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In the context of the journey toward digital transformation and the realization of a fully connected factory, concepts such as data science, artificial intelligence (AI), machine learning (ML) and even predictive models emerge as indispensable pillars. Given the relevance of these topics, the present study focused on the analysis of customer complaint data, employing ML techniques to anticipate complaint accountability. The primary objective was to enhance data accessibility, harnessing the potential of ML models to optimize the complaint handling process and thereby positively contribute to data-driven decision-making. This approach aimed not only to reduce the number of units to be analyzed and customer response time but also to underscore the pressing need for a paradigm shift in quality management. The application of AI techniques sought to enhance not only the efficiency of the complaint handling process and data accessibility but also to demonstrate how the integration of these innovative approaches could profoundly transform the way quality is conceived and managed within organizations.

To conduct this study, real customer complaint data from an automotive company was utilized. Our main objective was to highlight the importance of artificial intelligence (AI) techniques in the context of quality. To achieve this, we adopted a methodology consisting of 10 distinct phases: business analysis and understanding; project plan definition; sample definition; data exploration; data processing and pre-processing; feature selection; acquisition of predictive models; evaluation of the models; presentation of the results; and implementation. This methodology was adapted from data mining methodologies referenced in the literature, taking into account the specific reality of the company under study. This ensured that the obtained results were applicable and replicable across different fields, thereby strengthening the relevance and generalizability of our research findings.

The achieved results not only demonstrated the ability of ML models to predict complaint accountability with an accuracy of 64%, but also underscored the significance of the adopted approach within the context of Quality 4.0 (Q4.0). This study served as a proof of concept in complaint analysis, enabling process automation and the development of a guide applicable across various areas of the company. The successful integration of AI techniques and Q4.0 principles highlighted the pressing need to apply concepts of digitization and artificial intelligence in quality management. Furthermore, it emphasized the critical importance of data, its organization, analysis and availability in driving digital transformation and enhancing operational efficiency across all company domains. In summary, this work not only showcased the advancements achieved through ML application but also emphasized the pivotal role of data and digitization in the ongoing evolution of Quality 4.0.

This study presents a significant contribution by exploring complaint data within the organization, an area lacking investigation in real-world contexts, particularly focusing on practical applications. The development of standardized processes for data handling and the application of predictions for classification models not only demonstrated the viability of this approach but also provided a valuable proof of concept for the company. Most importantly, this work was designed to be replicable in other areas of the factory, serving as a fundamental basis for the company’s data scientists. Until then, limited data access and lack of automation in its treatment and analysis represented significant challenges. In the context of Quality 4.0, this study highlights not only the immediate advantages for decision-making and predicting complaint outcomes but also the long-term benefits, including clearer and standardized processes, data-driven decision-making and improved analysis time. Thus, this study not only underscores the importance of data and the application of AI techniques in the era of quality but also fills a knowledge gap by providing an innovative and replicable approach to complaint analysis within the organization. In terms of originality, this article stands out for addressing an underexplored area and providing a tangible and applicable solution for the company, highlighting the intrinsic value of aligning quality with AI and digitization.

This paper aims to investigate materiality judgement providing insights, critiques and future research paths in light of the open debate on the role of materiality in corporate financial disclosure, highlighting potential connections and implications with sustainability and intellectual capital (IC) reporting.

The research presents an overview of the analysis of financial materiality, including new stimuli from recent studies and regulatory requirements for financial and non-financial reporting. Accordingly, this study used a systematic literature review (SLR) based on a combination of content, text and bibliometric analysis of materiality in accounting research studies, collecting data from the Scopus database as one of the most relevant repositories.

The SLR identified four relevant research trends, concerning: (1) the relevance of materiality principles in corporate disclosure; (2) financial reporting practices and materiality; (3) theories and approaches in defining financial materiality and (4) the existence of quantitative and qualitative thresholds in the materiality judgement.

The results provide theoretical and practical implications when comprehending the development of the concept of financial materiality in financial statements and whether they can be appropriate in reporting IC as well. We identified future research paths.

From a practical perspective, this study is useful for companies implementing financial materiality based on stakeholder engagement and improving their transparency in financial and non-financial reporting practices.

The research investigates if the process for assessing materiality is in line with the expectations of all stakeholders involved in financial and non-financial reporting.

This research is the first to investigate the scientific basis and applicability of the concept of financial materiality to sustainability and IC reporting.

Studies showed that construction digitalisation could prevent or mitigate accidents rate on sites. Digitalisation applications may prevent or mitigate building project collapse (BPC) but with some encumbrances, especially in developing countries. There is a paucity of research on digital technologies application to prevent or mitigate BPC in Nigeria. Thus, the research aims to explore the perceived barriers that may hinder digital technologies from preventing or mitigating building collapse and recommend measures to improve technology applications during development.

