Finnish National Research Articles

Care leaders' moral distress in older adult care: A scoping review.

Moral distress among nurses is well researched and well documented, but there is limited research on the moral distress experienced by care leaders, who serve as intermediaries between patient care nurses and higher levels of administration. Healthcare professionals experience moral distress daily in the context of older adult care. The aim of this scoping review was to evaluate recent literature on moral distress in older adult care with the goal of revealing how care leaders' experiences of moral distress in older adult care have been conceptualized in earlier studies. The research questions were: How is the concept of moral distress as experienced by care leaders in older adult care defined in the existing literature? How is the concept of moral distress conceptualized in the literature? The research has been conducted in accordance with the guidelines set forth by the Finnish National Advisory Board on Research Ethics TENK. We saw that consensus on how moral distress is defined is lacking. Care leaders in older adult care experience substantial moral distress, which could be linked to the duality of their leadership role. Moral distress can be caused by a complex interplay of individual and structural factors and the challenging complex moral issues inherent to older adult care. Moral distress could impact care leaders' emotional health, job performance, overall job satisfaction and result in higher turnover rates, absenteeism, decreased quality of patient care, and increased organizational costs. Addressing moral distress on the individual, team, and organizational levels is crucial for enhancing care leaders' well-being and improving the overall quality of care for older adults. A focus on the identification of strategies whereby care leaders can be supported, exploration of the long-term effects of moral distress on healthcare professionals in general, and the organizational outcomes associated with moral distress should be included in future research.

Neoadjuvant Chemotherapy in Muscle-Invasive Bladder Cancer: A Nationwide Analysis of Eligibility, Utilization, and Outcomes

Objectives: To investigate neoadjuvant chemotherapy (NAC) eligibility, utilization, and survival outcomes for muscle-invasive bladder cancer patients undergoing radical cystectomy (RC) in a Finnish population. Materials and Methods: Data from the Finnish National Cystectomy Database (2005–2017) was combined with Finnish Cancer Registry survival data. NAC utilization rates were reported, and downstaging rates were calculated based on final pathological staging. Logistic regression analyzed NAC usage and complete response (CR) predictors. Results: Since 2011, 29% of 1157 patients received NAC. Its usage remained consistent, and the number of eligible patients not receiving NAC decreased during the study period. Among NAC patients, pathology T-category was pT0 (34%), pT1-Ta-Tis (16%), pT2 (23%), pT3 (20%), and pT4 (7%) tumors, with pN0 in 82%. In the RC + NAC group, the 5-year overall survival (OS) rates were 89% for patients with no residual disease (pT0N0), 82% for those with organ-confined residual disease (pT1, Tis, Ta, T2/N0), and 49% for patients with non-organ-confined residual disease (pT3+/N+). The corresponding cancer-specific survival (CSS) rates were 93%, 86%, and 57%, respectively. Patients with organ-confined residual disease after NAC had survival outcomes comparable to those who underwent RC alone. Higher age; odds ratio (OR) 0.93, [95% Confidence Interval (CI): 0.90–0.95] and Charlson Co-morbidity Index–score [OR 0.88 (0.79–0.98)] reduced the likelihood of receiving NAC, while a smaller center size increased the probability [OR 1.82 (1.02–3.28)]. More treatment cycles [OR 0.70, (95% CI: 0.51–0.93)] and a favorable GFR [OR 0.38 (0.16–0.88)] were associated with achieving CR. Conclusion: We report that NAC is well-utilized across Finland, with CR rates comparable to recent trials. Additionally, our survival rates are reasonable, and even with organ-confined residual disease after NAC, survival outcomes are similar to those who underwent RC alone.

