Resource Planning Research Articles

Project management and partner banking as tools for the recovery concept in Ukraine

The main purpose of this study is to investigate the effect of project management and partnership banking. Due to the shortage of investment, Ukraine always needs to plan for financial resources. All over the world, partnership banking is becoming an increasingly common and attractive type of investment in projects. In the conditions of modern Ukraine, with the need to restore industrial and infrastructure construction projects, one can predict significant growth in the construction services market and the need for investment. In this study, the ways of possible increasing investment in the construction industry have been discussed based on the use of a logical-structural approach in project management and partnership banking as a source of investment.

Evaluation and Prediction of Water Resources Carrying Capacity in Dongting Lake Area Based on County Unit and DWM-BP Neural Network Model

Evaluation of water resource carrying capacity is one of the methods used to measure the degree of realization of resource and environmental planning. This study takes 26 county-level units in the Dongting Lake area of Hunan Province as the evaluation object and screens 22 indicators for the evaluation of water resource carrying capacity. The comprehensive weight of the water resources carrying capacity of the Dongting Lake area is calculated using double weight methods (DWM) based on the analytical hierarchy process (AHP) and the entropy weighting method. And then, the back propagation (BP) neural network is used to predict the evolution of the alarm situation of the water resources carrying capacity. The results show the following: (1) In terms of time trend, the water resources carrying capacity of Dongting Lake area is generally stable and is between overload and critical state, but the change trends of water resources carrying capacity of each county are very different; (2) in terms of spatial distribution, the water resources carrying capacity in the western and southern regions of the Dongting Lake area is relatively high, while that in the Yueyang urban area in the eastern region is relatively low, showing a pattern of “high in the west and low in the east”; (3) among the criterion layers, the water resources support force has the highest weight and the smallest change, and the economic support force has the largest change in weight. The decline of water resources carrying capacity is closely related to the speed of economic development; (4) by 2025, the water resources carrying capacity of Nanxian County, Hanshou County, and Xiangyin County will be significantly increased from overload to critical state, and Dingcheng District will gradually turn from critical to overloaded status. This study is the first attempt to evaluate and predict the water resources carrying capacity of the Dongting Lake area at the county level so that the evaluation results are more applicable to grass-roots administrative units with decision-making power. The double-weighting method is used to determine the weights of the indicators so that the evaluation results can be more accurate.

Data-driven forecasting framework for daily reservoir inflow time series considering the flood peaks based on multi-head attention mechanism

Accurate and reliable daily reservoir inflow forecast plays an essential role in several applications involving the management and planning of water resources, such as hydroelectric generation, flood control, water supply, and basin ecological dispatching. Runoff usually exhibits strong non-linearity, high uncertainty, and spatial and temporal variability. Existing techniques fail to capture complete dynamics change processes effectively. A data-driven forecasting framework for daily reservoir inflow time series considering the flood peaks based on a multi-head attention mechanism was developed, referred to as the GWOCS-VMD-CNN-Transformer (GCVCT). First, the model utilize Grey Wolf Optimizer coupled with Cuckoo Search (GWO-CS) algorithms to optimize parameters in variational mode decomposition model (VMD). This approach helps obtain highly correlated intrinsic mode function (IMF) components, enhancing the frequency resolution of the input dataset. The proposed method overcomes the bottleneck of other available methods by decomposing the time series to capture the main long-term and short-term properties of hydrological processes. Second, the convolution neural network and Transformer (CNN-Transformer) are based on a multi-head attention mechanism as the objective predictive method. Finally, six evaluation indicators verify the performance of the proposed approach. The approach’s reliability was evaluated using the historical daily reservoir inflow data from the Xiluodu (XLD) and Wudongde (WDD) reservoirs in the Jinsha River Basin, China. Several single and hybrid models were developed for comparative analysis. The results indicate that the proposed ensemble approach fits better than other developed model methods. The GCVCT model showed excellent performance in forecasting the inflows of XLD and WDD reservoirs, with NSE values of 0.985 and 0.984, respectively. Furthermore, the GCVCT framework forecast capacity for peak inflow was further verified through discussion and analysis of the 48 peak flows during the validation period, consistently outperforming other models in predicting peak flow for both study reservoirs. This framework provides an effective method for the scientific optimal scheduling of hydropower reservoirs, enabling more sustainable and efficient management practices. It also demonstrates the potential of powerful deep-learning models in intelligent hydrological forecasting.

