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This study aims to present a new rapid enhancement digital elevation model (DEM) framework using Google Earth Engine (GEE), machine learning, weighted interpolation and spatial interpolation techniques with ground control points (GCPs), where high-resolution DEMs are crucial spatial data that find extensive use in many analyses and applications.

First, rapid-DEM imports Shuttle Radar Topography Mission (SRTM) data and Sentinel-2 multispectral imagery from a user-defined time and area of interest into GEE. Second, SRTM with the feature attributes from Sentinel-2 multispectral imagery is generated and used as input data in support vector machine classification algorithm. Third, the inverse probability weighted interpolation (IPWI) approach uses 12 fixed GCPs as additional input data to assign the probability to each pixel of the image and generate corrected SRTM elevations. Fourth, gridding the enhanced DEM consists of regular points (E, N and H), and the contour interval is 5 m. Finally, densification of enhanced DEM data with GCPs is obtained using global positioning system technique through spatial interpolations such as Kriging, inverse distance weighted, modified Shepard’s method and triangulation with linear interpolation techniques.

The results were compared to a 1-m vertically accurate reference DEM (RD) obtained by image matching with Worldview-1 stereo satellite images. The results of this study demonstrated that the root mean square error (RMSE) of the original SRTM DEM was 5.95 m. On the other hand, the RMSE of the estimated elevations by the IPWI approach has been improved to 2.01 m, and the generated DEM by Kriging technique was 1.85 m, with a reduction of 68.91%.

A comparison with the RD demonstrates significant SRTM improvements. The suggested method clearly reduces the elevation error of the original SRTM DEM.

We examined the effect of management practices on overt and subtle forms of discrimination in remote working contexts. Management practices (i.e. diversity and equality management systems) may influence employees’ perception of the diversity climate and affect the occurrence of discrimination.

To empirically investigate these associations, we administrated an online questionnaire with self-report measures via a 3-wave longitudinal research design.

Data analysis of our sample of N = 153 remotely working employees show that when managers invest in equality and diversity practices, employees perceive their workplace as more inclusive (i.e. diversity climate). In turn, this reduces the occurrence of subtle discriminations. Conversely, this relationship was not significant for overt forms of discrimination.

This result indicates that creating a diversity climate is especially important when combatting subtle forms of discrimination in remote work contexts. We discuss the theoretical and practical implications of our findings in light of managerial studies on discrimination at work and psychological literature on virtual environment and social networking.

This paper develops a neural network surrogate model based on a discrete lattice approach to investigate the influence of complex microstructures on the emergent behavior of biological networks.

The adaptability of network-forming organisms, such as, slime molds, relies on fluid-to-solid state transitions and dynamic behaviors at the level of the discrete microstructure, which continuum modeling methods struggle to capture effectively. To address this challenge, we present an optimized approach that combines lattice spring modeling with machine learning to capture dynamic behavior and develop nonlinear constitutive relationships.

This integrated approach allows us to predict the dynamic response of biological materials with heterogeneous microstructures, overcoming the limitations of conventional trial-and-error lattice design. The study investigates the microstructural behavior of biological materials using a neural network-based surrogate model. The results indicate that our surrogate model is effective in capturing the behavior of discrete lattice microstructures in biological materials.

The combination of numerical simulations and machine learning endows simulations of the slime mold Physarum polycephalum with a more accurate description of its emergent behavior and offers a pathway for the development of more effective lattice structures across a wide range of applications.

The novelty of this research lies in integrating lattice spring modeling and machine learning to explore the dynamic behavior of biological materials. This combined approach surpasses conventional methods, providing a more holistic and accurate representation of emergent behaviors in organisms.

This paper aims to examine various indicators related to corruption and determine their impact on financial globalization in emerging countries. It will consider other factors that may impact financial globalization and focus on how corruption within political, executive and public sector institutions can affect this process.

This paper uses a generalized method of moments (GMM) for a data sample of emerging countries covering 2000–2020. Corruption measurements are derived from the varieties of democracy data sets and Transparency International. It also includes data on foreign direct investment, portfolio flows, foreign exchange and international debt as separate indicators of financial globalization. These measures provide more detailed information on the types of financial transactions occurring across countries.