Large-scale flood risk assessment in data-scarce areas: an application to Central Asia

Abstract. The countries of Kazakhstan, Kyrgyz Republic, Tajikistan, Turkmenistan, and Uzbekistan in Central Asia are highly prone to natural hazards, particularly floods, earthquakes, and landslides. The European Union, in collaboration with the World Bank and the Global Facility for Disaster Reduction and Recovery (GFDRR), created the programme Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia (SFRARR) to advance disaster and climate resilience in the region. As part of the SFRARR project, the “Regionally consistent risk assessment for earthquakes and floods and selective landslide scenario analysis for strengthening financial resilience and accelerating risk reduction in Central Asia” was developed to achieve the project's objectives. This article presents the data, model, methodology, and results for the five Central Asian countries of the flood risk assessment, which represents the first high-resolution regional-scale transboundary risk assessment study in the area aiming to provide tools for decision-making. The output information will inform and enable the World Bank to initiate a policy dialogue. A fully probabilistic risk assessment for fluvial floods has been carried out for these countries to support regional and national risk financing and insurance applications, including potential indemnity and/or parametric risk financing solutions for a regional programme. A homogenised risk assessment methodology for the five countries and across multiple hazards (floods and earthquakes) and asset types has been adopted to obtain strategic financial solutions consistent across geographical areas and economic sectors. The largest relative (to the total exposed value) expected annual damages are found in Kazakhstan and Tajikistan, with values above 6 ‰. In the five considered countries, the largest relative expected annual damages by sector are found for the transport and agricultural sectors. Climate change is expected to have contrasting impacts, with increases in risk for some regions (the most severe increase is found in the Mangistauskaya region in Kazakhstan) and decreases for other regions (Lebap, Turkmenistan; Khatlon, Tajikistan; Samarkand, Uzbekistan; and Batken, Kyrgyz Republic).

A Case Study on Application of Bloom’s Taxonomy in Increasing Financial and Tax Knowledge in India

The ever-evolving economy of the Indian subcontinent is approaching a higher demographic dividend that can positively affect the per capita income of the nation. There is a possibility that of an increase in number of earning members in each household which would ultimately raise their cumulative income. With better income, the urge to purchase a new home, secure one’s children’s future, taking care of one’s health by purchasing insurance, planning for retirement, etc also increases. Hence, early education on this front can be helpful for the citizens of the country. This study explores the efforts put towards increasing financial and tax knowledge in the country and finding better ways to increase the reach of the information to the masses primarily to the youth as they are the flag bearers of development. The National Centre of Financial Education has introduced several schemes for the welfare of the students at the school and college level to introduce financial literacy in their school’s curriculum. The paper found out that students are allowed by the National Centre of Financial Education to appear in the National Financial Literacy Assessment test. The National Centre of Financial Education also has designed the Money Smart School Curriculum that inculcates a plethora of subject areas that can educate the students regarding finances and taxes. But all these programmes apply only to the schools and the students who have registered themselves with the National Centre of Financial Education. Thus, as all these programmes are voluntary, there is a chance that the above-mentioned initiatives might not reach the targeted students, i.e., students all over India. Hence, some suggestions given the recommendations of the New Education Policy have been made utilising Bloom's revised taxonomy that guides most of the curriculum design for educational institutions.

Cryptocurrency Investments: The Role of Advisory Sources, Investor Confidence, and Risk Perception in Shaping Behaviors and Intentions

The rapid growth and increasing adoption of cryptocurrencies have reshaped the investment landscape, presenting unique opportunities and challenges for investors. This study examines how advisory information sources influence cryptocurrency investment behaviors and intentions among U.S. investors. Using data from the 2021 National Financial Capability Study, it explores how reliance on financial professionals, media, and social networks shapes investment decisions. The motivation for this research lies in the need to understand the divergent roles of these sources in an era where traditional and emerging financial advice coexist. Findings reveal that reliance on financial advisors correlates with reduced cryptocurrency investment and future investment intentions, reflecting advisors’ cautious stance toward volatile assets. Conversely, reliance on media and social networks significantly increases both current investments and future intentions. The findings also highlight that investor confidence is positively associated with the likelihood and intentions to invest in cryptocurrency. Conversely, heightened risk perceptions associated with cryptocurrency reduce both the likelihood and intentions to invest. The study calls for financial professionals to enhance client education on cryptocurrency risks and for policymakers to strengthen regulations, ensuring accurate information dissemination through media and social networks. By providing a nuanced understanding of advisory influence and investors’ characteristics, this research offers valuable insights for financial professionals, policymakers, and investors navigating the complexities of cryptocurrency investments.

