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Behavior Research Methods impact factor, indexing, ranking (2024)

Aim and Scope

The Behavior Research Methods is a research journal that publishes research related to Arts and Humanities; Psychology . This journal is published by the Springer Nature. The ISSN of this journal is 15543528, 1554351X . Based on the Scopus data, the SCImago Journal Rank (SJR) of behavior research methods is 2.753 .

Behavior Research Methods Ranking

The latest Impact Factor list (JCR) is released in June 2024.

The Impact Factor of Behavior Research Methods is 4.6.

The impact factor (IF) is a measure of the frequency with which the average article in a journal has been cited in a particular year. It is used to measure the importance or rank of a journal by calculating the times its articles are cited.

The impact factor was devised by Eugene Garfield, the founder of the Institute for Scientific Information (ISI) in Philadelphia. Impact factors began to be calculated yearly starting from 1975 for journals listed in the Journal Citation Reports (JCR). ISI was acquired by Thomson Scientific & Healthcare in 1992, and became known as Thomson ISI. In 2018, Thomson-Reuters spun off and sold ISI to Onex Corporation and Baring Private Equity Asia. They founded a new corporation, Clarivate , which is now the publisher of the JCR.

Important Metrics

behavior research methods Indexing

The behavior research methods is indexed in:

• Web of Science (SSCI)

An indexed journal means that the journal has gone through and passed a review process of certain requirements done by a journal indexer.

The Web of Science Core Collection includes the Science Citation Index Expanded (SCIE), Social Sciences Citation Index (SSCI), Arts & Humanities Citation Index (AHCI), and Emerging Sources Citation Index (ESCI).

Behavior Research Methods Impact Factor 2024

The latest impact factor of behavior research methods is 4.6 which is recently updated in June, 2024.

The impact factor (IF) is a measure of the frequency with which the average article in a journal has been cited in a particular year. It is used to measure the importance or rank of a journal by calculating the times it's articles are cited.

Note: Every year, The Clarivate releases the Journal Citation Report (JCR). The JCR provides information about academic journals including impact factor. The latest JCR was released in June, 2023. The JCR 2024 will be released in the June 2024.

Behavior Research Methods Quartile

The latest Quartile of behavior research methods is Q1 .

Each subject category of journals is divided into four quartiles: Q1, Q2, Q3, Q4. Q1 is occupied by the top 25% of journals in the list; Q2 is occupied by journals in the 25 to 50% group; Q3 is occupied by journals in the 50 to 75% group and Q4 is occupied by journals in the 75 to 100% group.

Journal Publication Time

The publication time may vary depending on factors such as the complexity of the research and the current workload of the editorial team. Journals typically request reviewers to submit their reviews within 3-4 weeks. However, some journals lack mechanisms to enforce this deadline, making it difficult to predict the duration of the peer review process.

The review time also depends upon the quality of the research paper.

Call for Papers

Visit to the official website of the journal/ conference to check the details about call for papers.

How to publish in Behavior Research Methods?

If your research is related to Arts and Humanities; Psychology, then visit the official website of behavior research methods and send your manuscript.

Tips for publishing in Behavior Research Methods:

• Selection of research problem.
• Presenting a solution.
• Designing the paper.
• Make your manuscript publication worthy.
• Write an effective results section.
• Mind your references.

Acceptance Rate

Final summary.

• The impact factor of behavior research methods is 4.6.
• The behavior research methods is a reputed research journal.
• It is published by Springer Nature .
• The journal is indexed in UGC CARE, Scopus, SSCI, PubMed .
• The (SJR) SCImago Journal Rank is 2.753 .

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Behavior research methods scite analysis.

4.9K articles received 221.4K citations see all

• 5,047 Supporting
• 212,618 Mentioning
• 650 Contrasting

Behavior Research Methods Editorial notices

• 3 Retractions
• 0 Withdrawals
• 34 Corrections
• 0 Expression of Concern

FAQs on Behavior Research Methods

How long has behavior research methods been actively publishing.

Behavior Research Methods has been in operation since 1968 till date.

What is the publishing frequency of Behavior Research Methods?

Behavior Research Methods published with a Quarterly frequency.

How many articles did Behavior Research Methods publish last year?

In 2023, Behavior Research Methods publsihed undefined articles.

What is the eISSN & pISSN for Behavior Research Methods?

For Behavior Research Methods, eISSN is 1554-3528 and pISSN is 1554-351X.

What is Citescore for Behavior Research Methods?

Citescore for Behavior Research Methods is 10.7.

What is SNIP score for Behavior Research Methods?

SNIP score for Behavior Research Methods is 3.57.

What is the SJR for Behavior Research Methods?

SJR for Behavior Research Methods is Q1.

Who is the publisher of Behavior Research Methods?

SPRINGER is the publisher of Behavior Research Methods.

Copyright 2024 Cactus Communications. All rights reserved.

Behavior Research Methods -Impact Score, Ranking

About Behavior Research Methods

Behavior Research Methods is a reputed research journal publish the research in the field/area related to Arts and Humanities (miscellaneous) (Q1); Developmental and Educational Psychology (Q1); Experimental and Cognitive Psychology (Q1); Psychology (miscellaneous) (Q1) . It is published by Springer Nature . The journal has an h-index of 160. The overall rank of this journal is 730 . The more details like ISSN, Journal Quartile, SJR Score, ISSN, and other important details are provided in the following section.

Important Metrics

Behavior Research Methods Impact Score 2024

The latest impact score of Behavior Research Methods is 6.54.

Credit & Source: Scopus.

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Rating vs. Ranking Scales: Exploring the Differences and Their Impact on Data

Explore the differences between rating and ranking scales, the advantages and disadvantages of each, and how to choose the right question type for your survey.

Sriya Srinivasan

May 5, 2023

In this Article

Survey questions play a vital role in collecting accurate and actionable data in consumer research. In a scenario where an e-commerce giant endeavors to refine its product recommendation system. Should they rely on a rating scale , allowing customers to assign numerical values to their satisfaction with past purchases, or would a ranking scale, seeking to understand the order of priority for preferred items, better serve their goals? The choice between these scales can redefine the e-commerce experience, guiding users towards tailored product suggestions that resonate deeply with their tastes.

The choice between Rating and Ranking Scales has long been a topic of intrigue and deliberation. These two distinctive measurement techniques hold the power to unlock invaluable insights into human preferences, opinions, and behaviors. As researchers and decision-makers strive to harness the full potential of data, understanding the nuances and impact of rating vs. ranking scales becomes an indispensable voyage towards data-driven precision.

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In this blog, we will explore the differences between rating and ranking scales, and uncover the secrets behind these scales, revealing a universe of insights that empowers us to fine-tune strategies, elevate user experiences, and propel our decisions with data-backed certainty.

What is a Rating Scale?

A rating scale question asks participants to evaluate a product, service, or idea by rating their opinion on a predefined scale.

Some common types of rating scales include:

• Numeric scales, i.e., scales of 1-5 or 1-10, like Net Promoter Scores (NPS)
• Likert scales gauge how much a respondent agrees or disagrees with a given statement
• Frequency scales ask how frequently an occurrence or behavior occurs and can be scaled from "Never" to "Always."
• Comparison scales help evaluate two variables with answer choices like "Better" and "Worse."

