Behavioral Science Research Research Articles

Balancing the Use of Behavioural Research and Design Science Research to Solve the Relevance Problem in Marketing Research

Contemporary marketing research has a value problem. Claims for the managerial impact of research appear in practically every research article. Nevertheless, managers in the field do not consider scientific outputs as relevant in helping them to address the multiple challenges that organizations face. Marketing typically conducts behavioural research, aiming to understand and explain real-world problems. Other disciplines, such as engineering, focus primarily on building solutions to solve practical problems. Such practice is often termed design science. This study proposes that marketing research should focus more on building solutions, hence calling for a better balance between behavioural and design research. An improved balance between these two paradigms in marketing should increase the value of academic research to practice. Four typical case studies are presented to illustrate key differences between design science and behavioural science.

Exploring Motivational Triggers for Impulse Buying: A Study of Over the Counter Medicines in Pakistan

This quantitative, hypothesis testing study aims to investigate the motivational factors impacting impulse buying of over-the-counter medicines in Pakistan. The study analyzes the relationship of perceived product quality, store atmosphere and shelf display with impulse buying. This research also introduces a new mediator of ‘Consumer urge to buy’ to the literature of impulse buying motivation. Data was collected from a sample of 320 over-the-counter customers whereby the sample size was determined on basis of principles set by the item response theory. Data analysis was done in SPPS using Process Macro by Hayes mediation model 4. The results showed that there is a significant positive relationship between perceived product quality, store atmosphere, shelf display and impulse buying of over the counter medicines with the mediation of urge to buy; hence all the study hypotheses were supported. The study provides a novel theoretical contribution by applying Hawkin Sterns impulse buying theory to a new area of research in behavioral sciences. This research will also help the pharmacists and pharmaceutical brand managers to gain a better understanding of impulse triggers of impulse buying and will enable them to develop a better placement strategy. In addition, this research study is in line with the third Sustainable Development Goal of the United Nations i.e., well-being and good health.

Development in Designing Competency-Based Learning Management According to the Guidelines for Driving the Economy (BCG Model) by Using the Concept of Proactive Learning Management for Students Practice Teaching Professional Experience

This research piece has the following goals: (1) to create a curriculum for creating a competency-based learning management system based on the economically motivated approach (BCG Model) by utilizing the proactive learning management concept for students practice teaching professional experience, (2) to assess the success of the curriculum in creating competency-based learning management utilizing the idea of proactive learning management for student practice teaching professional experience in accordance with the economic driving model (BCG Model), namely; (2.1) to compare the understanding of developing compe-tency-based learning management in accordance with the economic driving model (BCG Model) utilizing the idea of proactive learning management before and after learning, and (2.2) to assess the capacity to create a competency-based learning man-agement plan utilizing the proactive learning management concept in accordance with the economy-driven approach (BCG Model) compared to the criteria of 80%. In the second semester of the academic year 2022, there are 50 first-year teaching professional internship students in attendance. In the second semester of the academic year 2022, a total of 30 first-year teacher training students made up the sample. It makes use of a straightforward random sampling technique and a research and development (R&D) paradigm, which is typical in behavioral and social science research. The following resources were used in the study: (1) a test of knowledge on competency-based learning management system design, and (2) an operational capacity assessment form for creating a competency-based learning management strategy. Using proactive learning management concepts and knowledge comparison in the design of learning management, manage pre-learn and post-learn competency-based learning, using the t-test for dependent, data analysis is used to evaluate the suitability and effectiveness of the curriculum in the design of competency-based learning management in accordance with the economic-driven approach (BCG Model). Then use the t-test for one sample to assess the capacity to create a learning management plan based on post-learning competency versus the threshold of 80%.
 
