Non-response Research Articles

Strengthening resilience in supply chains: the role of multi-layer flexibility, supply chain risks and environmental dynamism

PurposeThis study examines the relationship between multi-layer supply chain flexibility (MSCF) and Supply chain resilience (SCR). Further, it looks at the moderating effect of environmental dynamism (ED) and supply chain risks (SCRI) on the relationship between MSCF and SCR.Design/methodology/approachExecutives from the pharmaceutical, agri-food, electronics, automobile and textile industries were invited to complete a self-administered questionnaire. We received feedback from a total of 302 participants. Prior to conducting the primary analysis, we addressed the potential for nonresponse bias and verified the assumptions of homoscedasticity and normal distribution of the data. The reliability and validity of the constructs were established through confirmatory factor analysis. Structural equation modelling is employed for the purpose of conducting hypothesis testing.FindingsThe results demonstrate a notable influence of MSCF on SCR, particularly in settings characterized by high levels of ED and SCRI. The study highlights the importance of flexibility in multiple aspects of the supply chain to build resilience against a range of disruptions and uncertainties.Originality/valueThe study presents the fundamental role of Multi-Layer Flexibility in building up SCR. The results of this study reinforce the existing literature and offers empirical evidence for how ED, SCRI moderates the influence between MSCF to SCR. These results offer valuable information to both supply chain specialists and researchers for building comprehensive strategy to bring resilience in supply chains.

Switch to Web-Based Surveys During COVID-19 Pandemic Left Out the Most Religious, Creating a False Impression of Rapid Religious Decline

Abstract Religion appears to have taken a nosedive during the pandemic, including previously persistent forms of intense religion such as strong affiliation and biblical literalism. However, this apparent secularization is the result of mode effects. The gold standard General Social Survey (GSS) switched to online rather than face-to-face interviews and the response rate plunged to 17%. Parallel analyses of GSS panel data demonstrate that this mode switch introduced substantial nonresponse bias. Illustratively, biblical literalism was almost 50% higher among those who declined to participate (36%) than those who participated in the online survey (25%). Rather than declining, intense religion persisted if not rose over time among those willing to participate in a push-to-web survey. The apparent decline was simply a result of disillusioned, distrusting, disinformed, disadvantaged, and disconnected people being much less likely to agree to participate. Intense religion and other social phenomena are underrepresented and thereby underestimated in online surveys with substantial nonresponse, including those using population sampling methods. The trend in survey research toward these types of surveys could be expected to give a false impression of secularization and other social change going forward—including making society look less disillusioned, distrusting, disinformed, disadvantaged, and disconnected than it is.

Statistical primer: individual patient data meta-analysis and meta-analytic approaches in case of non-proportional hazards.

Individual patient data (IPD) meta-analyses build upon traditional (aggregate data) meta-analyses by collecting IPD from the individual studies rather than using aggregated summary data. Although both traditional and IPD meta-analyses produce a summary effect estimate, IPD meta-analyses allow for the analysis of data to be performed as a single dataset. This allows for standardization of exposure, outcomes, and analytic methods across individual studies. IPD meta-analyses also allow the utilization of statistical methods typically used in cohort studies, such as multivariable regression, survival analysis, propensity score matching, uniform subgroup and sensitivity analyses, better management of missing data, and incorporation of unpublished data. However, they are more time-intensive, costly, and subject to participation bias. A separate issue relates to the meta-analytic challenges when the proportional hazards assumption is violated. In these instances, alternative methods of reporting time-to-event estimates, such as restricted mean survival time should be used. This statistical primer summarizes key concepts in both scenarios and provides pertinent examples.

Impact of information system flexibility and dynamic capabilities in building net zero supply chains

PurposeThe main aim of this study is to explore the relationship between information system flexibility and dynamic capabilities to build sustainable and net zero supply chains under the influence of environmental dynamism.Design/methodology/approachWe have formulated a self-administered survey, with 359 participants contributing responses. Prior to delving into foundational assumptions, such as homoscedasticity and normality, a nonresponse bias analysis was executed. The integrity of the data, in terms of reliability and construct validity, was gauged using confirmatory factor analysis. Subsequent regression outputs corroborated all the proposed assumptions, fortifying the extant scholarly literature.FindingsThe empirical findings of this research underscore a positive correlation between Information system flexibility, dynamic capabilities and a net zero supply chain, especially in the context of environmental dynamism. Data sourced from the cement manufacturing sector support these observations. We also found that environmental dynamism moderates the relationship between data analytics capability and sustainable supply chain flexibility but does not moderate the relationship between Resource flexibility and sustainable supply chain flexibility. Additionally, this research strengthens the foundational principles of the dynamic capability theory.Originality/valueThe conceptual framework elucidates the interplay between information system flexibility, dynamic capabilities, and sustainable supply chain flexibility, emphasizing their collective contribution towards achieving sustainable chain net zero, introducing environmental dynamics as a moderating variable that augments the scholarly discourse with a nuanced layer of analytical depth.