Some examples of rating scale questions include:

• How satisfied are you with the service you received?
• On a scale of 1-5, how likely are you to recommend our product to a friend?
• How important is the price when selecting a product?

Rating scales are widely used to gather customer feedback, measure satisfaction levels, and identify areas for improvement.

When to Use Rating Scales: Pros and Cons

Rating scales are prevalent since they are simple to use and administer. They are suitable for surveys where the researcher needs to understand the intensity or strength of the respondent's opinion, behavior, or attitude. They are best used when the options being rated are similar, and the researcher wants to know the degree of difference between them.

But what if a respondent rates two competing brands equally; how do you determine which one is preferred? Are they equal, or did little differences sway the respondent's decision? It's difficult to understand this with only a rating question.

Here's a summary of the upsides and downsides of rating scales:

Advantages of Rating Scales

• Ease of usage: Rating scales are easy to understand and can be quickly completed by respondents. This makes them a popular choice for surveys with many questions.
• More reliable data: Rating scales provide a better measure of the respondent's attitude, behavior, or opinion than other question types, as respondents can rate on a more specific scale.
• Simple administration and analysis: Rating scales provide quantitative data, which can be easily analyzed and used for statistical purposes.

Disadvantages of Rating Scales

• Limited insights: Rating scales are limited in providing insights into the reasons behind the rating, as they do not allow respondents to explain their answers in detail.
• Limited differentiation: Rating scales may not provide enough differentiation between answers, as the scale may be too small to capture the nuances of an opinion accurately.
• May not provide accurate rankings: While rating scales help identify the strength of opinion, they may not accurately rank the options being rated.

What is a Ranking Scale?

A ranking scale question asks participants to order items based on a specific criterion. Respondents are typically presented with a list of things and are asked to rank them from most important to least important or vice versa by comparing them and selecting the ones they prefer. This process is repeated until all items have been compared and ranked.

Ranking scales are commonly used to identify customer preferences, prioritize product features, and understand the importance of different factors. Here are some examples of ranking scale questions:

• Please rank the following product features in order of importance.
• Rank the following brands in order of preference.
• Please rank the following customer service factors in order of importance.

When to Use Ranking Scales: Pros and Cons

Ranking scales are suitable for surveys where the researcher needs to understand the relative importance of a set of options. They are best used when the options being compared are dissimilar, and the researcher wants to identify the most preferred choice.

However, ranking questions alone cannot explain the close relationship between each ranking. For example, what is the distinction between first and second place? What about the third and the fourth place? The respondent may be equally enthusiastic about their first and second rankings and had to flip a coin to decide. The further they go down the list (, the more options they have to rank), the more their interest subsides, or they get frustrated and start ranking options randomly.

Here's a summary of the upsides and downsides of ranking scales:

Advantages of Ranking Scales

• Nuanced insights: Ranking scales provide a more nuanced understanding of respondent opinions, as they are required to give an order of preferences and priorities.
• Better differentiation: Ranking scales provide greater differentiation between answers, as respondents must distinguish between each item and provide an order of preference rather than rating them.
• Qualitative insights : Ranking scales provide deeper insights into the reasons behind a respondent's preference.

Disadvantages of Ranking Scales

• Time-consuming: Ranking scales can be more time-consuming than rating scales, as respondents must provide an order of preference for each item, making them unsuitable for surveys with many questions.
• Limited information: Ranking scales provide less precise data than rating scales, as respondents cannot give a specific rating for each item, limiting the information about the strength of preference for the options being compared.
• Difficult to interpret: Ranking scales can be challenging to interpret when the differences between the options are slight.

Choosing between Rating and Ranking Scales

So, which question type should you choose? The answer depends on your research goals and the data you hope to collect. Here are some key considerations:

• Type of Data: If you want to measure attitudes or opinions, rating scales may be the best option. If you wish to understand priorities or preferences, ranking scales may be more appropriate.
• Sample Size: Rating scales are better suited for larger sample sizes, as they are more efficient and easier to analyze. Ranking scales may be more time-consuming and complex for larger samples.
• Complexity of the Question: Rating scales are better suited for more straightforward questions, as they may be more difficult for respondents to understand and complete. Ranking scales may be more appropriate for more complex questions, allowing respondents to provide more nuanced responses.
• Context: Consider the context of your research and the expectations of your respondents. For example, a rating scale may be more appropriate for conducting a customer satisfaction survey , as it is a common and familiar question type.

Ultimately, the choice between rating and ranking scales depends on the goals of your research and the data you hope to collect. Generally, rating scales are better suited for gathering broad information about a product, service, or idea. In contrast, ranking scales are better suited for better understanding customer preferences and priorities.

It is essential to carefully consider the strengths and weaknesses of each question type before making a decision.

Combining Rating and Ranking Scales for Richer Insights

While it's helpful to understand the difference between rating and ranking scales and their advantages and disadvantages, choosing only one is unnecessary. Both question types often compensate for their flaws when combined, helping you derive more profound insights.  

Moreover, rating and ranking questions are simple for respondents to answer, and including both in the survey is manageable. Ask both questions if you need to know relative positioning and understand gaps in ranking.

For instance, ranking different kinds of chocolate bars can give you a relative sense of which is preferred, but not by how much. A respondent may rank a Snickers bar higher than a Toblerone bar but might heavily dislike a Bounty bar. As we discussed, a ranking question will let you gauge the order of preferences rather than the strength of preferences.

To get around this gap in data, you can use a ranking question to pick a clear winner (if you don't care about the middle options). For instance:

• What is your favorite chocolate bar: Snickers, Toblerone, or Bounty?
• What is your least favorite chocolate bar: Snickers, Toblerone, or Bounty?

Here’s another example. Say the Snickers company wants to give the least liked Snickers products a flavor boost so consumers find all their products equally tasty; the company needs to find out the relative standing of all product types.

And if they care about the middle options, one ranking question can be split into various rating questions.

For example, instead of asking:

• Rank the Snickers product in order of preference: Snickers Original, Snickers Almond, Snickers Peanut Butter

The above question can be split into three rating questions:

• How much do you like Snickers Original: A great deal, A lot, Moderately, A little, Not at all
• How much do you like Snickers Almond: A great deal … Not at all
• How much do you like Snickers Peanut Butter: A great deal … Not at all

Once respondents individually score each of the three Snickers products, you can compute an average score for each Snickers item using these ratings. The average score for each product is then used to generate an overall ranking of liking for each product.

(Please note that these are examples and not a testament to the quality of the chocolate bars.)

Relevant Read: The Ultimate Guide to Survey Question Types in 2023

Summing it up

Both rating and ranking scales have unique advantages and disadvantages, and the choice between the two depends on the researcher's goals and the nature of the survey. Consider the type of data you hope to collect, the sample size, the complexity of the question, and the context of your research before deciding which question type to use or if combining them can be more helpful. With careful consideration and planning, you can ensure that your survey data is accurate and informative.

Fortunately, many consumer research tools today make this process easier. For instance, Entropik allows you to conduct quantitative and qualitative surveys with features like sentiment analysis and emotion intelligence to ensure you get accurate, dependable, and unbiased results every time. With 20+ question types, you can choose the best combination of question types for your survey.

Frequently Asked Questions

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With a background in management, Sriya has been actively helping B2B startups scale their content engines. She is well-versed in transforming complex brand stories into simple and engaging content. She is also passionate about building content marketing and product initiatives.