 The results of the study showed that; (1) the development of a curriculum in the design of competency-based learning management according to the economic-driven approach (BCG Model) using the concept of proactive learning management for students practice teaching professional experience, the innovation of competency-based learning management combined with work to develop competence for students, practice, teaching, and professional experience was successful at 81.74/82.19, the mean was 4.60, the standard deviation was 0.50, and the level at which it was most suited was 4.60, and (2) use the idea of proactive learning management for students’ practice teaching professional experience to evaluate the efficiency of the curriculum in de-veloping competency-based learning management in accordance with the economic driving model (BCG Model), it was found that (2.1) students practice teaching professional experience knowledge in designing a competency-based learning management after learning was significantly higher than before learning at the .01 level, and (2.2) students practice teaching professional experience had the ability to prepare a learning management plan based on post-learning competency higher than the criteria of 80 percent at the statistical significance level of .01.

The Children's Picture Books Lexicon (CPB-LEX): A large-scale lexical database from children's picture books.

This article presents CPB-LEX, a large-scale database of lexical statistics derived from children's picture books (age range 0-8 years). Such a database is essential for research in psychology, education and computational modelling, where rich details on the vocabulary of early print exposure are required. CPB-LEX was built through an innovative method of computationally extracting lexical information from automatic speech-to-text captions and subtitle tracks generated from social media channels dedicated to reading picture books aloud. It consists of approximately 25,585 types (wordforms) and their frequency norms (raw and Zipf-transformed), a lexicon of bigrams (two-word sequences and their transitional probabilities) and a document-term matrix (which shows the importance of each word in the corpus in each book). Several immediate contributions of CPB-LEX to behavioural science research are reported, including that the new CPB-LEX frequency norms strongly predict age of acquisition and outperform comparable child-input lexical databases. The database allows researchers and practitioners to extract lexical statistics for high-frequency words which can be used to develop word lists. The paper concludes with an investigation of how CPB-LEX can be used to extend recent modelling research on the lexical diversity children receive from picture books in addition to child-directed speech. Our model shows that the vocabulary input from a relatively small number of picture books can dramatically enrich vocabulary exposure from child-directed speech and potentially assist children with vocabulary input deficits. The database is freely available from the Open Science Framework repository: https://tinyurl.com/4este73c .

Cause-specific mortality by county, race, and ethnicity in the USA, 2000–19: a systematic analysis of health disparities

Large disparities in mortality exist across racial-ethnic groups and by location in the USA, but the extent to which racial-ethnic disparities vary by location, or how these patterns vary by cause of death, is not well understood. We aimed to estimate age-standardised mortality by racial-ethnic group, county, and cause of death and describe the intersection between racial-ethnic and place-based disparities in mortality in the USA, comparing patterns across health conditions. We applied small-area estimation models to death certificate data from the US National Vital Statistics system and population data from the US National Center for Health Statistics to estimate mortality by age, sex, county, and racial-ethnic group annually from 2000 to 2019 for 19 broad causes of death. Race and ethnicity were categorised as non-Latino and non-Hispanic American Indian or Alaska Native (AIAN), non-Latino and non-Hispanic Asian or Pacific Islander (Asian), non-Latino and non-Hispanic Black (Black), Latino or Hispanic (Latino), and non-Latino and non-Hispanic White (White). We adjusted these mortality rates to correct for misreporting of race and ethnicity on death certificates and generated age-standardised results using direct standardisation to the 2010 US census population. From 2000 to 2019, across 3110 US counties, racial-ethnic disparities in age-standardised mortality were noted for all causes of death considered. Mortality was substantially higher in the AIAN population (all-cause mortality 1028·2 [95% uncertainty interval 922·2-1142·3] per 100 000 population in 2019) and Black population (953·5 [947·5-958·8] per 100 000) than in the White population (802·5 [800·3-804·7] per 100 000), but substantially lower in the Asian population (442·3 [429·3-455·0] per 100 000) and Latino population (595·6 [583·7-606·8] per 100 000), and this pattern was found for most causes of death. However, there were exceptions to this pattern, and the exact order among racial-ethnic groups, magnitude of the disparity in both absolute and relative terms, and change over time in this magnitude varied considerably by cause of death. Similarly, substantial geographical variation in mortality was observed for all causes of death, both overall and within each racial-ethnic group. Racial-ethnic disparities observed at the national level reflect widespread disparities at the county level, although the magnitude of these disparities varied widely among counties. Certain patterns of disparity were nearly universal among counties; for example, in 2019, mortality was higher among the AIAN population than the White population in at least 95% of counties for skin and subcutaneous diseases (455 [97·8%] of 465 counties with unmasked estimates) and HIV/AIDS and sexually transmitted infections (458 [98·5%] counties), and mortality was higher among the Black population than the White population in nearly all counties for skin and subcutaneous diseases (1436 [96·6%] of 1486 counties), diabetes and kidney diseases (1473 [99·1%]), maternal and neonatal disorders (1486 [100·0%] counties), and HIV/AIDS and sexually transmitted infections (1486 [100·0%] counties). Disparities in mortality among racial-ethnic groups are ubiquitous, occurring across locations in the USA and for a wide range of health conditions. There is an urgent need to address the shared structural factors driving these widespread disparities. National Institute on Minority Health and Health Disparities; National Heart, Lung, and Blood Institute; National Cancer Institute; National Institute on Aging; National Institute of Arthritis and Musculoskeletal and Skin Diseases; Office of Disease Prevention; and Office of Behavioral and Social Sciences Research, US National Institutes of Health.

