Time Investment Research Articles

Social media as a driver for local development: an entrepreneurial perspective from Ecuador

This research seeks to analyze the positive effect of social networks in starting a small business. According to this paper, social networks is of great help, especially for the entrepreneur, because of their wide reach and ease of dissemination. A potential customer can be transformed by this impact, becoming a real consumer, just by the impression of social networks. This article describes social networks and their environment and defines them as a tool that helps to reduce the risk of failure in ventures. Its usefulness is especially noted at the time of the first investment, due to its low cost compared to traditional means of communication. For this purpose, two case studies were conducted on entrepreneurship in the artisanal food sector in the city of Guayaquil, showing an improvement in the profitability of these businesses thanks to the help of social networks. Additionally, consumers were consulted through a survey to determine the use and importance given to small food businesses through social networks and the impact they have. In general, the results indicate that: 1) consumers in the Ecuadorian context have a propensity to use social networks to follow small food businesses; 2) consumers have or receive some influence from posts and promotions to make their purchasing decisions; 3) social networks are a tool with potential positive impact to promote small businesses, which in turn, generate local economic development for communities. This positive impact occurs even if there is no professional use strategy.

AI-Estimated Online Product Review Based on the Comments of the Customer

Abstract: The primary objective of “AI-powered product review summarization” is to efficiently analyze large volumes of product reviews, estimating positive and negative sentiment specific to a product identified by its product name. Using the Text Blob library for sentiment analysis and NLTK for summary generation, the tool increases the ease of analysis and simplifies decisionmaking processes for sellers and consumers. Desired outcomes include time investment, an informed customer base, and ultimately a reduction in the purchase of defective products. By categorizing sentiment and generating concise summaries, the tool equips users with valuable insights, streamlines the review analysis process, and contributes to a more efficient and trustworthy e-commerce marketplace.

Artificial Intelligence for Patient Flow

Why Is This an issue? Inefficient patient flow contributes to the overcrowding of health care settings and negative clinical outcomes and patient experiences downstream. Patient flow management aims to achieve seamless patient movement through the health care system and between acute and long-term settings, ensuring timely access to quality care. What Is the Technology? Artificial intelligence (AI)-based patient flow management tools are interventions designed to forecast and monitor patient movement from admission to discharge as they progress through different care settings. AI-driven tools can leverage big data and digital information systems (e.g., electronic health records) to facilitate effective patient flow. AI-based patient appointment scheduling tools, which can help improve patient flow, are created to automate appointment scheduling and optimize it by minimizing wait times and matching the demand for health services and hospital capacity. What Is the Potential Impact? AI tools for patient flow management can support volume forecasting of patients with various conditions, especially those experiencing chronic conditions that require different types of treatment or care in different settings over a long period of time. These AI tools can predict admissions, patient movement from the emergency department to inpatient beds, discharge, and transfers to different health care settings. Evidence for their effectiveness in patients with emergency admissions and those transferred to tertiary and quaternary care, as well as inpatients from the general, cardiology, and mental health departments, was reported. In addition, evidence suggested that AI tools can optimize appointment scheduling in general outpatient settings and operating rooms. In health care systems in Canada, AI tools are being used or investigated to enhance patient flow by predicting emergency admissions, transfers to alternate levels of care, and general inpatient discharges, as well as optimizing capacity planning for patients receiving oncology care. AI appointment scheduling tools are currently being used in some oncology care settings and operating rooms across Canada. The implementation of AI systems generally requires an upfront investment of time and other resources in addition to the financial cost of the system itself for set-up, integration, and staff training. The goal of these systems is to improve efficiency and save money, time, and human resources in the long run. What Else Do We Need to Know? Patient privacy and data security issues are concerns regarding widespread implementation of AI tools trained on electronic health records systems and patient datasets. AI algorithms trained on datasets lacking adequate representation of all relevant patients may not predict their flow accurately. Training datasets with sufficient data from all relevant patient groups can ensure the inputs and outputs of the algorithms accurately reflect patient care needs and mitigate potential bias. To accurately predict the care needs of local patients, AI algorithms, once deployed, should be retrained on site-specific datasets containing data for the patient populations that are representative of the hospital or health system in which they are being used. Not all institutions have the hardware or computing power available to adequately or efficiently process the large amounts of data that are required by these AI systems, or the infrastructure needed to use big data (e.g., from electronic health records), and may require additional resources for implementation, as well as for ongoing maintenance and updating of the systems.

