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Computing seepage discharge in earth dams involves inherent complexities and challenges that require the use of probabilistic algorithms to accurately capture their uncertain characteristics and identify optimal solutions. This study aims to investigate the impact of uncertainty in seepage flow estimation using a novel hybrid approach, combining the analysis of Laplacian equations with the probabilistic finite element method (PFEM) and a metaheuristic algorithm.

To achieve this purpose, a finite element-based FORTRAN program was developed to model the problem using the Galerkin finite element method, which was validated using laboratory findings. Subsequently, Monte Carlo loops were incorporated into each model, consisting of 2000 iterations and the probability distribution function and cumulative distribution function were computed for each sub-model. A total of 138 earth dams were analysed to investigate the influence of different characteristics on seepage, including variations in dam geometry, soil permeability and water levels (both downstream and upstream). Effective seepage flow (ESF), was introduced in both deterministic and probabilistic models.

The findings indicated that the downstream slope has a more significant impact on ESF than the upstream slope, with a difference of 1.29%. Additionally, the ratio of dam height to bottom width (H/B) directly affects the ESF, resulting in a 20% increase in ESF for every 16% increase in H/B.

This study introduces a novel hybrid approach for estimating seepage flow in earth dams by integrating the Unlike traditional deterministic models, which often overlook the inherent .PFEM with a metaheuristic algorithman advanced uncertainties in seepage characteristics, this research effectively captures these uncertainties throughprobabilistic framework.

This review investigates the effects of temporal aggregation and systematic sampling on time-series analysis, focusing on their influence on data accuracy, interpretability and statistical properties. The purpose of the study is to synthesise existing literature on the topic and offer insights into the trade-offs between these data reduction techniques.

The research methodology is based on an extensive review of theoretical and empirical studies covering univariate and multivariate time series models, focusing on unit roots, ARIMA, GARCH, cointegration properties and Granger Causality.

The key findings reveal that while temporal aggregation simplifies data by emphasising long-term trends, it can obscure short-term fluctuations, potentially leading to biases in analysis. Similarly, systematic sampling enhances computational efficiency but risks information loss, especially in non-stationary data, and may result in biased samples if sampling intervals coincide with data periodicity. The review highlights the complexities and trade-offs involved in applying these methods, particularly in fields like economic forecasting, climate modelling and financial analysis.

The originality and value of this study lie in its comprehensive synthesis of the impacts of these techniques across various time series properties. It underscores the importance of context-specific applications to preserve data integrity, offering recommendations for best practices in the use of temporal aggregation and systematic sampling in time-series analysis.

As the warehouse is increasingly viewed as a strategic component, retailers’ willingness to invest in automated warehouse systems (AWS) has increased. These investment decisions are influenced by well-known operational factors, but strategic factors, which have received limited attention in warehousing literature, also play a pivotal role. Addressing this gap, this study investigates how strategic factors influence AWS investment decisions in retail.

Based on a theoretical foundation of technology adoption, strategic intent, and automation strategy, an abductive multiple case study is conducted with eight purposefully selected retailers that had implemented or were in the process of implementing a large AWS.

The study ranks 10 competitive priorities and 21 AWS evaluation aspects and shows how the firm’s strategic intent and the AWS investment decisions can be connected via the formulation of a warehouse automation strategy. The findings reveal the content for such a strategy – including 7 categories and 17 considerations – related to, for example, technology innovativeness, efficiency versus adaptiveness, technology-supplier relationships, control and ownership, and risk exposure. The study empirically shows how manager characteristics and owner strategies influence retailers’ AWS investment decisions. Four strategic intent profiles are abductively developed: reliability and delivery service; profitable deliveries; scalable logistics for volume growth; and platform building for logistics services. The study also provides evidence of a reciprocal relationship between strategic intent and AWS investment decisions.

The study is conducted with a limited number of Swedish retailers, indicating a need for additional studies to test the findings across different contexts.

The study offers a framework for formulating a warehouse automation strategy. As a foundation for developing the framework, the study shares empirical insights from retailers in the forefront of AWS implementation.

The study contributes as a conversation changer by showing the importance of shifting from a tactical-operational focus to a strategic perspective on warehouse configuration in general and on AWS investment decisions in retail in particular.

This study aims to examine Africa’s environment, social and governance (ESG) research through a systematic literature review (SLR). The authors aim to identify and discuss influential aspects of ESG accounting in Africa, focusing on prominent themes, authors and journals in published articles using Africa’s setting. It also constructs agendas for future research to advance the literature and contribute to the ESG accounting practices in Africa.

