Day: January 30, 2024

Research Paper Formatting according to Journal or Conference Template

body {
font-family: Arial, sans-serif;
line-height: 1.6;
margin: 20px;
}

    h1 {
        color: #333;
    }

    img {
        max-width: 100%;
        height: auto;
        margin-bottom: 15px;
    }

    .rating {
        display: inline-block;
        unicode-bidi: bidi-override;
        color: #888;
        font-size: 24px;
        height: 1em;
        position: relative;
    }

    .rating span {
        display: inline-block;
        position: absolute;
        overflow: hidden;
        color: #FFD700;
    }

    .rating input {
        display: none;
    }

    .rating label {
        cursor: pointer;
        width: 1em;
        font-size: 24px;
        text-align: center;
        line-height: 1.6;
    }

    .rating label:before {
        content: '★';
    }

    .rating input:checked ~ label,
    .rating input:checked ~ label ~ label {
        color: #FFD700;
    }


<h1>Formatting a research paper according to a specific journal or conference template is crucial for successful submission and publication. Different journals and conferences may have their own guidelines and templates, but there are some common elements that are typically included. </p><div class="separator" style="clear: both;text-align: center"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiYs4d_UmJdl1dBzL0HHbnbTb8USijUB6xV_4S0V0hU4OmgTH2LYilL99IDa-W2LjaSbDXw2vvFo0k-zI7dPn3aeEGLfPOYr0A-nTpTe9hDhRZHcDF5nE7rT8fNmprPoM8Uc-pXS0wqi7eDzTyG-lT2jV2qgiUWk312JngTypfsk2opRbXXKz6_69va-lIK/s725/Screenshot%202024-01-30%20at%2011.28.56%20PM.png" style="margin-left: 1em;margin-right: 1em"><img border="0" height="307" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiYs4d_UmJdl1dBzL0HHbnbTb8USijUB6xV_4S0V0hU4OmgTH2LYilL99IDa-W2LjaSbDXw2vvFo0k-zI7dPn3aeEGLfPOYr0A-nTpTe9hDhRZHcDF5nE7rT8fNmprPoM8Uc-pXS0wqi7eDzTyG-lT2jV2qgiUWk312JngTypfsk2opRbXXKz6_69va-lIK/s320/Screenshot%202024-01-30%20at%2011.28.56%20PM.png" width="320" /></a></div><br /><p>

Here’s a general guide to research paper formatting:

1. Title:

  • The title should be concise, informative, and relevant to the content of the paper.
  • Use title case (capitalize major words).
  • Avoid unnecessary words.

2. Author Information:

  • Include the names, affiliations, and email addresses of all authors.
  • Clearly specify the corresponding author and their contact details.

3. Abstract:

  • Provide a brief summary of the research, including objectives, methods, results, and conclusions.
  • Typically, abstracts are limited to a certain word count.

4. Keywords:

  • Include a list of keywords relevant to the paper’s content.
  • Keywords help in indexing and searching for the paper.

5. Introduction:

  • Introduce the background and context of the research.
  • Clearly state the research problem, objectives, and the significance of the study.
  • End with a brief overview of the paper’s structure.

6. Literature Review:

  • Review relevant literature and previous research in the field.
  • Highlight gaps in existing knowledge that the current study aims to address.

7. Methodology:

  • Clearly describe the research design, methods, and materials used.
  • Include details on data collection, sampling, and statistical analyses.

8. Results:

  • Present the findings of the study in a clear and organized manner.
  • Use tables, figures, and graphs when necessary.
  • Provide statistical details and significance values.

9. Discussion:

  • Interpret the results and discuss their implications.
  • Compare findings with previous research.
  • Address limitations of the study.
  • Propose future research directions.

10. Conclusion:

  • Summarize the main findings and their significance.
  • Restate the research objectives.
  • Suggest practical applications or implications.

11. References:

  • List all the sources cited in the paper.
  • Follow the citation style specified by the journal or conference.

12. Acknowledgments:

  • Acknowledge individuals or institutions that contributed to the research.

13. Appendices:

  • Include supplementary material if necessary, such as raw data or additional details.

Formatting Guidelines:

  • Font and Size:

    • Use a standard font (e.g., Times New Roman, Arial) and size (often 12-point).
    • Check if the template specifies a different font or size.

  • Margins:

    • Follow the specified margin requirements (commonly 1 inch or 2.54 cm).

  • Line Spacing:

    • Typically, use double-spacing for the entire document.

  • Page Numbers:

    • Add page numbers in the specified format and location.

  • Headings and Subheadings:

    • Format headings and subheadings consistently.
    • Use the designated heading styles if provided in the template.

  • Figures and Tables:

    • Ensure that figures and tables are properly labeled and formatted.
    • Follow the template’s instructions for placement.

  • Citations and References:

    • Use the citation style specified by the journal or conference (APA, MLA, IEEE, etc.).
    • Verify the format for in-text citations and the reference list.

Additional Tips:

  • Read the Guidelines:

    • Thoroughly read and follow the formatting guidelines provided by the journal or conference.

  • Use the Provided Template:

    • If a template is provided, use it to ensure adherence to specific formatting requirements.

  • Proofread:

    • Carefully proofread the paper for grammatical errors, typos, and formatting issues.

  • Submission Checklist:

    • Create a checklist based on the journal or conference requirements to ensure nothing is overlooked before submission.

By adhering to the specific formatting guidelines provided by the journal or conference, researchers increase the chances of their papers being accepted and published. It is essential to be meticulous in following the instructions to meet the publication standards of the target venue.

Most Commonly Used Terms in Cricket

Bloganuary writing prompt
What do you complain about the most?
View all responses

Cricket, a popular sport played in many countries, has its own set of terms and terminology. Here are some key terms used in cricket:

Photo by Patrick Case on Pexels.com
  1. Batsman (or Batter): The player from the batting team who is currently in play and facing the bowler.
  2. Bowler: The player from the bowling team who delivers the ball to the batsman.
  3. Wicket: The set of three stumps and two bails at either end of the pitch. A wicket can refer to the dismissal of a batsman as well.
  4. Run: The unit of scoring in cricket. Batsmen score runs by running between the wickets after hitting the ball.
  5. Over: A set of six consecutive legal deliveries bowled by a bowler.
  6. Innings: One side’s or one player’s turn to bat or bowl. In limited-overs cricket, each team typically gets one or two innings, while in Test cricket, each team has two innings.
  7. No Ball: An illegal delivery by the bowler that results in the batting side being awarded an extra run. The batsman cannot be dismissed on a no-ball unless they are run out.
  8. Wide: A delivery that is too wide for the batsman to play a shot, resulting in the batting side being awarded an extra run. The ball is not counted as one of the six in the over.
  9. Extras: Runs scored by the batting team that are not attributed to any batsman’s individual score, such as wides, no-balls, and byes.
  10. Dismissal: The act of getting a batsman out. Common forms of dismissal include bowled, caught, lbw (leg before wicket), run out, and stumped.
  11. Fielding: The defensive aspect of the game, where players try to prevent the batting side from scoring runs by stopping the ball and attempting to dismiss batsmen.
  12. Captain: The leader of a cricket team responsible for on-field decision-making.
  13. Umpire: The officials responsible for ensuring that the game is played in accordance with the rules. There are usually two on-field umpires and a third umpire for TV referrals.
  14. Duck: When a batsman gets out without scoring any runs.
  15. Century: When a batsman scores 100 runs in an innings.
  16. Duckworth-Lewis Method: A mathematical formula used to adjust target scores in limited-overs matches affected by weather interruptions.
  17. Powerplay: A set number of overs at the beginning of an innings in limited-overs cricket during which fielding restrictions are in place.

