Jesse Robbins
Kathryn Proudfoot
Lauren Hemsworth
Grahame Coleman
Paul Hemsworth
Jennifer Van Os- 1Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI, United States
- 2Departments of Health Management and Companion Animals, Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, PE, Canada
- 3Animal Welfare Science Centre, Melbourne Veterinary School, Faculty of Science, The University of Melbourne, Parkville, VIC, Australia
Introduction: The dairy industry has responded to growing public concerns about animal welfare by developing various corporate social responsibility (CSR) initiatives aimed at demonstrating their commitment to animal welfare (AW). Unfortunately, little is known about how dairy consumers perceive these efforts. We conducted two studies of U.S. dairy consumers (n=344 and n=484) to assess their attitudes toward a fictitious AW-CSR initiative. Our objectives were to assess how attitudes toward the AW-CSR initiative and toward the company were affected by participants’ demographic factors and their perceived motivational attributions (i.e., strategic vs. altruistic) of the company for undertaking the AW-CSR initiative.
Methods: In both studies, adult participants were recruited online and were eligible if they consumed dairy products ≥ once/wk. Participants read a fictitious news article about an AW-CSR initiative focused on training dairy farm workers to appropriately handle cows. They then provided Likert-scale ratings of motivational attributions (whether they thought the initiative was strategic or altruistic) and their attitudes toward the initiative and the company. Spearman rank correlations followed by hierarchical multiple regressions were used to assess how attitudes toward the AW-CSR initiative and the company were associated with demographic factors and motivational attributions.
Results and discussion: Across both studies, attitudes toward the AW-CSR initiative and the company were, on average, positive. Female gender was consistently associated with more positive attitudes toward the AW-CSR initiative. Higher age was associated with more positive attitudes in Study 2, but income, education, and political ideology were not associated with attitudes in either study. Rather than demographics, attitudes toward the AW-CSR initiative appeared to be largely associated with motivational attributions, with altruistic motivational attributions being a stronger predictor of positive attitudes compared to strategic motivational attributions. Future research could seek to better elucidate the process by which these motivational attributions are formed, which would help inform how dairy industry CSR initiatives could improve both animal welfare and consumer perceptions.
1 Introduction
Research carried out across numerous countries has found a consistent pattern of growing public concern for the welfare of farmed animals (Cornish et al., 2016). The dairy industry has faced increased public scrutiny regarding the care and welfare of cattle. For the dairy industry to maintain its social license to operate, it must demonstrate that common practices are congruent with prevailing social norms about the treatment of animals (Weary and von Keyserlingk, 2017; Hampton et al., 2020). Failure to do so can diminish public trust, reduce consumer demand, and increase support for government regulation (Tonsor and Olynk, 2011; Coleman, 2018).
To address these growing public concerns, the dairy industry has engaged in corporate social responsibility (CSR) efforts aimed at providing assurances that animal welfare (AW) is a major priority (i.e., AW-CSR). Animal welfare quality assurance programs (e.g., the National Dairy FARM Program, NMPF, 2024; Validus Verification Services, 2024) represent the most common form of AW-CSR (Lever and Evans, 2017). These programs typically entail the establishment of standards or best management practices for how animals should be cared for, as well as mechanisms for evaluating adherence to these standards (e.g., standardized animal welfare assessments or audits). Participation in these programs may or may not be mandatory, and consequences for non-compliance vary considerably between programs in both their nature and severity (Fraser, 2006; Sørensen and Fraser, 2010). Standards are typically informed by scientific research aimed at understanding how both conventional and alternative dairy management practices affect dairy cattle welfare. However, we are not aware of any research examining how dairy consumers perceive these AW-CSR initiatives. The very limited research that has been conducted to date has been focused on sociodemographic factors associated with attitudes toward agricultural CSR but has not specifically addressed AW-CSR. Moreover, none of this past work has been conducted with U.S. consumer samples.
In addition to demographics, psychographic factors such as perceived motivations have also been suggested to influence evaluations of CSR efforts. Drawing on attribution theory (Malle, 2022), some authors have posited that CSR evaluations are influenced by perceptions about the motivation for engaging in the CSR activity (Story and Neves, 2015; Wut and Ng, 2023). This suggests that when unexpected information is delivered to consumers, as may be the case with most CSR efforts, consumers attempt to deduce the cause or motivation behind the CSR activity when evaluating the company, program, or product in question. This process is thought to occur relatively quickly, without effortful cognition (Evans and Stanovich, 2013).
Previous research has largely supported the predictions of attribution theory by showing that perceptions of the underlying motivation to engage in CSR play a major role in how these initiatives are evaluated (Groza et al., 2011). These motivations are typically categorized as strategic or altruistic (Kim et al., 2012). Strategic (i.e., self-serving) motivations refer to CSR initiatives perceived as primarily aiming to manage public perceptions, industry reputation, and/or economic performance. Altruistic (i.e., other-serving) motivations refer to CSR efforts undertaken because the firm believes doing so is the ethical or right thing to do. Initiatives perceived as emanating primarily from altruistic motivations tend to be viewed more favorably than those perceived as arising from strategic motivations (Kim et al., 2012; Xie and Wang, 2022; Dai and Guo, 2024). It should be noted these two types of motivations can and do occur together, and some authors postulate a broader set of perceived motivations (Becker-Olsen et al., 2006; Vlachos, 2012).
