West Africa faces devastating flood hazards that affect more than 400 million people. Yet flood risk assessment is hindered by sparse and often unreliable hydrological data. Regional flood frequency analysis (RFFA) is widely used to estimate design values at ungauged catchments and there is a need for a systematic intercomparison of RFFA approaches in this region. With an unprecedented dataset of 211 near-natural catchments, we compared a Direct Regression Approach (DRA) and three homogeneous region delineation methods using the index-flood methods based on spatial proximity, Principal Component Analysis (PCA), and Canonical Correlation Analysis (CCA) with catchment attributes. Each regional approach was paired with two regression models: (i) Stepwise Regression and (ii) Least Absolute Shrinkage and Selection Operator (LASSO), and four machine learning algorithms: (i) Random Forest (RF), (ii) eXtreme Gradient Boosting (XGB), (iii) Support Vector Regression (SVR), and (iv) a hybrid linear-tree ensemble (LinRF). Results show that index-flood methods consistently outperformed DRA, with the CCA-based framework achieving the highest accuracy. CCA-SVR combination is the best-performing regional model, yielding the lowest estimation errors (mean absolute relative error = 0.21 and relative bias = −0.03) for 20- or 50-year flood quantiles. Feature importance analysis revealed that subsurface properties, catchment area, and topographic attributes have stronger influence on regional flood estimation than surface features or land use patterns. The methodology and findings of this study offer practical tools for infrastructure design and climate adaptation, supporting more resilient flood risk management across vulnerable West African communities.
This study examines the role of business incubators in supporting start-ups in emerging markets, where resource limitations require entrepreneurs to either rely on entrepreneurial bricolage or invest in developing dynamic capabilities in their early stages. Although prior research has explored incubators, less attention has been given to how they help start-ups move beyond static short-term bricolage toward building long-term dynamic capabilities under resource-constrained conditions. Using data from 403 start-ups, the study tests the moderating effect of incubator support on the relationship between bricolage, dynamic capabilities, and venture performance. The findings suggest that incubators significantly strengthen the positive impact of dynamic capabilities on start-up performance compared to that of bricolage. In emerging markets, incubators thus enable start-ups to cultivate adaptive, growth-oriented capabilities rather than relying solely on static bricolage practices. The study offers implications for policymakers, founders, and incubator managers seeking to promote sustainable start-up development and scaling.
Successful management of invasive plants often requires broad societal buy-in. Hence, to facilitate management, it is important to understand what influences and drives perceptions of those involved or affected by invasive plants’ distribution, management, and impacts. In this paper, we focus on a sector central to this space—the ornamental industry—and on a geographic region that is highly affected by plant invasions but remains under-researched-Southern Africa. Using various techniques (semi-structured interviews, workshops, informal conversations, and questionnaires), we assessed key stakeholders' understanding of plant invasions and perceptions of invasive ornamental plants, collating data from 78 environmental specialists, 38 ornamental industry staff, and 72 ornamental gardeners. This data collection included participants from Botswana (104), Namibia (50), Zimbabwe (18), South Africa (13), and Zambia (3). We found that, across these groups, there is a broadly similar understanding of invasions consistent with definitions used in research and policymaking. People are often aware of ecological processes but do not necessarily use “scientific” terms to describe them. However, both the different groups and individuals within the groups differed in how they perceived specific plants. We argue that these differences are shaped by people’s interests, professions, and socioeconomic and cultural backgrounds, that are, in turn, shaped by broader socio-ecological and geopolitical dynamics. Such differences in perceptions can, of course, result in conflicts of interest, particularly when varying perceptions are informing conflicting actions. We believe that stakeholder relations would benefit from open, relational, balanced, and regular communication with a view to reaching agreements. Such communication should value and recognise the diversity in capacities, perceptions and knowledge as a step to removing power imbalances. This is particularly relevant in the Southern Africa context where successful invasive species management requires acknowledging and addressing racial and classist segregation that shape mutual perceptions and relations with invasive plants and landscapes.
PurposePrior Big Data and fraud research emphasises technical detection or generic analytics, offering limited empirical insight into how Big Data is operationalised as practices and controls in e-retail fraud prevention. This study examines how e-retail firms translate analytics capabilities into fraud prevention practices and controls.Design/methodology/approachThe study draws on 32 semi-structured interviews across 18 e-retail organisations and applies abductive thematic analysis to examine how Big Data resources are translated into fraud prevention practices and controls.FindingsFindings reveal that Big Data enables fraud prevention not merely through detection technologies, but through two empirically derived transformation practices: (1) Localised Exploitation, which translates complex analytics into simplified, role-specific reporting routines; and (2) Personalised Care Practice, which develops and cross-references business-unit-specific fraud risk profiles to identify emerging threats. These practices generate data-driven fraud controls by strengthening technical safeguards, refining formal policies, streamlining fraud reporting and reinforcing behavioural controls through analytics-informed training. Their effectiveness, however, is constrained by regulatory complexity, limited inter-organisational knowledge sharing and reliance on outsourced analytics providers.Originality/valueThis study makes three key empirical contributions. First, it moves beyond conceptual Big Data research by showing how analytics capabilities are enacted as organisational practices in e-retail fraud prevention. Second, it advances theory by integrating the Practice-Based View and the Balanced Control Paradigm into an empirically grounded Integrated Big Data-Enabled Fraud Prevention (IBDEFP) model. Third, it provides actionable insights into designing analytics, controls and human engagement to strengthen adaptive fraud management in volatile digital environments.
This study investigates the relationship between financial constraints and a firm's sustainability performance. Our empirical analysis utilises a panel of 40,445 observations from 9466 listed non-financial firms across 44 countries, spanning the period from 2002 to 2019. We provide strong evidence that financial constraints significantly hinder a firm's corporate sustainability performance. Further analyses show that a firm's climate exposure affects the negative impact of financial constraints on sustainability performance. In other words, firms exposed to climate change tend to show greater commitment to sustainability practices regardless of their financial constraints. However, we find no evidence that external and public attention to climate change issues persuades financially constrained firms to enhance their sustainability performance. Our study offers new insights into the link between financial constraints and corporate sustainability, as well as the implications of climate exposure and public attention to climate change.