Long-term trajectories of depressive symptoms in deployed military personnel: A 10-year prospective study

BackgroundMilitary missions, especially those involving combat exposure, are associated with an increased risk of depression. Understanding the long-term course of depressive symptoms post-deployment is important to improve decision-making regarding deployment and mental health policies in the military. This study investigates trajectories of depressive symptoms in the Dutch army, exploring the influence of factors such as demographics, early-life trauma, posttraumatic stress disorder (PTSD) symptoms, and deployment stressors. MethodsA cohort of 1032 military men and women deployed to Afghanistan (2005–2008) was studied from pre- to 10 years post-deployment. Depressive and PTSD symptoms were assessed using the Symptom CheckList-90 and the Self-Rating Inventory for PTSD. Demographics, early trauma, and deployment experiences were collected at baseline and after deployment, respectively. Latent Class Growth Analysis was used to explore heterogeneity in trajectories of depressive symptoms over time. ResultsFour trajectories were found: resilient (65%), intermediate-stable (20%), symptomatic-chronic (9%), and late-onset-increasing (6%). The resilient group experienced fewer deployment stressors, while the symptomatic-chronic group reported more early life traumas. Trajectories with elevated depressive symptoms consistently demonstrated higher PTSD symptoms. LimitationsPotential nonresponse bias and missing information due to the longitudinal design and extensive follow-up times. ConclusionsThis study identified multiple trajectories of depressive symptoms in military personnel up to 10 years post-deployment, associated with early trauma, deployment stressors, adverse life events and PTSD symptoms. The prevalence of the resilient trajectory suggests a substantial level of resilience among deployed military personnel. These findings provide valuable insights and a foundation for further research.

Abstract 1169: Increased immunotherapy sensitivity associated with damaging IFN-gamma pathway mutations in NSCLC

Abstract Introduction: In vivo CRISPR screens across multiple murine models of malignancy revealed that loss of interferon gamma (IFN-γ)-mediated sensing predisposes to immune elimination. We assessed the association between damaging mutations in the IFN-γ pathway and clinical outcomes after immune checkpoint inhibition in the Stand Up to Cancer-Mark Foundation non-small cell lung cancer (NSCLC) cohort. Methods: The sensitizing IFN-γ pathway genes were defined as IFNGR1, IFNGR2, JAK1, JAK2, STAT1, IRF1 & IRF9. Damaging mutations (including missense mutations) in any of these genes was considered as loss of pathway activity. We used the best observed response (measured by RECIST v1.1 criteria across time points) to define outcome. Complete and partial response were categorized as responses (R), while stable and progressive disease were categorized as non-responses (NR). The percentage change in target lesions (CTL) was also assessed. Response data was analyzed using additive logistic (LogR) and linear regression (LinR). Overall survival (OS) and progression-free survival (PFS) were analyzed using Cox proportional-hazards models. Three predictor variable groups were established; the final models comprised IFN-γ pathway loss, tumor mutational burden (TMB), programmed death-ligand 1 (PD-L1) tumor proportion score (TPS), prior platinum chemotherapy, age, sex, histological subtype, initial tumor stage, and smoking pack years. Results: 309 patients had evaluable whole-exome sequencing data, treatment with PD-(L)1 inhibitor regimens, and matched radiological responses (121 R [39%], 188 NR [61%]); 306 had matched OS data; 297 had matched PFS data; and 227 had matched CTL data. 27 (7%) patients had damaging mutations in the IFN-γ pathway [IFNGR1 (3); IFNGR2 (1); JAK1 (6); JAK2 (8); STAT1 (3); IRF1 (1); IRF9 (3); and JAK2 & IRF9 (1)]. Significant relationships were observed in univariable models between IFN-γ pathway loss and both response (LogR: OR 0.17, p=2 × 10−4*; LinR: OR 0.70, p=4.8 × 10−4*) and survival (PFS: HR 0.45, p=0.0067*; OS: HR 0.51, p=0.037*). IFN-γ pathway loss is significantly associated with TMB (p=9.7 × 10−8*, Wilcoxon signed rank test). Multivariable models incorporating TMB and PD-L1 TPS confirmed the significance of IFN-γ pathway loss in both response (LogR: OR 0.29, p=0.050*; LinR: OR 0.71, p=0.0052*) and survival (PFS: HR 0.37, p=0.036*; OS: HR 0.27, p=0.030*). These relationships persisted in the final multivariable models (LogR: OR 0.32, p=0.11; LinR: OR 0.69, p=0.012*; PFS: HR 0.39, p=0.068; OS: HR 0.15, p=0.0038*). Random gene sets, of similar total protein coding sequence length, significantly associated with TMB but not with multivariable models of response or survival. Conclusion: In this NSCLC cohort, damaging IFN-γ pathway mutations associate with increased immunotherapy sensitivity, independently of TMB and other co-variates, as evidenced by multiple clinical outcomes. Citation Format: Alexander Azizi, Arvind Ravi, Natalie Vokes, Stephen-John Sammut, Justin Gainor, Gad Getz. Increased immunotherapy sensitivity associated with damaging IFN-gamma pathway mutations in NSCLC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1169.