These are just a few examples, and there are many more cricket terms specific to the rules and nuances of the game.

Climate Impacts Awards: Unlocking urgent climate action by making the health effects of climate change visible

 The aim of this scheme is to make the impacts of climate change on physical and mental health visible to drive urgent climate policy action at scale. We will fund transdisciplinary teams to deliver short-term, high-impact projects that maximise policy outcomes by combining evidence generation, policy analysis, engaged research approaches and communication strategies.

Skip to contenScheme at a glance

Career stage:
Mid-career researcher, Established researcher
Where your administering organisation is based:
Anywhere in the world (apart from mainland China)
Level of funding:

Up to £2.5 million

Duration of funding:

Up to 3 years

Next deadline

Next deadline

Full application deadline: 3 April 2024

View all key dates

About this scheme

In 2023, Wellcome launched the Climate Impacts Awards and funded 11 innovative global projects.

In 2024, we will fund projects that generate context-specific evidence using community knowledge and experiences to deliver actionable policy outcomes that can be scaled to multiple settings. We will prioritise funding for research that involves and serves the needs of communities most impacted by the health effects of climate change, and advances stories and narratives that tend to be absent in the media or underrepresented in public discourse (Perga et al, 2023). This will include generating and/or synthesising relevant data and insights (preferably across multiple sites or countries) on significant health issues arising from climate impacts.

We are looking for proposals with a clear theory of change and strong understanding of policy levers. Policy outcomes should be achievable within the award period, innovative in their design and should support meaningful and sustainable change. Proposals should describe the intended policy outcomes and how new insights and effective communication will influence these outcomes.

Teams must have prior demonstrable success in work that combines science, policy and society (Serrao-Neumann, et al 2021). We use the Organisation for Economic Co-operation and Development (OECD) definition of transdisciplinary research. Transdiscipilary research combines knowledge from different scientific disciplines, citizens, public and private sector stakeholders to address complex societal challenges. By engaging key stakeholders from the outset and embedding different expertise in the research design, we expect that teams will use evidence and impactful narratives on the effects of climate change on health to drive urgent policy change that supports collaborative solutions for climate change adaptation and mitigation. 

This scheme aims to make the impacts of climate change on health visible. There are many reasons the impacts of climate change could be invisible.

These include but are not limited to:

  • distance: decision makers not being based where the impacts are happening
  • ideology: political polarisation results in missing voices, disinformation or lack of information
  • unseen: some of the climate impacts of environmental drivers of health outcomes (for example, certain chemicals, pollutants or microscopic organisms) may not be visible and therefore may be ignored
  • linkage: the links between climate change and health effects not being explicitly made or understood
  • low priority: climate change’s effects on health are not given much focus due to competing priorities, unconvincing analyses and communications challenges.

Motivation for this scheme

Proposals that this funding will support

Eligibility and suitability

Who can apply, who can’t apply, what’s expected of your organisation

About you

You can apply for this award if you are a team leader who wants to advance transdisciplinary research on the impacts of climate change on health.

As the lead applicant, you will be expected to:

  • have experience leading transdisciplinary teams and working in the science-policy-society interface
  • have prior experience of research engaging with policy partners
  • have knowledge brokering skills such as the ability to bring together research teams and impacted communities
  • actively promote a diverse, inclusive and supportive environment within your team and across your organisation.

Your team can include researchers from any discipline (natural, physical and social sciences as well as technology) but must be transdisciplinary (using the OECD definition) and include expertise in policy, public engagement and communications. In addition to strong health expertise, we are particularly interested in teams that can demonstrate strong climate expertise.

During your award we expect you to:

  • fill an important evidence gap where the data and insights generation and/or synthesis could help drive urgent action
  • work across a transdisciplinary team involving researchers, policymakers, communicators, and other key stakeholders including impacted communities
  • co-develop and co-produce evidence to fill the identified gap with the involvement of impacted populations and communities (Vargas et al, 2022)
  • deliver a public engagement and communications strategy that embeds key stakeholders within the design and maximises the intended policy outcomes
  • provide evidence that can help support collaborative solutions to drive urgent climate action.

The award will be held by a lead applicant from an eligible administering institution, on behalf of a team of coapplicants.

At the time of submission the lead applicant:

  • must be able to demonstrate that they have a permanent, open-ended, or long-term rolling contract for the duration of the award
  • must be able to contribute at least 20% of their time to this award
  • must be based at an eligible administering organisation that can sign up to Wellcome’s grant conditions
  • can only be a lead applicant on one application to this scheme. Lead applicants can be included as a coapplicant on one other application, but they must be able to demonstrate that they have sufficient capacity for both projects if funded.

Wellcome cannot make awards to teams with co-lead applicants.

Coapplicants

  • Can be based anywhere in the world (apart from mainland China).
  • Must be able to contribute at least 20% of their time to this project.
  • Must be essential for delivery of the proposed project and provide added value to the team. For example designing the research, writing the application, providing training, knowledge brokering or managing the programme.
  • Must have a guarantee of workspace from their organisation for the duration of the award.
  • Must be based at an eligible organisation that can sign up to Wellcome’s grant conditions.
  • Must include in-country policy actors and/or practitioners, civil servants, private sector, civil society actors.
  • Do not need to have a permanent, open-ended, or long-term rolling contract at their organisation.
  • Can be at any career stage (please clearly outline the career stage of all coapplicants in the application). We would encourage research teams to consist of at least 1 early-career stage researcher.
  • Coapplicants can be listed on a maximum of two applications only. 

Your application can have a maximum of 7 coapplicants. Lead applicants should ensure that each coapplicant provides added value to the team in terms of the expertise and experience outlined in the criteria.

The team

Team members (coapplicants, staff, consultants) must combine researchers from different disciplines, policymakers, community stakeholder representatives and/or engagement experts. We are looking for transdisciplinary teams that can demonstrate strong health as well as climate expertise (particularly climate and meteorological science).

Additional expertise could span across:

  • specific sectors (for example, housing or agriculture)
  • economics
  • political science
  • private sector
  • public engagement
  • media or communications.