Pendampingan Pemerintah Desa dalam Penyusunan Proposal Usaha dan Laporan Keuangan Pelaku UMKM Guna Peningkatan Pendapatan Masyarakat Kecamatan Dau, Kabupaten Malang

Micro, Small and Medium Enterprises (MSMEs) play an important role in Indonesia. MSMEs have shown their tenacity and survived the economic crisis. In line with the Covid-19 pandemic, it is hoped that MSMEs will survive again. Especially among MSMEs in Dau District, Malang Regency, whose fields include culinary, agriculture, fisheries and crafts. This group was chosen because of the high number of MSMEs in Dau District, Malang Regency and the abundant potential of natural resources (HR) in the area. The aim of this research is to examine and analyze the effectiveness of the results of assistance in preparing business proposals and analyzing financial reports for MSME actors in Dau District so that they can be used as a reference and make business improvements, become individual financial strength, so that they can make a big commitment to national finance. The research method used is field research carried out through direct interviews with village heads (or those who know the village program) and 1 MSME actor in the 5 selected villages. This activity stage begins with clarifying the obstacles that MSMEs often face, followed by clarifying the importance of strategic agreements, the efforts taken in preparing strategic plans, and the importance of auditing budget reports. The results of this research show that the majority of people in running their businesses do not understand how to make, prepare a feasible proposal, and how to classify financial reports in obtaining a capital strategy for MSMEs. Almost all MSMEs in Dau Village do not comply with the assistance and expectations of the village government. So the community does not have access to capital from BUMN or the government.

The FINRA Foundation’s National Financial Capability Study: Unpacking 12 years of data on U.S. financial capability

Abstract The National Financial Capability Study (NFCS) is a large-scale, ongoing research project of the FINRA Foundation that provides a broad range of stakeholders the ability to examine and better understand the financial capability of U.S. adults. First introduced in 2009, the NFCS has dozens of publicly available reports and data sets that are free to download. Data from the NFCS contain information on people’s ability to make ends meet, their management of financial products and services, the extent to which they plan ahead, their financial knowledge and decision-making, and numerous other topics. This paper provides an overview of the history, methodology, and some insights gained from the NFCS over the years. In addition, given the NFCS’ unique role as the birthplace of the Big 5 financial literacy questions that are present in all waves of the NFCS, we discuss the role of the NFCS in aiding our understanding of financial knowledge in the United States. The paper concludes with a look toward the future of the NFCS.

Not enough: Medicaid expansion, medical debt, and cost avoidance in rural American Indian and Alaska Native households.

Despite expanding health insurance coverage under the Patient Protection and Affordable Care Act (ACA), many Americans struggle with financial barriers to health care. Medicaid expansion was meant to help alleviate these barriers, particularly for rural communities, but has shown mixed results. The American Indian and Alaska Native (AI/AN) community, which faces both racial and geographic disparities, is a group that should benefit from Medicaid expansion. This paper examines differences in medical debt and cost avoidance for rural American Indian and Alaskan Native households in Medicaid expansion and nonexpansion states. This study uses data from the National Financial Capability Study in binomial logistic regression models to examine whether AI/AN households in rural areas are more likely to have medical and engage in cost avoidance than their urban counterparts. The results show no differences between Medicaid expansion status but do find a higher likelihood of medical debt for those living in rural areas. The results also show a higher likelihood of medical debt and cost avoidance for those living farther from Indian Health Services (IHS)/Tribal health care facilities. The results indicate that rural AI/AN individuals continue to face financial barriers to health care despite the expansion of Medicaid under the ACA. This suggests that rural AI/AN individuals are seeking care outside of the IHS/Tribal system due to limited access to IHS providers. Addressing these barriers may require policies and programs focusing on rural AI/AN communities.