Intuitively, AW-CSR initiatives perceived as emanating from altruistic motivations should result in more positive public perceptions. However, it is also possible they may be viewed with skepticism, especially if they are perceived as insincere or dishonest (i.e., “welfare-washing”; Bjørkdahl and Syse, 2021). Conversely, the perception of strategic motivations may also signal honesty, leading to more positive perceptions. Little is known about how dairy industry AW-CSR initiatives are perceived by consumers. As a result, communication strategies by the dairy industry, and animal agriculture more generally, are based largely on conjecture rather than empirical data. A more thorough scientific understanding of how CSR initiatives are perceived by dairy consumers could enhance the industry’s ability to effectively communicate about not only animal welfare, but also other regnant societal concerns affecting the industry’s social sustainability.
To begin to address this gap in the research, we set out to explore U.S. dairy consumer evaluations of AW-CSR efforts using a hypothetical CSR initiative describing a novel farm worker animal handling training program. Animal handling was selected as the topic of focus because it has been consistently identified as a major determinant of farm animal welfare (Boivin et al., 2003; Hemsworth and Coleman, 2011; Burton et al., 2012; Rushen and de Passillé, 2020) and represents the most significant source of negative publicity regarding animal welfare in the dairy industry and animal agriculture in general (Robbins et al., 2016). Recent research also suggests that, unlike other controversial dairy animal welfare issues, there may be significant agreement between both dairy industry and non- industry stakeholders with respect to the appropriateness of many controversial dairy animal handling situations (Robbins et al., 2024).
In Study 1, we set out to characterize U.S. dairy consumer attitudes toward a fictional AW-CSR initiative and toward the company undertaking it, and to explore the associations between those attitudes and both demographic factors and motivational attributions. Based on previous research (Cheah et al., 2011; Pérez and del Bosque, 2015; Choi et al., 2016) we hypothesized the following demographics would be associated with more positive evaluations of AW-CSR: age (hypothesis H1a), female gender (H1b), greater educational attainment (H1c), greater income (H1d), and liberal political ideology (H1e). As a test of attribution theory, we further hypothesized that both strategic (H2) and altruistic (H3) motivational attributions would be associated with AW-CSR evaluations. In Study 2, we attempted to replicate the findings of Study 1 for hypotheses H1 to H3, as well as to extend them by manipulating motivational attributions. We hypothesized AW-CSR evaluations would be more positive when the initiative was framed as being undertaken based on only altruistic motivations, as opposed to for strategic motivations only (H4). Following Feiler et al. (2012) we hypothesized that an initiative framed as motivated by a combination of both altruistic and strategic motivations would be evaluated less favorably than either altruistic- or strategic motivation-only treatments (H5). Lastly, we hypothesized that a control condition, where no information on the underlying motivation was provided, would be viewed least positively (H6). Since none of these hypotheses have been previously tested in the context of AW-CSR, they are most appropriately viewed as exploratory rather than confirmatory (Fife and Rodgers, 2022).
2 Materials and methods
This research was approved by the University of Wisconsin–Madison Educational and Social/Behavioral Science Institutional Review Board (submission ID 2023-0861). All participants provided written informed consent.
2.1 Participant recruitment
For both studies, U.S. dairy consumers (i.e., consumed dairy products ≥ once per week) were recruited using CloudResearch, a survey panel provider utilizing the Amazon Mechanical Turk worker pool to screen and aggregate participants. Mechanical Turk has been used in a variety of studies assessing public attitudes toward various farm animal welfare issues (e.g. Robbins et al., 2015; 2019; Suchyta, 2021; Weathers et al., 2020; Powers et al., 2020). Mechanical-Turk samples have been shown to be more diverse and attentive (i.e., by passing required attention-check questions) than typical convenience samples (Buhrmester et al., 2011; Hauser and Schwarz, 2016). To minimize self-selection bias and demand characteristics, survey invitations for both studies were described vaguely as a communication study aimed at understanding how people read and interpret media stories. CloudResearch was instructed to recruit non-overlapping samples of participants for Study 1 and Study 2.
2.2 Study 1 procedures
In Study 1, participants (n=480) were informed they would be reading an excerpt from a recent news story that was randomly selected from a database containing thousands of current news stories. In reality, there was only one fictional story describing an animal handling training program (“CowPro”) adopted by a dairy company (“Cedarbrook Dairy Company”). A fictional excerpt, mimicking the styling and appearance of an actual newspaper article, was developed to avoid pre-existing negative associations with an actual dairy company or AW-CSR initiative (Figure 1). Google search was used to check that the names of the fictional training program and dairy company were not already used by real programs or companies.
Figure 1. Fictional excerpt used in Study 1 and Study 2. In Study 2, additional text stated the purported motivation for the fictional dairy company to adopt the training program.
After reading the excerpt, participants responded to our primary outcome measures: attitude toward the AW-CSR, and strategic and altruistic motivational attributions. Global attitude toward the AW-CSR (8 items; Cronbach α=0.94) was our primary outcome measure used to test our hypotheses. This measure comprised two distinct, yet closely related subscales tapping attitudes toward the animal welfare initiative (4 items; Cronbach α=0.88) and attitudes toward the company (4 items; Cronbach α=0.92). Following Story and Neves (2015), single items (1=strongly disagree; 7=strongly agree) were used to measure strategic (i.e., “Cedarbrook Dairy Company is implementing the CowPro training program to generate a positive public image”) and altruistic motivational attributions (i.e., “Cedarbrook Dairy Company is implementing the CowPro training program because they care about the welfare of cows and want to make their lives better”).
