Research Questions Research Articles

Leveraging transformer models for enhanced temperature forecasting: a comparative analysis in the Beni Mellal region

The remarkable impact of transformers in artificial intelligence, exemplified by applications like GPT-3 in language processing, has sparked interest in their potential for time series analysis. This study aims to explore whether transformers, specifically temporal fusion transformers (TFT), can outperform conventional methods in this domain. The research question is whether TFT exhibits superior performance compared to conventional recurrent neural network (RNN) methods, specifically gated recurrent unit (GRU), and traditional machine learning approaches, notably autoregressive integrated moving average (ARIMA), in the context of time series analysis and temperature prediction. A comparative analysis is conducted among three models: ARIMA, GRU, and TFT. The study utilizes time series data spanning from 1984 to the end of 2022. The models’ performances are evaluated using multiple metrics: mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and the coefficient of determination (R2). The TFT model achieves the lowest MAE, indicating high accuracy in its predictions. It outperforms both the RNN and traditional machine learning in temperature prediction tasks. Integrating the TFT model with the FAO penman-monteith method could improve irrigation scheduling due to more accurate temperature predictions, potentially enhancing water efficiency and crop yields.

Navigating household food insecurity and environmental sustainability on a low income: An exploration of Sheffield mothers.

In 2023, 25% of adults in England, Wales and Northern Ireland experienced food insecurity. The concentration of food insecurity in both socioeconomically disadvantaged groups and households containing children raises concerns about its uneven nutritional and health impacts across different groups. In parallel with rising food insecurity over the past decade, concerns about the environmental consequences of human diets are intensifying, where urgent changes are needed to people's diets to avoid irreversible environmental damage. It is generally assumed that cost has a significant impact on people's ability to adopt more environmentally sustainable food practices. This UK Research Council-funded project seeks to gain insights into the ways in which low-income mothers (are able to) engage with environmentally sustainable food practices. RQ1 will examine the day-to-day food practices that mothers undertake for their families to offer insights into everyday food insecurity and the relevance of environmentally sustainable food practices. RQ2 will explore biographical experiences to highlight how mothers' life histories influence their familial food practices, including their current household food security and engagement with environmentally sustainable food practices. Finally, RQ3 will explore mothers' upcoming prospects of food insecurity and environmentally sustainable food practices. These research questions will be explored through a qualitative longitudinal, feminist study of 15 low-income mothers in Sheffield, UK, combining in-depth interviews with ethnographic elements. Gaining improved knowledge of mothers' food practices on a low income will be valuable to influence realistic, effective and meaningful philosophies, policies and practical action that prioritises equity, good nutrition and environmentally sustainable food practices.

Conceptualizing healthcare professionals’ relational competence in mental healthcare: An integrative review

IntroductionThe relationship between patients and mental health professionals is the cornerstone of all mental health treatment, and good patient-professional relationships are associated with several positive therapeutic outcomes for patients with mental health challenges. Mental health professionals’ relational competence is essential for high-quality services in mental healthcare. There is currently no unified understanding of the concept of relational competence. This study seeks to address this gap by exploring how relational competence is conceptualized within the context of mental healthcare. The research question was: How is relational competence described in the research literature within the context of mental healthcare? MethodsAn integrative review was conducted with systematic searches in the databases PsycInfo, Ovid Medline, Embase, CINAHL, ERIC, Academic Search Elite, IDUN, and Svemed+, spanning from January 2012 to October 2023, as well as hand searches in the reference lists of the included studies. A thematic synthesis was carried out based on the results in the included studies. ResultsOut of 2476 scientific studies screened, 30 were included, employing a variety of research methodologies to explore relational competence within mental healthcare. Four themes were found to describe relational competence in mental healthcare: having the ability to self-reflect and self-regulate, having a genuine interest in understanding the patient, engaging in reciprocal interaction with the patient, and meeting the patient so that they feel acknowledged. Each theme describes a central and important part of relational competence, but fully developed relational competence must be understood as a whole in which all the themes are present. ConclusionRelational competence in mental healthcare incorporates all the identified components. Each theme complements the others and contributes to the construction of a strong therapeutic relationship between patients and mental health professionals. To provide the best possible care for mentally ill patients, healthcare professionals must embrace and integrate these elements into their practice.