Substance use prediction using artificial intelligence techniques

Substance use poses a significant public health challenge worldwide, including in Finland. This study seeks to predict patterns of substance use, aiming to identify the driving factors behind these trends using artificial intelligence techniques. This research utilizes data from the 2022 Finnish National Drug Survey, comprising 3,857 participants, to develop predictive models targeting the use of cannabis, ecstasy, amphetamine, cocaine, and non-prescribed medications. Analysis of 23 questionnaire items yielded 76 features across four substance use dimensions: demographic attributes, experience and preferences of drug use, health-related aspects of drug use, and social attributes of drug use. In addition to traditional machine learning (ML) approaches previously applied in this field, three sophisticated deep learning models—standard LSTM, BiLSTM, and Recursive LSTM—were employed to evaluate their predictive performance. These LSTM models were further augmented with SHAP analysis to identify the primary influences on substance use patterns. While all these artificial intelligence models demonstrated superior predictive performance, our focus was specifically on the outcomes of the LSTM models due to their novel application in this field. The results underscore the exceptional performance of both LSTM and ML models in unraveling complex substance use behaviors, underlining their applicability in diverse public health contexts. This study not only sheds light on the predictors of substance uses but also furthers methodological innovation in drug research, charting new directions for crafting targeted intervention strategies and policies. The observed variability in predictor significance across different substances indicates the necessity for tailored prevention programs catering to particular user groups. Integrating machine learning with social science and public health policy, our research deepens the understanding of the factors influencing substance use and promotes effective strategies for its mitigation. Despite some limitations, this investigation establishes a foundation for future studies and accentuates the critical role of advanced computational techniques in addressing intricate social issues.

Adapting the National Financial Capability Test to Address Generational Differences in Cognitive Biases

This study examined the influence of cognitive biases on financial literacy test outcomes across four generational groups: Gen Z, Millennials, Gen X, and Baby Boomers. Using the National Financial Capability Test and an online in silico experiment, we analyzed how cognitive biases influence the likely responses of each generation. The results indicate that the current test format aligns more closely with Baby Boomers, who are less affected by certain biases but tend to exhibit resistance to new financial strategies. A key contribution of this research is the identification of generational bias profiles and actionable recommendations for tailoring financial literacy assessments to reflect these differences. Our approach not only advances behavioral finance literature but also introduces innovative methodology through AI-driven simulations, providing a replicable framework for exploring cognitive influences in decision-making. The findings underscore the need for tailored financial education programs that consider these cognitive biases, aiming to foster unbiased financial decision-making across age groups.

Navigating Fiscal Federalism in Pakistan: Balancing Decentralization and Economic Stability Post-18th Amendment

This paper critically examines Pakistan’s fiscal federalism and highlights the challenges posed by the 18th Constitutional Amendment 2010. The research is contextualized within the broader theoretical framework of fiscal federalism. Although intended to decentralize power and grant fiscal autonomy to provinces, the amendment has created structural fiscal issues, especially within the National Finance Commission. Locking of the provincial NFC share, lack of consensus building on the NFC Award since 2009, stagnant fiscal space and tax-to-GDP ratio, vertical fiscal imbalance, and absence of a joint fiscal responsibility mechanism have a negative bearing on the macroeconomic stability of Pakistan. Limited fiscal decentralization to local governments further restricts equitable development at the grassroots level. Key recommendations include operationalizing the NFC Secretariat, revising fiscal frameworks, incentivizing provincial tax efforts, and enhancing collaboration through the Council of Common Interest to strengthen fiscal management and cohesion.

The double-edged sword: how cryptocurrency investments could undermine the anxiety-reducing benefits of rainy-day savings in times of economic turbulence

PurposeRainy-day savings have been an effective measure for maintaining financial stability in times of emergency. Motivated by the rapid expansion of cryptocurrencies, the present study examines how crypto investments could moderate the beneficial outcomes of rainy-day savings for alleviating financial anxiety during the most recent economic turbulence caused by the COVID-19 pandemic.Design/methodology/approachThe present study carries out multivariate logistic regression with interaction effects on the most recent 2021 cohort data from the National Financial Capability Study (NFCS).FindingsWhile rainy-day savings relate to less financial anxiety, the effect varies depending on whether an individual has invested in cryptocurrencies. Specifically, this paper finds that crypto investors experience less relief in financial anxiety from rainy-day savings than non-crypto investors. Additionally, crypto investors are more susceptible to financial stressors like job loss and financial fragility, likely due to the financial loss from investing in cryptocurrencies.Practical implicationsThe findings highlight the necessity of implementing policies and regulations, such as the newly approved Markets in Crypto-Assets (MiCA) regulation, that could raise people’s awareness of the high-risk nature of cryptocurrencies as well as offering targeted financial education for crypto investors, especially during times of market downturn.Originality/valueThis is the first attempt to study how crypto investments may weaken the benefits of rainy-day savings in reducing financial anxiety. The findings offer new insights into the beneficial outcomes of rainy-day savings for emergencies in light of individual crypto investment backgrounds. Additionally, findings from the present study also contain important implications given the rapid expansion of the cryptocurrency market as well as future economic turbulence.

