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The aim of this study is to evaluate the adoption and quality of integrated reports in the European Union (EU).

The sample consists of 147 listed firms from the 18 EU countries during 2013–2020. This study creates a disclosure index – based on the balanced scorecard (BSC) that reflects the information content of integrated reports. The content analysis method is used to measure the integrated reporting quality (IRQ).

The findings demonstrate that the IRQ increased across the study’s time frame, going from 49.3% in 2013 to 77% in 2020. Furthermore, financial disclosures still get the most attention in the integrated reporting (IR), followed by learning and growth perspective disclosures. In addition, businesses in the financial and industrial sectors rely more on integrated reports. However, the utility sector has the highest IRQ score. By country, Spain has the highest rate of IR adoption, followed by France. Other countries, such as Austria and Hungary, have only implemented IR by one company each.

This study adds to the IR literature a new approach to measure IRQ by linking BSC with the IR framework. Empirically, businesses of any size can use this method to assess the degree of balance between the revealed financial and nonfinancial information in their reports.

Empirically, this study helps IR practitioners in determining how widely IR is used in Europe and in updating the database on the IR website. It helps them update and improve the IR framework by identifying the elements that have the least transparency and quality, investigating the causes and enhancing them.

To the best of the authors’ knowledge, this study is the first to examine the IRQ in EU countries by linking the BSC with IR elements. This is to split the elements into their own pillars, making it easier to track disclosure and evaluate the corporations’ interest in revealing these perspectives, on their own and collectively.

Image segmentation is one of the most essential tasks in image processing applications. It is a valuable tool in many oriented applications such as health-care systems, pattern recognition, traffic control, surveillance systems, etc. However, an accurate segmentation is a critical task since finding a correct model that fits a different type of image processing application is a persistent problem. This paper develops a novel segmentation model that aims to be a unified model using any kind of image processing application. The proposed precise and parallel segmentation model (PPSM) combines the three benchmark distribution thresholding techniques to estimate an optimum threshold value that leads to optimum extraction of the segmented region: Gaussian, lognormal and gamma distributions. Moreover, a parallel boosting algorithm is proposed to improve the performance of the developed segmentation algorithm and minimize its computational cost. To evaluate the effectiveness of the proposed PPSM, different benchmark data sets for image segmentation are used such as Planet Hunters 2 (PH2), the International Skin Imaging Collaboration (ISIC), Microsoft Research in Cambridge (MSRC), the Berkley Segmentation Benchmark Data set (BSDS) and Common Objects in COntext (COCO). The obtained results indicate the efficacy of the proposed model in achieving high accuracy with significant processing time reduction compared to other segmentation models and using different types and fields of benchmarking data sets.

The proposed PPSM combines the three benchmark distribution thresholding techniques to estimate an optimum threshold value that leads to optimum extraction of the segmented region: Gaussian, lognormal and gamma distributions.

On the basis of the achieved results, it can be observed that the proposed PPSM–minimum cross-entropy thresholding (PPSM–MCET)-based segmentation model is a robust, accurate and highly consistent method with high-performance ability.

A novel hybrid segmentation model is constructed exploiting a combination of Gaussian, gamma and lognormal distributions using MCET. Moreover, and to provide an accurate and high-performance thresholding with minimum computational cost, the proposed PPSM uses a parallel processing method to minimize the computational effort in MCET computing. The proposed model might be used as a valuable tool in many oriented applications such as health-care systems, pattern recognition, traffic control, surveillance systems, etc.

The purpose of this paper is to explore a novel storytelling approach that investigates lived experience at the intersection of motherhood/caregiving and Ph.D. pursuits. The paper contributes to the feminist tradition of writing differently through the process of care that emerges from shared stories.

Using a process called heartful-communal storytelling, the authors evoke personal and embodied stories and transgressive narratives. The authors present a composite process drawing on heartful-autoethnography, dialogic writing and communal storytelling.

The paper makes two key contributions: (1) the paper illustrates a novel feminist process in action and (2) the paper contributes six discrete stories of lived experience at the intersection of parenthood and Ph.D. studies. The paper also contributes to the development of the feminist tradition of writing differently. Three themes emerged through the storytelling experience, and these include (1) creating boundaries and transgressing boundaries, (2) giving and receiving care and (3) neoliberal conformity and resistance. These themes, like the stories, also became entangled.

The paper demonstrates how heartful-communal storytelling can lead to individual and collective meaning making. While the Ph.D. is a solitary path, the authors' heartful-communal storytelling experience teaches that holding it separate from other relationships can impoverish what is learnt and constrain the production of good knowledge; the epistemic properties of care became self-evident.

The purpose of this study is to examine the dynamic relationship between intellectual capital (IC) and its components on financial performance of banks within the selected eight countries of Association of Southeast Asian Nations (ASEAN).