The burden of stomach cancer mortality by county, race, and ethnicity in the USA, 2000–2019: a systematic analysis of health disparities

There are persistent disparities in stomach cancer mortality among racial-ethnic groups in the USA, but the extent to which these patterns vary geographically is not well understood. This analysis estimated age-standardised mortality for five racial-ethnic groups, in 3110 USA counties over 20 years, to describe spatial-temporal variations in stomach cancer mortality and disparities between racial-ethnic groups. Redistribution methods for insufficient cause of death codes and validated small area estimation methods were applied to death registration data from the US National Vital Statistics System and population data from the US National Center for Health Statistics to estimate annual stomach cancer mortality rates. Estimates were stratified by county and racial-ethnic group (non-Latino and non-Hispanic [NL] American Indian or Alaska Native [AIAN], NL Asian or Pacific Islander [Asian], NL Black [Black], Latino or Hispanic [Latino], and NL White [White]) from 2000 to 2019. Estimates were corrected for misreporting of racial-ethnic group on death certificates using published misclassification ratios. We masked (ie, did not display) estimates for county and racial-ethnic group combinations with a mean annual population of less than 1000; thus, we report estimates for 3079 (of 3110) counties for the total population, and 474, 667, 1488, 1478, and 3051 counties for the AIAN, Asian, Black, Latino, and White populations, respectively. Between 2000 and 2019, national age-standardised stomach cancer mortality was lowest among the White population in every year. Nationally, stomach cancer mortality declined for all racial-ethnic groups across this time period, with the most rapid declines occurring among the Asian (percent decline 48.3% [45.1-51.1]) and Black populations (42.6% [40.2-44.6]). Mortality among the other racial-ethnic groups declined more moderately, decreasing by 36.7% (35.3-38.1), 35.1% (32.2-37.7), and 31.6% (23.9-38.0) among the White, Latino, and AIAN populations, respectively. Similar patterns were observed at the county level, although with wide geographic variation. In 2019, a majority of counties had higher mortality rates among minoritised racial-ethnic populations compared to the White population: 81.1% (377 of 465 counties with unmasked estimates for both racial-ethnic groups) among the AIAN population, 88.2% (1295 of 1469) among the Latino population, 99.4% (663 of 667) among the Asian population, and 99.9% (1484 of 1486) among the Black population. However, the size of these disparities ranged widely across counties, with the largest range from 0.3 to 17.1 among the AIAN population. Stomach cancer mortality has decreased substantially across populations and geographies in the USA. However, disparities in stomach cancer mortality among racial-ethnic groups are widespread and have persisted over the last two decades. Local-level data are crucial to understanding the scope of this unequal burden among minoritised groups in the USA. National Institute on Minority Health and Health Disparities; National Heart, Lung, and Blood Institute; National Cancer Institute; National Institute on Aging; National Institute of Arthritis and Musculoskeletal and Skin Diseases; Office of Disease Prevention; and Office of Behavioral and Social Sciences Research, National Institutes of Health (contract #75N94019C00016).

