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This study aims to analyze the emergence of a new structural configuration of globalization, with the 2008 global financial crisis serving as the first symptom of this change. By introducing the “Evolutionary Structural Triptych” (EST), this research seeks to understand the basic components of the new evolutionary trajectory of global capitalism post-2008. The study places emphasis on its interdependent and coevolving economic, political and technological dynamic facets.

This research introduces the EST framework, critically contrasting it with conventional understandings in international political economy (IPE) to provide a comprehensive and structured analysis of global developments after 2008. It traces the phases of global capitalism since Second World War, examines the central dynamic dimensions during each evolutionary phase, identifies the basic patterns and delves into the foundational elements of the emerging era of globalization.

The analysis reveals three key findings. First, the emerging restructured globalization indicates a need for a new balance in the contemporary world system; however, this balance cannot be achieved within the architecture of the old system. Second, the new era of globalization necessitates a re-equilibrated approach across different dimensions of geopolitical stability, economic development and innovation. This approach should emphasize sustainability, adaptability, resilience and inclusivity and lean toward responsible, open and organic innovation models for a revamped global structure. Third, while many current IPE theories tend to compartmentalize aspects of the new globalization, the EST advocates for a holistic perspective that integrates politics, economics and technology within the framework of global trends. This perspective bridges existing gaps and offers actionable insights for a dynamic and inclusive global future.

The paper presents the EST as a novel analytical instrument in the realm of the modern IPE. This tool uniquely places technology and innovation at the forefront, parallel to economic and political spheres, to comprehend the progression of globalization. In doing so, it highlights the intertwined relationship of these structural dimensions in shaping the future of the subject of the IPE.

The present paper aims at the identification of the critical variables of risk-based auditing (RBA). The variables under examination are the internal audit (IA), the audit committee (AC) and the cooperation between the RBA and the stakeholders (audit committee, external auditors, internal auditors, board of directors, fraud investigators, chief risk manager) (COOP).

A questionnaire survey was conducted among 176 Greek companies. The questionnaires were addressed to accountants, internal auditors, managers, chief risk managers and the board of directors. A total of 96 questionnaires have been collected and analyzed. PLS-SEM modeling was used as a tool to test hypotheses and analyze the findings.

The results show that three variables, i.e. the internal audit, the audit committee and the RBA cooperation with stakeholders have a statistically significant and positive effect on risk-based auditing (RBA). Additionally, the existence of partial-complementary mediation of the internal audit in the audit committee-RBA interaction is confirmed.

This study is an original research that identifies the essential variables of risk-based auditing in Greek companies. It attempts to analyze the perceptions of all stakeholders in risk-based auditing, including the internal audit, the audit committee, etc. and is not restricted only on internal auditors. Furthermore, the analysis is conducted with PLS-SEM Modeling, which is an innovative tool of testing hypotheses and analyzing results.

The paper redefines workplace spirituality (WS/WPS) by transcending the existential vacuum (in psychiatric terms a sense of lack of meaning of human existence and thus of work), leading to the development of workplace creativity, productivity and satisfaction, targeting operational profitability and organizational optimization.

Spirituality is analyzed philosophically, following the Nietzschean definition in response to Schopenhauer’s primordial suffering. Philosophical syncretism yields a viable organizational culture change model of spiritualizing the workplace. For this purpose, specific techniques are proposed which are combined with those already applied to various large companies and organizations.

Spirituality in the workplace acts as a catalyst for developing beneficial qualities by increasing employee job satisfaction, organizational efficiency and business profitability, when equally responding to stakeholders’ needs.

The suggested change model holistically fosters organizational, operational, individual and collective effectiveness through work place spirituality redefined.

For the first time spirituality in the workplace is discussed under a brand new perspective, resulting in an interdisciplinary emerging model, contributing to the field by providing guidance to academics and practitioners to its auspicious implementation through organizational culture change.

The set of 2,509 documents related to the human-centric aspect of manufacturing were retrieved from Scopus database and systmatically analyzed. Using an unsupervised machine learning approach based on Latent Dirichlet Allocation we were able to identify latent topics related to human-centric aspect of Industry 5.0.

This study aims to create a scientific map of the human-centric aspect of manufacturing and thus provide a systematic framework for further research development of Industry 5.0.

In this study a 140 unique research topics were identified, 19 of which had sufficient research impact and research interest so that we could mark them as the most significant. In addition to the most significant topics, this study contains a detailed analysis of their development and points out their connections.

Industry 5.0 has three pillars – human-centric, sustainable, and resilient. The sustainable and resilient aspect of manufacturing has been the subject of many studies in the past. The human-centric aspect of such a systematic description and deep analysis of latent topics is currently just passing through.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

The use of additive manufacturing in many branches of industry is increasing significantly because of its many advantages, such as being able to produce complex parts that cannot be produced by classical methods, using fewer materials, easing the supply chain with on-site production, being able to produce with all kinds of materials and producing lighter parts. The binder jetting technique, one of the additive manufacturing methods researched within the scope of this work, is predicted to be the additive manufacturing method that will grow the most in the next decade, according to many economic reports. Although additive manufacturing methods have many advantages, they can be slower than classical manufacturing methods regarding production speed. For this reason, this study aims to increase the manufacturing speed in the binder jetting method.