The study utilizes the balanced panel data of 37 publicly listed banks from eight leading ASEAN economies for the period of 2017–2021. In this sense, the authors applied the Ante Pulic's typology, i.e. value-added intellectual coefficient (VAIC™) to evaluate the efficiency of intangible and tangible assets. While, investigating the dynamic nature of relationship, the authors employed the generalized system method of moments because of its power to account for the problem of endogeneity and heteroscedasticity.

The results of the study demonstrate that banks in ASEAN countries shed a varied degree of a spotlight on VAIC™ and its components to create value. The findings revealed that structural capital efficiency is significantly associated with earning per share (EPS), return on assets (ROA) and return on equity (ROE), compared to human capital efficiency (HCE) and capital employed efficiency of ASEAN banks. These results endorse the importance of resource- and knowledge-based views of organizations to leverage the financial performance of banks. However, contrary to theoretical expectations, this study found no positive relationship between HCE with ROA and ROE. Whereas, the relationship of VAIC™ is positive and significant with EPS and ROE but it remains statistically very marginal.

There are some inherent limitations in this study that could be opportunities for future research. The current study uses the VAIC™ typology, but future researchers can use the modified value-added intellectual coefficient (MVAIC) or triangulation approach to enhance the validity and reliability of the study. Additionally, future research can investigate the similarities and differences among countries in terms of their cultural backgrounds and regulatory frameworks regarding the disclosure of intangibles. Furthermore, future research can increase the length and sample size of the study to enhance its generalizability.

The robust empirical findings extend the academic debate on IC by unveiling the dynamic nature of relationship between IC and financial performance in context of ASEAN banking sector. The findings provide plausible recommendations for policy makers (managers, regulators and stakeholders) to understand how to increase the IC efficiently, especially human capital as a source to evaluate the firms’ ability in determining value-added and financial performance. Further, findings of this study also suggest that how can policy makers get the benefit by investing more on structural capital as a valuable strategic source to guarantee the optimal performance returns.

Prior studies on IC have been country- and firm-specific, utilizing cross-sectional research designs. However, this research contributes to the limited literature by investigating the dynamic nature of the relationship between IC and financial performance of banks in the context of ASEAN countries using micro-panel data.

This study aims to quantify and analyse the dynamics of land use and land cover (LULC) changes over three decades in the rapidly urbanizing city of Abha, Saudi Arabia, and to assess urban growth using Morphological Spatial Pattern Analysis (MSPA).

Using the Support Vector Machine (SVM) classification in Google Earth Engine, changes in land use in Abha between 1990 and 2020 are accurately assessed. This method leverages cloud computing to enhance the efficiency and accuracy of big data analysis. Additionally, MSPA was employed in Google Colab to analyse urban growth patterns.

The study demonstrates significant expansion of urban areas in Abha, growing from 62.46 km² in 1990 to 271.45 km² in 2020, while aquatic habitats decreased from 1.36 km² to 0.52 km². MSPA revealed a notable increase in urban core areas from 41.66 km² in 2001 to 194.97 km² in 2021, showcasing the nuanced dynamics of urban sprawl and densification.

The novelty of this study lies in its integrated approach, combining LULC and MSPA analyses within a cloud computing framework to capture the dynamics of city and environment. The insights from this study are poised to influence policy and planning decisions, particularly in fostering sustainable urban environments that accommodate growth while preserving natural habitats. This approach is crucial for devising strategies that can adapt to and mitigate the environmental impacts of urban expansion.

Due to the continuous and rapid evolution of telecommunication equipment, the demand for more efficient and noise-robust detection of dual-tone multi-frequency (DTMF) signals is most significant.

A novel machine learning-based approach to detect DTMF tones affected by noise, frequency and time variations by employing the k-nearest neighbour (KNN) algorithm is proposed. The features required for training the proposed KNN classifier are extracted using Goertzel's algorithm that estimates the absolute discrete Fourier transform (DFT) coefficient values for the fundamental DTMF frequencies with or without considering their second harmonic frequencies. The proposed KNN classifier model is configured in four different manners which differ in being trained with or without augmented data, as well as, with or without the inclusion of second harmonic frequency DFT coefficient values as features.

It is found that the model which is trained using the augmented data set and additionally includes the absolute DFT values of the second harmonic frequency values for the eight fundamental DTMF frequencies as the features, achieved the best performance with a macro classification F1 score of 0.980835, a five-fold stratified cross-validation accuracy of 98.47% and test data set detection accuracy of 98.1053%.

The generated DTMF signal has been classified and detected using the proposed KNN classifier which utilizes the DFT coefficient along with second harmonic frequencies for better classification. Additionally, the proposed KNN classifier has been compared with existing models to ascertain its superiority and proclaim its state-of-the-art performance.