The development of an immersive mixed-reality application to improve the ecological validity of eating and sensory behavior research.

The modern eating environment has been implicated as a driving force of the obesity epidemic. Mixed reality applications may improve traditional methodological assessments of eating behavior by improving the ecological validity of the laboratory setting. Research experts evaluated the utility and ecological validity of a mixed reality application that allowed immersion within virtual environments through utilizing the passthrough cameras of the head mounted display to view and interact with real foods. An initial evaluation was conducted that involved three virtual environments: a traditional laboratory booth, a non-textured restaurant, and a full-textured restaurant. The feedback from the initial evaluation was used to create a new virtual restaurant environment and a subsequent evaluation was conducted. Nearly all research experts suggested adding social cues such as people and background noise to create a more authentic and ecologically valid experience. The experts scored the new virtual restaurant environment to be more acceptable than eating or conducting research in a sensory booth but scored lower when compared to conducting research in a real-world restaurant setting. The results of this evaluation suggest that mixed reality applications may be a new methodology to assess environmental influences of eating behavior and may be a promising direction for eating behavior and sensory science research.

Ecological momentary interventions for smoking cessation: a systematic review and meta-analysis.

Tobacco use is an important cause of preventable mortality and morbidity worldwide. Only 7% of smokers successfully quit annually, despite numerous evidence-based smoking cessation treatments. An important reason for failure is barriers to accessing appropriate smoking cessation interventions, which can be minimized by technology-delivered interventions, such as ecological momentary interventions. Ecological momentary interventions provide the right type and intensity of treatment in real time, based on ecological momentary assessments of relevant variables. The aim of this review was to assess the effectiveness of ecological momentary interventions in smoking cessation. We searched MEDLINE, Scopus, CENTRAL, psychINFO, and ProQuest without applying any filters on 19 September, 2022. One author screened search results for obvious irrelevant and duplicate studies. The remaining studies were independently reviewed by two authors to exclude irrelevant studies, and then they extracted data from the included studies. We collated study findings, transformed data into a common rubric, and calculated a weighted treatment effect across studies using Review Manager 5. We analyzed 10 studies with a total of 2391 participants. Assessment methods included exhaled CO analyzers, bidirectional SMS, data input in apps, and hand movement detection. Interventions were based on acceptance and commitment therapy and cognitive behavioral therapy. Smoking abstinence was significantly higher in participants of intervention groups compared to control groups (RR = 1.24; 95% CI 1.07-1.44, P = 0.004; I2 = 0%). Ecological momentary intervention is a novel area of research in behavioral science. The results of this systematic review based on the available literature suggest that these interventions could be beneficial for smoking cessation.

Predicting and reasoning about replicability using structured groups

This paper explores judgements about the replicability of social and behavioural sciences research and what drives those judgements. Using a mixed methods approach, it draws on qualitative and quantitative data elicited from groups using a structured approach called the IDEA protocol (‘investigate’, ‘discuss’, ‘estimate’ and ‘aggregate’). Five groups of five people with relevant domain expertise evaluated 25 research claims that were subject to at least one replication study. Participants assessed the probability that each of the 25 research claims would replicate (i.e. that a replication study would find a statistically significant result in the same direction as the original study) and described the reasoning behind those judgements. We quantitatively analysed possible correlates of predictive accuracy, including self-rated expertise and updating of judgements after feedback and discussion. We qualitatively analysed the reasoning data to explore the cues, heuristics and patterns of reasoning used by participants. Participants achieved 84% classification accuracy in predicting replicability. Those who engaged in a greater breadth of reasoning provided more accurate replicability judgements. Some reasons were more commonly invoked by more accurate participants, such as ‘effect size’ and ‘reputation’ (e.g. of the field of research). There was also some evidence of a relationship between statistical literacy and accuracy.