A Review of Modern and Traditional Approaches for the Management of Pulmonary Hypertension

Research Problem: Hypertension, either high blood pressure or pulmonary hypertension, both affect the life of patients in many ways. Research Questions: Does Pulmonary hypertension similar to high blood pressure? : Does it affect old age people or the neonatal age groups also? : What were the reasons for pulmonary hypertension? : Does pulmonary hypertension manageable with modern and traditional drugs? Literature Review: The purpose of the literature review is to identify the targeted therapy for pulmonary hypertension and also include the plants that may be effective for the management of pulmonary hypertension. Conclusion: The author has reviewed many targeted drugs that are presently used, and many are in research that was found effective in making the life of patients as normal as possible. : They also reviewed about 15 herbal plants which were found effective in the management of the disease. : As pulmonary hypertension is not treatable, it can only be managed by changing lifestyle and food habits; therefore, the authors emphasized reviewing the traditional approach for the management of pulmonary hypertension.

The two-way path between Benefit and BC: virtuous behavior or opportunism?

Following the example of selected US states, Italy was among the pioneers in regulating Benefit corporations (BECs), ushering in a new breed of companies committed to integrating positive stakeholder impact into their corporate missions. While obtaining BEC status is not obligatory for acquiring B-corp certification, Italian firms pursuing this certification must adhere to the BEC legal framework to uphold their certified status. This study delves into the BEC landscape, aiming to address the following research questions: i) Does possessing either pure BEC status or B-corp (BC) certification impact (financial) performance? ii) Does being a leader or a follower, characterized by different paths toward BC or BEC status, have an effect on performance?Our comprehensive analysis indicates that holding a B-corp certification positively correlates with economic and financial performance, while standalone BEC status lacks a significant impact on performance. Within the domain of hybrid organizations, certification emerges as a potent mechanism for enhancing performance. This trend can be attributed to the acknowledgment by external stakeholders—including consumers, investors, customers, and suppliers—that external certification, as opposed to mere legal structuring, adds tangible value to the enterprise.

A Survey on Self-Supervised Learning: Algorithms, Applications, and Future Trends.

Deep supervised learning algorithms typically require a large volume of labeled data to achieve satisfactory performance. However, the process of collecting and labeling such data can be expensive and time-consuming. Self-supervised learning (SSL), a subset of unsupervised learning, aims to learn discriminative features from unlabeled data without relying on human-annotated labels. SSL has garnered significant attention recently, leading to the development of numerous related algorithms. However, there is a dearth of comprehensive studies that elucidate the connections and evolution of different SSL variants. This paper presents a review of diverse SSL methods, encompassing algorithmic aspects, application domains, three key trends, and open research questions. First, we provide a detailed introduction to the motivations behind most SSL algorithms and compare their commonalities and differences. Second, we explore representative applications of SSL in domains such as image processing, computer vision, and natural language processing. Lastly, we discuss the three primary trends observed in SSL research and highlight the open questions that remain.

Avoiding “a piece of paper on the wall that everyone ignores”: a qualitative study on the barriers for implementing open fracture guidelines

BackgroundOrtho-plastic evidence-based clinical guidelines for open fractures have demonstrated to standardise care and improve outcomes for patients admitted following lower extremity trauma. Despite its benefits, very few countries have introduced such guidance. The aim of this study was to explore the attitudes, barriers and limitations to the development and implementation of guidelines for lower limb open fractures MethodsTwelve renowned orthopaedic and plastic surgeons, based in countries with no guidelines at present, underwent semi-structured interviews. A qualitative appraisal was conducted using reflexive thematic analysis methodology. Systematic coding led to the development and refinement of themes to address the research question. ResultsIndividualistic decision-making, reliance on multidisciplinary interpersonal relationships, and the presence of immobile determinants of open fracture care emerged as three themes that define how patients are currently managed in settings with no guidelines in place. Although guidelines can potentially improve care by presenting evidence-based recommendations, introducing audit practices, establishing pathways for multidisciplinary collaboration, and enhancing effective leadership; if barriers to the implementation are not considered, they may end up as a “piece of paper on the wall that everyone ignores” ConclusionsThis study is the first to explore the challenges of introducing ortho-plastic guidelines for open extremity trauma. The themes presented describe the status quo in settings with no such protocols in place, establishing the foundation for future initiatives aiming to provide a practical strategy to aid the development and introduction of clinical guidelines for open lower limb fractures.