Voluntary E-Learning Exercises Support Students in Mastering Statistics

University students often learn statistics in large classes, and in such learning environments, students face an exceptionally high risk of failure. One reason for this is students’ frequent statistics anxiety. This study shows how students can be supported using e-learning exercises with automated knowledge of correct response feedback, supplementing a face-to-face lecture. To this end, we surveyed 67 undergraduate social science students at a German university and observed their weekly e-learning exercises. We aggregated students’ exercise behavior throughout the semester to explain their exam performance. To control for participation bias, we included essential predictors of educational success, such as prior achievement, motivation, personality traits, time preferences, and goals. We applied a double selection procedure based on the machine learning method Elastic Net to include an optimal but sparse set of control variables. The e-learning exercises indirectly promoted the self-regulated learning techniques of retrieval practice and spacing and provided corrective feedback. Working on the e-learning exercises increased students’ performance on the final exam, even after controlling for the rich set of control variables. Two-thirds of students used our designed e-learning exercises; however, only a fraction of students spaced out the exercises, although students who completed the exercises during the semester and were not cramming at the end benefited additionally. Finally, we discuss how the results of our study inform the literature on retrieval practice, spacing, feedback, and e-learning in higher education.

Nonresponse Bias Analysis in Longitudinal Studies: A Comparative Review with an Application to the Early Childhood Longitudinal Study

SummaryLongitudinal studies are subject to nonresponse when individuals fail to provide data for entire waves or particular questions of the survey. We compare approaches to nonresponse bias analysis (NRBA) in longitudinal studies and illustrate them on the Early Childhood Longitudinal Study, Kindergarten Class of 2010–2011 (ECLS‐K:2011). Wave nonresponse with attrition often yields a monotone missingness pattern, and the missingness mechanism can be missing at random (MAR) or missing not at random (MNAR). We discuss weighting, multiple imputation (MI), incomplete data modelling and Bayesian approaches to NRBA for monotone patterns. Weighting adjustments can be effective when the constructed weights are correlated with the survey outcome of interest. MI allows for variables with missing values to be included in the imputation model, yielding potentially less biased and more efficient estimates. We add offsets in the MAR results to provide sensitivity analyses to assess MNAR deviations. We conduct NRBA for descriptive summaries and analytic model estimates in the ECLS‐K:2011 application. The strength of evidence about our NRBA depends on the strength of the relationship between the fully observed variables and the key survey outcomes, so the key to a successful NRBA is to include strong predictors.

Lightweight federated learning for STIs/HIV prediction

This paper presents a solution that prioritises high privacy protection and improves communication throughput for predicting the risk of sexually transmissible infections/human immunodeficiency virus (STIs/HIV). The approach utilised Federated Learning (FL) to construct a model from multiple clinics and key stakeholders. FL ensured that only models were shared between clinics, minimising the risk of personal information leakage. Additionally, an algorithm was explored on the FL manager side to construct a global model that aligns with the communication status of the system. Our proposed method introduced Random Forest Federated Learning for assessing the risk of STIs/HIV, incorporating a flexible aggregation process that can be adjusted to accommodate the capacious communication system. Experimental results demonstrated the significant potential of a solution for estimating STIs/HIV risk. In comparison with recent studies, our approach yielded superior results in terms of AUC (0.97) and accuracy (93%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$93\\%$$\\end{document}). Despite these promising findings, a limitation of the study lies in the experiment for man’s data, due to the self-reported nature of the data and sensitive content. which may be subject to participant bias. Future research could check the performance of the proposed framework in partnership with high-risk populations (e.g., men who have sex with men) to provide a more comprehensive understanding of the proposed framework’s impact and ultimately aim to improve health outcomes/health service optimisation.