Assessment of reference evapotranspiration forecasting in the Mediterranean climate of Central Chile using the ASCE standardized Penman-Monteith equation, the Hargreaves-Samani equation, and weather predictions from the Global Forecast System model

Accurate estimation of crop evapotranspiration (ETc) is essential for effective agricultural water resource planning. To determine ETc it is common multiplying the reference evapotranspiration (ETref) by an appropriate crop coefficient (Kc). Forecasting ETref could be particularly beneficial for irrigation scheduling. In this work, daily and cumulative (7 days) ETref predictions were estimated with the Penman-Monteith (PM) and the Hargreaves-Samani (HS) models using environmental variables obtained from the Global Forecast System (GFS). Results were compared with ETref calculated with the ASCE (American Society of Civil Engineers) PM equation from measured daily values of solar radiation, air temperature, relative humidity and wind speed. Four weather stations located in Central Chile during 2020 to 2022 were used. The best results were obtained using the HS equation, for all weather stations, the statistical indicators for daily ETref predictions fall within the following ranges: Accuracy (defined as the percentage of daily ETref predictions with an absolute error within 1mmd−1) varies between 72.8% and 91.8%; the root mean square error (RMSE) ranges from 0.59mmd−1 to 0.98mmd−1; the normalized root mean square error (NRMSE) is between 16.1% and 24.1%; the mean bias error (MBE) is between 0.10mmd−1 and 0.36mmd−1; and the normalized mean bias error (NMBE) varies from 2.5% to 8.3%. On the other hand, the indicators for the 7-day cumulative ETref forecasts were: RMSE from 2.86mm to 5.08mm; NRMSE from 11.0% to 16.3%; MBE from 0.66mm to 2.57mm; and NMBE from 2.4% to 8.3%. These results suggest that HS ETref values predicted from GFS temperature forecasts could be effective for foreseeing near-future water demand and enhancing the accuracy of irrigation scheduling and management in Central Chile.

Evaluation of Individual Impacts of Human Activities on Streamflow Changes Using SWAT Model

Understanding streamflow behavior under the influence of climate change and human activities is crucial for developing adaptation strategies and policies for water resource planning and management. However, detecting natural or negligible impact periods is challenging. We aimed to distinguish human impacts on streamflow changes during the affected period using direct data for each human activity by using a hydrological modeling approach in the Gamcheon watershed in South Korea. To this end, a physical-based, semi-distributed hydrological model, the Soil and Water Assessment Tool (SWAT), was applied. The model was set up using climatic and spatial data on watershed characteristics and data on human activities, such as dam operation and water use. Several simulations of runoff, each considering only one human activity, were performed, and the results were compared with those of the naturalized streamflow to determine the individual impacts on changes in streamflow. The combined effects of human activities on streamflow changes were not significant; however, the individual impacts were evident. The 10th lowest flow rate in a year, affected by stream water intake, groundwater pumping, dam operation, and treated sewage water discharge, changed by −13.7%, −54.0%, 34.0%, and 38.4% relative to the natural flow, respectively.