This study uses an SLR approach, where accounting research journal articles are collated and compiled according to pre-determined criteria and analysed using bibliometric techniques. After carefully reviewing 1,387 articles, the authors selected and examined 246 academic articles published from 2006 to 2024 in 32 accounting journals indexed in the Web of Science.

The authors identify four main streams of ESG accounting research in Africa, namely, ESG disclosure in primary-based economies; corporate governance dynamics in Africa; internal mechanisms in ESG reporting; and external mechanisms in ESG disclosure. According to the analysis, the authors propose future research agendas to discuss institutional perspective of ESG reporting standards implementation and enforcement; value creation impact on sustainability performance; ESG reporting effect on conflict resolution; and ESG reporting quality and environmental sustainability.

This study assists policymakers, academics, managers, accounting professionals and investors in comprehensively understanding the current state and projecting future actions to develop ESG accounting in Africa.

To the best of the authors’ knowledge, this study is perhaps the first to examine Africa’s ESG research through an SLR. This study contributes to the body of knowledge by providing a comprehensive analysis of the existing ESG accounting landscape and tailoring future research agendas based on the distinctive characteristics of Africa.

This study aims to develop a maturity model to assess manufacturing companies’ adoption of digital technologies for energy efficiency and to formulate strategies to facilitate progress toward higher maturity levels. To achieve this goal, the study will identify and analyze the challenges inherent in the adoption and implementation of digital technologies for energy efficiency.

This study used a mixed methodology, combining analysis of the literature for building a maturity model and a questionnaire for validating the model and developing strategies for advancing maturity. The questionnaire was answered by 101 Swedish manufacturing companies.

The findings reveal that the aluminum industries and iron and steel industries exhibit higher maturity levels in adopting digital technologies. Most companies are intermediate adopters utilizing core technologies such as the Internet of things, cloud and big data for energy use monitoring, analysis and reporting. A smaller subset of companies, identified as leading adopters, reached the highest maturity level, integrating artificial intelligence, predictive analytics and machine learning into their energy management systems to optimize both production and energy use. A key challenge identified is the “lack of knowledge” regarding the adoption and implementation of these technologies.

It is essential to emphasize that the developed maturity model does not prioritize the adoption of multiple types of digital technologies. From a maturity standpoint, what truly matters is how effectively the information obtained from digital technologies is utilized in energy efficiency and energy management work to create knowledge and, thus, add value to the organization.

The maturity model and the strategies for advancing maturity related to the adoption of digital technology for energy efficiency are designed to be applicable to all types of manufacturing industries regardless of what sector or country the company is active in. The model can also be used by academia or other actors interested in evaluating the maturity level for the adoption of digital technologies for energy efficiency in companies in the manufacturing industry. The developed strategies offer guidance on determining which activities to undertake within the organization based on its current level of maturity.

This study’s main contributions are: (1) the maturity model to assess digital technology adoption for energy efficiency, (2) a set of strategies to advance maturity in adoption and (3) empirical investigation of maturity levels in the adoption of digital technologies for energy efficiency in 101 Swedish manufacturing companies.

The circular economy (CE) has been endorsed as representing a model that is able to achieve environmental protection through decreased use of raw materials, together with changing economic values and social inclusion thanks to its demand for a wide variety of skill profiles. This has motivated many policy initiatives to support the implementation of the CE. The purpose of this study is to follow such policy initiatives in three geographically anchored industry-specific networks.

The study contributes to the research debate on the CE through a spatial approach with a focus on how the implementation of the CE is conditioned by spatial and regional contexts. The authors investigate three different networks in Sweden for CE with different locations and industrial profiles.

The findings reveal the difficulty that exist in relation to the implementation of the CE. The network and support functions in combination with private industry are vital. The risk of sustaining an uneven regional economic development is evident.

Although research on the development of the CE has proliferated, geographical approaches to this development are comparably rare to date. The authors seek to contextualise the strategy development and policy implementation of a CE policy.

The article aims to measure the effect of innovation obstacles on the probability of patenting by using an integrative framework that considers a variety of financial and non-financial obstacles and emphasizes the role of firm ownership in moderating the effect of obstacles on innovation.

The empirical analysis exploits a sample of 6,772 European manufacturing firms from the period 2007–2011, integrating data from the EFIGE survey with the Orbis Intellectual Property (Orbis IP) dataset from Bureau van Dijk. In terms of methodology, we carry out the analysis by applying a logistic regression with standard errors clustered at country level.