Adverse Childhood Experiences and Diabetes Risk in Mississippi Adults.

Despite Mississippi's high diabetes prevalence and the growing literature finding significant associations between adverse childhood experiences (ACEs) and diabetes, no research has examined the relationship between ACEs and diabetes risk in Mississippiadults. This study utilized data from the 2020 Behavioral Risk Factor Surveillance System (BRFSS) to determine if such a relationship existed. Data for Mississippi respondents were weighted to account for nonresponse bias and non-coverage errors. Each respondent's total ACE exposure score was calculated based on the number of ACE categories experienced. Multivariate logistic regression was utilized to model the relationship between diabetes and ACE categories and diabetes and total ACE exposure scores. Variables that were significant at p<0.05 were retained in the final (best-fitting) models. All models were adjusted for sex, age, race, level of education, income, and body mass index (BMI). After adjusting for covariates, those experiencing physical abuse (adjusted odds ratio (AOR) 1.72, 95% CI 1.69; 1.75) or sexual abuse (AOR 1.56, 95% CI 1.53; 1.58) had the highest odds of ever being diagnosed with diabetes. Experiencing one ACE (AOR 1.02, 95% CI 1.01; 1.03) was associated with slightly higher odds of having diabetes, while experiencing seven ACE categories (AOR 2.20, 95% CI 2.10; 2.31) had the highest odds. Overall, this study shows a strong association between ACEs and a diagnosis of diabetes in the state of Mississippi. This relationship represents an important focus area for prevention efforts in legislation, public health campaigns, and universal screening procedures in primary care that may decrease the prevalence and burden of diabetes in Mississippi.

Nonresponse Bias of Japanese Wage Statistics

This article analyzes unit nonresponse bias of establishment surveys, drawing on two major government wage surveys in Japan. We find that unit nonresponse is prevalent among establishments with fewer employees, in urban prefectures, and operating in the service industry. Further, while low-wage establishments are less likely to respond, the resulting bias is quantitatively negligible. Regarding establishment sampling of employees, we find that the sampled establishments randomly choose workers in an appropriate manner. Overall, with proper weighting, nonresponse bias is negligible in Japanese wage statistics to the extent that the mean estimate is concerned. In addition to nonresponse bias, we also assess potential bias due to limited coverage of the sampling population and find substantial bias at the tails of the wage distribution. In particular, excluding corporate executives and establishments with few employees results in an underestimation of wage inequality.

How does the dramatic rise of nonresponse in the Current Population Survey impact labor market indicators?

SummaryWithin a decade, the share of households refusing to participate in the Current Population Survey (CPS) tripled. We show households that refuse 1 month but respond in an adjacent month account for an important part of the rise. Leveraging the labor force status of survey participants in the months surrounding their nonresponse, we find that rising refusals suppressed the measured labor force participation rate and employment–population ratio but had little effect on the unemployment rate. Notably, nonresponse bias accounts for at least 10% of the reported decline in the labor force participation rate from 2000 to 2020.

The structure of psychiatric comorbidity without selection and assortative mating

The widespread comorbidity observed across psychiatric disorders may be the result of processes such as assortative mating, gene-environment correlation, or selection into population studies. Between-family analyses of comorbidity are subject to these sources of bias, whereas within-family analyses are not. Because of Mendelian inheritance, alleles are randomly assigned within families, conditional on parental alleles. We exploit this variation to compare the structure of comorbidity across broad psychiatric polygenic scores when calculated either between-family (child polygenic scores) or within-family (child polygenic scores regressed on parental polygenic scores) in over 25,000 genotyped parent-offspring trios from the Norwegian Mother Father and Child Cohort study (MoBa). We fitted a series of factor models to the between- and within-family data, which consisted of a single genetic p-factor and a varying number of uncorrelated subfactors. The best-fitting model was identical for between- and within-family analyses and included three subfactors capturing variants associated with neurodevelopment, psychosis, and constraint, in addition to the genetic p-factor. Partner genetic correlations, indicating assortative mating, were not present for the genetic p-factor, but were substantial for the psychosis (b = 0.081;95% CI [0.038,0.124]) and constraint (b = 0.257;95% CI [0.075,0.439]) subfactors. When average factor levels for MoBa mothers and fathers were compared to a population mean of zero we found evidence of sex-specific participation bias, which has implications for the generalizability of findings from cohort studies. Our results demonstrate the power of the within-family design for better understanding the mechanisms driving psychiatric comorbidity and their consequences on population health.