ORBICULES IN SOUTH AMERICAN BALANOPHORACEAE: ANALYSIS OF ITS POSSIBLE ROLE AND ITS TAXONOMIC VALUE IN THE FAMILY

Balanophoraceae are root parasites, lacking both chlorophyll and typical cormophytic organization. There have been few studies of pollen dispersal mechanisms in Balanophoraceae, and no studies on structures accompanying pollen, such as orbicules. Anthers were analyzed in five South American taxa of Balanophoraceae (Langsdorffia hypogaea, Lophophytum pyramidale, L. mirabile. subsp. bolivianum, Ombrophytum subterraneum and Helosis cayennensis), in order to: (1) determine the presence of orbicules, characterize them and relate their presence with the tapetum type, characteristics of the anther wall and dehiscence, and the pollination mode; (2) and analyze its possible taxonomic value in relation to the phylogeny of the family Balanophoraceae. In three genera (Lophophytum, Langsdorffia and Ombrophytum), the anthers were found to be dithecous, tetrasporangiate with longitudinal dehiscence. In these taxa the endothecium is present and is responsible for dehiscing the anthers. In Helosis the pollen sacs are welded in a synandrium with apical groves and lacking an endothecium. In all species, a secretory tapetum is responsible for nourishing the pollen grains during their formation. The presence of orbicules is variable among the species studied: it is found in Lophophytum and Langsdorffia, and is absent in Ombrophytum and Helosis. In Lophophytum the orbicules appear as small and spheroidal corpuscles with smooth surfaces. In Langsdorffia hypogaea the orbicules are conspicuous spherical, ellipsoid to ovoid forms. This study shows that there is no relationship between layers present in mature anthers, dehiscence type, pollination mode and orbicules presence. We report for the first time the presence of orbicules in the holoparasitic family Balanophoraceae belonging to the order Santalales, in which there is scarce information available regarding the presence of orbicules. Given the intergeneric variations and the consistency at generic level observed in the taxa here analyzed, orbicules may have predictive value and potential for systematically oriented research questions.

Sigmoidal learning rate optimizer for deep neural network training using a two-phase adaptation approach

The optimization of machine learning models is an open research question since an optimal procedure to change the learning rate throughout training is still unknown. Manually defining a learning rate schedule involves troublesome, time-consuming try-and-error procedures to determine hyperparameters, such as learning rate decay epochs and learning rate decay rates, in order to navigate the intricate landscape of loss functions. Although adaptive learning rate optimizers automatize this process, recent studies suggest they may produce overfitting and reduce performance compared to fine-tuned learning rate schedules. Considering that the new machine learning loss function approaches present landscapes with much more saddle points than local minima, we proposed the Training Aware Sigmoidal Optimizer (TASO). This automated two-phase learning rate adaptation mechanism significantly reduces the need for manual hyperparameter tuning. The first phase uses a high learning rate to quickly traverse the numerous saddle points in the error surface, while the second phase uses a low learning rate to gradually approach the center of the local minimum previously found. We compared the proposed approach with commonly used adaptive learning rate schedules such as Adam, RMSProp, and Adagrad. The validation experiments were performed in image and text datasets and showed that TASO outperformed all competing methods in optimal (i.e., performing hyperparameter validation) and suboptimal (i.e., using default hyperparameters) scenarios. In our benchmark tests, TASO demonstrated promising performance, achieving an 8.32% increase in accuracy and a significant 46.62% decrease in training loss on average across various datasets and models. This remarkable performance positions TASO ahead of well-established adaptive optimizers, suggesting higher effectiveness and consistency in performance.

A primer: peer review process for Advances in Physiology Education.

The dissemination of discipline-focused educational scholarship advances theory and stimulates pedagogical application. The aim of Advances in Physiology Education is to publish manuscripts that advance knowledge and inform educators in the field. This primer is tailored for individuals new to manuscript reviewing, early in their careers, or experienced in reviewing research but not educational manuscripts. Peer reviewing for basic and applied science is akin to evaluating research questions and rigor in teaching and learning studies, with differences in approach and analysis similar to those between biophysics and molecular physiology or cell and integrated physiology. Our purpose is to provide an overview of the review process and expectations. The submission and peer review process involves several steps: authors submit a manuscript and the Editor assigns an Associate Editor, who then assigns peer Reviewers. Reviewers are contacted via email and can accept or decline the invitation. Reviewers evaluate the work's strengths and weaknesses and then independently submit comments and recommendations to the Associate Editor. After review, the Associate Editor collects and weighs Reviewers' comments, sometimes garners additional reviews and input, to make a recommendation to the Editor. The Editor reviews the process, comments, and recommendations to render a final decision. Both authors and Reviewers receive an email with the decision. The editorial staff assists with communication and helps track the overall process. Peer review is integral to scientific publishing, ensuring quality and rigor, and reviewing is both a privilege and a responsibility of all in the scientific community.NEW & NOTEWORTHY This mini-review offers a comprehensive and current overview of the peer review process and the qualifications required to serve as a journal reviewer for Advances in Physiology Education. The guidelines are specifically designed for early career professionals new to manuscript reviewing, as well as seasoned research manuscript reviewers who are new to educational manuscript evaluation. Peer review is a cornerstone of scientific publishing, ensuring both quality and rigor. It is both a privilege and a responsibility for all members of the scientific community.