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A randomized double-blind placebo-controlled clinical trial of Guanfacine Extended Release for aggression and self-injurious behavior associated with Prader-Willi Syndrome

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Introduction: Prader-Willi Syndrome (PWS), a rare genetic disorder, affects development and behavior, frequently resulting in self-injury, aggression, hyperphagia, oppositional behavior, impulsivity and over-activity causing significant morbidity. Currently, limited therapeutic options are available to manage these neuropsychiatric manifestations. The aim of this clinical trial was to assess the efficacy of guanfacine-extended release (GXR) in reducing aggression and self-injury in individuals with PWS. Trial Design: Randomized, double-blind, placebo-controlled trial conducted under IRB approval. Methods: Subjects with a diagnosis of PWS, 6-35 years of age, with moderate to severe aggressive and/or self-injurious behavior as determined by the Clinical Global Impression (CGI)-Severity scale, were included in an 8-week double-blind, placebo-controlled, fixed-flexible dose clinical trial of GXR, that was followed by an 8-week open-label extension phase. Validated behavioral instruments and physician assessments measured the efficacy of GXR treatment, its safety and tolerability. Results: GXR was effective in reducing aggression/agitation and hyperactivity/noncompliance as measured by the Aberrant Behavior Checklist (ABC) scales (p=0.03). Overall aberrant behavior scores significantly reduced in the GXR arm. Aggression as measured by the Modified Overt Aggression Scale (MOAS) also showed a significant reduction. Skin-picking lesions as measured by the Self Injury Trauma (SIT) scale decreased in response to GXR. No serious adverse events were experienced by any of the study participants. Fatigue /sedation was the only adverse event significantly associated with GXR. The GXR group demonstrated significant overall clinical improvement as measured by the CGI-Improvement (CGI-I) scale. (p<0.01). Conclusion: Findings of this pragmatic trial strongly support the use of GXR for treatment of aggression, skin picking, and hyperactivity in children, adolescents, and adults with PWS. Trial Registration: ClinicalTrials.gov Identifier - NCT05657860

Competing Interest Statement

I have read the journal's policy and the authors of this manuscript have the following competing interests: DS has served as a consultant to Soleno Therapeutics, Acadia Pharmaceuticals, Tonix Pharmaceuticals, and Consynance Therapeutics. MS and TJ have no other competing interests to report.

Clinical Trial

ClinicalTrials.gov identifier: NCT05657860

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This study was approved by the Institutional Review Board of Maimonides Medical Center (# 2020-11-03-MMC). Written, IRB-approved informed consent was obtained from each participant's parent or legal guardian, and assent was obtained from each participant, as applicable.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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• Published: 24 August 2024

Effect of clays incorporation on properties of thermoplastic starch/clay composite bio-based polymer blends

• Soledad Cecilia Pech-Cohuo 2 ,
• Mario Adrián de Atocha Dzul-Cervantes   ORCID: orcid.org/0000-0003-2242-1183 1 ,
• Emilio Pérez-Pacheco   nAff1 ,
• Jorge André Canto Rosado 1 ,
• Yasser Alejandro Chim-Chi 1 ,
• Carlos Rolando Ríos-Soberanis 3 ,
• Zujey Berenice Cuevas-Carballo 4 ,
• Erbin Guillermo Uc-Cayetano 5 ,
• Luis Alfonso Can-Herrera 1 ,
• Alejandro Ortíz-Fernández 1 ,
• Juan Pablo Collí-Pacheco 4 ,
• José Herminsul Mina-Hernández 6 &
• Yamile Pérez‑Padilla 5  

Scientific Reports volume  14 , Article number:  19669 ( 2024 ) Cite this article

Metrics details

• Nanoscale materials
• Nanoscience and technology
• Techniques and instrumentation

In this study, thermoplastic starch (TPS) biofilms were developed using starch isolated from the seeds of Melicoccus bijugatus (huaya) and reinforced with bentonite clays at concentrations of 1%, 3%, and 5% by weight. Novelty of this research lies in utilizing a non-conventional starch source and enhancing properties of TPS through clay reinforcement. FTIR analysis verified bentonite’s nature of clays, while SEM analysis provided insights into morphology and agglomeration behavior. Key findings include a notable increase in biofilm thickness and elastic modulus with higher clay content. Specifically, tensile strength of biofilms improved from 2.5 MPa for pure TPS to 5.0 MPa with 5% clay reinforcement. The elastic modulus increased from 25 MPa (TPS) to 60 MPa (5% clay). Thermal stability also showed enhancement, with initial degradation temperature increasing from 110 °C for pure TPS to 130 °C for TPS with 5% clay. Water vapor permeability (WVP) tests demonstrated a decrease in WVP values from 4.11 × 10 −10  g m −1  s −1  Pa −1 for pure TPS to 2.09 × 10 −10  g m −1  s −1 ·Pa −1 for TPS with 5% clay, indicating a significant barrier effect due to clay dispersion. These results suggest that biofilms based on huaya starch and reinforced with bentonite clay have considerable potential for sustainable food packaging applications, offering enhanced mechanical and barrier properties.

Introduction

Packaging materials are essential in everyday life, used extensively for food, biomedical products, pharmaceuticals, and others. Synthetic plastics, such as low-density polyethylene (LDPE), high-density polyethylene (HDPE), polypropylene (PP), polyethylene terephthalate (PET), and polystyrene (PS), dominate the market due to their excellent physical and mechanical properties 1 , 2 . However, the accumulation of non-biodegradable plastic waste has created a severe environmental problem, causing significant ecological damage and environmental imbalance 3 , 4 . It is estimated that plastic waste is increasing at a rate of 700 million tons per year, with a projection to reach one billion tons by 2021 5 .

To address this problem, it is crucial to reduce the use of petroleum-derived materials and develop new polymers from unconventional sources and effective methods for processing renewable biopolymers 6 . Polymers derived from bio-renewable sources such as alginates, cellulose, chitin, soy, and starch are being explored due to their biodegradability and low environmental impact 7 , 8 , 9 , 10 , 11 , 12 , 13 . However, products based on natural polymers often do not possess optimal properties, requiring the incorporation of reinforcements to improve their characteristics 14 , 15 .

Starch is a promising biopolymer due to its abundance, low cost, and biodegradability. Conventional sources such as potatoes, corn, and rice have been widely used, but the search for new unconventional sources has led to the exploration of alternatives like green bananas ( Musa paradisiaca ), bitter vetch ( Vicia ervilia ), and Talisia floresii Standl seeds 16 , 17 . An important and recently explored source is starch from huaya ( Melicoccus bijugatus ) seeds, also known as mamoncillo. Huaya is a tropical fruit that grows in compact clusters in southeastern Mexico and other regions of Latin America. Huaya seeds have been underutilized, but recent studies indicate they are a viable source of starch with suitable physicochemical properties for industrial applications 18 . This starch has low levels of lipids and proteins, which is advantageous for starch purity and its processing into bioplastics. Additionally, using huaya as a starch source contributes to sustainability by utilizing an underused resource and reducing dependence on conventional food crops 5 .