Investigating sources of non-response bias in a population-based seroprevalence study of vaccine-preventable diseases in the Netherlands

BackgroundPIENTER 3 (P3), conducted in 2016/17, is the most recent of three nationwide serological surveys in the Netherlands. The surveys aim to monitor the effects of the National Immunisation Programme (NIP) by assessing population seroprevalence of included vaccine preventable diseases (VPDs). The response rate to the main sample was 15.7% (n = 4,983), following a decreasing trend in response compared to the previous two PIENTER studies (P1, 55.0%; 1995/1996 [n = 8,356] and P2, 33.0%; 2006/2007 [n = 5,834]). Non-responders to the main P3 survey were followed-up to complete a “non-response” questionnaire, an abridged 9-question version of the main survey covering demographics, health, and vaccination status. We assess P3 representativeness and potential sources of non-response bias, and trends in decreasing participation rates across all PIENTER studies.MethodsP3 invitees were classified into survey response types: Full Participants (FP), Questionnaire Only (QO), Non-Response Questionnaire (NRQ) and Absolute Non-Responders (ANR). FP demographic and health indicator data were compared with Dutch national statistics, and then the response types were compared to each other. Random forest algorithms were used to predict response type. Finally, FPs from all three PIENTERs were compared to investigate the profile of survey participants through time.ResultsP3 FPs were in general healthier, younger and higher educated than the Dutch population. Random forest was not able to differentiate between FPs and ANRs, but when predicting FPs from NRQs we found evidence of healthy-responder bias. Participants of the three PIENTERs were found to be similar and are therefore comparable through time, but in line with national trends we found P3 participants were less inclined to vaccinate than previous cohorts.DiscussionThe PIENTER biobank is a powerful tool to monitor population-level protection against VPDs across 30 years in The Netherlands. However, future PIENTER studies should continue to focus on improving recruitment from under-represented groups, potentially by considering alternative and mixed survey modes to improve both overall and subgroup-specific response. Whilst non-responder bias is unlikely to affect seroprevalence estimates of high-coverage vaccines, the primary aim of the PIENTER biobank, other studies with varied vaccination/disease exposures should consider the influence of bias carefully.

The impact of assortative mating, participation bias and socioeconomic status on the polygenic risk of behavioural and psychiatric traits.

To investigate assortative mating (AM), participation bias and socioeconomic status (SES) with respect to the genetics of behavioural and psychiatric traits, we estimated AM signatures using gametic phase disequilibrium and within-spouses and within-siblings polygenic risk score correlation analyses, also performing a SES conditional analysis. The cross-method meta-analysis identified AM genetic signatures for multiple alcohol-related phenotypes, bipolar disorder, major depressive disorder, schizophrenia and Tourette syndrome. Here, after SES conditioning, we observed changes in the AM genetic signatures for maximum habitual alcohol intake, frequency of drinking alcohol and Tourette syndrome. We also observed significant gametic phase disequilibrium differences between UK Biobank mental health questionnaire responders versus non-responders for major depressive disorder and alcohol use disorder. These results highlight the impact of AM, participation bias and SES on the polygenic risk of behavioural and psychiatric traits, particularly in alcohol-related traits.

Calibration and XGBoost reweighting to reduce coverage and non-response biases in overlapping panel surveys: application to the Healthcare and Social Survey

BackgroundSurveys have been used worldwide to provide information on the COVID-19 pandemic impact so as to prepare and deliver an effective Public Health response. Overlapping panel surveys allow longitudinal estimates and more accurate cross-sectional estimates to be obtained thanks to the larger sample size. However, the problem of non-response is particularly aggravated in the case of panel surveys due to population fatigue with repeated surveys.ObjectiveTo develop a new reweighting method for overlapping panel surveys affected by non-response.MethodsWe chose the Healthcare and Social Survey which has an overlapping panel survey design with measurements throughout 2020 and 2021, and random samplings stratified by province and degree of urbanization. Each measurement comprises two samples: a longitudinal sample taken from previous measurements and a new sample taken at each measurement.ResultsOur reweighting methodological approach is the result of a two-step process: the original sampling design weights are corrected by modelling non-response with respect to the longitudinal sample obtained in a previous measurement using machine learning techniques, followed by calibration using the auxiliary information available at the population level. It is applied to the estimation of totals, proportions, ratios, and differences between measurements, and to gender gaps in the variable of self-perceived general health.ConclusionThe proposed method produces suitable estimators for both cross-sectional and longitudinal samples. For addressing future health crises such as COVID-19, it is therefore necessary to reduce potential coverage and non-response biases in surveys by means of utilizing reweighting techniques as proposed in this study.