Your team should be able to demonstrate:

  • a history of collaborating together and successfully delivering projects among members of the team
  • a strong record of working in climate change and health research
  • a strong record of working with communities most affected by climate change
  • a strong record of working in collaboration with policymakers or decision makers involved in delivering climate solutions
  • experience designing and planning research projects with major policy implications
  • experience designing and delivering communications and/or public engagement activities, co-produced with impacted communities and key stakeholders with clear policy impact.

We will be looking across the team (including lead applicant and coapplicants) for the criteria identified on this page.

Administering organisations

The lead applicant must be based at an eligible administering organisation that can sign up to Wellcome’s grant conditions (can be based in any country apart from mainland China). The project must have a lead applicant based in all countries where the research activities are taking place.

Eligible administering organisations for the proposal can be:

  • higher education institutions
  • research institutes
  • non-academic healthcare organisations
  • not-for-profit or non-governmental organisations

One organisation can submit multiple different applications. 

What’s expected of the administering organisation:

We also expect your administering organisation to:

  • give you, and any staff employed on the grant, at least 10 days a year (pro rata if part-time) to undertake training and continuing professional development (CPD) in line with the Concordat. This should include the responsible conduct of research, research leadership, people management, diversity and inclusion, and the promotion of a healthy research culture
  • provide a system of onboarding, embedding and planning for you when you start the award
  • provide you with the status and benefits of other staff of similar seniority
  • if your administering organisation is a core-funded research organisation, this award should not replace or lead to a reduction in existing or planned core support.

Time spent away from research and part-time working

You can apply if you’ve been away from research (for example, a career break, maternity leave or long-term sick leave). We’ll allow for this when we consider your application. Lead and coapplicants can be part-time. There is no formal minimum, but part-time working needs to be compatible with delivering the proposal successfully.

Inclusive research design

The proposed research should be equitable, diverse and inclusive in a way that is appropriate to the place in which the research is conducted and the aims of the research or other activities.

This should focus on:

  • Who defines and does the research: we expect our partners to demonstrate to us that their research community has substantive input from, and engagement with, the primary end users or subjects of their research, be they patients, participants or policymakers.
  • How the research is done: we expect our partners to demonstrate to us that their research agenda and the design and conduct of their research substantively engages with the needs and values of the people and communities who are participating in, or are the subject of, their research.
  • Who benefits from the research: Wellcome already has a commitment to focusing on those most affected by our health challenges. Accordingly, we expect our research partners to be able to demonstrate within their research and activity plans that their outputs will be made meaningfully accessible and used by those who most need it and, if appropriate, those who participated in the research.

Who can’t apply

You cannot apply if you intend to carry out activities that involve the transfer of grant funds into mainland China.

Other Wellcome awards

  • An early-career researcher can be a lead applicant on one Wellcome award and a coapplicant on one other Wellcome award, or a coapplicant on two Wellcome awards.
  • A mid-career researcher can be a lead applicant on one Wellcome award and a coapplicant on two other Wellcome awards, or a coapplicant on three Wellcome awards.
  • An established researcher can be:
    • a lead applicant on two Wellcome awards, one as the sole applicant and one as lead applicant for a team, or both as the lead applicant for a team. They can also be a coapplicant on two other Wellcome awards; or
    • a lead applicant on one Wellcome award, as the sole applicant or lead for a team, and a co-applicant on three other Wellcome awards; or
    • a coapplicant on four Wellcome awards.  

The awards should be for different research projects, with no overlap in work packages.

Resubmissions

For teams that were shortlisted in the 2023 Climate Impacts Awards, we will only accept resubmissions if there are significant amendments to the application based on the feedback provided.

About your proposal

What is in scope and full application assessment criteria

Wellcome’s Climate & Health team will continue to modify the award each year, guided by learnings and insights from the past year and broader trends in the climate and health space. What is in/out of scope this year may not be the same in subsequent years, as well as the remit and criteria. 

In scope

  • Proposals where the primary focus is on the current or future direct and environmentally mediated physical or mental health outcomes attributable to climate change (Haines & Ebi 2019 for definitions), making the health effects of climate change visible.
  • Proposals that include the four key elements of:
    1. an evidence gap that can be filled in the short time available
    2. a clear policy pathway
    3. engaged research approach with key stakeholders identified
    4. a communications strategy that can drive change.

Out of scope

  • Proposals where the primary focus is on:
    • Socially mediated health effects (such as migration and livelihoods) – we are aware that all health outcomes have a social context but are looking for research where environmentally driven aspects of climate change are the primary driver(s) of a given health outcome.
    • Current or future health effects attributable to the consequences of climate change action (mitigation or adaptation). Wellcome is not looking to fund research on these unintended consequences of maladaptation through this award. We may consider funding opportunities on those topics in the future.
    • Current or future health effects attributable to the drivers of climate change (for example, fossil fuel emissions).
  • Proposals where the goal of the project is general advocacy for a specific issue, rather than specific policy opportunities that can be achieved in a realistic timeframe through targeted and co-produced evidence and communications activities.
  • Proposals where the four key elements are not articulated.
  • Proposals submitted in the first round of the scheme that were not shortlisted.
  • Proposals that were shortlisted in the first round that have not undergone major revision.

How applications will be assessed

Applications will be triaged internally at Wellcome with expert methods advisors. Shortlisted applications will be submitted for review by the Funding Advisory Committee which will make funding recommendations to Wellcome’s Climate & Health team. The team will use these as a basis for final funding decisions. The total number of projects we fund through this award will depend on several factors, such as the number and quality of applications received.

Wellcome has a preference for proposals focused on policy outcomes informed by communities most impacted by climate change in both HICs and LMICs. Wellcome does not have a preference for single or multi-country studies but does have a preference for proposals that aim to demonstrate the scale of the problem and the potential for climate action at scale.

There is no preference for proposals that generate new data versus synthesise available data. Data should be managed/collected following the FAIR Guiding Principles for scientific data management and stewardship.

The Funding Advisory Committee will assess applications based on the following criteria:

Theory of change (25%):

  • Problem articulation: ability to articulate the problem and identify the evidence gap. For example, if your proposal outlines a solution/s, guided by policy analysis and insight. Clarity about the policy opportunity and implications of the proposed activities.
  • Potential to have policy impact in the timeframe of the award. For example, is this work scalable or transferable?
  • Evidence of demand for this research.
  • Relevance of the proposed work in driving context-specific climate action.

Approach and methods (50%):

  • The quality, innovation and mix of methodologies proposed. For example, is the presented theoretical and conceptual framework informed by different perspectives (such as natural sciences, social sciences, epidemiological analysis, economic analysis, political analysis and climate sciences).
    • Justification for the chosen methods, including qualitative and quantitative work packages.
  • Relevance and innovation of the proposed communication strategy. For example, the ability to communicate the policy opportunity, implications of the proposed activities and engagement with key stakeholders.
  • The approach to engaged research:
    • Clear identification and justification of key stakeholders and impacted communities’ involvement (for example, local, or national governments, civil society, community-based organisations, international or multilateral organisations, private sector, local or national government).
    • Evidence of stakeholders and impacted communities contributing to the research design and research questions and their involvement is clearly shown throughout the lifespan of the proposed activities. For example, if the project responds to the needs, interests and capacities of the stakeholders and impacted communities.
    • The engagement methods and framework that will be used and how these are integrated and beneficial to the wider ambitions of the project.
  • Monitoring and evaluation to track and assess the results of planned activities throughout the lifetime of the project.