Voice analytics in the wild: Validity and predictive accuracy of common audio-recording devices.

The use of voice recordings in both research and industry practice has increased dramatically in recent years-from diagnosing a COVID-19 infection based on patients' self-recorded voice samples to predicting customer emotions during a service center call. Crowdsourced audio data collection in participants' natural environment using their own recording device has opened up new avenues for researchers and practitioners to conduct research at scale across a broad range of disciplines. The current research examines whether fundamental properties of the human voice are reliably and validly captured through common consumer-grade audio-recording devices in current medical, behavioral science, business, and computer science research. Specifically, this work provides evidence from a tightly controlled laboratory experiment analyzing 1800 voice samples and subsequent simulations that recording devices with high proximity to a speaker (such as a headset or a lavalier microphone) lead to inflated measures of amplitude compared to a benchmark studio-quality microphone while recording devices with lower proximity to a speaker (such as a laptop or a smartphone in front of the speaker) systematically reduce measures of amplitude and can lead to biased measures of the speaker's true fundamental frequency. We further demonstrate through simulation studies that these differences can lead to biased and ultimately invalid conclusions in, for example, an emotion detection task. Finally, we outline a set of recording guidelines to ensure reliable and valid voice recordings and offer initial evidence for a machine-learning approach to biascorrection in the case of distorted speech signals.

A factored regression model for composite scores with item-level missing data.

Composite scores are an exceptionally important psychometric tool for behavioral science research applications. A prototypical example occurs with self-report data, where researchers routinely use questionnaires with multiple items that tap into different features of a target construct. Item-level missing data are endemic to composite score applications. Many studies have investigated this issue, and the near-universal theme is that item-level missing data treatment is superior because it maximizes precision and power. However, item-level missing data handling can be challenging because missing data models become very complex and suffer from the same "curse of dimensionality" problem that plagues the estimation of psychometric models. A good deal of recent missing data literature has focused on advancing factored regression specifications that use a sequence of regression models to represent the multivariate distribution of a set of incomplete variables. The purpose of this paper is to describe and evaluate a factored specification for composite scores with incomplete item responses. We used a series of computer simulations to compare the proposed approach to gold standard multiple imputation and latent variable modeling approaches. Overall, the simulation results suggest that this new approach can be very effective, even under extreme conditions where the number of items is very large (or even exceeds) the sample size. A real data analysis illustrates the application of the method using software available on the internet. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Towards a Psychology of Corporate Governance: Theoretical Insights on Non-Executive Director and CEO Performance

Generally, corporate governance research has been considered the preserve of economics, accounting, and finance scholars. However, attempts are being made to apply behavioral science knowledge in this domain that has traditionally been considered economics and finance. The purpose of this article is to further the efforts in establishing a tradition of behavioral science research in corporate governance. To accomplish this objective, the article establishes the gaps in the extant literature on corporate governance. The article argues from theoretical insights emanating from organizational psychology in particular and behavioral sciences in general. As a result, this article attempts to apply some of the existing theories in social and organizational psychology to explain factors influencing the performance of non-executive directors on boards as well as executive performance and remuneration. Principally, we utilized theories of motivation (expectancy and equity theories) and social influence theory (conformity) to highlight the potential contributions that the psychology of corporate governance can make to advance knowledge and practice. Further, applications and practical ways of testing derived assumptions are also discussed.