Results indicate that financial and non-financial obstacles negatively impact innovation. We also find that family firms have a lower probability of applying for a patent compared to non-family firms. Moreover, when considering the effect of each obstacle, the evidence varies depending on the firm’s ownership. The negative effect is more pronounced for non-FFs regarding market and regulation obstacles and organizational rigidities. However, it is weakly significant for lack of qualified personnel and information on technology. The perception of economic risks and the lack of suitable financial resources consistently impact businesses, regardless of ownership.

We contribute to the existing literature in a twofold way. First, by considering firm patenting activity as an innovation profile, we provide new insights into the obstacles that hinder the patent process, while previous research instead focused on product and process innovation or broader innovation activities. Second, we evaluate whether the influence of innovation obstacles is dependent upon the firm’s ownership structure. No previous research has provided evidence on this issue.

This paper aims to further our understanding of policy–practice gaps in organizations from an organizational learning perspective. The authors conceptualize and analyze policy–practice gaps in terms of what they label the dual challenge of organizational learning, i.e. the organizational tasks of both adapting ongoing practices to prescribed policy demands and adapting the policy itself to the needs of practice. Specifically, the authors address how this dual challenge can be understood in terms of organizational learning and how an organization can be managed to successfully resolve the dual learning challenge and, thereby, bridge policy–practice gaps in organizations.

This paper draws on existing literature to explore the gap between policy and practice. Through a synthesis of theories and an illustrative practical example, this paper highlights key conceptual underpinnings.

In the analysis of the dual challenge of organizational learning, this study provides a conceptual framework that emphasizes the important role of tensions and contradictions between policy and practice and their role as drivers of organizational learning. To bridge policy–practice gaps in organizations, this paper proposes five key principles that aim to resolve the dual challenge and accommodate both deployment and discovery in organizations.

Because this is a conceptual study, empirical research is called for to explore further and test the findings and conclusions of the study. Several avenues of possible future research are proposed.

This paper primarily contributes by introducing and elaborating on a conceptual framework that offers novel perspectives on the dual challenges of facilitating both discovery and deployment processes within organizations.

To avoid indecisiveness and a lack of direction in making a successful career transition, it may be necessary to modify the career transition process through career mentorship (CM). The study aims to investigate career adaptability as pathways through which CM is related to trainees’ career transition.

A three times cross-sectional data were collected from 480 national industrial skills development program (NISDP) trainees among five industrial training fund (ITF) trainees in Southeastern Nigeria.

The analysis revealed that CM positively associated with the trainees’ career transition behaviors. CM is also positively associated with the trainees’ career adaptability. Also, career adaptability is positively associated with trainees’ career transition behaviors. Likewise, career adaptability mediated the link between CM and career transition behaviors.

Conducting the study in a previously neglected context extended our understanding of the indirect relationship between CM and trainees’ career transition behavior.

This paper analyses Rwandan farmers’ perceptions of historical drivers of landscape vulnerability (past), current livelihood assets (present) and existing or potential capacities (future) to increase resilience to drought. The specific focus is on linking experiences from the past and present with ideas for a drought-resilient future. It explores how farmers' perceptions of past droughts and future visioning can contribute to rural development policy and multi-level collaborations.

This study was conducted in Bugesera, a drought-prone district in south-eastern Rwanda. Empirical data was collected through participatory observation, semi-structured interviews and focus groups. The analytical points of departure are based on sustainable landscapes and livelihood approaches, combining spatial and temporal perspectives on challenges and opportunities identified by farmers’ communities in addressing droughts.

All respondents had a high awareness of the impact of droughts. Perceived drivers of landscape change include historical climate events, such as droughts and floods, immigration and agricultural expansion, which have led to demographic pressure on land, deforestation and infringement on natural resources. Factors enhancing resilience capacities include access to diversified sources of livelihood, knowledge of appropriate irrigation techniques and availability of safety nets and credits. Furthermore, farmers identified collaborative opportunities as important for resilience capacity, including peer learning, and sharing best practices through knowledge exchange and on-field training. In addition, farmers brought up the need for innovative institutions that can facilitate access to markets and enable collaboration between different agricultural sectors.

This study analyses farmers’ perceptions of resilience capacities to droughts through a spatiotemporal lens of past droughts, present capital and future challenges by linking scales, knowledge and human–environment nexus. This paper contributes to the knowledge of climate adaptation in Rwanda and to discussions about smallholder farming in the literature on climate change adaptation.