To enhance data quality, a commitment request (“Can you commit to carefully reading the article and providing thoughtful answers to questions in this survey?”) and one multiple-choice comprehension check (“What is the name of the animal training program described in the excerpt?”) were included and interspersed among several distractor questions (e.g., “Overall, how interesting did you find the article?”; “When shopping, how important are food animal welfare labels?”). We also included questions about general attitudes toward dairy animal welfare, and the efficacy and importance of animal handling training. In addition to mitigating demand characteristics, these questions were included to obtain information for future extension programming.
Unless otherwise stated, all items utilized 7-point Likert-type scales and were presented singly on their own page. A hyperlink to the article was provided with each question in case participants wanted to re-read the excerpt. In the final section, participants answered a series of demographic questions (i.e., age, gender, income, education, and political ideology). The full survey instrument can be found in Supplementary Material S1 (http://digital.library.wisc.edu/1793/96310).
2.3 Study 2 procedures
In Study 2, a novel sample of participants (n=640; 160 in each of 4 treatments) was recruited by CloudResearch to follow the same protocol described in Study 1, with several modifications. Most notably, participants were randomly assigned to 1 of 4 treatments with text about the company’s motivation for initiating the AW-CSR program. Our goal was to manipulate perceived motivations by adding the following text to the end of the excerpt used in Study 1: altruistic (ALT: “Dr. Smith said, ‘Cedarbrook Dairy Company hopes the implementation of the CowPro Program will improve the lives of both dairy cattle and the workers that care for them.’”), strategic (STR: “Dr. Smith said, ‘Cedarbrook Dairy Company hopes the implementation of the CowPro Program will help promote consumer confidence in dairy production.’”); combination (ALT-STR: “Dr. Smith said, ‘Cedarbrook Dairy Company hopes the implementation of the CowPro Program will improve the lives of both dairy cattle and the workers that care for them and promote consumer confidence in dairy production.”); and CON where no information on motivation was provided (as in Study 1, Figure 1). In the ALT-STR combination treatment, the order in which the strategic and altruistic motivation text appeared was randomized to control for possible order effects. Images of the articles used in the 4 treatments are shown in Supplementary Material S2 (http://digital.library.wisc.edu/1793/96310).
After reading the excerpt, participants were presented with the same primary outcome measures used in Study 1, with several slight modifications. Attitudes toward the AW-CSR initiative were assessed using a 9-item scale that tapped attitudes toward the program (3 items; Cronbach α=0.93), attitudes toward the company (3 items; Cronbach α=0.96), and purchase likelihood (3 items; Cronbach α=0.95). The latter, a measure of behavioral intention, was included to improve metric validity in the dairy consumer context. Following Kim and Choi (2018), strategic and altruistic motivational attributions were both measured using 3-item scales (strategic; Cronbach α=0.72 and altruistic; Cronbach α=0.79). As in Study 1, a commitment request and one comprehension check were interspersed among distractors. All questions appeared singly on their own page with a hyperlink to the fictional excerpt. Unless otherwise stated, all response options were 7-point Likert-type. Lastly, participants completed the same standard demographic questions used in Study 1. The full survey instrument for Study 2 is available as Supplementary Material S3 (http://digital.library.wisc.edu/1793/96310).
2.4 Statistical analysis
Data from participants failing the commitment request (n=15 and n=21 failed in Study 1 and 2, respectively) and/or the comprehension check (n=121 and n=135, respectively) were excluded from analysis, resulting in n=344 and n=484 surveys (STR: n=111, ALT: n=112, ALT-STR: n=150, and CON: n=111) retained for analysis in Studies 1 and 2, respectively.
All statistical analyses were conducted using R software (Version 1.4.1717). Descriptive statistics were summarized using the psych package. All variables were plotted and visually inspected to assess distributions prior to analysis. Spearman rank correlations were used to evaluate correlations prior to inferential tests. Hierarchical multiple regression was used to test hypotheses. Our primary dependent variable was global attitudes toward the AW-CSR. Steps 1–3 were conducted for both Study 1 and Study 2. In Step 1, demographic factors hypothesized to impact CSR evaluations (i.e. age, gender, education, income and political ideology) were entered simultaneously to test hypotheses H1a-e. To maximize cell size and facilitate interpretation, several demographic variables were re-coded prior to analysis (Table 1). In Step 2, strategic motivation was added and assessed (H2). In Step 3, altruistic motivational attributions were added (H3). Model fit changes between steps were tested using ANOVA. Statistical assumptions were assessed by inspection of residual plots and variable inflation factors using the plot and vif (car package) functions in R.
Table 1. Demographic variable coding used in inferential statistical testing.
In Study 2, one-way ANOVA with post-hoc Tukey tests was used to test experimental treatments. We had originally sought to test hypotheses H4-H-6; however, since our experimental manipulation of motivational attributions in Study 2 was largely unsuccessful (see Section 3.3), instead of testing our original hypotheses H4-H6, we sought to replicate our findings from Study 1 by collapsing data across all treatments and conducting a similar analysis to Study 1 following the same procedures for Steps 1–3 above. Given the consumer context, we also analyzed responses about purchase likelihood as well as general attitudes.