Enhancing Customer Relationship Management in ERP Systems with AI: Personalized Interactions, Predictive Modeling, And Service Automation

This research paper delves into the integration of Artificial Intelligence (AI) in Customer Relationship Management (CRM) systems within Enterprise Resource Planning (ERP) frameworks, with a focus on personalized interactions, predictive modeling, and service automation. The primary objective was to empirically investigate how AI enhancements in CRM functionalities can contribute to more efficient and effective customer relationship management within ERP systems. A quantitative research design was adopted, utilizing a structured questionnaire distributed among CRM managers and IT professionals across various industries, with the data analyzed using the Statistical Package for the Social Sciences (SPSS). Key findings from the study reveal significant positive impacts of AI integration on CRM functionalities, notably in personalized customer interactions, predictive modeling accuracy, and service automation efficiency. These enhancements were found to contribute to improved customer satisfaction and operational efficiency, suggesting that AI - driven CRM strategies within ERP systems offer substantial benefits to organizations. The research fills a critical gap in existing literature by providing empirical evidence of the specific benefits of AI in CRM and underscores the strategic value of AI integration in enhancing CRM functionalities. The broader implications of this study highlight the transformative potential of AI in CRM, encouraging organizations to leverage AI technologies to gain a competitive edge in customer relationship management.

Nursing Services in Kahramanmaras Earthquakes in Turkiye: A Needs Analysis.

The injuries that occurred in earthquakes caused an accumulation in hospitals and the need for health services increased. The most needed human resource in the provision of health services in disasters is nurses. The aim of this study is to determine the scope of nursing services in earthquakes and to identify the service needs in hospitals during the February 6 earthquakes in Turkey. In this study, Delphi technique was used for needs analysis. The managers of health institutions in 11 provinces that experienced the earthquake were interviewed to determine how nursing services are carried out during earthquakes. As a result of this study, it was determined that there were inadequacies in triage, identification of earthquake victims, medical intervention and keeping records, identification of deceased earthquake victims, storage of personal belongings, communication with relatives of earthquake victims, and psychosocial support services in disasters such as earthquakes where many people were seriously injured. It has been observed that there is a need for disaster nurses and forensic nurses to work in these areas and it is thought that these 2 nursing fields should be taken into consideration in the planning of health professional resources in disasters.

Enhancing Salary Prediction Accuracy with Advanced Machine Learning Models

Abstract. Accurate salary prediction is crucial for navigating the complexities of the job market and ensuring fair compensation practices. This research focuses on evaluating advanced machine learning models to improve salary prediction accuracy. The study integrates demographic, educational, and professional experience data to offer a comprehensive analysis of earning potential, aiming to foster equitable job markets and enhance strategic human resource planning. The methodology involves employing various models, including Linear Regression, Decision Tree, and Random Forest (RF). Key steps include preprocessing the dataset to address missing values, categorize data, and remove irrelevant features. The study finds that the RF model excels in predicting salaries, surpassing other models in performance metrics. This superior efficacy is attributed to RFs ability to handle complex, high-dimensional data and mitigate overfitting. The results of this study have significant implications for establishing fairer compensation practices and improving job market efficiency. By offering a reliable tool for understanding earning potential, this research contributes to better career decisions and strategic planning for both individuals and organizations. Future research will explore further refinements and applications of these models in real-world scenarios.

Climate shapes baseflows, influencing drought severity

Abstract Baseflow, the sustained flow from groundwater, lakes, and snowmelt, is essential for maintaining surface water flow, particularly during droughts. Amid rising global water demands and climate change impacts, understanding baseflow dynamics is crucial for water resource management. This study offers new insights by assessing baseflow controls at finer temporal scales and examining their relationship with hydrological drought flows. We investigate how climatic factors influence seasonal baseflow in 7138 global catchments across five major climate regions. Our analysis identifies precipitation as the primary driver, affecting 58.3% of catchments, though its impact varies significantly across different climates. In temperate regions, precipitation dominates (61.9% of catchments), while in tropical regions, evaporative demand is the leading factor (47.3%). Snow fraction is particularly crucial in both snow-dominated (20.8%) and polar regions (48.5%). Negative baseflow trends generally emerge where the effects of evaporative demand or snow fraction outweigh those of precipitation. Specifically, in northern regions and the Rocky Mountains, where snow fraction predominantly controls baseflow changes, a negative trend is evident. Similarly, in tropical catchments, where evaporative demand drives baseflow changes, this also leads to a negative trend. Additionally, our findings indicate that baseflow changes are closely linked to hydrologic drought severity, with concurrent trends observed in 69% of catchments. These findings highlight the relationship between baseflow changes, the severity of hydrologic drought and shifts in precipitation, evaporative demand, and snow dynamics. This study provides crucial insights for sustainable water resource planning and climate change adaptation, emphasizing the importance of managing groundwater-fed river flows to mitigate drought impacts.