The Danger of the Interpretation of Facts: Legal Uncertainty in the Spanish Saga Cases

Enhancing legal certainty is one of the main values that are sought in the investor–state dispute settlement system. The importance of legal certainty is strengthened in the case of renewable energy investments, which are in the global public interest, long-term and capital-intensive up-front. The first part of the paper presents the importance of legal certainty in investment arbitration in general, its limits and its importance in the context of the green energy transition. In addition, it addresses the special features of renewable energy investments. The second part of the paper analyses from the perspective of legal certainty the Spanish renewable energy cases initiated under the Energy Charter Treaty (ECT), which deal with similar factual and legal issues. In this respect, the paper presents the varying weight tribunals gave to the important facts that led them further to conclude whether Spain breached the fair and equitable treatment standard, and if so, whether the investor was entitled to full compensation or a reasonable rate of return. In addition, it presents different approaches to perceiving the stability provision of Article 10 (1) of the ECT. The paper concludes that it remains uncertain to what extent RE investors will be protected under the ECT’s stability condition in the case of fundamental or small-scale changes. Although one group of arbitrators may argue that the fundamental change triggers per se a breach of a stability condition, others may argue that for the breach to be established, the host state’s measures must be arbitrary, unreasonable or discriminatory. Moreover, the threat to legal certainty might not only be the vague provisions of the ECT but also the significant discretion tribunals have towards the interpretation of facts, leading to different outcomes. Indeed, it is at the discretion of arbitrators to consider whether the timing of investment, presence of evidence indicating possible regulatory changes, and the reasonable rate of return prescribed in Spain’s domestic law will be relevant or irrelevant.

Benefits of Give Circle: Exploring the impact of collaborative redistribution platforms on user willingness to donate to charity and tendency towards consumer minimalism

This study investigated the motivations of Taiwanese consumers who choose to redistribute their possessions via Give Circle, a free collaborative redistribution platform. We aimed to understand why users prefer using a free platform to donate and solicit items over traditional second-hand exchange platforms. Additionally, we sought to identify the behavioral changes that occur from using this platform within the context of the sharing economy. A mixed-methods approach was adopted, starting with semi-structured interviews with frequent users of the platform to identify independent and dependent variables for the formal research framework. Based on this evidence, we selected the extended UTAUT2 model and gamification affordance theory to explore the factors of continued use intentions. We applied structural equation modeling to 691 valid questionnaires. Predictors of continued use intentions included performance expectations, convenience, time and effort investment, and autonomy and self-expression. Continued use intention positively influenced charitable donation behavior and consumers' minimalist tendencies. These findings represent a novel contribution to the literature on the sharing economy, filling gaps in research on non-monetary collaborative redistribution platforms, extending the application of the UTAUT2 model, and deepening our understanding of consumer psychology and behavioral patterns in Asian Chinese societies.

GENERATIONAL INVESTMENT PATTERNS IN MUTUAL FUNDS: A COMPARATIVE STUDY OF GENERATION X AND MILLENNIALS

This Master’s thesis examines the investment patterns in mutual funds across two distinct generational cohorts: Generation X and Millennials. The primary aim of this study is to analyze and compare the investment behaviors and preferences between these two groups, focusing on factors such as risk tolerance, investment time horizon, and preferred fund types. Utilizing a mixed-methods approach that combines quantitative data analysis of investment portfolios with qualitative surveys and interviews, this research provides a comprehensive view of how generational differences influence investment decisions. The quantitative analysis involves a statistical examination of mutual fund holdings and transactions sourced from a proprietary database covering the period from 2010 to 2020. Meanwhile, the qualitative component features insights from over 200 individual interviews with representatives of each generation, supplemented by surveys to gauge attitudes towards various investment strategies and market outlooks. Initial findings suggest significant divergences in the investment strategies between Generation X and Millennials. Generation X investors tend to favor income-focused and balanced funds, displaying a preference for stability and long-term growth, influenced possibly by their proximity to retirement. In contrast, Millennials are more inclined towards equity-heavy portfolios and show a greater propensity for embracing emerging market funds and ESG (environmental, social, and governance) themed investments, driven by higher risk tolerance and a longer investment horizon. This thesis not only highlights the distinct investment preferences of these generations but also delves into the sociopolitical and economic factors that might influence these patterns. Furthermore, it discusses the implications of these generational differences for financial advisors, mutual fund managers, and policymakers. By identifying and understanding these generational investment behaviors, industry professionals can better tailor their approaches to meet the needs and preferences of each demographic group, enhancing client satisfaction and engagement. This comparative study contributes to the financial industry’s understanding of demographic shifts and their impacts on market trends, offering valuable insights for developing more targeted and effective investment products and strategies.