Team, skill and experience (25%):

  • Transdisciplinary teams: the team composition includes an appropriate combination of individuals and organisations with the capacity, skills and experience to deliver the project and its intended outcomes. Outline how your team will work across the science-policy-society interface and has expertise in climate and health.
  • Successful partnerships: evidence of a history of working together and using a transdisciplinary approach.
  • Evidence that the team has the relevant expertise to deliver the approach and methods outlined. For example, triallists, policy analysis, policy practice, engagement practices and communication strategies.
  • Evidence of a commitment to equity, diversity and inclusion. For example, your approach to recruiting a diverse team and how you will promote inclusion of members in the research and outputs produced.
  • Clear articulation of what a positive research culture is and how teams will foster this through their future work.

The maximum word count for the programme of work description is 3,000 words.

Applicants do not need to submit ethics approval to the administering organisation by the deadline but should give some consideration to potential ethical issues that may arise through the proposed work in the application.

Please provide any relevant links including publications, websites, social media and videos. We advise you to use links strategically, and be sure to include all of the crucial information in the text of the application as the reviewers are not required to go through each link. Any links must be written out in full URL format.

How to apply

Stages of application

1. Before you apply

2. Submit your application to your administering organisation for approval

  • Complete your application on Wellcome Funding.
  • View the sample application form.
  • Submit it to the ‘authorised organisational approver’ at your administering organisation for approval. Make sure you leave enough time for the approver to review and submit your application before the deadline. The approver may ask you to make changes to your application.

3. Administering organisation reviews your application and submits it to us

  • Your application must be submitted by 17:00 BST on the deadline day.  

4. Shortlisting

  • Shortlisting will be carried out internally as the application assessment criteria outlines above.  

5. Funding decision

  • An external expert committee will make funding recommendations to us based on which we will make final funding decisions.
  • You will receive an email notification of the funding decision soon after the decision has been made.
  • The reasons for a decision will be provided to unsuccessful applicants in writing.
Start your application

Log in to our online grants system. You can save your application and return to it any time.

Top Fully Funded PhD and Postdoctoral Programs in Environment and Sustainable Development

By Shashikant Nishant Sharma

 As the global community continues to grapple with pressing environmental challenges, the need for qualified professionals in the field of environment and sustainable development becomes increasingly crucial. Pursuing a PhD or postdoctoral program in this field not only offers individuals the opportunity to contribute meaningfully to addressing environmental issues but also opens doors to diverse career paths in academia, research, policy, and more. In this article, we will explore some of the top fully funded PhD and postdoctoral programs in environment and sustainable development.

  1. Fulbright Scholar Program

The Fulbright Scholar Program is renowned for providing fully funded opportunities for scholars, including those in the field of environment and sustainable development. This program promotes international collaboration and cultural exchange, allowing scholars to conduct research, teach, or pursue advanced studies in the United States and other countries.

  1. European Environmental Agency (EEA) PhD Studentship Program

The EEA offers fully funded PhD studentships in collaboration with various universities across Europe. These programs focus on a wide range of environmental topics, including climate change, biodiversity conservation, and sustainable resource management. Scholars benefit from access to cutting-edge research facilities and a collaborative network of experts.

  1. Swiss Federal Institute of Technology (ETH Zurich) – Doctoral Programs

ETH Zurich is a prestigious institution known for its commitment to sustainability and environmental research. The university offers fully funded doctoral programs in environmental science and engineering, providing students with the opportunity to work on interdisciplinary projects and contribute to sustainable development.

  1. MIT Environmental Solutions Initiative – Postdoctoral Fellowships

The Massachusetts Institute of Technology (MIT) Environmental Solutions Initiative offers postdoctoral fellowships for researchers interested in addressing global environmental challenges. This program provides funding and mentorship to scholars working on innovative and impactful projects related to environmental sustainability.

  1. Australian Research Council (ARC) Centre of Excellence for Environmental Decisions – Postdoctoral Fellowships

The ARC Centre of Excellence for Environmental Decisions in Australia provides postdoctoral fellowships for researchers in the field of environmental science and sustainable development. This program supports projects aimed at enhancing decision-making processes for biodiversity conservation and ecosystem management.

  1. Chinese Academy of Sciences (CAS) – International Postdoctoral Exchange Fellowship Program

The CAS International Postdoctoral Exchange Fellowship Program encourages international collaboration in environmental research. This fully funded program allows postdoctoral researchers to work with leading Chinese institutions on projects related to environmental protection, climate change, and sustainable development.

  1. United Nations University – PhD Fellowships in Sustainability Science

The United Nations University offers fully funded PhD fellowships in Sustainability Science, focusing on research that addresses global sustainability challenges. Fellows have the opportunity to work with leading experts and contribute to policy-relevant research in areas such as climate change, sustainable development, and natural resource management.

Conclusion

Embarking on a fully funded PhD or postdoctoral program in environment and sustainable development opens up exciting opportunities for researchers to make meaningful contributions to the global effort to address environmental challenges. These programs not only provide financial support but also offer access to cutting-edge research facilities, expert mentorship, and a network of like-minded professionals. As the demand for skilled individuals in this field continues to grow, these top programs play a crucial role in nurturing the next generation of leaders and innovators committed to creating a more sustainable and resilient world.

References

Åkerlind*, G. S. (2005). Postdoctoral researchers: roles, functions and career prospects. Higher Education Research & Development, 24(1), 21-40.

Fairman, J. A., Giordano, N. A., McCauley, K., & Villarruel, A. (2021). Invitational summit: Re-envisioning research focused PHD programs of the future. Journal of Professional Nursing, 37(1), 221-227.

Ginther, D. K., & Heggeness, M. L. (2020). Administrative discretion in scientific funding: Evidence from a prestigious postdoctoral training program✰. Research policy, 49(4), 103953.

Gould, J. (2015). How to build a better PhD. Nature, 528(7580), 22.

Universities offering doctoral and post doctoral courses in health economics and sustainable development. (2024, January 29). Edupub.org. https://articles.edupub.org/2024/01/universities-offering-doctoral-and-post.html




Impact of Financial Literacy on Retirement Planning of Women Employees in Public Electricity Companies in Telangana

By S. Kavitha Devi & M. Priyanka

Abstract:

The purpose of this research is to investigate the Impact of financial literacy on retirement planning of women employees in public Electricity companies in Telangana. The current research study is an investigative and exploratory research. It uses primary data. The study examined partial least square-structural equation modelling (PLS-SEM) obtained by sampling data from 406 women employees of Public Electricity Companies in Telangana. The findings of this study have important inferences for both researchers and practitioners in the field of personal finance. They highlight the significance of FL in influencing individuals’ Retirement Planning. Moreover, the role of psychological factors emphasizes the need to consider these factors when examining the relationship between FL and Retirement Planning. These findings suggest that interventions aimed at improving FL should also focus on enhancing individuals’ Psychological Factors and cultivating positive Retirement Planning Behavior.