The results reveal that foreign investors may be less likely to enter certain sectors of the economy due to concerns about unethical practices and difficulties navigating the regulatory landscape in countries with high levels of corruption. This can lead to underdevelopment in sectors that are attractive to foreign investment and a reliance on a narrow range of sectors.

This paper offers valuable insights by integrating corruption and financial globalization indicators, using the GMM for robust analysis. It highlights how corruption influences foreign investment decisions, potentially leading to sectoral underdevelopment and overreliance in emerging countries.

This paper aims to clarify the relationship between foreign direct investment (FDI), democracy and carbon intensity. This study examines the influence of types of democracy on the relationship between inward FDI and carbon intensity. For this purpose, it uses five varieties of democracy, including a composite democracy indicator as moderating variables.

This study applies the fixed-effects panel quantile regression approach that considers unobserved heterogeneity and distributional heterogeneity using panel data from 160 countries during 1990–2020. By taking into account sudden changes in the volume of inward FDI, an event study is conducted across various sub-samples of democracy to check the robustness of the results.

The results show that FDI has a significantly negative impact on carbon intensity of the host country in the upper quantiles. In general, different types of democracy have a significant positive impact on carbon intensity across different quantiles. After considering the other factors, including industry intensity, trade openness, green technology, fossil fuel dependency and International Environmental Agreements, there is evidence that all types of democracy moderate the relationship between FDI and carbon intensity, thereby supporting the halo effect hypothesis. In addition, the interaction effects have a significant negative impact on carbon intensity of low- and high-carbon-intensive countries.

This paper offers several contributions to the literature on the effect of FDI and democracy on carbon intensity. This study overcomes the limitations related to the conceptualization and measurement of democracy found in the literature. While prior research has predominately concentrated on how democracy promotes the selection of FDI host-country locations, this study seeks to answer the question of whether democracy type has any effect on inward FDI, thus contributing to improving carbon intensity. Furthermore, this paper analyses the interaction effect on carbon intensity in different countries with different carbon intensity levels separately.

Knowledge production in project studies is continuously challenged to combine scientific rigour and practical relevance, and a professional graduate programme in Project Management is a suitable environment for addressing this issue. This research aims to generate a framework of the Ecosystem of a University Research Centre in Project Studies (URC-PS) to enhance the benefits of research developed in a professional graduate programme.

The research was developed under the paradigm of Design Science and operationalised through a method of Design Science Research. The prescriptive approach was used to identify, design, develop, demonstrate, and evaluate the framework.

The framework comprises four macro-elements: Project Studies; Impact Generation Process; Circumstances, Governance and Management; and Context, broken into sixty elements. It provides a structure that is simultaneously holistic, integrative, and procedural. It also develops a perspective of knowledge co-creation between academics and practitioners in an engaged scholarship approach.

The framework provides a more thorough understanding of the ecosystem university management to the research centre itself, to engaged academics, and to external actors, which allows them to discuss, plan, execute, and evaluate the co-creation of knowledge in Project Studies.

The framework contributes to Organisational Knowledge Creation Theory by including and discussing outcomes and impacts from co-created knowledge in a URC-Project Studies environment. It also explores the concept of “Ba” in its proposal for structuring, organising, and operationalising the “Ba”.

This paper describes a model of “Learning to care” derived from a study exploring how care workers in care homes learn to care for people living with dementia. The “Learning to care” model is primarily informal in nature in which influences such as formalised training and organisational culture impact care outcomes indirectly rather than directly.

This study used a focused, critical ethnographic approach in two care homes in England resulting in 63 h of observation of care of people living with advanced dementia, 15 semi-structured interviews and 90 in-situ ethnographic interviews with care staff.

The findings reveal a three-level model of learning to care. At the level of day-to-day interactions is a mechanism for learning that is wholly informal and follows the maxim “What Works is What Matters”. Workers draw on resources and information within this process derived from their personal experiences, resident influences and care home cultural knowledge. Cultural knowledge is created through a worker’s interactions with colleagues and the training they receive, meaning that these organisational level influences affect care practice only indirectly via the “What Works is What Matters” mechanism.