Strategic determinants of military expenditure in Indonesia: Insights from AHP

This study investigates the key determinants of defense spending in Indonesia using the Analytical Hierarchy Process (AHP) to prioritize factors influencing budget allocations. Indonesia’s unique strategic position, resource wealth, and internal security challenges necessitate a comprehensive approach to defense budgeting. The AHP methodology was employed to assess the relative importance of four main criteria: National Resources, Internal Geopolitics, Finance, and Global Geopolitics. Data were gathered from a combination of literature reviews and insights from key stakeholders, including government agencies and academic experts. The results indicate that National Resources, particularly natural wealth such as oil, are the most critical factors driving military expenditure, highlighting Indonesia’s dependence on resource-based revenues. Internal Geopolitics, focusing on political stability and internal conflicts, also significantly influence defense spending, emphasizing the need for a stable domestic environment. The findings further reveal varying perspectives among stakeholders, particularly in financial and geopolitical considerations, underscoring the challenges in aligning strategic priorities. This research offers a structured framework for optimizing defense spending, providing valuable insights for policymakers in Indonesia and other nations with similar budgetary constraints. The study highlights the importance of a cohesive and informed defense budgeting strategy to enhance national security and economic sustainability.

PyPSA-BD: A customized model to explore decarbonized energy transition for developing country

This article provides high-resolution, evidence-based insights towards power sector planning for a developing country. We consider the PyPSA-BD model as a cutting-edge contribution as it’s a fully customized adaptation of PyPSA-Earth for Bangladesh to identify challenges and opportunities for transitioning to a decarbonized power system through counterfactual validation of inputs from national official statistics with a spatial resolution of 30km x 30km and an hourly temporal resolution. Its open-source framework is helpful for future researchers and decision-makers in developing countries like Bangladesh to develop more scenarios to answer any policy-relevant questions as per national need. With 2019 as a reference year, scenarios for 2030, 2041, and 2050 align with national renewable energy integration and decarbonization targets revealing cost-effective generation expansions, diversification of installed capacity through renewable energy penetration, net employment generation, additional land and investment requirement. Model results show that the 2019 installed capacity of 18.94 GW will grow to 61.45 GW by 2030, 102.36 GW by 2041, and 281.52 GW by 2050. By 2050, a storage capacity of 28.5 GW will be required to maintain grid stability. This transition could create approximately 6.7 million jobs and reduce generation costs to 7.63 BDT/kWh by 2050, requiring 3690.85 sq.km of land. Achieving these outcomes will demand an annual investment of approximately 1.99% of Bangladesh’s 2023 GDP from 2025, underscoring the need for national and international finance mobilization. The results guide policymakers to develop sustainable energy transition strategies for Bangladesh that provide power supply security at both spatial and temporal scale.

The efficacy of short-term in-service teacher training in implementing a behavioural intervention

ABSTRACT Teachers feel inadequate to face pupils’ behavioural problems in schools, yet teachers are expected to play significant roles in providing and supporting pupils’ appropriate social behaviour. Unfortunately, teacher education in Finland and globally seems to offer little content for behavioural support, and in-service training sessions are filling this educational gap. The key context in this study is the in-service training that was directed at primary school teachers and teaching staff. The aim of this study was to explore the efficacy of short-term in-service training and discuss the support that in-service training can provide for behavioural problems that are experienced in school. From 2017 to 2021, over 200 teachers or teaching staff members participated in check-in, check-out (CICO), in-service training sessions that were funded by the Finnish National Agency of Education and organised by the Niilo Mäki Institute. Afterwards, the participants were asked to answer a questionnaire concerning their use of the CICO model in their own schools. The post-training questionnaire reached over 80 participants. The results indicated that CICO support was introduced well to schools after the training; however, for some teachers, the short in-service training did not sufficiently support this implementation. Also, factors that complicated and contributed the implementation were examined. According to the results of the study, implementation can be supported with active learning, e.g. opportunities to practise new skills and the use of models.

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.