 

Keywords:  financial literacy; financial risk tolerance; retirement planning; herding behavior.

Introduction

Financial education or financial literacy has gained relevance in recent years as a result of the rising complexity of the financial products and services available, as well as information asymmetry between financial service providers and consumers. Financial education is the process of obtaining the information and abilities needed to handle and use money in an educated and efficient manner. It is a lifelong process that assists people and households in becoming more knowledgeable about the financial goods and services offered in the market in order to make wise decisions regarding their use. Financial education is broadly defined as the understanding of financial market products, particularly rewards and risk, in order to make educated decisions. Organisation for Economic Co-operation and Development (OECD, 2013) has defined financial education as “the process by which financial consumers/ investors improve their understanding of financial products, concepts and risks through information, instruction and/or objective advice, develop the skills and confidence to become more aware of financial risks and opportunities, to make informed choices, to know where to go for help, and to take other effective actions to improve their financial well-being”. According to Standard & Poor’s Ratings Services Global Financial Literacy Survey, 2014 “76% of Indian adults do not understand key financial concepts like inflation, compounded interest rate, and risk diversification adequately. This finding says that financial literacy is lower than the worldwide average”. Authors Lusardi and Mitchell, 2011, Bucher-Koenen and Lusardi, 2011, Grohmann et al. have revealed in their papers that there is a positive impact of financial literacy on retirement planning.

The development and expansion of any country is heavily influenced by its economic condition. Proper capital formation is necessary to stimulate the process of economic growth. The financial market is crucial in accelerating capital development by encouraging savings and using investment alternatives, which contributes to speeding up the process of wealth creation.

Being a developing country, India needs rapid capital generation. This could only be accomplished by encouraging smart planning and guiding people’s financial habits. The Indian economy has expanded at a quicker rate from the previous decade, however in order to achieve the goal, economic growth alone is not enough must improve citizen living standards. According to Singh (2008) “development cannot be measured only in terms of growth, instead the objective must be to achieve the improvement in the standard living of people.”

According to Ahuwalia (2008) “Indians are poor investors but smart savers. They do not prepare for the long term and do not invest in long-term investment products. Furthermore, it was stated that Indians like to save their money into their houses instead of saving in banks or other investments. This will be a major issue in India, where social security is non-existent”.

Indian Population Context:

 

(Source: IMPORTANCE OF SAVINGS FOR RETIREMENT AND EARLY DECISION

MAKING IN HUMAN LIFE, N Sheikh & S Karnati – 2021)

India is young demographically with 90% of population under the age of 60 years but ageing gradually, it is estimated that persons above the age of 60 would increase from ~8.9% of the population now to ~15% by 2050. Those above 80 are likely to increase from ~0.9% to ~2.8%. According to United Nations World Population Prospects, India’s 60-plus population is expected to reach 323 million by 2050 – a number greater than US Population of 2012.

Figure above shows historical data and future forecasts on the Indian population’s dependency from 1980 to 2050. It can be seen that the percentage of dependent people gradually increased between1980 to 2015. However, the share of the dependent population is predicted to rise faster between 2015 and 2050. In 2050, 15% of India’s elderly population would be dependent on the working population.

Despite the fact that the transition from a young to an older age structure indicates a successful and satisfying outcome of health improvement, the rate of old and the size of the Older population with diverse requirements and resources creates various obstacles for health care providers and Government officials. The percentage of old age people has increased and is expected to increase further, while the percentage of the young age group is decreasing, resulting in a slow but continuous shift to an older population structure in the country. Furthermore, the transition from a young age structure is not uniform across the country. A rising old population requires increased quantity and quality of elder services, income security, and overall improved quality of life. The necessity for social pension payments and the resulting financial outlays to meet expanding old-age dependency and a decreasing support base is more demanding for policy consideration now and in the future.

Research Gap

According to the review of the literature, even though women’s literacy rates have improved significantly in recent years, there are still significant gender gaps in financial education in

India. More research is needed on the factors that contribute to these gaps and an apparent gap is observed in understanding the retirement financial planning of women in India. Previous research on financial literacy usually focuses on its potential effects on financial decision-making; however, little research is done on its effects on retirement planning. Therefore, the present study having spotlight on Financial Literacy and Retirement planning aimed and focused on women employees in electricity companies in Telangana. Majorly it considers respondents awareness levels towards financial literacy and retirement planning decisions of respondents using three components to calculate the financial literacy (financial knowledge, financial attitude and financial behaviour) of women employees to assess the holistic impact on retirement planning decisions. We examine the potential effects of financial literacy on retirement planning of women employees in Public electricity companies in Telangana. This study will fill in this research gap. 

Objectives of Research

1)         To find the relationship between financial literacy levels and retirement financial planning.

2)         To study the impact of psychological constructs variables on the retirement planning of women employees in public electricity companies of Telangana and analyses the financial literacy levels.

Hypotheses

Hypotheses are considered to be the most significant tool in a research study. It makes a difference in representing new tests and their views. Hypotheses are based on fundamental assumptions in every research study. Following a thorough analysis of the relevant literature, an attempt was made to create the conditional assumption in constructing the test and its reasonable consequences. The following hypotheses have been developed for the aim of the research.

H01: There is no significant relationship between financial literacy levels and retirement financial planning.

H02: There is no significant influence of psychological constructs on retirement financial planning.

H02a: There is no significant influence of Future time prospective on retirement financial planning.

H02b: There is no significant influence of Attitude towards Retirement on retirement financial planning.

H02c: There is no significant influence of Risk tolerance on retirement financial planning.

H02d: There is no significant influence of Retirement Goal Clarity on retirement financial planning.

Methodology

Primary Data

Primary Data collected through a Survey Questionnaire from the respondents of women employees in Public Electricity Companies in Telangana

For current study both convenience and snowball sampling methods (non-probability) sampling techniques were used to recruit potential samples for the achievement of the research objectives. Convenience sampling refers to the collection of data from immediately available representative respondents of the population of the study. Convenience sampling would help a researcher when he could not have access to the entire population of the study and/or when a researcher had difficulty identifying the representative sample of the study.

Snowball sampling refers to the researcher initially recruiting participants, and these initial participants help to recruit future respondents for the study. This technique helps the researcher when he is facing challenges or difficulties to collect data from the target potential population of the study. The researcher may be face difficulty due to unknown to the respondents and hesitate to give important personal information to strangers.

This study involved the collection of personal and financial information of the respondents. Some respondents may be unwilling to provide their personal and financial information.