The study is exploratory because of the unexplored approach. The researchers collected data from knowledgeable participants in digitalisation and building collapse in Nigeria. The research employed a phenomenology approach and analysed collected data via a thematic approach. The study achieved saturation at the 29th interviewee.

Findings show that lax construction digitalisation implementation, absence of regulatory framework, lax policy, unsafe fieldworkers' behaviours, absence of basic infrastructure, government attitude, hesitation to implement and high technology budget, especially in developing countries, are threats to curbing building collapse menace via digitalisation. The study identified technologies relevant to preventing or mitigating building collapse. Also, it proffered measures to prevent or mitigate building collapse via improved digital technology applications during development.

This research contributes to the construction digitalisation literature, especially in developing countries, and investigates the perceived barriers that may hinder digital technologies usage in preventing or mitigating building collapse in Nigeria.

This paper aims to identify how a public client’s use of control systems (process, output and social control) affect innovation possibilities in construction projects.

Semi-structured interviews about six infrastructure projects were conducted to identify respondents’ views on innovation possibilities. These possibilities were then analyzed from an organizational control perspective within principal–agent relationships between the Swedish Transport Administration (STA) and their contractors.

How the client uses control systems affects innovation possibilities. Relying on process control could negatively affect innovation opportunities, whereas output control could have a positive influence. In addition, social control seems to have a weak effect, as the STA appears not to use social control to facilitate joint innovation. Public clients must comply with the Public Procurement Act and, therefore, retain the requirements specified in the tendering documents. Much of the steering of the execution is connected to the ex ante phase (before signing the contract), which affects innovation possibilities in the design and execution phases for the contractor.

This study was conducted with only one client, thus limiting its generalizability. However, the findings provide an important stepping stone to further investigation into balancing control systems and creating innovation possibilities in a principal–agent relationship.

Although public procurement has increasingly been emphasized as a major potential source of innovation, studying how a public client’s use of organizational control systems affects innovation possibilities in the construction sector has received scant attention.

Process mining (PM) has emerged as a leading technology for gaining data-based insights into organizations’ business processes. As processes increasingly cross-organizational boundaries, firms need to conduct PM jointly with multiple organizations to optimize their operations. However, current knowledge on cross-organizational process mining (coPM) is widely dispersed. Therefore, we synthesize current knowledge on coPM, identify challenges and enablers of coPM, and build a socio-technical framework and agenda for future research.

We conducted a literature review of 66 articles and summarized the findings according to the framework for Information Technology (IT)-enabled inter-organizational coordination (IOC) and the refined PM framework. The former states that within inter-organizational relationships, uncertainty sources determine information processing needs and coordination mechanisms determine information processing capabilities, while the fit between needs and capabilities determines the relationships’ performance. The latter distinguishes three categories of PM activities: cartography, auditing and navigation.

Past literature focused on coPM techniques, for example, algorithms for ensuring privacy and PM for cartography. Future research should focus on socio-technical aspects and follow four steps: First, determine uncertainty sources within coPM. Second, design, develop and evaluate coordination mechanisms. Third, investigate how the mechanisms assist with handling uncertainty. Fourth, analyze the impact on coPM performance. In addition, we present 18 challenges (e.g. integrating distributed data) and 9 enablers (e.g. aligning different strategies) for coPM application.

This is the first article to systematically investigate the status quo of coPM research and lay out a socio-technical research agenda building upon the well-established framework for IT-enabled IOC.

The purpose of this study is to analyze the first major federal education policy, the Elementary and Secondary Education Act of 1965, and the most recent federal policy, the Every Student Succeeds Act of 2015, through a Black critical theory (BlackCrit) lens to understand better how these educational policies have served as antiblack projects. Furthermore, this study locates examples of educational Freedom Dreams in the past and present to imagine new possibilities in Black education.

By analyzing education policy documents and history through BlackCrit methods, the authors expose how education policy is inherently an antiblack project. Freedom Dreams catalyze possibilities for future education.

The data confirms that while these policies purport equity and accountability in education, they, in practice, exacerbate antiblackness through inequitably mandated standardized testing, distributed funding and policed schooling.

This paper applies BlackCrit analysis of education policy to reimagine Black educational possibilities.

Coronavirus disease (COVID-19) has challenged leadership in hospitals worldwide. The experiences of leadership during the pandemic changed leadership significantly. This study aims to describe nurse leaders’ perceptions of what future leadership in hospital settings in the post-pandemic era needs to be like.