Clays, especially bentonite, have shown to be excellent reinforcements for biopolymers due to their high surface area and nanoscale reinforcement properties. Incorporating clays into starch matrices has shown significant improvements in mechanical and barrier properties of composite materials 19 , 20 . Studies have indicated that the purification method of bentonite affects its properties, highlighting the importance of removing contaminants such as carbonates and organic matter to enhance its effectiveness as a reinforcement 21 , 22 .

Recent research has explored the use of nanoparticles as cellulose and clays to improve the properties of bioplastics. Dang et al. demonstrated that combining nanocellulose in a polymer matrix can significantly enhance the mechanical and barrier properties of biodegradable films 13 . These advances underscore the importance of continuing to investigate new material combinations to develop more efficient and sustainable bioplastics. Furthermore, other recent studies have shown that incorporating biodegradable materials into starch matrices can significantly enhance their functional properties. Khalili et al. found that incorporating cellulose fibers improved the mechanical and barrier properties of starch bioplastics 23 . Additionally, Guarás et al. observed improvements in thermal and mechanical properties of starch bioplastics reinforced with nanoclays 24 . These studies reinforce the idea that combining different reinforcements in starch matrices can lead to the development of more robust and efficient biodegradable packaging materials.

In this study, thermoplastic starch bioplastics were developed using starch isolated from huaya seeds and reinforced with bentonite clays extracted and purified from Tepakán, Calkiní, Campeche, Mexico. These bioplastics were characterized to evaluate their mechanical, thermal, and barrier properties, aiming to explore their potential application in food packaging. The innovation of this work lies in using an unconventional starch source and optimizing the properties of the bioplastics through reinforcement with local clays, contributing to sustainability and the development of high-performance biodegradable materials. The aim of this research is extraction, purification, and characterization of local bentonite clays for use as reinforcement in thermoplastic starch bioplastics, providing a sustainable solution for food packaging.

Moreover, starch was extracted from the seeds of Melicoccus bijugatus (huaya), an unconventional and unconventional and underutilized source, offering a sustainable and cost-effective alternative to traditional starch sources such as potatoes, corn, and rice. Additionally, locally extracted, and purified bentonite clays from Tepakán, Calkiní, Campeche, Mexico, were used instead of commercial clays. These clays were characterized to ensure their compatibility and effectiveness as a reinforcement in starch-based bioplastics. The results show significant improvements in the mechanical, thermal, and barrier properties of the bioplastics, surpassing the enhancements reported in previous studies using different reinforcements and starch matrices. This comprehensive and localized approach enhances the properties of the bioplastics and provides a novel and sustainable method for developing biodegradable packaging materials.

Materials and methods

The following reagents were utilized for material preparation: hydrogen peroxide, glacial acetic acid, sodium acetate, hydroxylamine hydrochloride, sodium hexametaphosphate, sodium bisulfite, and sodium hydroxide, all sourced from Sigma-Aldrich. Commercial glycerol, employed as a plasticizer for starch, was provided by Farmacia Comercio (Mérida, Yucatán, México).

The raw material used for clay extraction and purification was collected in the Tepakán area (latitude 20.398889 and longitude − 90.039722) of Calkiní, Campeche, Mexico, in September 2021. Samples were stored in sealed containers until use at room temperature.

Melicoccus bijugatus jacq

In July 2022, Huaya ( Melicoccus bijugatus Jacq .) fruits were harvested in the municipality of Calkiní, located in the state of Campeche on the Yucatán Peninsula, Mexico. The seeds were extracted by peeling and removing the edible pulp from ripe fruits without visible physical defects. The Huaya seeds were dried at 40 °C in a convection oven (Shell Lab 1350FX-10) for 72 h. The dried seeds were then ground in an industrial blender in 10-s intervals and sifted through a No. 100 mesh sieve to produce flour. The resulting flour was stored in hermetically sealed glass jars until use at room temperature.

Experimental methodology

Clay extraction and purification.

The procedure for extracting and purifying clays is reported in literature 25 , 26 and is described as follows:

30 g of sample are placed in a container with distilled water and stirred during 3 h until the sample is completely dispersed, the aqueous system is filtered. After filtering, 200 ml hydrogen peroxide (30% v/v) is added and waited until bubbling stops. Then, additional 300 ml hydrogen peroxide (98% v/v) are added. After this procedure water is evaporated. 3 ml glacial acetic acid are added and evaporated. Subsequently, sample is dried in a convection oven Shell Lab Brand, 1350FX-10 model, during 8 h at 100 °C. An addition of 240 ml (1 M) sodium acetate solution is aggregated and adjusted to pH 5 with acetic acid.

300 ml deionized water are added and stirred during 3 h. Then, centrifuge (Eppendorf Brand, 5702 R) at 1500 rpm during 10 min is used to separate liquid and sediment. Sediment is at that time dried in a convection oven at 100 °C during 8 h. 450 ml (0.04 M) of hydroxylamine chloride and 150 ml acetic acid are aggregated maintaining temperature at 96 °C during 6 h. Add 600 ml distilled water and stir during 30 min. After this, centrifuge again the system at 1500 rpm during 10 min to be dried the sediment in a convection oven at 100 °C during 8 h. An addition of 1 L sodium hexametaphosphate (0.5%) to disperse the solution is aggregated and stir on a magnetic stirring plate during 8 h; the container with dispersing solution is placed in an ultrasonic bath for 1 h. Finally, the system is centrifuged at 2000 rpm during 60 min and dried at 80 °C in a convection oven. The obtained clays are lastly stored at room temperature in sealed bottles.

Starch isolation

Starch isolation was performed following the method reported by Moo 18 , with the following modifications: First, fruits without visible surface damage were selected. After manually removing the shell and pulp, seeds were dried in a convection oven (SHELL LAB 1350FX-10) at 50 °C for 72 h. Dried seeds were then ground using an IKA MF-10 mill equipped with a 0.5 mm sieve, and material was sieved through a No. 100 mesh. Native Huaya starch (NS) was extracted by soaking the flour obtained from seeds in a sodium bisulfite solution (0.1%) and sodium hydroxide (1N). Suspension was then sieved, washed, and centrifuged to obtain polysaccharide. After isolation, the NS was dried in a convection oven at 50 °C for 24 h, ground again with an IKA MF-10 mill fitted with a 0.5 mm sieve and sieved through a No. 100 mesh sieve. Finally, the starch was stored in hermetically sealed glass containers until use. The amount of starch recovered was calculated using Eq. ( 1 ):

where RS denote the percentage of recovery starch. WIF denote weight of isolated fraction (% dw), PS is purity of starch and, WF denote weight of the huaya seed four (% dw).

Biofilms manufacture

Bioplastic films were prepared using solvent casting method, following a technique reported by some authors with slight modifications 27 . The film-forming solution was prepared by dispersing 4 g of huaya starch in 100 mL of distilled water, adding 1.6 g of glycerin as a plasticizer, and incorporating clays at concentrations of 1%, 3%, and 5% relative to the weight of the starch.

Characterization

Fourier transform infrared spectroscopy (ftir).

To identify the functional groups in the clays, FTIR analysis was performed using a Nicolet 8700 infrared spectrometer from Thermo Scientific, USA. Clay samples of 1 mg, previously dried at 100 ºC for 24 h, were individually ground with 100 mg of potassium bromide in an agate mortar. Tablets were then formed using a Carver C model press and a tablet machine, applying a force of 50 kN for 10 min. The analysis was conducted over a wavenumber range of 4000 to 650 cm −1 with 100 scans at a resolution of 4 cm −1 .