Medical Image Segmentation Review: The Success of U-Net.

Automatic medical image segmentation is a crucial topic in the medical domain and successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the most widespread image segmentation architecture due to its flexibility, optimized modular design, and success in all medical image modalities. Over the years, the U-Net model has received tremendous attention from academic and industrial researchers who have extended it to address the scale and complexity created by medical tasks. These extensions are commonly related to enhancing the U-Net's backbone, bottleneck, or skip connections, or including representation learning, or combining it with a Transformer architecture, or even addressing probabilistic prediction of the segmentation map. Having a compendium of different previously proposed U-Net variants makes it easier for machine learning researchers to identify relevant research questions and understand the challenges of the biological tasks that challenge the model. In this work, we discuss the practical aspects of the U-Net model and organize each variant model into a taxonomy. Moreover, to measure the performance of these strategies in a clinical application, we propose fair evaluations of some unique and famous designs on well-known datasets. Furthermore, we provide a comprehensive implementation library with trained models. In addition, for ease of future studies, we created an online list of U-Net papers with their possible official implementation.

Maximising the potential of deep geothermal energy: Thermal output increase by large-scale heat pumps

Due to the high share of the heating market in Europés final energy consumption, it is mandatory to intensify the decarbonisation in this sector. Large-scale heat pumps could significantly contribute to different areas of application, like the supply of existing heating networks or the utilisation of industrial waste heat, by using various energy sources like geothermal, air or running water. However, there are still open research questions regarding the technical aspects like fluid selection or part load behaviour as well as economic aspects.This study investigates the potential of maximising the thermal capacity of existing geothermal heating plants by integrating large-scale heat pumps. To consider a realistic performance of the heat pump, experimental results are implemented into a techno-economic model. Annual simulations are carried out based on real data for a district heating network and the geothermal source. The experimental investigations of a high-temperature heat pump show the high impact of temperature lift, temperature glides and part load operation on the COP. Additionally, this investigated part load behaviour is implemented in the techno-economic model.For the techno-economic analyses, the levelized costs of heat (LCOH) are calculated. For a base scenario, 68 €/MWh are estimated. Additionally, sensitivity analyses were conducted to quantify the influence of selected geological, design and economic parameters on the LCOH. The electricity price shows the most significant impact, with a potential reduction of the LCOH of 39 %. In general, the study points out the great potential of integrating large-scale heat pumps into geothermal energy systems and district heating networks to extend the renewable system’s thermal capacity efficiently and cost-effectively.

The Paradoxical Leader, Crafting Human Worker, and Robot Teammate: A Commentary on the Future of Leader Behaviors

Robot utilization is expected to result in significant changes in the way organizations and teams operate. The increasing prevalence of robots in the workplace offers an opportunity to research robot utilization and employee response behaviors to this phenomenon. A timely research question is, “What reactionary behaviors would organizational leaders and employees exhibit as they begin to regard robots as coworkers and teammates?”. The purpose of this commentary is to offer theoretical perspectives on leader response behaviors when leading human-robot teams as well as to understand employee reactions to working alongside a robot teammate. Drawing on paradox and Job Demands-Resources (JD-R) theories and through postulation statements, we show the (contradictory) duality of leader and employee behaviors under conditions of high/low job demands in a human-robot team environment. In doing so, this discussion bears implications for research on team leadership, job crafting, the presence of behavioral robotics, and for the practice of organizational leadership.