Therefore, convenience and snowball sampling techniques were employed in this study to gather the data to evaluate the research hypothesis. The blend of convenience and snowball sampling methods helps to achieve reliable results for the research investigation.

Secondary Data:

Secondary data collected from various Publications, Journals, Articles, Newspapers and official websites Viz. RBI, SEBI, IRDAI, PFRDA, NCFE, etc.,

Period of the study is between July 2022 and November 2022.

Calculation of Sample Size

The present research study is an investigative in nature, the study is done based on four public electricity companies in Telangana selected on the basis of population as criteria. In order to study the perception of women employee’s financial retirement planning from each company, sample variables are selected proportionately. Hence the total sample size is 406.

Sl.

No.

Name of the        company

Population (women

employees)

1

TSSPDCL

1320

2

TSNPDCL

1182

3

TSGENCO

2429

4

TSTRANSCO

2125

TOTAL

7056

                          (Source: collected from respective HR Department by Researcher)

 

The total women employees of Public Electricity Companies in Telangana is 7056, out of that population the sample is detrained and drawn according to Krejcie Morgan table, at Confidence Level of 95%, Confidence Interval is 4%, Proportion is 5% and if Population is below 8000,

Sample size determined is 367 respondents. In present study 430 respondents sample size was taken, among them 406 were found to be relevant for study.

Proportionately the sample is determined from each company as follows:

 

Sl.

 

No.

Name        of        the company

Population

(womenemployees)

Proportionatesample

1

TSSPDCL

1320

80

2

TSNPDCL

1182

72

3

TSGENCO

2429

131

4

TSTRANSCO

2125

123

TOTAL

7056

406

 

Measurement of Reliability

Cronbach’s Alpha

No of Items

0.867

45

The degree of consistency between multiple measurements of variables was measured by the reliability test. Reliability calculates the accuracy and precision of a measurement procedure. Cronbach’s Alpha is widely used to measure thereliability of data. The coefficient of Cronbach’s Alpha value for financial literacy and retirement planning of womenemployees in public electricity companies of Telangana for 45 variables was 0.867 as presented in the above table.

Analytical Tools and Software

The current research study is an investigative and exploratory research. It uses primary data. Thus data would be analyzed through descriptive statistics, structural equation modeling, factor analysis and frequency tables etc, The software package like SmartPLS is used to analyze the data.

Data Analysis and Results:

Correlation Between Latent Constructs

Correlation refers to the extent to which two variables move together in a systematic way. It quantifies the strength and direction of the relationship between variables. Correlation coefficients, often represented as path coefficients in SEM, indicate the extent to which the latent constructs are related.

 Correlation between latent constructs

Constructs

Financial Literacy

FUTURE TIMEPERSPECTIVE

ATTITUDETOWARDSRETIREMENT

RISKTOLERANCE

RETIREMENTGOALCLARITY

SOCIALGROUPSUPPORT

PLANNINGACTIVITY

SAVINGS

Financial Literacy

1.000

0.320

0.303

0.417

0.272

0.449

0.443

0.268

FUTURE TIMEPERSPECTIVE

0.320

1.000

0.326

0.299

0.293

0.322

0.318

0.288

ATTITUDETOWARDSRETIREMENT

0.303

0.326

1.000

0.284

0.277

0.305

0.301

0.274

RISKTOLERANCE

0.417

0.299

0.284

1.000

0.255

0.420

0.414

0.251

RETIREMENTGOALCLARITY

0.272

0.293

0.277

0.255

1.000

0.274

0.270

0.245

SOCIALGROUPSUPPORT

0.449

0.322

0.305

0.420

0.274

1.000

0.445

0.270

PLANNINGACTIVITY

0.443

0.318

0.301

0.414

0.270

0.445

1.000

0.266

SAVINGS

0.268

0.288

0.274

0.251

0.245

0.270

0.266

1.000

 

These correlations provide insights into the relationships between the latent constructs. For example, Retirement Planning is positively associated with Financial Literacy. As well as, FTP, ATR, RT, RGC, SGS, PA and Savings shows positive associations with Financial Literacy. However, it’s important to note that correlation does not imply causation, and further analysis is needed to understand the underlying factors influencing these relationships.

Common Method Bais (CMB)

The Common method bias can be caused by different groups responding differently to the same questions or scales, leading to inaccurate results(Podsakoff & Organ, 1986). Another source of bias is the researcher’s own expectations or preconceptions about the data. This could lead to a researcher interpreting the data in a way inconsistent with the actual results. (MacKenzie & Podsakoff, 2012)  (Spector, 2006).

Inner Model VIF Values using Random Variable method

Constructs

Random Variable

Financial Literacy

1.720

Future Time perspective

1.303

Attitude Towards Retirement

1.507

Risk Tolerance 

1.635

Retirement Goal Clarity

1.121

Social Group Support

1.565

Planning Activity

1.626

Savings

1.747

 

To mitigate the CMB, used different anchors of constructs while collecting the data from respondents, different scales were also adopted, research instrument was pre-tested with two academicians in the field and six respondents. and report a full collinearity measure by reporting that all inner and Outer VIF values are less than 3.3(Kock & Lynn, 2012) (Kock, 2015). 

Hence the model is free from CMB.

Factor Loading and AVE ( From author collected data)

 

 

These results indicate that the indicators generally have strong to moderate relationships with their respective constructs, and the constructs explain a substantial amount of variance in their indicators.

Model Assessment Procedure:

The Model Assessment Procedure introduced by Hair et al. in 2017a is a methodology used to evaluate the performance and validity of a statistical model. This procedure involves several steps to ensure the accuracy and reliability of the model’s results. The Model Assessment Procedure by Hair et al. provides a systematic framework for developing and evaluating statistical models, ensuring that they are robust, reliable, and appropriate for the research objectives at hand.

1.     Evaluation of the Measurement Model:

1.1.Internal Consistency & Reliability: Internal consistency and reliability are important concepts in the field of measurement and psychometrics. They refer to the extent to which a measurement instrument, such as a questionnaire or a test, consistently and reliably measures a particular construct or attribute.

 

 

 

 

Reliability Thresholds

Constructs

Cronbach’s alpha

Composite reliability (rho_a)

Composite reliability (rho_c)

Future Time Prospective

0.702

0.783

0.812

Attitude Towards Retirement

0.700

0.711

0.752

Risk Tolerance

0.720

0.743

0.753

Retirement Goal Clarity

0.909

0.923

0.931

Social Group Support

0.702

0.719

0.749

Planning Activity

0.726

0.730

0.731

Savings

0.715

0.721

0.765

Cronbach’s alpha values greater than 0.60 for the early stages of the research, values of at least 0.70 required, values higher than 0.95 are not desirable(Nunnally,1978)

Cronbach’s alpha can be considered the lower bound and composite reliability(rho_c) the upper bound of the exact internal consistency and reliability.                               