Adaptive slicing and variable binder amount algorithm (VBAA) were used to increase manufacturing speed in binder jetting. Taguchi method was used to optimize the layer thickness and saturation ratio in VBAA. According to the Taguchi experimental design, 27 samples were produced in nine different conditions, three replicates each. The width of the samples in their raw form was measured. Afterward, the samples were sintered at 1,500 °C for 2 h. After sintering, surface roughness and density tests were performed. Therefore, the methods used have been proven to be successful. In addition, measurement possibilities with image processing were investigated to make surface roughness measurements more accessible and more economical.

As a result of the tests, the optimum printing condition was decided to be 180–250 µm for layer thickness and 50% for saturation. A separate test sample was then designed to implement adaptive slicing. This test sample was produced in three pieces: adaptive (180–250 µm), thin layer (180 µm) and thick layer (250 µm) with the determined parameters. The roughness values of the adaptive sliced sample and the thin layer sample were similar and better than the thick layer sample. A similar result was obtained using 12.31% fewer layers in the adaptive sample than in the thin layer sample.

The use of adaptive slicing in binder jetting has become more efficient. In this way, it will increase the use of adaptive slicing in binder jetting. In addition, a cheap and straightforward image processing method has been developed to calculate the surface roughness of the parts.

This study aims to examine the relationship between climate change disclosure performance (CCDP) and audit pricing. The moderating effect of corporate governance characteristics on that relationship is also investigated.

Using a sample of top 300 Australian Securities Exchange listed non-financial firms over the period 2008–2019, this study investigates the association between CCDP and audit fees. The findings are robust to a difference-in-difference test thereby alleviating potential endogeneity concerns.

CCDP is found to be significantly positively related to external auditor fees.

The findings show some important implications for firm management, regulators, investors and auditors. This study presents empirical evidence that climate change, as a factor of external risk, influences audit fees.

Firms with governance structures characterized by larger more independent boards, larger audit committees and audit committees with a higher level of independence significantly moderate the relationship between CCDP and audit fees.

Investors’ demand for firm transparency and disclosure of information regarding the risks of climate change, effects and opportunities has increased significantly over the past decade, as these factors could have a significant effect on valuation and investment decisions.

Importantly, stakeholders need to be aware of the costs of climate change, the quantification of climate change impacts and how firms address climate change in their business risk management processes. This study quantifies the impact of CCDP on auditor risk assessments via audit fees.

The purpose of this study involves analysing the factors of and barriers to innovation and their respective impacts (directly and indirectly) on the competitiveness of both destination and their host companies.

Based on primary data collected by questionnaire from a sample of 119 companies operating in the tourism sector in the autonomous region of Madeira (Portugal), the authors applied a quantitative methodology with recourse to econometric models and multiple linear regression.

Comprehensive results based on a conceptual model are obtained through the analysis of competitiveness tourism-based and innovation factors. The study identifies and empirically tests the existing and underlying relationships between innovation (factors and barriers) as the means of leveraging the competitiveness of destinations and their companies.

The results hold important theoretical and practical implications contributing towards innovation for competitiveness and filling a shortcoming identified in the literature.

The purpose of this study is to examine various key aspects associated with entrepreneurs’ behaviour following a long-term crisis. Specifically, the study compares the perceptions of female and male entrepreneurs operating in Cyprus and Greece concerning success factors and firm performance in the aftermath of the global financial crisis. Conceptually, the study considers the organisational adaptation literature (Miles and Snow’s typology).

The views of female and male micro and small firm owners-managers operating in Greece and Cyprus, a total of 406, were gathered through a questionnaire. To analyse the quantitative data, independent samples t-test and exploratory factor analysis were applied.

Participants’ responses reveal similar levels of perceived importance between genders regarding adaptive measures and strategies to confront a long-term crisis, as well as perceived firm performance. Nevertheless, exploratory factor analysis highlights differences in how male/female entrepreneurs perceive actions that, as in the case of financial management, can safeguard the immediate outlook of the firm.

While scholarly discourses on gender and entrepreneurship abound, important knowledge gaps still exist, for instance, in entrepreneurs’ problem-solving strategies adopted by female and male entrepreneurs following crises. In addressing this scholarly gap cross-culturally, that is, drawing on cross-national data (Cyprus and Greece); the present study makes an important contribution. Empirically, the study ascertains similar entrepreneurial behavioural characteristics between female-male entrepreneurs. Theoretically, the study validates Miles and Snow’s typology and develops a theoretical framework linking the typology and dimensions emerging from the empirical findings.