Investment Decision for Long-Term Battery Energy Storage System Using Least Squares Monte Carlo

The use of renewable energy sources to achieve carbon neutrality is increasing. However, the uncertainty and volatility of renewable resources are causing problems in power systems. Flexible and low-carbon resources such as Energy Storage Systems (ESSs) are essential for solving the problems of power systems and achieving greenhouse gas reduction goals. However, ESSs are not being installed because of Korea’s fuel-based electricity market. To address this issue, this paper presents a method for determining the optimal investment timing of Battery Energy Storage Systems (BESSs) using the Least Squares Monte Carlo (LSMC) method. A case study is conducted considering the System Marginal Price (SMP) and Capacity Payment (CP), which are electricity rates in Korea. Revenue is calculated through the arbitrage of a 10 MW/40 MWh lithium-ion BESS, and linear programming optimization is performed for ESS scheduling to maximize revenue. The ESS revenue with uncertainty is modeled as a stochastic process using Geometric Brownian Motion (GBM), and the optimal time to invest in an ESS is determined using an LSMC simulation considering investment costs. The proposed method can be used as a decision-making tool for ESS investors to provide information on facility investments in arbitrage situations.

Knowing me, knowing you: how school educators cope with the challenges of inclusion in deprived and non-deprived areas

ABSTRACT Inclusive classrooms have become a focus in mainstream education in recent years. However, challenges for educators are often overlooked. This study utilised an exploratory, qualitative research approach following Strauss and Corbin’s variant of Grounded Theory [Corbin, J., and A. Strauss. 2015. Basics of Qualitative Research. Sage] to investigate this; semi-structured interview data was collected from 19 educational professionals, utilising theoretical sampling techniques. Participants were working in deprived and non-deprived areas and were interviewed about their experiences of facilitating inclusion. Data was analysed using Grounded Theory; ‘Knowing’: yourself and your students emerged as the core category, with main categories of attitudes, sacrifices, student empowerment, meeting needs and barriers. Boundaries around time and emotional investment were paramount, as was available non-judgemental support. Student empowerment alleviated pressure on educators. Deprivation exacerbated challenges such as workload, student emotional and social needs, and emotional toll on educators.

Complete Neuron Reconstruction Based on Branch Confidence.

In the past few years, significant advancements in microscopic imaging technology have led to the production of numerous high-resolution images capturing brain neurons at the micrometer scale. The reconstructed structure of neurons from neuronal images can serve as a valuable reference for research in brain diseases and neuroscience. Currently, there lacks an accurate and efficient method for neuron reconstruction. Manual reconstruction remains the primary approach, offering high accuracy but requiring significant time investment. While some automatic reconstruction methods are faster, they often sacrifice accuracy and cannot be directly relied upon. Therefore, the primary goal of this paper is to develop a neuron reconstruction tool that is both efficient and accurate. The tool aids users in reconstructing complete neurons by calculating the confidence of branches during the reconstruction process. The method models the neuron reconstruction as multiple Markov chains, and calculates the confidence of the connections between branches by simulating the reconstruction artifacts in the results. Users iteratively modify low-confidence branches to ensure precise and efficient neuron reconstruction. Experiments on both the publicly accessible BigNeuron dataset and a self-created Whole-Brain dataset demonstrate that the tool achieves high accuracy similar to manual reconstruction, while significantly reducing reconstruction time.

Low-Cost Approach to an Instream Water Depth Sensor Construction Using Differential Pressure Sensors and Arduino Microcontrollers.