A qualitative descriptive study was used. A total of 20 nurse leaders from the Finnish central hospital were interviewed from June to October 2021. The data were analysed using inductive content analysis.

The analysis revealed five main categories describing nurse leaders’ perceptions of future leadership in hospital settings in the post-pandemic era: digitalisation and hybrid working culture, development of sustainable working conditions, moving smoothly to the post-pandemic era, dissolution of traditional regimes of organisation and flexibility in leadership.

In the post-pandemic era, the constantly changing demands and challenges currently facing healthcare systems have significantly increased the complexity of hospital organisations. This requires critical evaluation and change to traditional leadership. Enhancing flexibility and authenticity in leadership, strengthening competencies, implementing a wide range of digital resources and increasing the appeal of the nursing profession to build the next generation of nurses – all of these are needed to provide sustainability in future healthcare.

The results identify the critical points of leadership that need to be developed for future challenges and for maintaining a sufficient supply of qualified professionals. Acting on this information will enhance flexibility in organisations and lead to acceleration of changes and the development of new kinds of leadership in the future

This paper aims to investigate how congruent keywords are used in information security policies (ISPs) to pinpoint and guide clear actionable advice and suggest a metric for measuring the quality of keyword use in ISPs.

A qualitative content analysis of 15 ISPs from public agencies in Sweden was conducted with the aid of Orange Data Mining Software. The authors extracted 890 sentences from these ISPs that included one or more of the analyzed keywords. These sentences were analyzed using the new metric – keyword loss of specificity – to assess to what extent the selected keywords were used for pinpointing and guiding actionable advice. Thus, the authors classified the extracted sentences as either actionable advice or other information, depending on the type of information conveyed.

The results show a significant keyword loss of specificity in relation to pieces of actionable advice in ISPs provided by Swedish public agencies. About two-thirds of the sentences in which the analyzed keywords were used focused on information other than actionable advice. Such dual use of keywords reduces the possibility of pinpointing and communicating clear, actionable advice.

The suggested metric provides a means to assess the quality of how keywords are used in ISPs for different purposes. The results show that more research is needed on how keywords are used in ISPs.

The authors recommended that ISP designers exercise caution when using keywords in ISPs and maintain coherency in their use of keywords. ISP designers can use the suggested metrics to assess the quality of actionable advice in their ISPs.

The keyword loss of specificity metric adds to the few quantitative metrics available to assess ISP quality. To the best of the authors’ knowledge, applying this metric is a first attempt to measure the quality of actionable advice in ISPs.

This work aims to integrate the concepts generated by a systematic literature review on patient flows in emergency departments (ED) to serve as a basis for developing a generic process model for ED.

A systematic literature review was conducted using PRISMA guidelines, considering Lean Healthcare interventions describing ED patients’ flows. The initial search found 141 articles and 18 were included in the systematic analysis. The literature analysis served as the basis for developing a generic process model for ED.

ED processes have been represented using different notations, such as value stream mapping and workflows. The main alternatives for starting events are arrival by ambulance or walk-in. The Manchester Triage Scale (MTS) was the most common protocol referred to in the literature. The most common end events are admission to a hospital, transfer to other facilities or admission to an ambulatory care system. The literature analysis allowed the development of a generic process model for emergency departments. Nevertheless, considering that several factors influence the process of an emergency department, such as pathologies, infrastructure, available teams and local regulations, modelling alternatives and challenges in each step of the process should be analysed according to the local context.

A generic business process model was developed using BPMN that can be used by practitioners and researchers to reduce the effort in the initial stages of design or improvement projects. Moreover, it’s a first step toward the development of generalizable and replicable solutions for emergency departments.

This study aims to establish whether accounting research articles can be potentially generated by artificial intelligence. If artificial intelligence can produce quality work, the integrity of academic research may be compromised.

ChatGPT was used to create a paper on a meta-analysis of the relationship between sustainability reporting and value relevance. After the paper was generated, references had to be added by hand based on the citations created by ChatGPT. The paper was then presented as-is for review.

ChatGPT was able to create a relatively good-quality research paper that received two major revisions from independent specialists in the field of accounting and finance. Even though there is uncertainty regarding the appropriateness of all the references and the results cannot be confirmed, there is a risk that a reviewer may find the paper publishable because reviewers are not compelled to check references and the accuracy of results if proper methods were used that appear to be sufficient at face value.

Artificial intelligence for academic writing is still relatively new, and there is still significant uncertainty as to the impact it may have on scholarly research. This is especially problematic because artificial intelligence applications improve by the second.