1.2.Discriminant validity: Discriminant validity is a concept in measurement and psychometrics that assesses the extent to which different measures or indicators of distinct constructs are distinct or discriminate from each other. It examines whether measures designed to capture different constructs are truly measuring separate concepts and not converging or overlapping.

                                                Heterotrait-Monotrait Ratio (HTMT)

Constructs

Attitude Towards Retirement

F L

F T P

P A

R G C

R P

R T

Savings

Financial Literacy

0.61

 

 

 

 

 

 

 

Future Time Prospective

0.60

0.84

 

 

 

 

 

 

Planning Activity

0.57

0.83

0.86

 

 

 

 

 

Retirement Goal Clarity

0.52

0.76

0.41

0.80

 

 

 

 

Retirement Planning

0.51

0.65

0.54

0.72

0.74

 

 

 

Risk Tolerance

0.49

0.97

0.69

0.53

0.63

0.66

 

 

Savings

0.45

0.66

0.57

0.85

0.55

0.59

0.68

 

Social Group Support

0.44

0.71

0.60

0.65

0.54

0.62

0.61

0.78

 

Based on the HTMT values and their confidence intervals, it can be concluded that all the constructs (Financial Literacy, Future Time Prospective, Planning Activity, Retirement Goal Clarity, Retirement Planning, Risk Tolerance, Savings, Social Group support) exhibit discriminant validity. This suggests that these constructs are distinct from each other and do not overlap significantly in measurement.

 

2.     Evaluation of the Structural model:

Evaluation of the Structural Model involves assessing collinearity among constructs, significance and relevance of path coefficients, predictive accuracy (R-squared, F-squared, Q-squared, PLS predict), predictive model selection, and goodness-of-fit.

2.1. Collinearity among constructs:

The Variance Inflation Factor (VIF) is a measure of the degree of multicollinearity between predictor variables in a linear regression model. A VIF of 1 indicates no correlation between the predictor variable and other predictor variables in the model, while a VIF more significant than 1 indicates some degree of multicollinearity. Typically, a VIF value of 5 or greater indicates high multicollinearity and may require corrective action. The VIF values were, listed in Table 5.6, below 5 confirm there was non-existence of multi-collinearity between constructs in this study. . For this, we report a full collinearity measure by reporting that all inner VIF values are less than 3.3 (Kock & Lynn, 2012)(Kock, 2015).

Inner Model VIF Values

Constructs

Attitude Towards Retirement

F L

FTP

PA

RGC

RP

RT

Savings

SGS

Financial Literacy

 

 

 

 

 

1.458

 

 

 

Retirement Planning

1.659

 

1.885

1.215

1.632

 

1.145

1.745

1.656

Source: Calculated by Author

In summary, based on the VIF values provided, there is no substantial collinearity issue among the constructs in the model. The VIF values are all relatively low, indicating that the variables are not highly correlated, and the model is not affected by multicollinearity.

2.2.  Hypotheses Testing:

 

After confirmation of the reliability and validity of the outer model, the significance of research model (hypothesized) relationships was examined with standardized path coefficient (b) and critical value (T-Value) at the significant level of 5 % (P-Values) by using the PLS bootstrapping. 

The first hypothesis (H1) is supported by (β=0.626, P<0.05) Financial Literacy positively effects Retirement Planning.The second hypothesis (H2) is supported by (β=0.932, P<0.05) Retirement Planning positively effects Future Time Prospective.The third hypothesis(H3) is supported by (β=0.905, P<0.05)  Retirement Planning positively effects Savings. The fourth hypothesis(H4) is also supported (β=0.817, P<0.05) as Retirement Planning has a positive significant effect on ATR. The fifth hypothesis (H5) is also supported (β=0.874, P<0.05) as Retirement Planning has a positive significant effect on Planning Activity.

The sixth hypothesis (H6) is also supported (β=0.839, P<0.05) as Retirement Planning has a positive significant effect on Risk Tolerance. 

The seventh hypothesis (H7) is supported by (β=0.921, P<0.05), as Retirement Planning has a positive significant effect on Retirement Goal Clarity. 

The eighth hypothesis(H8) is supported by (β=0.892, P<0.05), as Retirement Planning has a positive significant effect on Social Group Support.

Hypothesis Results

Hypothesis

Relationship

Path Coefficients  (b)

Standard Deviation (STDEV)

T Value (|b/STDEV|)

P Values

Decision

H1

Financial Literacy – Retirement Planning

0.626

0.057

10.982

0.000

supported

H2

Retirement Planning Future Time Prospective

0.932

0.043

21.674

0.000

supported

H3

Retirement Planning –Savings

0.905

0.039

23.205

0.000

Supported

H4

Retirement Planning-> Attitude Towards Retirement

0.817

0.046

17.760

0.001

supported

H5

Retirement Planning-> Planning Activity

0.874

0.048

18.208

0.000

supported

H6

Retirement Planning- Risk Tolerance

0.839

0.071

11.816

0.012

supported

H7 

Retirement planning-Retirement Goal Clarity

0.921

0.083

11.096

0.000

supported

H8 

Retirement planning- Social Group Support

0.892

0.049

18.204

0.000

supported

2.3.Goodness-of-fit: For PLS-SEM SRMR will give a goodness-of-fit index.

Standardized root mean square residual (SRMR): squared discrepancy between the observed correlations and the model implied indicator correlations.

SRMR assessing the quality of the whole model results (i.e., jointly evaluating the outer and inner model results). It Should be less than 0.08 (Hair et al.,2019).

As per PLS algorithm results, the research model’s SRMR is 0.075, which is less than the threshold limit (0.08). Hence it is concluded as our model has a good fit.

Discussion:

The frequency statistics of age represent that most of the women working in Public Electricity companies in Telangana were aged between 31 to 40 years representing almost 32.5 %; aged between 41 to 50 years represented 29.3 %, 21.2 % of respondents were from the age group of 51-60 years and 7 % of respondents were above the age 60 who were near to retirement and 10.0% of individuals falls under the age group 20 to 30 years. All the respondents were below their retirement age. The Profession of the respondents were either financial or non-financial. Maximum respondents i.e., 61.33% respondents were from non-financial background. The rest 38.66% respondents were from financial background. Findings of the study reveal that most of the respondents were from non-financial background. 

The findings of this study have important inferences for both researchers and practitioners in the field of personal finance. They highlight the significance of FL in influencing individuals’ Retirement Planning. Moreover, the role of psychological factors emphasizes the need to consider these factors when examining the relationship between FL and Retirement Planning. From a practical standpoint, these findings suggest that interventions aimed at improving FL should also focus on enhancing individuals’ Psychological Factors and cultivating positive Retirement Planning Behavior. This could be achieved through targeted educational programs, financial counselling, and promoting a financial environment that fosters positive financial behaviors.