3 Results
3.1 Sample descriptions
After data exclusions, responses available for analysis in Study 1 and Study 2 were n=344 and n=484, respectively. Complete demographic information for both studies can be found in Table 2. Demographic characteristics of samples for both studies were similar. Female participants were slightly overrepresented (55%). The modal response for household income bracket was $50,000 to $74,999, similar to the median U.S. income of $75,000 (U.S. Census Bureau, 2024). The proportion of participants who spent most of their lives living in a rural area (27%) exceeded the U.S. national average (19%; U.S. Census Bureau, 2024). After binning and excluding those who selected the middle point on the scale, the political ideology of our sample (27% liberal and 37% conservative) was consistent with representative polling data (25% liberal and 37% conservative; Brenan, 2025). According to Jones (2023; n=1,105), 4% of Americans identify as vegetarians, and across our studies, 3.7% of participants reported being either vegetarian or vegan. A total of 43% of participants reported consuming dairy products at least once a day during a typical week, and 7.7% of participants reported having direct or indirect experience with the dairy industry. In sum, both samples had characteristics similar to the general U.S. population.
Table 2. Participant demographics for Study 1 (n=344) and Study 2 (n=484). Unless otherwise noted, values are reported as percentages with counts in parentheses.
3.2 Study 1 results
Descriptive statistics showed that attitudes about how cows are treated on U.S. dairy farms were approaching neutral (3.92 ± 1.44, mean ± SD). Participants reported positive attitudes toward the program (5.38 ± 1.26) and the company (5.30 ± 1.15) and thought the program was important (5.81 ± 1.41) and likely to improve how cows were treated on dairy farms (5.05 ± 1.55). Participants were more likely than not to indicate they would purchase dairy products from the company if they were available to them (5.17 ± 1.43) and to vote for a law that would require all dairy farms to participate in the program (4.84 ± 1.87). Participants indicated that labels (e.g., animal welfare certifications) were of middling importance to them when shopping for groceries (4.13 ± 1.96).
Participants tended to agree that both strategic (4.91 ± 1.40) and altruistic (5.55 ± 1.23) motives were driving the AW-CSR initiative. A positive relationship was observed between strategic and altruistic motivational attributions (rs=0.25, P < 0.001). Strategic motivational attributions were positively correlated with attitudes toward both the AW-CSR initiative (rs=0.22, P < 0.001) and the company (rs=0.26, P < 0.001). Altruistic motives were also positively correlated with attitudes toward both the CSR initiative (rs=0.56, P < 0.001) and the company (rs=0.68, P < 0.001). See Table 3 for complete correlational results.
Table 3. Correlation matrix of motivational attributions and attitudes toward an animal welfare corporate social responsibility (AW-CSR) initiative (and constituent subscales) in Study 1.
In Step 1, the demographics-only model was not significant (R²=0.02, F4, 339 = 1.6, P=0.124), indicating demographics did not predict additional variation in global AW-CSR attitudes beyond the intercept-only model. Within this model, however, the coefficient for female gender was positive (b=0.25, P=0.053), suggesting female consumers tended to hold more positive attitudes toward the AW-CSR (hypothesis H1b). Neither age (H1a), education (H1c), income (H1d), nor political ideology (H1e) were associated with attitudes toward AW-CSR in Step 1.
In Step 2, the addition of strategic motivation (hypothesis H2) to the model explained variance above and beyond the demographics-only model (R2 change=0.04, F1, 337 = 15.1, P < 0.001). The coefficient for female gender remained significant and positive (b=0.29, P=0.025), suggesting both gender and strategic motivational attributions are independently associated with attitudes.
In Step 3, altruistic motivational attributions were added (hypothesis H3). The overall model fit was significant (R²=0.40, F7, 336 = 31.3, P < 0.001) and represented a substantial improvement from Step 2 (R2 change=0.33, F1, 336 = 186.0, P < 0.001). The coefficient for altruistic motivation was positive and significant (b=0.56; P < 0.001), as was the coefficient for strategic motives (b=0.09; P=0.012). The coefficient for politics was also negative and marginally significant (b=-0.06; P=0.083), indicating a possible tendency for more favorable attitudes among more liberal participants, when accounting for motivational attributions. See Table 4 for complete regression results for Study 1.
Table 4. Results of hierarchical regression explaining variance in attitudes toward a hypothetical animal welfare corporate social responsibility (AW-CSR) initiative as a function of demographic and motivational attributions in Study 1.
3.3 Study 2 results
Regardless of treatment, descriptive results indicated that participants reported positive attitudes toward the program (5.69 ± 1.19, mean ± SD) and the company (5.45 ± 1.21). They were more likely than not to express willingness to purchase dairy products from the company if they were available to them (5.42 ± 1.33). Participants appeared to agree that both strategic (4.92 ± 1.22) and altruistic (5.82 ± 0.95) motives were involved in the AW-CSR.
The order in which strategic and altruistic motive text appeared in the combination treatment did not affect either strategic (P=0.852) or altruistic (P=0.506) motivational attributions, and thus these were analyzed as a single treatment (ALT-STR). ANOVA showed differences in strategic motivational attributions between the four treatment groups (F3, 480=2.74, P=0.043), which post-hoc tests revealed were between the ALT-STR treatment (5.11 ± 1.27, mean ± SD) and the ALT treatment (4.68 ± 1.22, mean ± SD; P=0.025). All other treatments did not differ from each other (P=0.54). There was no significant difference in altruistic motivational attributions between at least two treatment groups (F3, 480 = 0.56, P=0.641). Strategic (P=0.69) and altruistic motivation (P=0.45) scores did not differ between subjects who received any additional information about purported motives (i.e., STR, ALT, and ALT-STR) and those who did not (i.e., CON). Complete results for all treatments can be found in Table 5.
