain’s wealth creation potential; and directing researchers by outlining prospective study pathways.

We propose a machine learning based methodology to deal with data collected from a mobile application asking users their opinion regarding fashion products. Based on different machine learning techniques, the proposed approach relies on the data value chain principle to enrich data into knowledge, insights and learning experience.

Online interaction and the usage of social media have dramatically altered both consumers’ behaviors and business practices. Companies invest in social media platforms and digital marketing in order to increase their brand awareness and boost their sales. Especially for fashion retailers, understanding consumers’ behavior before launching a new collection is crucial to reduce overstock situations. In this study, we aim at providing retailers better understand consumers’ different assessments of newly introduced products.

By creating new product-related and user-related attributes, the proposed prediction model attends an average of 70.15% accuracy when evaluating the potential success of new future products during the design process of the collection. Results showed that by harnessing artificial intelligence techniques, along with social media data and mobile apps, new ways of interacting with clients and understanding their preferences are established.

From a practical point of view, the proposed approach helps businesses better target their marketing campaigns, localize their potential clients and adjust manufactured quantities.

The originality of the proposed approach lies in (1) the implementation of the data value chain principle to enhance the information of raw data collected from mobile apps and improve the prediction model performances, and (2) the combination consumer and product attributes to provide an accurate prediction of new fashion, products.

This paper aims to explore how historical context influences the content and selection of rhetorical legitimation strategies. Using case study method, this paper will focus on how insurance companies and labor tried to defend their legitimacy in the context of enactment of Medicare in the USA. What factors influenced the strategic (rhetorical) decisions made by insurance companies and labor unions in their institutional work?

The study is empirically grounded in archival research, involving an analysis of over 9,000 pages of congressional hearings on Medicare covering the period 1958–1965.

The authors show that rhetorical legitimation strategies depend significantly on the specific historical circumstances in which those strategies are used. The historical context lent credibility to certain arguments and organizations are forced to decide either to challenge widely held assumptions or take advantage of them. The authors show that organizations face strong incentives to pursue the latter option. Here, both the insurance companies and labor unions tried to show that their positions were consistent with classical liberal ideology, because of high respect of classical liberal principles among different stakeholders (policymakers, voters, etc.).

It is uncertain how much the results of the study could be generalized. More information about the organizations whose use of rhetorics the authors studied could have strengthened our conclusions.

The practical relevancy of the revised paper is that the authors should not expect hegemony challenging rhetorics from organizations, which try to influence legislators (and perhaps the larger public). Perhaps (based on the findings), this kind of rhetorics is not even very effective.

The paper helps to understand better how organizations try to advance their interests and gain acceptance among the stakeholders.

In this paper, the authors show how historical context in practice influence rhetorical arguments organizations select in public debates when their goal is to influence the decision-making of their audience. In particular, the authors show how dominant ideology (or ideologies) limit the options organizations face when they are choosing their strategies and arguments. In terms of the selection of rhetorical justification strategies, the most pressing question is not the “real” broad based support of certain ideologies. Insurance company and labor union representatives clearly believed that they must emphasize liberal values (or liberal ideology) if they wanted to gain legitimacy for their positions. In existing literature, it is often assumed that historical context influence the selection of rhetorical strategies but how this in fact happens is not usually specified. The paper shows how interpretations of historical contexts (including the ideological context) in practice influence the rhetorical strategies organizations choose.