Accurate hydrological data with high spatial resolution is important for flood risk and water resource management, particularly under the context of climate change. The cost of monitoring networks, as well as the characteristics of the hydrological environment itself, can be a barrier to meeting these data requirements, however. This study covers the design and testing of a low-cost, "build-it-yourself", instream water depth sensor providing an assessment of its potential in future hydrological monitoring projects. The low-cost sensor was built using an Arduino microcontroller, a differential pressure sensor and a thermistor, a real-time clock, and an SD card module. The low-cost logger was deployed in tandem with a factory-calibrated Solinst®LevelLogger® 5 Junior for 6 months in the River Wissey, UK. We found the mean absolute error of the Arduino-based logger relative to the commercial setup to be ±0.69 cm for water depth and ±0.415 °C for water temperature. Economically, the Arduino-based logger offers an advantage, costing a total of £133.35 (USD 168.26 at time of publication) comparative to the industrial comparison's cost of £408 (USD 514.83 at time of publication). This study concludes that the low cost of the Arduino-based logger gives a strong advantage to its incorporation in hydrological data collection, if the trade-offs (i.e., time investment and accuracy) are considered acceptable and appropriate for a project.

Participatory methods used in the evaluation of medical devices: a comparison of focus groups, interviews, and a survey

BackgroundStakeholder engagement in evaluation of medical devices is crucial for aligning devices with stakeholders’ views, needs, and values. Methods for these engagements have however not been compared to analyse their relative merits for medical device evaluation. Therefore, we systematically compared these three methods in terms of themes, interaction, and time-investment.MethodsWe compared focus groups, interviews, and an online survey in a case-study on minimally invasive endoscopy-guided surgery for patients with intracerebral haemorrhage. The focus groups and interviews featured two rounds, one explorative focussing on individual perspectives, and one interactive focussing on the exchange of perspectives between participants. The comparison between methods was made in terms of number and content of themes, how participants interact, and hours invested by all researchers.ResultsThe focus groups generated 34 themes, the interviews 58, and the survey 42. Various improvements for the assessment of the surgical procedure were only discussed in the interviews. In focus groups, participants were inclined to emphasise agreement and support, whereas the interviews consisted of questions and answers. The total time investment for researchers of focus groups was 95 h, of interviews 315 h, and survey 81 h.ConclusionsWithin the context of medical device evaluation, interviews appeared to be the most appropriate method for understanding stakeholder views since they provide a scope and depth of information that is not generated by other methods. Focus groups were useful to rapidly bring views together. Surveys enabled a quick exploration. Researchers should account for these methodological differences and select the method that is suitable for their research aim.

THE END OF TRADITIONAL FOCUS GROUPS?

The key benefits of qualitative research are rich insights and thick data. However, some argue these come at the cost of small sample sizes and low generalisability of findings. With traditional FGDs (FGDs), this could be addressed by conducting multiple groups. However, this requires significant investment of time and manpower. We explored methods to gather thick data quickly, aiming to increase the number of respondents without increasing manpower or lengthening fieldwork while maintaining data quality. In this paper, we detail our experience running a pilot study of a 1.5 hour online discussion with N=100 respondents, to capture in-depth responses at scale. Using pre-programmed questions and artificial intelligence (AI) to provide instant visual analyses of responses and additional probes to respondents live, we ran a full qualitative study with a bigger sample in the same duration required for a typical FGD. The discussion was text-based, with respondents being able to view and give their agreement or disagreement to what others may have said without interaction between them. The data was compared to data collected from a previous study with a similar topic and analysed from an operational aspect of conducting research and gathering insights from both methodologies. While this methodology does not replace traditional FGD, it has proven effective in scaling up qualitative research by gathering large amounts of qualitative data within a short duration, in real-time. The methodology has its limitations, primarily the inability to further nuance responses. Despite this, the pilot study appears to be a successful attempt conceptually, as the AI generated valuable instant insights while the study was ongoing, particularly from open-ended (OE) responses. It may add value to specific use cases such as quick sensing which require both breadth and scalability.

ChatGPT-Enhanced ROC Analysis (CERA): A shiny web tool for finding optimal cutoff points in biomarker analysis.

Diagnostic tests play a crucial role in establishing the presence of a specific disease in an individual. Receiver Operating Characteristic (ROC) curve analyses are essential tools that provide performance metrics for diagnostic tests. Accurate determination of the cutoff point in ROC curve analyses is the most critical aspect of the process. A variety of methods have been developed to find the optimal cutoffs. Although the R programming language provides a variety of package programs for conducting ROC curve analysis and determining the appropriate cutoffs, it typically needs coding skills and a substantial investment of time. Specifically, the necessity for data preprocessing and analysis can present a significant challenge, especially for individuals without coding experience. We have developed the CERA (ChatGPT-Enhanced ROC Analysis) tool, a user-friendly ROC curve analysis web tool using the shiny interface for faster and more effective analyses to solve this problem. CERA is not only user-friendly, but it also interacts with ChatGPT, which interprets the outputs. This allows for an interpreted report generated by R-Markdown to be presented to the user, enhancing the accessibility and understanding of the analysis results.