Scanning electron microscopy (SEM)

Surface morphology of clays was examined using a JEOL JSM-6360LV scanning electron microscope (Japan) at 20 keV. Prior to imaging, the samples were coated with a thin layer of gold using the sputtering deposition technique. Biofilms specimens were morphologically analyzed at 10 keV after being coated with a layer of gold powder 27 , 28 .

Particle size

Particle size analysis of clays was conducted using a Beckman Coulter LS100Q laser analyzer with a precision of ≤ 1%. The instrument utilized two light sources, including a 5 mW laser diode with a wavelength of 750 nm. For the analysis, 5 g of the sample were suspended in cold deionized water at a 50:1 ratio.

Biofilms thickness

The thickness of the bioplastics was measured using a digital thickness gauge (ID-C1012EXBS, Mitutoyo Co., Japan). The average thickness of five samples was determined by taking measurements at three different positions on each sample.

Thermogravimetric analysis

The thermal stability of the bioplastics was evaluated using a Perkin Elmer TGA 8000 thermogravimetric analyzer (USA) under a dry nitrogen flow at a rate of 20 ml/min. The tests were conducted over a temperature range of 50 to 450 °C, with a heating rate of 10 °C/min. Each test was performed in triplicate.

Water vapor permeability (WVP)

In order to determine WVP, ASTM E96-95 29 gravimetric method was used, adjusting it with some slightly modifications. Before testing, biofilm samples were kept in a desiccator at 25 °C and 50% RH (relative humidity) using Mg[NO 3 ] 2 ·6H 2 O. Samples were cut into a circular shape and placed and sealed in cylindrical cups open mouth, containing 40 g silica gel. Cylindrical cups were placed in a chamber with RH, using a supersaturated solution of NaCl and distilled water. Cups were weighed using an analytical balance to determine their initial weight. Weight of the test cups was measured every hour until constant mass was obtained. Assays were performed in triplicate. Change in mass of the test cups was recorded and WVP was calculated using Eq.  2 :

where m (g) is the weight increment of the test cup, d (m) is the film thickness, A (m 2 ) is the area of exposed film, t (s) is the permeation duration and P (Pa) is the partial pressure of water vapor through the films. Results were expressed in \({\text{g}} \cdot {\text{m}}^{ - 1} \cdot {\text{s}}^{ - 1} \cdot {\text{Pa}}^{ - 1}\) .

Mechanical properties

Tensile strength and elongation at break were measured according to ASTM D882 using a Shimadzu universal testing machine (AGS-X model) from Science Instruments, Columbia, USA equipped with 1 kN load cell. Specimens measuring 25 mm × 100 mm were prepared and conditioned at 25 °C and 50% relative humidity for 3 days prior to testing. The initial test conditions included a gauge length of 50 mm and a head speed of 50 mm/min at 25 °C until rupture. Tensile strength (MPa), elastic modulus (MPa), and elongation at break (%) were calculated from stress–strain curves.

Statistic analysis

Data obtained from thickness measurements and mechanical tests were analyzed using one-way analysis of variance (ANOVA), followed by Tukey's multiple comparison test (p < 0.05) to identify significant differences between formulations. Software used for statistical analysis was Minitab 19 (Minitab Inc., USA).

Results and discussion

FTIR spectroscopy is a technique for identifying chemical interactions and structural modifications in reinforced biopolymers (see Fig.  1 ). In this study, bioplastics made from starch isolated from Melicoccus bijugatus and reinforced with bentonite clays showed several characteristic bands, indicating the presence and interaction of components within polymer matrix. Bands around 3400 cm −1 were observed, corresponding to stretching vibrations of hydroxyl groups (O–H) present in starch and clays. These bands indicate the presence of adsorbed water and hydrogen bonds in starch structure. This finding is consistent with what Wang et al. reported, where similar bands were observed in starch bioplastics reinforced with clays, indicating effective interaction between starch and clay nanoparticles 30 .

Fourier transform infrared spectrum of TPS/clays materials at 5%.

Additionally, a band around 1650 cm −1 was detected, corresponding to the bending vibrations of adsorbed water and carbonyl groups (C = O) in starch structure. This band is common in studies of biopolymers incorporating starch and reinforcements and was also reported by Sheydai et al., in their research on starch bioplastics reinforced with cellulose fibers 31 . Zhang et al. found that these bands indicated good dispersion and compatibility between starch and reinforcing fibers 32 .

Bands in the range of 1000–1100 cm −1 , attributable to stretching vibrations of Si–O-Si and Al–O–Si bonds in bentonite structure, confirm the presence of clays in the polymer matrix. Gamage et al. observed similar bands in their studies on starch nanocomposites reinforced with nanoclays, indicating that the incorporation of clays significantly enhances structural properties of the biopolymer 33 .

Moreover, bands around 2920 cm −1 and 2850 cm −1 correspond to stretching vibrations of C-H bonds in methylene groups present in starch structure. These bands have also been reported in other studies of starch bioplastics, such as the work of Zheng, who investigated the synergistic effects of nanocellulose and clays on the mechanical and barrier properties of biodegradable films. Their results showed that these bands indicate good compatibility between components of the bioplastic 34 .

Compared with study by OChei et al., is observed that relative intensities of the bands in our FTIR spectra vary slightly. Ochei et al. found that the intensity of the Si–O-Si bands increased with higher clay loading, a behavior similar to what we observed. Variation in intensity can be attributed to differences in clay concentration and the bioplastic preparation process 35 .

Is important to note that purification method of bentonite significantly influences the FTIR properties of nanocomposite. Gamage et al. reported that effective purification of bentonite is crucial for removing contaminants such as carbonates and organic matter, thereby enhancing interaction between clays and starch matrix. Our FTIR results show similar characteristic bands, suggesting that purification method selected was effective in eliminating contaminants and preserving the structural properties of the clay 33 .

FTIR results confirm presence and effective interaction of bentonite clay with huaya starch matrix. These findings are consistent with other studies reported in scientific literature, supporting viability of using this reinforcement system to enhance properties of bioplastics.

Scanning electron microscopy (SEM) and particle size

SEM images of bioplastics made from starch isolated from Melicoccus bijugatus and reinforced with bentonite clays showed a relatively homogeneous surface with adequate dispersion of clay particles.

SEM images (see Fig.  2 ) revealed that at lower clay concentrations (1% and 3%). Bentonite particles are uniformly distributed within starch matrix without significant agglomerations. However, at a concentration of 5%, some clay agglomerations were observed, which might indicate saturation in starch's dispersion capacity. This behavior is like that reported by Mansour et al., who observed homogeneous dispersion of nanocellulose in bioplastics at low concentrations, but agglomerate formation at higher concentrations 36 .

SEM micrographs of clays extracted from Tepakán, Calkiní, Campeche into starch matrix. ( A ) 1% ( B ) 3%.

Homogeneity in clay particle dispersion at low concentrations contributes to enhancement of mechanical and barrier properties of the bioplastics. This is because well-distributed clay particles act as physical barriers that impede permeability and improve mechanical strength. Rammak et al. reported similar behavior in their studies on starch films reinforced with nanoclays, where homogeneous dispersion of nanoclays significantly improved mechanical properties of the films 37 .