2024 Mpox outbreak: A rapidly evolving public health emergency of international concern

The declaration of a second Public Health Emergency of International Concern (PHEIC) for mpox in August 2024 underscores the urgent need for a comprehensive understanding of the evolving epidemiology [1] clinical manifestations, and zoonotic potential of this emerging threat [2]. This work delves into the intricate interplay between human and animal mpox infections, with a specific focus on the unique characteristics of various viral clades and their implications for individual and public health.There is a critical need to elucidate the factors driving multiple spillover events and the subsequent emergence of new clades better adapted to human-to-human transmission. We hypothesize that anthropogenic changes, including deforestation, urbanization, and climate change are facilitating increased human-to-animal contact, leading to more frequent zoonotic transmissions and viral adaptations. Our conceptual framework integrates One Health principles, evolutionary virology, and epidemiological modeling to investigate the demographic, clinical, and treatment differences among mpox clades in both humans and animals. We employ a mixed-methods approach, combining genomic analysis, clinical data review, and ecological surveys to construct a comprehensive picture of mpox's changing dynamics. The research questions explore the differences in epidemiological and clinical profiles among mpox clades and the factors that likely contribute to successful cross-species transmission and human adaptation.This manuscript introduces an updated Identify, Isolate, Inform (3I) Tool meticulously redesigned to significantly improve the early detection, containment, and reporting of mpox cases across diverse settings. By integrating clinical, virological, and ecological data, this work aims to lay the groundwork for enhanced risk assessment, targeted interventions, and global preparedness strategies in the face of this evolving zoonotic threat.

Digital learning using ChatGPT in elementary school mathematics learning: a systematic literature review

Digital learning with ChatGPT in elementary school mathematics learning is urgently implemented. Several studies have explored digital learning with ChatGPT in elementary school mathematics learning, but studies using SLR are minimal. This article presents the 2022-2024 SLR study on digital learning with ChatGPT in elementary school mathematics learning. This SLR and PRISMA method is supported by Publish or Perish 8, VOSviewer version 1.6.20, Mendeley version 1.19.8, and ATLAS.ti version 7.5.16. The search results obtained 1,259 Scopus articles, which were filtered to 40 and analyzed using ATLAS.ti, then the results were described according to the research question. Digital learning with ChatGPT is a learning approach using the synchronous-asynchronous mode, virtual classrooms, distance, use of interactive digital tools, digital methods and media, innovation, digital modeling, use of robotics and AI ChatGPT for children with the principle of collaboration digital, and problem-solving with the support of digital resources. ChatGPT features multilingual, natural language, advanced AI, 24/7 availability, answering math questions, recurring training, and helping students with various math tasks. Implementation of digital learning with ChatGPT in elementary school mathematics learning for problem-solving, geometry, function limits in algebra, the material on flat shapes, geometric shapes, integrated PjBL, online, mixed and flipped classes.

Transition from Human to AI-Judge - The Future Automated Adjudication Mechanism in India

In the current technological landscape, artificial intelligence is rapidly evolving and reshaping the way we operate. Nevertheless, AI also presents challenges that necessitate human involvement and unique problem-solving approaches. This study explores the potential future impact of Human to AI Judges. It begins by global approach on AI as automated decisionmaking authority, the acceptance models that helps to adopt such advanced technology in India and also examining AI's legal personality. The submission of evidence is a critical aspect of any adjudication process. Therefore, it is essential to authenticate such evidence through a reliable mechanism to ensure the secure collection of evidence. The use of chain of custody and blockchain technology has been widespread for this purpose, although countries like China have differing methods for utilizing such technology. However, India currently lacks such efficiency. Predictive analysis has been employed to gather data and develop algorithms. However, AI lacks the cognitive ability to evaluate cases based on emotions by relying solely on these algorithms. The paper also delves into the inability of AI judges to detect emotions in criminal and family cases. The appeals process has been evaluated, as individuals who disagree with a judge's decision typically lodge an appeal. The issue of accountability is a significant concern, prompting discussions about whether the programmer or AI should bear the responsibility. This research paper also explores the appealability of decisions made by AI judges, offering a viable solution to address the research question.

Adaptive demand planning models: Utilizing reinforcement learning to address contingencies and dynamic market conditions in supply chains

There are remarkable opportunities in supply chain management associated with using the reinforcement learning (RL) approach in demand planning. As opposed to numerous other techniques, such as modeling and forecasting and applying them over a certain period, RL allows for the making and adapting these decisions in real-time, depending on the current demand and other conditions in the market. Supplementary to this, RL-based models enable supply chains to constantly adapt to shifts since they directly update knowledge from incoming data, making them less susceptible to economic shocks or other supply uncertainties. This flexibility is particularly important for contemporary supply chains in uncertain global environments where conventional, deterministic demand planning techniques cannot address changing needs. In this research, we discover how RL-based models could reduce demand volatility and react to contingencies. As such, it is best suited for industries that are required to respond flexibly and faster. This paper enlightens RL on the benefits of demand planning. It provides live examples that give insight into how it may be used to improve inventory, reduce cost, and improve decision-making. Regarding the specific research questions, this study helps investigate the methodologies, evaluation metrics, and case outcomes that shape RL's potential for changing demand planning and improving supply chain adaptability for future research on adaptive supply chain management.