Accurate Paddy Rice Mapping Based on Phenology-Based Features and Object-Based Classification

Highly accurate rice cultivation distribution and area extraction are essential to food security. Moreover, Inner Mongolia, whose slogan is “from scientific rice to world rice”, is an essential national rice production base. However, high-quality rice mapping products at high resolutions are still scarce around the Inner Mongolia Autonomous Region. This condition is not conducive to rational planning of farmland resources, maintaining food security, and promoting sustainable growth of the local agricultural economy. In this study, the rice backscattering intensity difference index from the vertically polarized backscatter intensity of Sentinel-1 and the phenology differential index from the spectral indices of two critical rice phenological phases of Sentinel-2 images were constructed. Other spectral features, including spectral indices, tasseled cap, and texture features, were computed using simple non-iterative clustering (SNIC) to achieve image segmentation. These variables served as input features for the random forest (RF) algorithm. Results reveal that employing the RF with the SNIC segmentation algorithm and combining it with optical and synthetic aperture radar data is an effective way to extract data on rice in mid-latitude regions. The overall accuracy and kappa coefficient are 0.98 and 0.967, correspondingly. The accuracy for rice is 0.99, as proven by empirical data. These results meet the requirements of regional rice cultivation assessment and area monitoring. Furthermore, owing to its resilience against longitude-associated influences, the model discerns rice across diverse regions and multiple years, achieving an R2 of 0.99. This capability significantly bolsters efforts to improve regional food security and the pursuit of sustainable development.

Multispectral Assessment of Net Radiations Using Comprehensive Multi-Satellite Data

Precise estimation of net radiation (Rn) is fundamental to understanding surface energy balance and is critical for accurately determining crop water requirements, especially using remote sensing and geospatial techniques. The core objective of this study is to evaluate multi-satellite-based net radiations on major cropped areas of the Punjab and Sindh provinces of Pakistan. In this study, overlapping scenes from the Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat 8, and Sentinel 2 were used from 2016 to 2020 along with three temperature products MOD11A1, Landsat 8 (brightness temperature), and ERA5. The multi-satellite-based net radiation estimations on overlapping days were compared with the Global Land Data Assimilation System (GLDAS) dataset. The models based on Landsat 8 and Sentinel 2 data exhibited good performance, with a Nash–Sutcliffe Efficiency (NSE) of 68.9%, a mean error (ME) of 13.918 W/m2, and a bias of 50.669 W/m2. The results indicated that Landsat 8 and Sentinel 2 data produced reliable estimations of net radiation, while MODIS data tended to overestimate due to its higher spatial resolution and broader coverage area. Landsat 8-based estimations are good compared to others, as it has good correlation coefficient and lower RMSE values. The study concludes that Landsat 8 provides the most reliable estimates of net radiation for determining crop water requirements, outperforming other datasets in accuracy. The findings underscore the importance of using high-resolution multi-satellite data for precise agricultural water management, recommending its use in future studies and water resource planning in Pakistan.