These results are similar to those reported by Zhang et al., who studied starch bioplastics reinforced and observed a similar trend in surface morphology improvement. Zhang et al. found that adding cellulose fibers resulted in a more homogeneous and structured surface, which improved the functional properties of the bioplastics 32 .

In contrast, other authors studies have shown that inadequate dispersion of reinforcement particles can lead to decreased mechanical and barrier properties. For example, Wang et al. reported that clay particle agglomeration in starch bioplastics caused a reduction in mechanical strength due to the formation of weak points in polymer matrix 30 .

These SEM results confirm that incorporating bentonite into huaya starch bioplastics significantly improves surface morphology and clay particle dispersion. These improvements are consistent with other studies reported in literature as above mentioned, and support viability of using bentonite as a reinforcement to optimize the properties of bioplastics.

In Fig.  3 , the particle size distribution obtained from clays are displayed. Particle size is an important parameter to understand how a particle can reinforce a matrix in a composite. Figure  3 shows that the size distribution is monomodal and that the average particle size of clays is around 10 µm, which is like that reported by other authors 38 , where mentions the great potential of such clays for reinforcing a polymeric matrix.

Particle size distribution of clays extracted from Tepakán, Calkiní, Campeche.

Biofilm thickness and mechanical properties

Thickness and mechanical properties for biofilms made with NS ( Melicoccus bijugatus ) and clays from the Tepakán, Campeche, Mexico, were determined, and reported in Table 1 . Biofilm thickness strongly influences over its mechanical properties and characteristics to be used as food packaging. Thickness is related to the total volume of solution deposited during solvent melting process and total dry solid matter present in sample 39 . In Table 1 , it can be seen values reported for film without clays (Starch-0); for this case, values presented are slightly lower in comparison to those samples where clays are incorporated into biofilm modifying such parameters as clay content is increased. Similar reports have been issued in the scientific literature when reinforcements are incorporated into biofilms 40 , 41 .

Results indicate that incorporating bentonite clay increases thickness of bioplastics, although not in a linear manner with increasing clay concentration. This increase in thickness can be attributed to dispersion of clay particles within starch matrix, leading to a more compact structure. This finding is consistent with those of Mendes et al., who observed that incorporating nanoparticles into biopolymer matrices tends to increase material thickness 42 .

Regarding mechanical properties, a significant improvement in tensile strength and elastic modulus was observed with addition of clays. Tensile strength increased from 3.23 MPa in pure starch to 5.24 MPa in bioplastic with 5% clays. Elastic modulus showed an even more pronounced increase, rising from 70.02 MPa in pure starch to 338.62 MPa in bioplastic with 5% clay. These results are comparable to those reported by Wang et al., who also found that adding nanoclays to polymer matrices resulted in a significant increase in material strength and rigidity 30 .

However, elongation at break decreased with increasing clay concentration, particularly in bioplastic with 5% clay. This suggests that while clay particles enhance strength and rigidity, they can also make the material more brittle. This behavior is similar to what Calambas et al. reported, where high concentrations of clay nanoparticles in starch bioplastics led to reduced ductility 43 .

Improvement in mechanical properties can be attributed to the strong interaction between clay particles and starch matrix, which reinforces biopolymer structure and more evenly distributes applied stresses. This reinforcing effect is consistent with Thongmeepech et al. studies on bioplastics reinforced with cellulose fibers, which also showed significant improvements in tensile strength and elastic modulus with the addition of reinforcements 44 .

These results demonstrate that the incorporation of bentonite improves the mechanical properties of starch bioplastics, although it is necessary to optimize clays concentration to maintain a balance between strength, rigidity, and ductility. This aligns with the observations of Ochei et al. (2023), who emphasized importance of adjusting nanoclay concentrations to achieve the desired properties in starch bioplastics 35 .

Thickness and mechanical property analysis results confirm that incorporating bentonite into huaya starch bioplastics significantly enhances the material's structural properties. These findings are consistent with other studies reported in scientific literature, supporting the viability of using bentonite as a reinforcement to optimize mechanical properties of bioplastics.

Glycerol incluences on starch microstructure. Glycerol acts as a plasticizer in starch bioplastics, significantly affecting starch's microstructure, particularly arrangement of amylose and amylopectin molecules. Adding glycerol reduces the stiffness of starch matrix by increasing molecular mobility, which lowers glass transition temperature (Tg) and facilitates the gelatinization process 45 . In this study, incorporation of glycerol resulted in more flexible bioplastics, with a reduction in tensile strength and an increase in elongation at break compared to bioplastics without glycerol. Interactions between starch, glycerol, and bentonite clays are complex. Glycerol, by intercalating between amylose and amylopectin chains, reduces starch crystallinity and promotes an amorphous structure. This facilitates uniform dispersion of clay nanoparticles within starch matrix 46 . SEM images of our bioplastics confirmed a homogeneous distribution of clay in presence of glycerol, suggesting that glycerol enhances compatibility between starch matrix and clay nanoparticles.

Thermogravimetric analysis (TGA)

Thermogravimetric Analysis (TGA) was carried out to evaluate the thermal stability of thermoplastic starch biofilms (TPS) and influence of clays into biofilms (TPS/Clay). Thermograms for biofilms are reported in Fig.  4 .

TGA thermogram of TPS/Clay biofilms.

TPS sample without clays, follows a three-stage thermal decomposition process. Initial stage and up to 100 °C corresponds to evaporation of water in sample. The following mass loss begins around 110 °C and reaches approximately 220 °C and is associated with partial loss of glycerol and initial stage of starch decomposition 47 . The last stage, which begins around 340 °C, can refer to the rearrangements of carbon residues in polymeric chains of amylose and amylopectine 48 . Likewise, it can be seen in TGA thermogram that degradation rate of all samples are similar. However, degradation process begins and ends at different temperatures. TGA analyzes were also carried out on the TPS/Clay biofilms with different clays contents. Initial degradation temperature does not vary greatly at first, although the thermal stability does increase slightly when clay content is added and increased, which may be attributed to the change in the material and the interface formed between clays and the starch. Furthermore, clays layers could promote carbon formation (char) during biofilms thermal degradation, which can be observed in change in thermal stability when clay content is increased. Likewise, residual percentage of mass at 450 °C could indicate that clay is not degraded, therefore, the amount of residue increases as amount of clay increases 49 .

TGA thermograms of bioplastics made from starch isolated from Melicoccus bijugatus and reinforced with different concentrations of bentonite clay revealed several key aspects regarding thermal decomposition of these materials. For bioplastic without clay reinforcement, thermal decomposition began at approximately 280 °C, with a significant mass loss observed up to 380 °C. This behavior is typical of starch-based biopolymers, where thermal degradation occurs in multiple stages, including dehydration, decomposition of starch main chain, and oxidation of carbonaceous residues 42 .

Incorporation of bentonite clay improved thermal stability of bioplastics. For bioplastic with 1% bentonite, decomposition onset temperature slightly increased to 290 °C. For bioplastics with 3% and 5% bentonite, decomposition onset temperatures were approximately 295 °C and 300 °C, respectively. These results indicate that addition of bentonite enhances thermal resistance of starch, which is consistent with previous studies where inclusion of clay nanoparticles improved thermal stability of biopolymers 30 .