Development of a regionally consistent and fully probabilistic earthquake risk model for Central Asia

Abstract. A fully probabilistic earthquake risk model was developed for five countries in Central Asia, providing updated earthquake loss estimates with a higher level of detail on all components with respect to previous studies in the region. Additionally, a regionally consistent approach that, on the one hand, allows for direct comparisons at different disaggregation levels (e.g., country and oblast) was used. On the other hand, this updated earthquake risk assessment aims to facilitate initiating a policy dialogue regarding national and regional disaster risk financing and insurance applications. This earthquake risk model made use of a regional probabilistic seismic hazard analysis and a comprehensive exposure database on which different types of assets and sectors were included and for which two scenarios (the years 2020 and 2080) were modeled. For each type of asset, a unique vulnerability function was derived and later used for convolution with the hazard data, which allowed for the estimation of the loss exceedance curve, at different disaggregation levels, from where other risk metrics such as the average annual loss (AAL) and specific return period losses were obtained. The regional earthquake AAL for the five countries and for the 2020 exposure scenario has been estimated to be around USD 2 billion, with the Kyrgyz Republic and Tajikistan being the countries with the highest earthquake risk levels in the region. Besides the probabilistic earthquake risk results, as-if scenarios were modeled using a pseudo-deterministic approach to assess the human and economic losses for realistic and representative earthquakes for the main cities within earthquake-prone regions in the five countries of the study area.

Technical Feasibility Analysis of Digital Twin Technology in the Field of Urban Disaster Prevention

Abstract. In recent years, disasters have occurred frequently around the world, causing great harm to national finances and people's safety. There is still a lack of research on digital disaster prevention technology, but with the development of technologies such as artificial intelligence and computer vision, it has gradually become possible to use digital twin technology in urban disaster prevention. This paper is mainly based on digital twin related technologies, briefly introduces the concept and importance of digital twins, and the applicable fields, and then discusses the mode of data interaction between real entities and twins and the feasibility of digital disaster prevention, so as to prevent and simulate urban disasters and derive strategies for dealing with disasters. This paper reviews the existing research on digital twin technology through the method of literature retrieval and analysis. By analyzing technologies such as the Internet of Things, sensors, three-dimensional laser technology, urban monitoring technology, satellite remote sensing technology, drone remote sensing technology, mobile device technology, lidar technology, and oblique photography, a systematic review of critical analysis is obtained, and a macro method of data interaction and modeling for digital twin technology for disaster prevention is obtained, thereby deriving the feasibility of disaster prevention based on digital twin systems.

Likert Scale Variables in Personal Finance Research: The Neutral Category Problem

Personal finance research often utilizes Likert-type items and Likert scales as dependent variables, frequently employing standard probit and ordered probit models. If inappropriately modeled, the “neutral” category of discrete dependent variables can bias estimates of the remaining categories. Through the utilization of hierarchical models, this paper demonstrates a methodology that accounts for the econometric issues of the neutral category. We then analyze the technique through an empirical exercise relevant to personal finance research using data from the National Financial Capability Study. We demonstrate that ignoring the “neutral” category bias can lead to incorrect inferences, hindering the progression of personal finance research. Our findings underscore the importance of refining statistical modeling techniques when dealing with Likert-type data. By accounting for the neutral category, we can enhance the reliability of personal finance research outcomes, fostering improved decision-relevant insights.

Beyond the hashtags: social media usage and cryptocurrency investment

PurposeCryptocurrencies have gained popularity among investors despite their high risk and volatility. Social media wields substantial influence over investors' attitudes, judgments and decisions related to investment. This study aims to investigate the associations between social media usage and cryptocurrency investment behavior.Design/methodology/approachUtilizing the main dataset of the 2021 National Financial Capability Study and its supplementary Investor Survey, this study analyzed social media usage in general. Additionally, it separately examined 11 different social media platforms as potential sources of information for investments. Logistic regressions were performed to explore the relationship between social media, previous experiences and future considerations in investing in cryptocurrencies. Robustness checks were conducted with additional analyses.FindingsInvestors who used social media for investment information were more likely to invest in cryptocurrencies and consider investing in cryptocurrencies in the future. The likelihood increased with the number of social media platforms used. Different social media platforms exhibited distinct associations with cryptocurrency investment experiences and future considerations.Originality/valueThis study is one of the initial attempts to examine the role of social media platforms in cryptocurrency investment. The findings offer unique and important theoretical and practical insights for policymakers, researchers and practitioners, which can benefit consumer well-being.