Analysis of Planning for Health Workforce Needs at Lasinrang Pinrang Regional General Hospital

Hospital is a health service unit as a place where hospital personnel perform their main tasks and functions. Integration between hospitals and health workers is closely related in terms of health services. How the face of the hospital depends on the quality of its health workers. This research is descriptive research. Research location at the Health Service Office of Lasinrang Pinrang Regional General Hospital. The study was conducted from May 7, 2021 to July 7, 2021. The population in this study were all health workers at the Health Service Office of the Lasinrang Pinrang Regional General Hospital with a sample size of 22 respondents. The results of the study indicate that the Inventory of new health workforce resources is limited to the type and number, while others are not yet such as the skills and abilities of each existing health worker. The forecast of health workforce resources that has been carried out is only informal and immediate, and this is not a systematic planning effort. The suggestion in this research is that there needs to be team work or specialist planning staff or a section/department in the organizational structure. which deals with human resource planning and development and the need for the formulation of strategic plans to serve as a basis/reference for planning health workforce resources.

Enhancing Organizational Performance Through Integrated ERP-Based Balanced Scorecard Systems: A Case Study

Organizations must continuously thrive to remain successful in the highly competitive business landscape. An integral aspect of achieving this success is implementing an efficient performance management system (PMS) that regularly monitors organizational performance. Large companies often employ Enterprise Resource Planning (ERP) systems to integrate data and information across various departments and business units within the company group, providing easy, comprehensive, and structured data collection. However, despite the advantages of ERP systems, a considerable amount of valuable data goes to waste as it is not integrated with the company’s PMS. This study aims to develop a balanced scorecard-based performance management system that utilizes the existing ERP system in a furniture company. In-depth interviews with the board of directors and general managers were conducted to assess the need for the PMS, formulate a strategic map and scorecard, determine scorecard weights, and establish targets for each category. During the implementation phase, it was observed that the financial indicators consistently fell below the baseline values, signaling an urgent need for management intervention. However, indicators from three other perspectives demonstrated relatively better performance than those from the financial perspective. This discrepancy emphasized the need to revise the strategic map, as solid performance in the other perspectives should translate into positive financial indicators. The management of the company validated the initial implementation results of the performance management system by noting a concerning decline in the company’s liquidity. This study argues for the importance of integration between the ERP system and the BSC performance management system, where the implementation of the ERP system alone cannot indicate the critical conditions experienced by the company. Keywords: Enterprise Resource Planning (ERP), performance management systems, balanced scorecard, case study

Effects of public data open on performance of government functions in China: the roles of digital economy and market economic system adopting the perspective of resource- based view

IntroductionThe emergence of the digital economy necessitates effective government guidance and regulation of enterprise behavior to ensure fair market competition. Understanding how public data open can enhance the performance of government functions is crucial, particularly in understanding the roles of key factors like the development of the digital economy and the current state of the market economic system. This study explores the effects of public data open on performance of government functions from a resource-based perspective.MethodsUsing a dataset of 237 Chinese cities from 2017 to 2021, we employed a two-way fixed-effects model to investigate the mechanisms. This includes its interaction with the development of the digital economy and the moderating role of the market economic system.ResultsThe findings reveal that public data open significantly improves performance of government functions, as validated by a multi-temporal DID model and various robustness checks. Notably, the development of the digital economy mediates this relationship, while the market economic system has a strong positive moderating effect. The effects of public data open on performance of government functions also exhibits significant variability.DiscussionRecommendations include coordinating regional public data open resource planning and allocation, enhancing public data open rules and objectives by region, and promoting synergy between public data open, development of digital economy and the market economic system.

Disaggregation of rainfall from daily to 1-hour scale through integrated MMRC-copula modelling

Rainfall intensity is one of the most crucial meteorological parameters which is extensively used by water resource planners, hydrologists, irrigation experts, flood and draught regulatory authorities. Particularly, sub-daily temporal rainfall time series is quite essential for detailed planning of urban drainage design, storm water management. However, due to non-availability of reliable fine resolution rainfall data, under current scenario temporal disaggregation of existing rainfall record using various stochastic techniques is emerging as one of the most sought after option. In the present study, the Microcanonical Multiplicative Random Cascade (MMRC) model has been adopted for disaggregation of daily rainfall values to 1 h scale. Though MMRC is capable of generating statistically reliable rainfall time series, it is not competent enough to preserve the extreme rainfall characteristics.In this paper, a new model has been presented where copula theory has been integrated with MMRC model to capture the dependence structure between coarse time step rainfall and its corresponding finer time steps. The procedure of disaggregation of coarse resolution rainfall series has been accomplished by this new Integrated MMRC-copula model with higher accuracy as it accounts for the random splitting procedure of cascade generator more precisely compared to MMRC model which generally leads to overestimation of extreme rainfall. An overall improved performance of the Integrated MMRC-Copula model in contrast to MMRC supports the model’s pertinence in the field of temporal disaggregation of rainfall.