Evaluasi Sistem Informasi Akademik Satya Wacana Menerapkan Model for Mandatory Use of Software Technologies

There are many theories and methods that can be used to evaluate information systems. For example TAM, Wixom and Todd, DeLone and McLean, etc. These models are not suitable to evaluate information systems that are implemented in mandatory environments where the users have to use the system whether they prefer to use it or not. This study aims to explore the variables that can affect the level of use of SIASAT (Satya Wacana Academic Information System). This research was carried out to find out how significant the influence of the factors that can influence SIASAT users. This study using BSR (Behavioral Science Research) approach with quantitative methods through data collection using questionnaire. For data analysis, statistical analysis was carried out using PLS-SEM through SmartPLS 4 software. Based on the results of the study it can be interpreted that there is a significant relationship between performance expectations and user attitudes in SIASAT usage. This can be seen from the relationship between the variables PE -> AT which has the highest T-Statistic value, 24,877. Variabel with highest effect is the Information Satisfaction variable with an R-Square value of 0.655. Of the eight hypotheses tested, it was concluded that six of them were accepted and two were rejected. This is because the two hypotheses using the Use variable with a Cronbach's Alpha value below 0.7 (0.576) these variables considered unreliable thus two hypotheses are rejected.

Associations of socioeconomic disparities with buccal DNA-methylation measures of biological aging

BackgroundIndividuals who are socioeconomically disadvantaged are at increased risk for aging-related diseases and perform less well on tests of cognitive function. The weathering hypothesis proposes that these disparities in physical and cognitive health arise from an acceleration of biological processes of aging. Theories of how life adversity is biologically embedded identify epigenetic alterations, including DNA methylation (DNAm), as a mechanistic interface between the environment and health. Consistent with the weathering hypothesis and theories of biological embedding, recently developed DNAm algorithms have revealed profiles reflective of more advanced aging and lower cognitive function among socioeconomically-at-risk groups. These DNAm algorithms were developed using blood-DNA, but social and behavioral science research commonly collect saliva or cheek-swab DNA. This discrepancy is a potential barrier to research to elucidate mechanisms through which socioeconomic disadvantage affects aging and cognition. We therefore tested if social gradients observed in blood DNAm measures could be reproduced using buccal-cell DNA obtained from cheek swabs.ResultsWe analyzed three DNAm measures of biological aging and one DNAm measure of cognitive performance, all of which showed socioeconomic gradients in previous studies: the PhenoAge and GrimAge DNAm clocks, DunedinPACE, and Epigenetic-g. We first computed blood-buccal cross-tissue correlations in n = 21 adults (GEO111165). Cross-tissue correlations were low-to-moderate (r = .25 to r = .48). We next conducted analyses of socioeconomic gradients using buccal DNAm data from SOEP-G (n = 1128, 57% female; age mean = 42 yrs, SD = 21.56, range 0–72). Associations of socioeconomic status with DNAm measures of aging were in the expected direction, but were smaller as compared to reports from blood DNAm datasets (r = − .08 to r = − .13).ConclusionsOur findings are consistent with the hypothesis that socioeconomic disadvantage is associated with DNAm indicators of worse physical health. However, relatively low cross-tissue correlations and attenuated effect sizes for socioeconomic gradients in buccal DNAm compared with reports from analysis of blood DNAm suggest that in order to take full advantage of buccal DNA samples, DNAm algorithms customized to buccal DNAm are needed.

Rigour and reproducibility in perinatal and paediatric epidemiologic research using big data.

Rigour and reproducibility in perinatal and paediatric epidemiologic research using big data.

Climate change views examined through a behavioral medicine frame: are there potential target mechanisms for change beyond political ideology?

ABSTRACT The threat of climate change is associated with both profound health consequences and failures by many individuals to take preventive actions. Behavioral science research on health behavior engagement may serve as a lens through which to better understand attitudes associated with the threat of climate change. This study was designed to examine individual differences in attitudinal responses to climate change, understanding the degree to which these responses can be predicted by both political beliefs and more readily modified psychological factors commonly associated with health behavior engagement: locus of control, anxiety sensitivity, delay discounting, and intolerance of uncertainty. Participants (N = 234) were US adults (62% male; 57% Non-Hispanic White; 44% Democrat) who completed an online survey. Stepwise multiple linear regressions examined which variables provided non-redundant prediction in models of climate change beliefs and concerns. In addition to providing support for the role of political affiliation and related ideology in climate change views (9–23% variance), this study underscores the importance of a behavioral health frame in understanding climate change concerns and beliefs. Known risk factors for negative health behaviors – prominently, locus of control, anxiety sensitivity, and delay discounting – contributed strongly to the understanding of these views, accounting for 4–28% of variance. Our findings encourage greater attention to health behavior-related constructs for understanding attitudes relevant to climate change action.