A Single-Session, 2-Hour Version of Mindfulness-Oriented Recovery Enhancement (One MORE) Improves Chronic Pain Patients' Pain-Related Outcomes Through 3-Month Follow-Up in a Randomized Controlled Trial.

Introduction: Traditional, 8-week, mindfulness-based interventions can effectively treat chronic pain, but require a time and resource investment too burdensome for many patients and providers. The solution to this logistical challenge may be to distill the core, therapeutic elements of an efficacious 8-week, mindfulness-based intervention, Mindfulness-Oriented Recovery Enhancement (MORE), into a 2-h, single-session intervention. Methods: In this study, the authors conducted a waitlist-controlled, randomized clinical trial to assess the impact of a 2-h, single-session adaptation of MORE (i.e., One MORE) on chronic pain patients' (N = 40) pain-related outcomes through 3-month follow-up. Results: Results indicated that One MORE significantly improved chronic pain patients' pain catastrophizing (i.e., primary outcome; F = 9.97, p = 0.002), pain intensity (F = 26.58, p < 0.001), pain interference (F = 39.43, p < 0.001), physical function (F = 16.29, p < 0.001), sleep (F = 16.66, p < 0.001), anxiety (F = 12.54, p < 0.001), and depression (F = 17.48, p < 0.001). One MORE also significantly increased theoretically indicated therapeutic mechanisms through the 3-month follow-up: mindfulness, positive reappraisal, savoring, self-transcendence. Discussion: Study results are promising, and if replicated, would suggest that One MORE is a highly scalable, low-cost (e.g., sustainable), nonpharmacologic treatment for chronic pain. Clinical Trial Registration: NCT05194241.

Evaluation of bromocriptine and plumbagin against the monogenean Rhabdosynochus viridisi: Computational drug repositioning and in vitro approaches

Monogeneans are parasitic platyhelminths that can harm the health of farmed fish. Few treatments are available against monogeneans, and the incentive to develop new antiparasitic agents is similar or even lower than the incentive for neglected parasitic diseases in humans. Considering that searching for and developing new antimonogenean compounds may require enormous investments of time, money, and animal sacrifice, the use of a computer-guided drug repositioning approach is a reasonable alternative. Under this context, this study aimed to evaluate the effectiveness of plumbagin and bromocriptine against adults and eggs of the monogenean Rhabdosynochus viridisi (Diplectanidae). Plumbagin is a phytochemical compound that has recently emerged as a potent antimonogenean; however, further investigation is required to determine its effects on different monogenean species. Bromocriptine was selected through a computational approach that included molecular docking analyses of 77 receptors of monogeneans (putative drug targets) and 77 ligands (putative inhibitors). In vitro experiments showed that bromocriptine does not exhibit mortality at concentrations of 0.1, 1, and 10 mg/L whereas plumbagin at 2 and 10 mg/L caused 100% monogenean mortality after 3 h and 30 min, respectively. The most effective concentration of plumbagin (10 mg/L) did not completely inhibit egg hatching. These findings underscore plumbagin as a highly effective agent against adult monogeneans and highlight the need for research to evaluate its effect(s) on fish. Although computational drug repositioning is useful for selecting candidates for experimental testing, it does not guarantee success due to the complexity of biological interactions, as observed here with bromocriptine. Therefore, it is crucial to examine the various compounds proposed by this method.

Optimal investment and exit decision of venture capitals with multiple heterogeneous Beliefs

Generally, traders hold different beliefs about the same project in venture capital activities. We propose a two-stage decision model with multiple heterogeneous beliefs using real options game theory. Based on this model, we analyse the optimal timing and amount of invested capital for venture capital in stage 1 (investing) and stage 2 (exiting). The results show that different beliefs decrease the optimal amount of invested capital but have little influence on the optimal investment timing, which accounts for the “agree to disagree” phenomenon. This negative effect can be offset by the learning process but can also be exacerbated by the differences in required returns.