Patient Enrollment to Industry-Sponsored Versus Federally-Sponsored Cancer Clinical Trials.

The conduct of cancer clinical research in the United States is supported by both private and public sponsors. Industry aims to obtain new drug approvals. Federally-sponsored trials examine a broad set of research questions that are not typically addressed by industry; these trials, which are also more commonly conducted in diverse populations, were recently shown to have contributed to gains of 14 million life-years for patients with cancer. Despite the different mandates, the proportion of patients who might participate in industry-sponsored versus federally-sponsored cancer studies is unknown. We evaluated trial enrollment patterns from 2008 to 2022 using ClinicalTrials.gov data. The ratio of enrollments attributable to industry versus federal sponsors was estimated. A large set of estimates on the basis of different combinations of study characteristics were generated. Point estimates were determined as the mean of combinations and confidence limits by the IQR. Five-year intervals were examined to smooth annual variation. In total, N = 26,080 studies were examined. The estimated enrollment ratio from 2018 to 2022 for all industry-sponsored versus federally-sponsored trials was 8.1 (IQR, 6.2-9.9). For adult trials, the ratio increased from 4.8 (IQR, 4.4-5.3) during 2008-2012 to 9.6 (IQR, 7.4-11.8) during 2018-2022; for trials in children, the ratio increased from 0.7 (IQR, 0.6-0.7) to 2.3 (IQR, 1.8-2.7). Despite increasing cancer incidence, enrollment counts for federally-sponsored trials were flat over the study period. In the United States, there is a growing reliance on industry to conduct cancer clinical research. Underinvestment in federally-sponsored research comes at a cost for both patients and researchers, with lost opportunities for scientific, clinical, and population advances.

Sustainable bonds as a sustainable financial instrument: thematic insights and future prospects

Purpose This study aims to explore the development and significance of sustainable finance instruments, mainly sustainable bonds. The purpose is to provide policymakers, regulators and researchers with insights into the current state of sustainable finance research and also provide future research directions. Design/methodology/approach This study used Scientific Procedures and Rationales for Systematic Literature Reviews as a review protocol and addressed four research questions concerning publication and citation trends, major themes and future research directions in sustainable bonds. Findings This study indicated growing attention in sustainable bond research, with increasing publication and citation trends. Along with identifying research themes, the findings include future direction on pricing and risk assessment, market dynamics and growth potential, policy and regulatory environments and global perspectives with local context. Research limitations/implications Although this study provides a robust analysis of the current literature, it relies on existing publications and may not capture the latest developments in sustainable bond research. However, policymakers can benefit from insights into the growth and dynamics of sustainable bonds, enabling them to implement effective policies and regulations. Investors and businesses can use this research to inform their environmental, social and governance investment strategies and decision-making processes. Originality/value This paper suggests a comprehensive overview of the state of research in sustainable bonds, highlighting the emerging trends and research priorities. It also underlines the significance of sustainable finance in achieving sustainability goals and provides a roadmap for future research.

Technology and happiness: the association between online budget planner and financial satisfaction

PurposeThis study examines the impact of online budget planning platforms (e.g. Goodbudget, Mint) on households’ financial satisfaction. Furthermore, the authors identify the channels and the cross-sectional heterogeneity of this impact based on households’ income, financial literacy and minority groups.Design/methodology/approachThis study utilizes data from the National Financial Capability Study (NFCS) for 2018 and 2021, which encompasses over 50,000 households. The authors apply logit and ordered logit regression techniques to examine the research questions and use propensity score matching and entropy balancing to address potential sample selection bias.FindingsThe authors find a substantial correlation between the adoption of financial budgeting tools and a notable improvement in households’ financial satisfaction, driven by the promotion of healthy financial behaviors and enhanced financial self-efficacy. The empirical findings underscore that the positive effects of online budget planners are more pronounced among low-income, financially illiterate and Black households.Originality/valueThis study is the first to examine the impact of online budget planners on household financial satisfaction. It contributes to the literature by offering valuable insights into how these tools influence financial satisfaction within households.