This study makes an original contribution by explaining the nature of day-to-day informal learning processes as experienced by care workers and those living with dementia in care homes. In particular, it illuminates the specific mechanisms by which organisational culture has an effect on care practice and the limitations of formal training in influencing such practice.

Social enterprises live in the limbo between social and market objectives, posing many operations management challenges. This study extends the discussion of operational priorities, which has focused on purely for-profit organisations, to the context of social enterprises by exploring, from a resource-based perspective, which resources and operational priorities are most important to them and how they are used to respond to conflicting demands.

Multiple case study research was carried out involving five Portuguese social enterprises, representative of the main sectors in which social enterprises operate in Europe. Ten semi-structured interviews with directors and other high-ranking respondents were conducted, and content was analysed to gather evidence on the key resources and operational priorities pursued by social enterprises. Cross-case conclusions were drawn, resulting in theoretical propositions and a conceptual framework.

Findings suggest that social enterprises rely on intangible resources and combine different operational priorities, which may vary throughout their lifecycle. Community engagement has emerged as a specific operational priority, in addition to those already reported in the manufacturing and services literature. To balance conflicting demands, most social enterprises studied combine innovation with community engagement or customer focus.

The study contributes to the development of knowledge about the operations strategy in the specific context of social enterprises, an organisational model that has not been systematically addressed in the operations management literature, and brings the discussion of operational priorities into the social enterprise field, thus strengthening the link between these two fields.

This paper aims to explore explainable artificial intelligence (XAI) in democracy, proposing an applicable framework. With artificial intelligence’s (AI) increasing use in democracies, the demand for transparency and accountability in AI decision-making is recognized. XAI addresses AI “black boxes” by enhancing model transparency.

This study includes a thorough literature review of XAI. The methodology chosen was design science research to enable design theory and problem identification about XAI’s state of the art. Thereby finding and gathering crucial information to build a framework that aims to help solve issues and gaps where XAI can be of major influence in the service of democracy.

This framework has four main steps to be applied in the service of democracy by applying the different possible XAI techniques that may help mitigate existing challenges and risks for the democratic system. The proposed artifact intends to display and include all the necessary steps to select the most suitable XAI technology. Examples were given for every step of the artifact to provide a clear understanding of what was being proposed.

An evaluation of the proposed framework was made through interviews with specialists from different areas related to the topics in the study. The interviews were important for measuring the framework’s validity and originality.

Given the sudden increase in plant investments across the manufacturing sector due to recent disruptions in the global supply chain caused by the COVID pandemic, the Ukraine/Russian war, ocean piracy incidents, and others, there is a need to strategically align the investments decisions with the overall business strategy. This study aims to establish whether strategic alignment enhances the effect of plant investments on operational performance.

Using the fifth version of the Global Manufacturing Research Group (GMRG V) dataset, we employ a structural equation model (SEM) to establish the moderating role of strategic alignment in plant investments-operational performance relationships.

The results suggest that strategic alignment enhances the positive effect of plant investments on operational performance, especially in cost, delivery, flexibility, and innovation. However, we found partial support for the moderating role of strategic alignment on quality performance.

Although the study was based on the GMRG global dataset and numerous papers have been published using the same dataset, the use of a convenience sample on a select group of companies may limit the generalizability of the results. With the increase in new plant investments in computer chip manufacturing, electronics, and electrical in recent years, the findings could help corporate executives maximize the benefits of their investment decisions. Companies should ensure that their plant investment decisions are strategically aligned with their overall business strategy to achieve optimal outcomes.

With the increase in new plant investments in computer chip manufacturing, electronics, and electrical in recent years, the findings could help corporate executives maximize the benefits of their investment decisions. Companies should ensure that their plant investment decisions are strategically aligned with their overall business strategy to achieve optimal outcomes.

The study comprehensively demonstrates that the extent of improvement in operational performance depends on how closely plant investment decisions align with the overall company strategy. Manufacturers should align major investment decisions with competitive priorities driven by market requirements to enhance operational performance.