Introduction to the special issue on the ACBS taskforce report

In 2021, the Journal of Contextual Behavioral Science (JCBS) published a white paper titled “Report of the ACBS Task Force on the strategies and tactics of contextual behavioral science research.” The focus of this report was to guide research done by members of the Association of Contextual Behavioral Science. This report offered 33 specific recommendations under five themes. This paper is the introduction to a special issue of JCBS that includes eight commentaries written by leaders in their respective fields on the taskforce report. These commentaries offer praise and suggestions based on the ACBS report.

African researchers must be full participants in behavioural science research.

Behavioural science involves understanding humans. However, it fails if it develops a limited understanding of humanity — 17% of whom who live in Africa. Africa’s voice must therefore be included in behavioural science research. Collaborations with African researchers should be grounded in respect.

Evolution and development of methodologies in social and behavioural science research in relation to oral health

The aim of this introductory paper is to provide an overview of key methodological developments in social and behavioural research in oral health. In the first section, we provide a brief historical perspective on research in the field. In the second section, we outline key methodological issues and introduce the seven papers in the theme. Conceptual models can contextualize research findings and address the 'why' and 'how' instead of 'what' and 'how many'. Many models exist, albeit they need to be evaluated (and adapted) for use in oral health research and in specific settings. The increasing availability of big data can facilitate this with data linkage. Through data linkage, it is possible to explore and understand in a broader capacity the array of factors that influence oral health outcomes and how oral health can influences other factors. With advances in statistical approaches, it is feasible to consider casual inferences and to quantify these effects. There is a need for not only individual efforts to embrace causal inference research but also systematic and structural changes in the field to yield substantial results. The value of qualitative research in co-producing knowledge with and from human participants in addressing 'the how' and 'the why' factors is also key. There have been calls to employ more sophisticated qualitative methods together with mixed methods approaches as ways of helping to address the complex or Wicked Problems in population oral health. In the final section, we outline possible future methodological directions in social and behavioural oral health research including participatory approaches and the development of core outcome sets. Our overriding goal in the paper is to facilitate a critical debate in relation to methodological issues which can be used to improve understanding and generate knowledge in population oral health and that this, in turn, will help inform oral health policy and practices.

Usability Analysis using Usefulness, Satisfaction, and Ease-of-Use Questionnaire on the Digital Banking

In order to give a seamless transaction, particularly during COVID-19 pandemic, private and public bank institution in Indonesia released digital banking application that is easier to use by smartphone. Nonetheless, there are several complaints from the customer regarding the dissatisfying experience of using digital banking apps. Hence, this research aims to analyze the effect of usability in user experience on customer satisfaction based on 4 variables namely Usefulness, Ease-of-Use, Ease-of-Learning as independent variables, and Satisfaction as a dependent variable on the USE Questionnaire. These variables can be used as references to usability enhancement for digital banking apps to give quality services to customers and be able to compete with competitors. Behavioral Science Research (BSR) is the approach used in this research using the quantitative method. The statistical method used in this research is multiple linear regression analysis. The result of this research shows that 1 out of 4 research hypotheses is rejected (Hypothesis 3) proving that there is no effect of the Ease-of-Learning variable on the Satisfaction variable. However, the value of the t-calculated of Ease-of-Learning variable is 2,478 nearly approach the value of t-table 2,600 showing that this variable is almost capable to give the effect of the Satisfaction variable.