Stochastic economic sizing and placement of renewable integrated energy system with combined hydrogen and power technology in the active distribution network

The current study concentrates on the planning (sitting and sizing) of a renewable integrated energy system that incorporates power-to-hydrogen (P2H) and hydrogen-to-power (H2P) technologies within an active distribution network. This is expressed in the form of an optimization model, in which the objective function is to reduce the annual costs of construction and maintenance of integrated energy systems. The model takes into account the planning and operation model of wind, solar, and bio-waste resources, as well as hydrogen storage (a combination of P2H, H2P, and hydrogen tank), and the optimal power flow constraints of the distribution network. Electrical and hydrogen energy are administered in an integrated energy system. The modeling of the uncertainties regarding the quantity of load and renewable resources is achieved through stochastic optimization using the Unscented Transformation method. The novelties of the scheme include the sizing and placement of a combined hydrogen and power-based renewable integrated energy system, the consideration of the impacts of bio-waste units, P2H, and H2P systems on the planning of the integrated energy system and the operation of the active distribution network, and the modeling of uncertainties using the Unscented Transformation method to reduce the calculation time. The study’s results demonstrate the scheme’s ability to improve the technical conditions of the distribution network by considering the optimal planning of integrated energy systems. In comparison to the network power flow, the operation status of the network has been improved by approximately 23-45% through the optimal siting, sizing, and energy management of hydrogen storage equipment, as well as renewable resources in the form of integrated energy systems. In other words, optimal energy management and planning of the integrated energy systems in the distribution network has been able to reduce energy losses and voltage drop by 44.5% and 42.4% compared to the load flow studies. In this situation, peak load carrying capability has increased by about 23.7%. In addition, compared to the case of the network with renewable resources, the overvoltage has decreased by about 43.5%. Also, Unscented Transformation method has a lower calculation time than scenario-based stochastic optimization.

Strategic excellence and technological integration: Sustaining competitive advantage for private universities in Indonesia

The competition among private universities in Indonesia is intensifying, requiring institutions to establish a sustainable competitive advantage through the right strategies, strong vision and mission, and effective use of information technology. This study analyzes the influence of strategy, vision and mission, university rankings, and information technology on the sustainable competitive advantage of private universities. It also examines the moderating role of information technology in strengthening these relationships. Using a quantitative approach with a descriptive and causal design, data were collected from 204 respondents across private universities in Indonesia via a questionnaire. Analysis using Partial Least Squares (PLS) and SmartPLS software reveals that strategy, vision and mission, university rankings, and information technology significantly impact sustainable competitive advantage. Furthermore, information technology strengthens the influence of strategy, vision, and mission but does not moderate the relationship between university rankings and competitive advantage. These findings indicate that while information technology supports strategic implementation and the achievement of institutional vision and mission, its direct role in enhancing university rankings is limited. The study emphasizes the importance of formulating innovative, sustainable strategies and integrating information technology effectively to maintain competitiveness. Practical implications include the necessity of resource planning and investment in technology to achieve strategic objectives. This study contributes to the literature by exploring the moderating role of information technology in private universities, providing valuable insights for managers on leveraging technology to navigate competitive challenges and sustain growth.