Table 5. Mean (SD) perceived motivations and attitudes1 toward a hypothetical animal welfare corporate social responsibility initiative for each treatment2 in Study 2.
A positive relationship was observed between strategic and altruistic motivational attributions (rs=0.30, P < 0.001). Strategic motivational attributions were positively correlated with attitudes toward both the AW-CSR initiative (rs=0.19, P < 0.001) and the company (rs=0.18, P < 0.001). Altruistic motives were also correlated with attitudes toward both the CSR initiative (rs=0.65, P < 0.001) and the company (rs=0.59, P < 0.001). Attitudes toward the program (rs=0.73, P < 0.001) and the company (rs=0.78, P < 0.001) were both positively associated with the likelihood of purchasing dairy products from the company. See Table 6 for complete correlational results.
Table 6. Correlation matrix of motivational attributions and attitude toward the animal welfare corporate social responsibility (AW-CSR) initiative (and constituent subscales) in Study 2.
In Step 1, the demographics-only model was significant (R²=0.03, F5, 478 = 2.65, P=0.023), indicating the model predicted additional variation in AW-CSR attitudes beyond the intercept-only model. The coefficient for age (hypothesis H1a; b=0.01, P=0.044) was positive and significant, indicating attitudes toward AW-CSR were more favorable among older participants. The coefficient for female gender (H1b; b=0.21; P=0.052) was marginally significant and positive, indicating a possible tendency for female participants to hold more positive attitudes toward AW-CSR. Neither education (H1c), income (H1d), nor political ideology (H1e) were associated with attitudes toward AW-CSR (P > 0.12). The demographics-only model for purchase likelihood was also significant (R²=0.02, F5, 478 = 2.41, P=0.044). The coefficient for age was positive and significant (b=0.01; P=0.027), indicating older consumers were more likely to purchase dairy products from the fictional company.
In Step 2, the addition of strategic motivation to the model (hypothesis H2) explained variance above and beyond the Step 1 (demographics-only) model. The coefficients for both age (b=0.01, P=0.012) and female gender (b=0.23, P=0.034) remained significant and positive in Step 2. The addition of strategic motivation to the purchase likelihood model also improved model fit (R2 change=0.03, F1, 477 = 9.0, P < 0.001), with the coefficient for age remaining significant and positive (b=0.01; P=0.014).
In Step 3, when altruistic motives were added in the final model (hypothesis H3), the overall model was significant (R²=0.40, F7, 476 = 37.9, P < 0.0001), and the coefficient for altruistic motivational attributions was positive and significant (b=0.71 P < 0.001). Neither strategic motivations nor any demographics were significant in the final model (P > 0.18). The addition of altruistic motivations in Step 3 significantly improved model fit from Step 2 (R2 change=0.33 F1, 476 = 226.6, P < 0.001). A very similar pattern of results was found using purchase likelihood, except gender was not significant at any step, and age was marginally significant in the final model (b=0.005, P=0.091). The addition of altruistic motivations in Step 3 of the purchase likelihood model also significantly improved model fit (R2 change=0.21, F1, 476 = 134.1, P < 0.001). Complete regression results for Study 2 are provided in Tables 7, 8 (attitudes and purchase intention, respectively).
Table 7. Results of hierarchical regression explaining variance in attitudes toward a hypothetical animal welfare corporate social responsibility (AW-CSR) initiative as a function of demographic and motivational attributions in Study 2.
Table 8. Results of hierarchical regression explaining variance in purchase likelihood toward a hypothetical animal welfare corporate social responsibility (AW-CSR) initiative as a function of demographic and motivational attributions in Study 2.
4 Discussion
Using attribution theory as our theoretical framework across two studies, we explored relationships between U.S. dairy consumer attitudes toward a hypothetical animal welfare initiative and dairy company, perceived motivational attributions for these initiatives, and demographic factors. In Study 1, we found that perceptions of dairy cattle care and welfare in the U.S. were ambivalent overall. However, perceptions of the importance and potential efficacy of a training program for dairy workers about appropriate cow handling were positive. Likewise, across both studies, general consumer attitudes toward the fictitious animal handling training initiative were positive.
These findings are consistent with a growing body of research suggesting increased public concern for dairy animals and support for measures to improve their welfare (Vanhonacker and Verbeke, 2013; Weary and von Keyserlingk, 2017). In the specific context of humane cow handling, our findings have positive practical implications for the dairy industry. Inappropriate animal handling is a significant source of negative publicity across animal agriculture sectors (Robbins et al., 2016). However, another study by our group suggests that there is substantial agreement between public and dairy industry stakeholders on what constitutes appropriate cow handling (Robbins et al., 2024). Annual documentation of continuing education on stockmanship, or appropriate cattle handling, is required by the FARM Animal Care Program (NMPF, 2024) for all U.S. dairy farm workers who handle cattle. The findings from our present studies suggest that consumers would likely hold positive views toward this type of industry expectation.