Conclusion:

Result shows that those who practice constructive financial habits tend to achieve good Retirement Planning. The well Retirement Planning can be enhanced through sound FL, FTP, ATR, SGS, RGC, Planning Activity, Savings. Among the predictors of Retirement Planning, Psychological factors has a higher impact followed by financial literacy of women employees. It is very important to understand the concepts like the impact of simple and compound interest rates, understands inflation, risk diversification, and the time value of money, have a positive perception of money, budget money in a planned manner, and explore financial products/services like a savings account, debit card, credit card, and insurance, to achieve the Retirement Planning of women employees.  The research model has explained 39.2% of the variance in financial wellbeing. So, it can be concluded as Retirement Panning is a long-term goal to achieve by admitting financial literacy, psychological factors. By prioritizing financial literacy, psychological factors individuals can achieve Retirement Planning and improve their overall quality of life.

References:

[1.]       Hogarth, R. M., & Karelaia, N. (2006). “Take-the-best” and other simple strategies: why and when they work “well” with binary cues. Theory and Decision, 61, 205-249.

[2.]       Lusardi, A., & Tufano, P. (2015). Debt literacy, financial experiences, and overindebtedness. Journal of Pension Economics & Finance, 14(4), 332-368.

[3.]       Lusardi, A. (2008). Financial literacy: an essential tool for informed consumer choice? (No. w14084). National Bureau of Economic Research.

[4.]       Lusardi, A. (2008). Household saving behavior: The role of financial literacy, information, and financial education programs (No. w13824). National Bureau of Economic Research.

[5.]       Thomson, S., De Bortoli, L., Underwood, C., & Schmid, M. (2020). PISA 2018: Financial Literacy in Australia.

[6.]       Awais, M., Laber, M. F., Rasheed, N., & Khursheed, A. (2016). Impact of financial literacy and investment experience on risk tolerance and investment decisions: Empirical evidence from Pakistan. International Journal of Economics and Financial Issues, 6(1).

[7.]       Garman, E. T., MacDicken, B., Hunt, H., Shatwell, P., Haynes, G., Hanson, K. C., … & Woehler, M. B. (2007). Progress in measuring changes in financial distress and financial well-being as a result of financial literacy programs. Consumer interests annual, 53, 199-211.

[8.]       Beal, D., & Delpachitra, S. (2003). Financial literacy among Australian university students. Economic Papers: A journal of applied economics and policy, 22(1), 65-78.

[9.]       Bianchi, M. (2018). Financial literacy and portfolio dynamics. The Journal of Finance, 73(2), 831-859.

[10.]    Bialowolski, P., Cwynar, A., Xiao, J. J., & Weziak‐Bialowolska, D. (2020). Consumer financial literacy and the efficiency of mortgage‐related decisions: New evidence from the Panel Study of Income dynamics. International Journal of Consumer Studies.

[11.]    Lusardi, A., & Tufano, P. (2015). Debt literacy, financial experiences, and overindebtedness. Journal of Pension Economics & Finance, 14(4), 332-368.

[12.]    Taft, M. K., Hosein, Z. Z., Mehrizi, S. M. T., & Roshan, A. (2013). The relation between financial literacy, financial wellbeing and financial concerns. International journal of business and management, 8(11), 63.

[13.]    Chu, Z., Wang, Z., Xiao, J. J., & Zhang, W. (2017). Financial literacy, portfolio choice and financial well-being. Social indicators research, 132(2), 799-820.

[14.]    Frijns, B., Gilbert, A., & Tourani-Rad, A. (2014). Learning by doing: The role of financial experience in financial literacy. Journal of Public Policy, 34(1), 123-154.

[15.]    Ameliawati, M., & Setiyani, R. (2018). The influence of financial attitude, financial socialization, and financial experience to financial management behavior with financial literacy as the mediation variable. KnE Social Sciences, 811-832.

[16.]    Khan, F., & Surisetti, S. (2020). Financial well-being of working women: mediating effect of cashless financial experience and digital financial self-socialization. Khan, F and Surisetti, s. Financial well-being of working women: mediating effect of cashless financial experience and digital financial self-socialization. MDIM Business Review, 1(2), 51-68.

[17.]    Nikolaos D. Philippas & Christos Avdoulas (2020) Financial literacy and financial well-being among generation-Z university students: Evidence from Greece, The European Journal of Finance, 26:4-5, 360-381, DOI:  10.1080/1351847X.2019.1701512

[18.]    Yakoboski, P. J., Lusardi, A., & Hasler, A. Financial literacy and well-being in a five generation America.

[19.]    Pandey, A., Ashta, A., Spiegelman, E., & Sutan, A. (2020). Catch them young: Impact of financial socialization, financial literacy and attitude towards money on financial well‐being of young adults. International Journal of Consumer Studies, 44(6), 531-541.

[20.]    Chhatwani, M. (2022). Mortgage delinquency during COVID-19: do financial literacy and personality traits matter?. International Journal of Bank Marketing.

[21.]    Thongrak, N., Chancharat, S., & Kijkasiwat, P. (2021). Financial Literacy: Does It Improve Well-being? A Case Study of Farmers in Khon Kaen, Thailand. In Environmental, Social, and Governance Perspectives on Economic Development in Asia. Emerald Publishing Limited

[22.]    Nunnally, J. C. (1978). Psychometric Theory 2nd ed. Mcgraw hill book company.

[23.]    Gutiérrez‐Nieto, B., Serrano‐Cinca, C., & de la CuestaߚGonzález, M. (2017). A multivariate study of over‐indebtedness’ causes and consequences. International Journal of Consumer Studies, 41(2), 188-198.

[24.]    Huston, S. J. (2010). Measuring financial literacy. Journal of consumer affairs, 44(2), 296-316.

[25.]    Fornero, E., Monticone, C., & Trucchi, S. (2011). The effect of financial literacy on mortgage choices.

[26.]    Lusardi, A., & Mitchell, O. S. (2014). The economic importance of financial literacy: Theory and evidence. Journal of economic literature, 52(1), 5-44.

[27.]    Bhalla, G. S., & Chadha, G. K. (1982). Green Revolution and the Small Peasant: A Study of Income Distribution in Punjab Agriculture: II. Economic and Political Weekly, 870-877.

[28.]    Madi, A., & Yusof, R. M. (2018). Financial Literacy and Behavioral Finance: Conceptual Foundations and Research Issues. Journal of Economics and Sustainable Development, 9(10), 81-89.

[29.]    Wolfe-Hayes, M. A. (2010). Financial literacy and education: An environmental scan. The International Information & Library Review, 42(2), 105-110.

[30.]    Amisi, S. A. R. A. H. (2012). The effect of financial literacy on investment decision making by pension fund managers in Kenya (Doctoral dissertation).

[31.]    Musundi, K. M. (2014). The effects of financial literacy on personal investment decisions in real estate in Nairobi count (Doctoral dissertation, University of Nairobi).

[32.]    Financial Express Bureau, November 24,2020

[33.]    Atkinson, A., & Messy, F. A. (2012). Measuring financial literacy: Results of the OECD/International Network on Financial Education (INFE) pilot study.

 

 

 

 

LINK to DOWNLOAD PDF