Online images amplify gender bias

Each year, people spend less time reading and more time viewing images1, which are proliferating online2–4. Images from platforms such as Google and Wikipedia are downloaded by millions every day2,5,6, and millions more are interacting through social media, such as Instagram and TikTok, that primarily consist of exchanging visual content. In parallel, news agencies and digital advertisers are increasingly capturing attention online through the use of images7,8, which people process more quickly, implicitly and memorably than text9–12. Here we show that the rise of images online significantly exacerbates gender bias, both in its statistical prevalence and its psychological impact. We examine the gender associations of 3,495 social categories (such as ‘nurse’ or ‘banker’) in more than one million images from Google, Wikipedia and Internet Movie Database (IMDb), and in billions of words from these platforms. We find that gender bias is consistently more prevalent in images than text for both female- and male-typed categories. We also show that the documented underrepresentation of women online13–18 is substantially worse in images than in text, public opinion and US census data. Finally, we conducted a nationally representative, preregistered experiment that shows that googling for images rather than textual descriptions of occupations amplifies gender bias in participants’ beliefs. Addressing the societal effect of this large-scale shift towards visual communication will be essential for developing a fair and inclusive future for the internet.

Reducing Social Stratification Bias in Referendum Participation: Evidence from the German Local Level

Political participation is socially distorted: Socio-economic resources affecting the probability of individuals becoming politically active question the principle of political equality. Such inequality in participation based on social stratification is well documented for elections, while research on inequality in referendum participation is still scarce. Based on the observation that such inequality varies between referendums, this paper explores referendum-specific contextual factors that may affect socially distorted referendum participation. We leverage information from an original dataset covering 1788 districts in 35 German municipalities for 68 local referendums held between 2000 and 2019. The results of our multilevel models indicate that concurrent first-order elections boost referendum turnout and decrease social stratification, while the closeness of the decision increases turnout but does not affect stratification bias. Moreover, we find a curvilinear relationship between turnout and stratification, with stratification only diminishing from participation levels above 40–50%.

Participant Bias in Community-Based Physical Activity Research: A Consistent Limitation?

Physical activity is a beneficial, yet complex, health behavior. To ensure more people experience the benefits of physical activity, we develop and test interventions to promote physical activity and its associated benefits. Nevertheless, we continue to see certain groups of people who choose not to, or are unable to, take part in research, resulting in "recruitment bias." In fact, we (and others) are seemingly missing large segments of people and are doing little to promote physical activity research to equity-deserving populations. So, how can we better address recruitment bias in the physical activity research we conduct? Based on our experience, we have identified 5 broad, interrelated, and applicable strategies to enhance recruitment and engagement within physical activity interventions: (1)gain trust, (2)increase community support and participation, (3)consider alternative approaches and designs, (4)rethink recruitment strategies, and (5)incentivize participants. While we recognize there is still a long way to go, and there are broader community and societal issues underlying recruitment to research, we hope this commentary prompts researchers to consider what they can do to try to address the ever-present limitation of "recruitment bias" and support greater participation among equity-deserving groups.

The impact of corporate social responsibility on the performance of mergers and acquisitions: European evidence

Corporate social responsibility (CSR) and its impact on corporate value has been widely debated. Two opposing views have emerged: stakeholder value maximization and shareholder expense. In this study, we explore these perspectives within the realm of mergers and acquisitions (M&A), one of the most important and far-reaching corporate decisions. Specifically, we examine whether the CSR of the acquiring company impacts M&A performance by analyzing a European sample of 1549 M&A deals announced between 2005 and 2019. Our findings indicate that CSR has a negative effect on stock performance around the M&A announcement, which aligns with the shareholder expense view. We argue that in Europe, where corporate social responsibility (CSR) is already elevated as a result of EU policies and regulations, additional CSR investments may not align with the best interests of shareholders. This serves as an illustration of a Too-Much-Of-A-Good-Thing Effect. However, we also see that CSR has no significant effect on operating performance, which may indicate a financial bias of capital market participants or suggests that investors weigh their own cost-benefit considerations when assessing acquirers' CSR performance.