In Study 2, we attempted to manipulate motivational attributions by adding quotations from a fictitious dairy company representative indicating the purported motivation for the AW-CSR. However, we were unsuccessful, as indicated by a lack of treatment differences in perceived motives. The reasons why this was the case were not clear. It may be that a sentence stating the reasons why a company is undertaking an animal welfare initiative is insufficient to modify actual consumer motivational attributions. Previous research has found that skepticism or disbelief among consumers causes them to engage in more complex attributional processing, which may weaken or exclude motivational claims (Fein and Hilton, 1994). Including measures of skepticism or disbelief (e.g., Rim and Kim, 2016) in future research could help to assess this possibility. More generally, novel approaches to manipulate motivational attributions are needed to improve future research in this area. Despite the manipulation failure in Study 2, when we combined data across treatments, we were able to replicate many of our findings from Study 1.
Across both studies, female gender was associated with more positive attitudes toward the animal welfare initiative. The effect of gender on CSR perceptions has been mixed in the literature, with some studies finding females hold more favorable attitudes toward CSR initiatives than males (Singhapakdi et al., 2001), while other research has failed to show any association with gender (Mueller and Theuvsen, 2014). Previous cross-cultural research has found that female consumers tend to evaluate CSR more favorably than male consumers (Jones III et al., 2017). Diehl et al. (2013) surveyed Americans on humane advertising and found that female consumers evaluated the advertisement significantly more favorably than did male consumers in three of the four countries. This is consistent with other research showing that women (vs. men) tend to be more concerned about the very types of issues that CSR often seeks to address, namely animal welfare (Herzog, 2007) and environmental issues (Stern et al., 1993).
It should be noted that while female gender was positively associated with attitudes toward the animal welfare initiative, this was not the case for purchase likelihood, where female consumers did not differ from males. Whether this represents a more generalizable gender difference in attitude-behavior consistency in the context of ethical food purchasing is worthy of further study, especially since women make most household food purchasing decisions (Silverstein and Sayre, 2009).
Age was positively associated with attitudes toward the animal welfare initiative, but only in Study 2. This is consistent with previous research showing older consumers reported more support of CSR (Vitell et al., 1991; Mueller and Theuvsen, 2014). However, cross-cultural research has found that younger people (up to age 30) held more favorable attitudes toward CSR than did older individuals (Diehl et al., 2013). It is important to note that even these differences were not significant in all countries surveyed, and U.S. consumers were not included in any samples (Diehl et al., 2013).
Income was not found to be significantly associated with any outcomes in either study. This may seem at odds with previous studies, which have found a positive association between income and willingness to pay (WTP) for improved animal welfare (Napolitano et al., 2010; Clark et al., 2017). However, the lack of association may be because our measure, purchase likelihood, differs from standard WTP measures in that the implied monetary tradeoff is not as salient. If potential price premiums associated with the animal welfare initiative had been made salient, as with WTP, consumers’ responses may have been different. Future research should explore whether there is evidence to support this conjecture.
Educational attainment also failed to show a significant association with attitudes in our studies. Previous research has reported mixed results. Mohr et al. (2001) reported that people with a higher interest in CSR also had higher levels of formal educational attainment. A U.S. survey found attitudes toward environmental CSR were positively associated with educational attainment (Panwar et al., 2010). However, other investigators have found no (Kinnear et al., 1974; Diehl et al., 2013) or negative associations (Samdahl and Robertson, 1989; Mueller and Theuvsen, 2014) between attitudes toward CSR and education levels. Clearly, more research is needed to better understand the role of educational attainment on attitudes toward animal welfare initiatives.
It is unclear why we did not observe an association between political ideology and attitudes. Furman et al. (2020) found that personal values explained attitudes toward CSR better than demographics, and personal values are closely related to political ideology. Several earlier studies have found that political ideology was associated with perceptions of CSR, with more liberal consumers holding more positive attitudes toward CSR (Creyer, 1997; Mueller and Theuvsen, 2014). There is also ample evidence suggesting that liberal political ideology is associated with greater concern for animal welfare (Bovay and Sumner, 2019; Lusk, 2012; Deemer and Lobao, 2011; Heleski et al., 2004). Political ideology also appears to be associated with motivational attributions. Choi et al. (2016) found collectivistic (as opposed to individualistic) consumers tend to make more altruistic (but not strategic) attributions about CSR motives, and collectivism tends to be more closely associated with liberal politics (Ebeling, 1993).
Lastly, and most significantly, we found that attitudes toward the fictitious animal welfare initiative were associated with perceived motivations for the company to engage in the initiative. In both studies, composite attitudes toward the initiative and dairy company were weakly correlated (rs range: 0.18 to 0.26) with strategic motivational attributions, and more strongly (rs range: 0.56 to 0.68) associated with altruistic attributions. Results for the constituent attitude subscales (i.e., separately for the training initiative and the dairy company) followed a similar pattern of results. Bolstering the correlational results, the addition of strategic and altruistic motivational attributions significantly improved the model fit above and beyond the demographics-only model. In both studies, the addition of strategic motivational attributions resulted in an R2 increase of 0.04 to 0.05, whereas the subsequent addition of altruistic motivations resulted in an R2 increase of 0.33 to 0.34. Thus, while both types of motivational attributions are important, altruistic motivations explain a much larger share of variation in attitudes toward the animal welfare initiative. These findings are consistent with earlier research suggesting that the more CSR is perceived as emanating from altruistic motivations (as opposed to strategic ones), the more favorably it is likely to be viewed by consumers (Hur and Kim, 2017; Story and Neves, 2015; Vázquez Burguete et al., 2013). This disproportionate impact of altruistic attributions may be due, in part, to strategic motivations being taken for granted in the context of CSR, with these initiatives presumed to benefit a company’s bottom line.