Human Resource Planning Strategy to Improve the Quality of Online Library Management Information Systems (E-Libraries) of Higher Education in the Industrial Era 5.0

Library management information systems in universities are essential for managing e-libraries in universities. The world of education will face one of the challenges after 2030: the use of information technology or robotics in various sectors of human life, one of which is university libraries. This research was conducted using a literature approach by collecting data from various relevant theoretical sources about human resource management and higher education management information systems in the context of university library human resource planning strategies. This article delves into the practical application of library human resource planning strategies in universities. It discusses the formulation of university library human resource planning and the development of human resources in the era of Society 5.0. Furthermore, it explores the challenges and opportunities in implementing these strategies, such as change management, adaptive higher education management information system policies, and the development of librarians' professionalism in the era of society 5.0. This study uses a literature approach by collecting data from relevant theoretical sources about library human resource management and higher education management information systems in the educational context of university library human resource planning strategies. Data is analyzed through systematic literature synthesis to identify university library human resource planning strategies that can improve the quality of management e-library in the era of Society 5.0. In this era, adaptive and technology-oriented human resource management strategies are the key to improving the quality of university e-library management.

A model for scheduling the resource deployment in a multi-stage ramp-up production system

Rapid and continuous changes in customer product requirements affect demand, requiring the supply chain to be more responsive. A new product ramp-up is integral to responsiveness, where it is paramount to implement it successfully and manage it effectively. A smooth ramp-up process minimises problems and delays, leading to lower costs and higher profitability. This study develops and analyses a model that describes a multi-stage ramp-up production system to identify the most cost-effective policy for controlling multiple ramp-ups. We propose a search-based optimisation approach to solve the problem. Through numerical analyses, we develop a decision framework to classify the patterns of resource deployment and planning, aiming to make the ramp-up process efficient and responsive to increasing demand. Additionally, we conduct sensitivity analyses to examine how variations in input parameters affect system behaviour. Our findings offer managerial implications and insights based on the numerical results.

Exploring peer-to-peer lending: key influences of firm uncertainty, loan features and venture quality

PurposeThe purpose of this study is to identify the determinants of success in peer-to-peer (P2P) lending campaigns, especially amid global financial disruptions like the COVID-19 pandemic. Addressing a notable gap in current research, we explore how factors such as firm uncertainty, loan characteristics (interest rates and maturity) and venture quality (human, social and intellectual capital) influence P2P lending effectiveness. Using multiple regression analysis on data from 523 projects on the October platform, our study aims to enhance the understanding and operational efficiency of P2P platforms, contributing to a more resilient financial ecosystem.Design/methodology/approachThis study employs a quantitative research design using multiple regression analysis to examine the impact of specific variables on the success of P2P lending campaigns. Data were collected from 523 concluded P2P lending projects on the October platform, spanning from 2015 to 2021. Variables of interest include the level of uncertainty of the firm, loan characteristics such as interest rate and maturity and the quality of the venture assessed through human, social and intellectual capital. This method allows for a robust analysis of the factors contributing to the success of P2P lending within a dynamic financial context.FindingsThe findings of this study reveal that the success of P2P lending campaigns is significantly influenced by the level of uncertainty of the firm, the interest rate of the loan and the quality of the venture. Specifically, higher uncertainty in firms correlates negatively with campaign success, while competitive interest rates positively impact funding outcomes. Furthermore, ventures that demonstrate robust human capital, particularly those with management teams that possess diverse skills and high qualifications, tend to attract more funding. These results underscore the critical role of strategic financial and human resource planning in enhancing the effectiveness of P2P lending platforms.Originality/valueThis study contributes uniquely to the literature by integrating multiple variables – firm uncertainty, loan characteristics and venture quality – into a comprehensive analysis of success factors in P2P lending. It addresses the scarcity of research examining the combined effects of these factors, particularly in the context of global financial disruptions like the COVID-19 pandemic. By focusing on a specific European platform during a dynamic period, this research provides new insights into how P2P lending can adapt to and thrive amid financial crises. The findings offer valuable guidance for both practitioners and policymakers aiming to optimize P2P lending practices in uncertain economic landscapes.