Design of Transfer Learning based Deep CNN Paradigm for Brain Tumor Classification

Brain tumor is a fatal illness causing worldwide fatalities. The existing neuroimaging modalities to detect brain tumors are invasive and are observer-biased. Automatic CAD frameworks using sophisticated AI techniques lessen human intervention and can effectively handle large amounts of data. Automatic CAD frameworks using Machine learning techniques require the use of time-consuming and error-prone manual feature extraction procedures. Deep learning techniques involve automatic feature extraction; hence, appreciable classification results are attained quickly. However, training DL models from scratch takes a significant investment of time, money, and large datasets, which are difficult to attain in the medical domain. Therefore, the trade-off is utilizing the well exhaustively learned models like VGG16, VGG19, AlexNet, etc. to design a novel framework for the classification of brain tumors. The paper aims to develop a CNN-based deep learning framework by fine-tuning the pre-trained VGG16 architecture via transfer learning for brain tumor detection. The designed framework employing the transfer-learning technique gives better results with less data in less time. The brain tumor binary classification using brain MR images using transfer learning achieved an appreciable accuracy of 97%. The training and validation accuracy obtained was 100% and 97%, respectively, with 30 epochs. The loss for classification was as low as 0.0059% and the run time of 32ms/step time, much less than the existing models.

Music-evoked emotions classification using vision transformer in EEG signals.

The field of electroencephalogram (EEG)-based emotion identification has received significant attention and has been widely utilized in both human-computer interaction and therapeutic settings. The process of manually analyzing electroencephalogram signals is characterized by a significant investment of time and work. While machine learning methods have shown promising results in classifying emotions based on EEG data, the task of extracting distinct characteristics from these signals still poses a considerable difficulty. In this study, we provide a unique deep learning model that incorporates an attention mechanism to effectively extract spatial and temporal information from emotion EEG recordings. The purpose of this model is to address the existing gap in the field. The implementation of emotion EEG classification involves the utilization of a global average pooling layer and a fully linked layer, which are employed to leverage the discernible characteristics. In order to assess the effectiveness of the suggested methodology, we initially gathered a dataset of EEG recordings related to music-induced emotions. Subsequently, we ran comparative tests between the state-of-the-art algorithms and the method given in this study, utilizing this proprietary dataset. Furthermore, a publicly accessible dataset was included in the subsequent comparative trials. The experimental findings provide evidence that the suggested methodology outperforms existing approaches in the categorization of emotion EEG signals, both in binary (positive and negative) and ternary (positive, negative, and neutral) scenarios.

Real option pricing under the regime-switching model with jumps on a finite time horizon

We consider an irreversible investment decision problem on a finite time horizon where an instantaneous cash flow process of a firm follows a regime-switching jump-diffusion (RSJD) model. The value of a project can be derived from a partial integro-differential equation (PIDE) and then we obtain a closed-form solution of the PIDE. It is proved that the value of the project converges to the solution on an infinite time horizon problem as the lifetime of the project tends to infinity. Moreover, the value function of a real option with a finite expiration date can be evaluated by solving a linear complementarity problem (LCP) under the RSJD model. We apply a 2-step backward differentiation formula (BDF2 method) combined with an operator splitting method to solve the LCP and then it has the second-order convergence rate in the discrete ℓ2-norm with respect to the time and spatial variables. The stability of the proposed method to solve the LCP is also proved in the discrete ℓ2-norm. Finally, a variety of numerical experiments are carried out to determine the optimal investment time and the numerical results on the finite time horizon are analyzed.

A New Model for Volunteerism

ABSTRACT As with many Volunteer Programs, volunteerism at Filoli began in support of a fledgling organization in 1975. Initially a separate nonprofit, Friends of Filoli was created with the purpose of fundraising and other support, but was dissolved in 1989 as it was not self-sufficient. The leadership structure, however, remained and later merged with the Board of Directors, creating confusion and discontent. The program peaked at 1,500 volunteers, and was outsizing service needs, not aligned with Filoli’s strategic direction, and a considerable investment of time and management. Volunteers were largely self-managed, but were not following industry best practices and were unsupportive of needed changes for the organization to be viable. As staff implemented best practices, volunteers responded negatively and publicly. Based on staff investment, diminished returns, and negative culture, the model was unsustainable, so the organization took action to assess the needs through a professional consultant to reframe volunteerism. In 2022 Filoli launched Service Learning, a new project-based program that provides an opportunity for learning and volunteerism. Now, Filoli has no dedicated volunteer coordinator, no docents, and no dedicated volunteer database. Yet there is still a robust program for volunteerism, with 650 participants and 2,400 service hours in 2023 alone.