Finally, some caution is advised when generalizing our results, as both studies were limited to a single, fictional example of an animal welfare initiative. Although our scenario was realistic, the external validity of future research would be enhanced with field experiments and surveys investigating actual dairy industry animal welfare initiatives (e.g., the FARM Animal Care Program, NMPF, 2024). With our results establishing the primacy of the influence of altruistic motivational attributions on consumer attitudes, future research could investigate how these attributions are formed. Equipped with such information, animal welfare CSR initiatives could hold promise for not only enhancing animal welfare, but also consumer perceptions, both of which are necessary to ensure the social sustainability of the dairy industry.
4.1 Conclusions
Results from two cohorts of participants reacting to a fictional dairy employee training program indicate that U.S. dairy consumer perceptions of hypothetical animal welfare corporate social responsibility initiatives are largely positive. Except for gender and, in one study, age, demographic factors were generally not associated with attitudes toward the initiative. Instead, consumer perceptions of hypothetical animal welfare initiatives appear to be driven more by motivational attributions, or the degree to which they perceive the initiative as emanating from a company’s self-serving (strategic) or other-serving (altruistic) motivations. Although both types of motivations were associated with consumer perceptions, altruistic motivations exerted a larger influence on consumer perceptions of the hypothetical animal welfare initiative. Furthermore, these motivational attributions varied among individual participants, regardless of information provided in the fictitious news release about the animal welfare initiative. Understanding how these motivations are formed, and the factors that lead to altruistic motivational attributions, could provide important information for the development of future dairy industry animal welfare initiatives.
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics statement
The studies involving humans were approved by University of Wisconsin–Madison Educational and Social/Behavioral Science Institutional Review Board. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
Author contributions
JR: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft. KP: Conceptualization, Funding acquisition, Methodology, Writing – review & editing. LH: Methodology, Writing – review & editing. GC: Conceptualization, Funding acquisition, Methodology, Writing – review & editing. PH: Conceptualization, Funding acquisition, Methodology, Writing – review & editing. JO: Conceptualization, Data curation, Funding acquisition, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – review & editing.
Funding
The author(s) declared financial support was received for this work and/or its publication. This work is supported by the Agriculture and Food Research Initiative competitive award no. 2020-68014–31413 from the USDA National Institute of Food and Agriculture.
Acknowledgments
We gratefully acknowledge the infrastructure support of the University of Wisconsin-Madison Department of Animal and Dairy Sciences (College of Agricultural and Life Sciences).
Conflict of interest
The authors declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declare that no Generative AI was used in the creation of this manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: http://digital.library.wisc.edu/1793/96310, https://www.frontiersin.org/articles/10.3389/fanim.2025.1677465/full#supplementary-material
References
Becker-Olsen K. L., Cudmore B. A., and Hill R. P. (2006). The impact of perceived corporate social responsibility on consumer behavior. J. Bus Res. 59, 46–53. doi: 10.1016/j.jbusres.2005.01.001
Bjørkdahl K. and Syse K. V. L. (2021). Welfare washing: Disseminating disinformation in meat marketing. Soci Anim. 32, 37–55. doi: 10.1163/15685306-BJA10032
Boivin X., Lensink J., Tallet C., and Veissier I. (2003). Stockmanship and farm animal welfare. Anim. Welf. 12, 479–492. doi: 10.1017/S0962728600026075
Bovay J. and Sumner D. A. (2019). Animal welfare, ideology, and political labels: Evidence from California's Proposition 2 and Massachusetts's Question 3. J. Agric. Resour. Econ. 44, 246–266.
Brenan M. (2025). U.S. political parties historically polarized ideologically (Gallup). Available online at: https://news.gallup.com/poll/655190/political-parties-historically-polarized-ideologically.aspx (Accessed July 31, 2025).