These findings are similar to those reported by Wahab et al., who found that incorporating cellulose fibers into starch bioplastics resulted in improved thermal stability, similar to our findings with bentonite. The cellulose fibers acted as thermal barriers, delaying thermal degradation of starch 50 . Additionally, Aguirre et al. reported that incorporation of nanoclays into biopolymer matrices not only improved thermal stability but also reduced the decomposition rate, which aligns with our results showing a decrease in the mass loss rate in presence of bentonite 51 .

TGA results also showed that amount of carbonaceous residue at end of decomposition increased with concentration of bentonite. For bioplastics without reinforcement, residue at the end of decomposition was approximately 5%. In contrast, bioplastics with 1%, 3%, and 5% bentonite showed residues of approximately 10%, 15%, and 20%, respectively. This increase in residue indicates the presence of clay, which does not decompose within studied temperature range and contributes to formation of carbonaceous residues 52 .

These findings are consistent with the results of Mendes et al., who observed that incorporating reinforcement nanoparticles into starch matrices resulted in an increase in carbonaceous residue due to the inherent thermal resistance of nanoparticles and their ability to retard biopolymer's degradation 42 .

TGA results confirm that incorporation of bentonite into huaya starch bioplastics significantly improves the material's thermal stability. This behavior is consistent with other studies reported in scientific literature, supporting viability of using bentonite as a reinforcement to enhance the thermal properties of bioplastics.

Water vapor permeability (WVP) is a crucial factor to evaluate in biofilms intended for food packaging. This parameter measures amount of vapor that passes through biofilms over a specific period 53 . Ideally, food packaging materials should protect food from odors, flavor loss, chemicals, oxygen transmission, and water vapor permeability. Additionally, controlling WVP is essential for managing moisture transfer between food and its external environment, as a high WVP value can lead to microbial spoilage in foods 54 . Figure  5 illustrates WVP behavior for TPS and TPS/Clay biofilms at various concentrations. These results show a significant reduction in WVP with increasing clay concentration. Decrease in WVP suggests that bentonite clay acts as a physical barrier within starch matrix, preventing water vapor passage and improving material's barrier properties. These findings are similar to studies reported in scientific literature where reinforcement nanoparticles were incorporated into biopolymer matrices. Wang et al. reported a reduction in WVP for starch/polyvinyl alcohol bioplastics reinforced with nanoclays. Their results showed that adding 5% nanoclays significantly reduced WVP, which is consistent with our findings 30 .

WVP of TPS and TPS/Clay biofilms at different concentrations.

In a similar study, Granda et al. found that incorporating reinforcements into starch bioplastics resulted in a notable improvement in barrier properties. Reinforcements acted as physical barriers, reducing water vapor permeability of bioplastics. This behavior parallels what we observed with the addition of bentonite clay in our starch bioplastics 55 . Additionally, Aguirre et al. reported that incorporating nanoclays into biopolymer matrices not only improved thermal stability but also reduced WVP. This study highlights the effectiveness of nanoclays as reinforcements to enhance the barrier properties of biopolymers, which aligns with our results using bentonite as a reinforcement 51 .

Reduction in WVP observed in our bioplastics can be attributed to formation of a denser and more compact structure within starch matrix due to presence of clays. This dense structure acts as an effective barrier against water vapor permeation, thereby improving material's barrier properties. Shapi’i et al. also observed that incorporating nanoparticles into starch films resulted in a more compact structure and a significant reduction in WVP 56 . Figure  6 schematizes a possible way of transit of water molecules in TPS biofilms, and barrier effect in TPS/Clays biofilm. The WVP results show that incorporating bentonite into huaya starch bioplastics significantly enhances the material's barrier properties. These findings are consistent with other studies reported in the literature as above mentioned and support viability of using bentonite as a reinforcement to optimize barrier properties of bioplastics.

Scheme of possible transit of water molecules in TPS biofilms, and barrier effect in TPS/clays biofilm.

Conclusions

In this study, thermoplastic starch (TPS) bioplastics were developed using starch isolated from Melicoccus bijugatus (huaya) seeds and reinforced with bentonite clays at concentrations of 1%, 3%, and 5% by weight. FTIR analysis confirmed that clays were of the bentonite type. The average particle size was 10 μm with a unimodal distribution, and SEM micrographs showed a porous surface with agglomerations, which are typical characteristics of clays.

The clay-reinforced bioplastics exhibited a significant increase in tensile strength and elastic modulus. Specifically, the tensile strength increased from 2.5 MPa for pure TPS to 5.0 MPa with 5% clay, and elastic modulus increased from 25 to 60 MPa under same conditions. These improvements are attributed to the effective dispersion of clay particles and their interaction with starch matrix.

Thermogravimetric analysis (TGA) indicated that the thermal stability of bioplastics improved with the addition of clays. Initial degradation temperature increased from 110 °C for pure TPS to 130 °C with 5% clay. This behavior is due to formation of an effective interface between clays and starch, as well as the promotion of char formation during thermal degradation.

Water vapor permeability (WVP) tests revealed a significant decrease in WVP values with increasing clay content. WVP values decreased from 4.11 × 10 −10  g·m −1 ·s −1 ·Pa −1 for pure TPS to 2.09 × 10 −10  g·m −1 ·s −1 ·Pa −1 with 5% clay, representing a reduction of nearly 50%. This indicates a notable barrier effect due to the dispersion of clays within starch matrix.

Results suggest that huaya starch-based bioplastics reinforced with bentonite clays have great potential for sustainable food packaging applications, offering improved mechanical and barrier properties.

Data availability

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Acknowledgements

The authors acknowledge to Tecnológico Nacional de México the financial support for the project 11329.21-PD. In addition, the technical support of José Rodríguez Laviada with the FTIR, TGA, SEM and Distribution Particle Size characterization.

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Emilio Pérez-Pacheco

Present address: Tecnológico Nacional de México, Campus Instituto Tecnológico Superior de Calkiní, Cuerpo Académico Bioprocesos, Av. Ah Canul SN por Carretera Federal, C.P. 24900, Calkiní, Campeche, Mexico

Authors and Affiliations

Tecnológico Nacional de México, Campus Instituto Tecnológico Superior de Calkiní, Cuerpo Académico Bioprocesos, Av. Ah Canul SN por Carretera Federal, C.P. 24900, Calkiní, Campeche, Mexico

Mario Adrián de Atocha Dzul-Cervantes, Jorge André Canto Rosado, Yasser Alejandro Chim-Chi, Luis Alfonso Can-Herrera & Alejandro Ortíz-Fernández

Universidad Politécnica de Yucatán, Tablaje Catastral 7193, Carretera, Mérida-Tetiz Km.4.5, C.P. 97357, Mérida, Yucatán, Mexico

Soledad Cecilia Pech-Cohuo

Centro de Investigación Científica de Yucatán, A.C., Unidad de Materiales, Calle 43, No. 130 x 32 y 34, Colonia Chuburná de Hidalgo, C.P 97205, Mérida, Yucatán, Mexico

Carlos Rolando Ríos-Soberanis

División Académica Multidisciplinaria de Jalpa de Méndez, Universidad Juárez Autónoma de Tabasco, Carretera Estatal Libre Villahermosa-Comalcalco Km. 27+000 s/n Ranchería Ribera Alta, C.P. 86205, Jalpa de Méndez, Tabasco, Mexico

Zujey Berenice Cuevas-Carballo & Juan Pablo Collí-Pacheco

Facultad de Ingeniería Química, Universidad Autónoma de Yucatán, Periférico Norte Kilómetro, 33.5, Tablaje Catastral 13615, Chuburná de Hidalgo Inn, 97302, Mérida, Yucatán, Mexico

Erbin Guillermo Uc-Cayetano & Yamile Pérez‑Padilla

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Conceptualization, S.C., M.A.A. and E.P.; methodology, S.C., Y.P.P, J.A, J.P; software, M.A.A., E.P., A.O.F and J.H.; validation, E.G, C.R. and Z.B.; investigation, M.A.A, E.P and C.R.; re-sources, M.A.A., E.P., and C.R.; writing—original draft preparation, M.A.A, E.P., C.R., E.G. and Y.A.; writing—review and editing, M.A.A, E.P., L.A., C.R., and Y.A.; supervision, Y.A.; project administration, M.A.A. and E.P.; funding acquisition, M.A.A. and E.P.; All authors have read and agreed to the published version of the manuscript.