This paper explores the indigenous climate knowledge (ICK) of the Gwadar fishing community in Pakistan. The main purpose of this paper is to explore the accuracy of ICK and how climatic change brings changes to it and the social lives of local fishers.

Qualitative research methods, including participant observation, in-depth interviews and oral histories, were used to collect the data.

Finding from this long fieldwork shows that this fishing community has a harmonious relationship with nature and local ecology. Their knowledge of local ecology enables them to have equal access to natural resources, sustainable resource management, disaster risk reduction and strong social organization on the coast of Gwadar. Recently their deep relationship with local ecology and sociocultural organization has been disturbed due to huge climate changes caused by human manipulation of the environment. Their ability to foresee climatic events has been reduced. They are finding it impossible to estimate fish availability due to massive climate changes. Local communities are losing their traditional livelihoods and socioeconomic autonomy as a result of growing climate change. Climatic change adds to the existing poverty situation and increases political instability in the region.

The study suggests using the fishermen’s valuable indigenous knowledge of local ecology, climate and its ties to local traditions, culture and resource management for a scientific understanding of climate change and marine resource management in Gwadar, Pakistan.

This is an ethnographic study based on a long term field work. Fishing community is passing through catastrophic climatic changes in the region. This community has been ignored by both government and researchers to record their problems and bring them to academia and media. Therefore, this study will help them raise their voices.

Family business sustainability is a critical issue. This study considers if adopting a strategic entrepreneurship orientation can support the sustainability of a family business.

A qualitative approach is used, in which semi-structured interviews were conducted with twelve family business owners. Data collected during the interviews provides insights into understanding, practices, motivations, behaviours and attitudes relating to sustainability.

Although awareness of sustainability processes and procedures is found to be low, sustainability is important to the family business. However, sustainability is not managed or implemented systematically.

The paper presents a new model to describe the sustainability practices of family businesses. Adoption of strategic entrepreneurship is advocated as mechanism for improving sustainability. Practical and policy implications are suggested to enhance the effectiveness of sustainability initiatives in family business settings.

Firms embedded in networks of relations are theorized through Gnyawali and Madhavan’s (2001) (G&M) structural embeddedness model to gain competitive advantage from topological characteristics. Empirical studies to support their theory have never been executed in full. Our study provided a full empirical test of their model in a digital trading network to achieve a higher degree of certainty that those network structural characteristics can have a major impact on the degree to which certain firms lead to competitiveness in a digital trading network environment.

To examine how firms respond in competitive situations, we chose the hyper-active digital trading network, eBay as our empirical context. We used eBay auction data to analyze how the network characteristics of eBay resellers impact their competitive behaviors.

Our study found strong support for the G&M model of competitiveness. We offer explanations for where support was not as strong as the Gynawali and Madavan theory proposes.

Our research is limited by our chosen context and findings in support of part of G&M model. Future studies in other digital contexts are needed to enhance the modeling of network topologies and further study the impacts of network density and structural autonomy on competitive action.

Our study suggests that managers proceed cautiously in forming partnerships, weighing circumstances where the firm can find itself with increased information power and avoiding, to the greatest extent possible, situations where the playing field is roughly equal.

Theory-making in this domain has begun as well as initial empirical testing. Much more needs to be accomplished, though, before embeddedness modeling can be thought of as being well established.

The G& M Model of competitiveness is an SNA explanation of why some competitive units succeed and others do not. Our study is the first, full blown empirical analysis of the theory.

Small businesses are facing evolving environments, with a resulting need to shift their traditional approaches toward new business models (BMs). Many face difficulties within this transition process due to their specific resource constraints. Based on this, incremental changes to the BM – business model transition (BMT) – are proposed as comprising a suitable framework for entrepreneurial small businesses.

This study conducts a systematic literature review (SLR) to cover a broad range of relevant literature within a final sample of 89 articles. The SLR method was chosen to integrate research in a systematic, transparent and reproducible way. For qualitative analysis and framework derivation, the study draws on a thematic ontological analysis.

The broad search criteria, focusing on BM, incremental BM changes and small businesses, pave the way for a comprehensive overview of multiple research streams of BM concepts (e.g. digital and sustainable BM). The main contribution of this work is the resulting holistic BMT framework, comprising the main parts BM innovation, external antecedents (transition of environment, entrepreneurial ecosystem), internal antecedents (dynamic capabilities, entrepreneurial orientation, resilience, strategy) and output (firm performance).

The framework provides guidance for entrepreneurs and entrepreneurial managers to implement and complete BMT in small businesses. Furthermore, the presented paper sets a future research agenda focusing on small businesses structured according to the derived framework.

This study provides the first SLR of existing BM concepts with a small-business specific perspective on BMI and a focus on various incremental BM changes.