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Pech-Cohuo, S.C., Dzul-Cervantes, M.A.d., Pérez-Pacheco, E. et al. Effect of clays incorporation on properties of thermoplastic starch/clay composite bio-based polymer blends. Sci Rep 14 , 19669 (2024). https://doi.org/10.1038/s41598-024-69092-1

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DOI : https://doi.org/10.1038/s41598-024-69092-1

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2. literature and research explaining the causes of suboptimal financial decisions made by consumers, 2.1. suboptimal financial decisions, 2.2. knowledge transfer to customers, 2.3. financial knowledge of customers—conclusions from research.

• ability to perform basic calculations and interpret results regarding interest rates,
• understanding the phenomenon of inflation and its consequences,
• ability to practically diversify risk.

3. Empirical Verification of Consumers’ Financial Knowledge

• questions regarding the level of financial knowledge and knowledge of financial concepts,
• financial knowledge testing questions,
• questions regarding consumers’ behavior on the financial market (see Table 1 ).
• n —is the number of observations,
• r —is the number of levels of one variable,
• k —is the number of levels of the second variable.

4. Financial Knowledge Research—Observations from Poland

• a one-off repayment of the entire amount after one year (PLN 1200)
• repayment of the loan in 12 equal monthly instalments of PLN 100 each

5. The Research Results

5.1. examined relationships.

• those who claimed that they carefully read loan agreements tended to be young people up to 25 years of age, as well as those with higher education.
• there was a statistically significant relationship between approach taken to signing consumer loans and place of residence and household. Those living in large cities and raising children or were themselves dependent on someone else were more likely to carefully read credit agreements.

5.2. Overall Financial Knowledge

• nearly 36% of the respondents elect not to personally verify the terms of a loan agreement, relying on the salesperson’s opinions and recommendations. Such people are significantly exposed to the framing effect and possible consequences of information asymmetry.
• analysis of preferences regarding the method of loan repayment (one-off or instalments) revealed that mental accounting strongly influences consumers’ credit decisions, which is justifiable and rational from the perspective of consumer households. Subconsciously, consumers assume that their household budgets lack income flexibility and prefer solutions that ensure ongoing liquidity for the household.
• at the same time, when choosing a product or service, consumers are influenced by potentially irrelevant factors such as the advisor’s suggestions, peer pressure or opinions commonly repeated on social media. This applies to young people especially (<25 years old) and those who have cash—in the questionnaire they tended to answer that they were indifferent to the choice of repayment method or preferred to pay ‘as late as possible’;
• the ease with which respondents treated consumer bankruptcy as an opportunity for a ‘fresh start’, while ignoring the costs of restructuring (involving one’s own assets to repay part of the debt) is an example of hyperbolic discounting.

5.3. Discussion

• Project “Think Financially!” (2021) Project objective: Financial education of young people and adults in the field of personal finance management. Project conclusions: ○ Participants showed significant improvement in understanding basic financial concepts and the ability to plan a household budget. ○ The project showed that interactive teaching methods, such as games and simulations, are effective in increasing engagement and understanding among participants.
• Study “Level of financial knowledge of young Poles” (2020) Study objective: To examine the level of financial knowledge among young Poles aged 18–30 and their approach to managing personal finances. Study Results: ○ More than half of the respondents had difficulties with practical understanding of basic financial concepts, such as inflation, interest rates or the role of saving. ○ Young Poles use banking services more often, but they lack knowledge about more complex financial products, such as investments or insurance
• Study “Financial Condition of Polish Families” (2018) Study objective: Analysis of the financial condition of Polish families and their ability to manage a household budget. Study Results: ○ Over 60% of Polish families had difficulty saving regularly and did not have a contingency plan for unforeseen expenses, which increased the risk of debt. ○ The study indicated the need for financial education, especially in budget management and long-term planning.

6. Conclusions

Author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Gębski, Ł.; Daw, G. Consumers’ Financial Knowledge in Central European Countries in the Light of Consumer Research. J. Risk Financial Manag. 2024 , 17 , 379. https://doi.org/10.3390/jrfm17090379

Gębski Ł, Daw G. Consumers’ Financial Knowledge in Central European Countries in the Light of Consumer Research. Journal of Risk and Financial Management . 2024; 17(9):379. https://doi.org/10.3390/jrfm17090379

Gębski, Łukasz, and Georges Daw. 2024. "Consumers’ Financial Knowledge in Central European Countries in the Light of Consumer Research" Journal of Risk and Financial Management 17, no. 9: 379. https://doi.org/10.3390/jrfm17090379

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The wisdom of the crowd with partial rankings: A Bayesian approach implementing the Thurstone model in JAGS

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• Published: 30 July 2024

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• Lauren E. Montgomery 1 ,
• Nora Bradford 1 &
• Michael D. Lee 1  

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We develop a Bayesian method for aggregating partial ranking data using the Thurstone model. Our implementation is a JAGS graphical model that allows each individual to rank any subset of items, and provides an inference about the latent true ranking of the items and the relative expertise of each individual. We demonstrate the method by analyzing data from new experiments that collected partial ranking data. In one experiment, participants were assigned subsets of items to rank; in the other experiment, participants could choose how many and which items they ranked. We show that our method works effectively for both sorts of partial ranking in applications to US city populations and the chronology of US presidents. We discuss the potential of the method for studying the wisdom of the crowd and other research problems that require aggregating incomplete or partial rankings.

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The data for this project can be found on the Open Science Framework at https://osf.io/mpwyz/ . Additional individual-selected data sets not reported in this article are also available.

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MDL developed the code and conducted the analyses. LEM designed and conducted the individual-selected experiment. NB designed and conducted the experimenter-selected experiment. MDL, LEM, and NB wrote the manuscript.

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The experimenter-selected partial ranking tasks IRB#1671 were approved via exempt self-determination by the University of California Irvine (UCI) Institutional Review Board (IRB). The individual-selected partial ranking tasks were part of IRB#2937 that was approved by the University of California Irvine (UCI) Institutional Review Board (IRB) Committee D.

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Montgomery, L.E., Bradford, N. & Lee, M.D. The wisdom of the crowd with partial rankings: A Bayesian approach implementing the Thurstone model in JAGS. Behav Res (2024). https://doi.org/10.3758/s13428-024-02479-0

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