Buhrmester M., Kwang T., and Gosling S. D. (2011). Amazon’s Mechanical Turk: A new source of inexpensive, yet high-quality, data? Perspect. Psychol. Sci. 6, 3–5. doi: 10.1037/14805-009
Burton R. J., Peoples S., and Cooper M. H. (2012). Building ‘cowshed cultures’: A cultural perspective on the promotion of stockmanship and animal welfare on dairy farms. J. Rural Stud. 28, 174–187. doi: 10.1016/j.jrurstud.2011.12.003
Cheah E. T., Jamali D., Johnson J. E., and Sung M. C. (2011). Drivers of corporate social responsibility attitudes: The demography of socially responsible investors. Br. J. Manag. 22, 305–323. doi: 10.1111/j.1467-8551.2011.00744.x
Choi J., Chang Y. K., Li Y. J., and Jang M. G. (2016). Doing good in another neighborhood: Attributions of CSR motives depend on corporate nationality and cultural orientation. J. Int. Mark 24, 82–102. doi: 10.1509/jim.15.0098
Clark B., Stewart G. B., Panzone L. A., Kyriazakis I., and Frewer L. J. (2017). Citizens, consumers and farm animal welfare: A meta-analysis of willingness-to-pay studies. Food Policy 68, 112–127. doi: 10.1016/j.foodpol.2017.01.006
Coleman G. (2018). Public animal welfare discussions and outlooks in Australia. Anim. Front. 8, 14–19. doi: 10.1093/af/vfx004
Cornish A., Raubenheimer D., and McGreevy P. (2016). What we know about the public’s level of concern for farm animal welfare in food production in developed countries. Animals 6, 74. doi: 10.3390/ani6110074
Creyer E. H. (1997). The influence of firm behavior on purchase intention: do consumers really care about business ethics? J. Consum. Mark 14, 421–432. doi: 10.1108/07363769710185999
Dai L. and Guo Y. (2024). Perceived CSR impact on purchase intention: The roles of perceived effectiveness, altruistic attribution, and CSR-CA belief. Acta Psychol. 248, 104414. doi: 10.1016/j.actpsy.2024.104414
Deemer D. R. and Lobao L. M. (2011). Public concern with farm-animal welfare: Religion, politics, and human disadvantage in the food sector. Rural Sociol. 76, 167–196. doi: 10.1111/j.1549-0831.2010.00044.x
Diehl S., Mueller B., and Terlutter R. (2013). The influence of demographic factors on the perception of humane-oriented (CSR) appeals in advertisements: a multi-country analysis. Adv. Advert Res. 4, 313–327. doi: 10.1007/978-3-658-02365-2_24
Ebeling R. M. (1993). Liberalism and collectivism in the 20th century. Political Stud. 41, 66–77. doi: 10.1111/j.1467-9248.1993.tb01804
Evans J. and Stanovich K. E. (2013). Dual-process theories of higher cognition: Advancing the debate. Perspect. Psychol. Sci. 8, 223–241. doi: 10.1177/1745691612460685
Feiler D. C., Tost L. P., and Grant A. M. (2012). Mixed reasons, mixed givings: The costs of blending egoistic and altruistic reasons in donation requests. J. Exp. Psychol. 48, 1322–1328. doi: 10.1016/j.jesp.2012.05.014
Fein S. and Hilton J. L. (1994). Judging others in the shadow of suspicion. Motiv. Emot. 18, 167–198. doi: 10.1007/BF02249398
Fife D. A. and Rodgers J. L. (2022). Understanding the exploratory/confirmatory data analysis continuum: Moving beyond the “replication crisis. Am. Psychol. 77, 453–466. doi: 10.1037/amp0000886
Fraser D. (2006). Animal welfare assurance programs in food production: a framework for assessing the options. Anim. Welf. 15, 93–104. doi: 10.1017/S0962728600030177
Furman A., Maison D., and Sekścińska K. (2020). Segmentation based on attitudes toward corporate social responsibility in relation to demographical variables and personal values–quantitative and qualitative study of Polish consumers. Front. Psychol. 11. doi: 10.3389/fpsyg.2020.00450
Groza M. D., Pronschinske M. R., and Walker M. (2011). Perceived organizational motives and consumer responses to proactive and reactive CSR. J. Bus Ethics 102, 639–652. doi: 10.1007/s10551-011-0834-9
Hampton J. O., Jones B., and McGreevy P. D. (2020). Social license and animal welfare: Developments from the past decade in Australia. Animals 10, 2237. doi: 10.3390/ani10122237
Hauser D. J. and Schwarz N. (2016). Attentive Turkers: MTurk participants perform better on online attention checks than do subject pool participants. Behav. Res. Methods 48, 400–407. doi: 10.3758/s13428-015-0578-z
Heleski C. R., Mertig A. G., and Zanella A. J. (2004). Assessing attitudes toward farm animal welfare: A national survey of animal science faculty members. J. Anim. Sci. 82, 2806–2814. doi: 10.2527/2004.8292806x
Hemsworth P. and Coleman G. (2011). “Human-animal interactions and animal productivity and welfare,” in Human-Livestock Interactions: The Stockperson and the Productivity and Welfare of Intensively Farmed Animals, 2nd ed (CABI, Wallingford, England), 47–83.
Herzog H. A. (2007). Gender differences in human–animal interactions: A review. Anthrozoös 20, 7–21. doi: 10.2752/089279307780216687
Hur W. M. and Kim Y. (2017). How does culture improve consumer engagement in CSR initiatives? The mediating role of motivational attributions. Corp. Soc. Responsib. Environ. Manag. 24, 620–633. doi: 10.1002/csr.1432
Jones J. (2023). In U.S., 4% identify as vegetarian, 1% as vegan (Gallup). Available online at: https://news.gallup.com/poll/510038/identify-vegetarian-vegan.aspx (Accessed July 31, 2025).
Jones R. J. III, Reilly T. M., Cox M. Z., and Cole B. M. (2017). Gender makes a difference: Investigating consumer purchasing behavior and attitudes toward corporate social responsibility policies. Corp. Soc. Responsib. Environ. Manag. 24, 133–144. doi: 10.1002/csr.1401
Kim S., Chaiy C., and Chaiy S. (2012). “Developing a corporate social responsibility process scale of individual stakeholder’s perception,” in Proc Am Mark Assoc Winter/Summer Educators’ Conference, (American Marketing Association, Chicago, IL: Marketing in the Socially-Networked World: Challenges of Emerging, Stagnant, and Resurgent Markets) Vol. 23. 271–280.
Kim S. and Choi S. M. (2018). Congruence effects in post-crisis CSR communication: The mediating role of attribution of corporate motives. J. Bus Ethics 153, 447–463. doi: 10.1007/s10551-016-3425-y
Kinnear T. C., Taylor J. R., and Ahmed S. A. (1974). Ecologically concerned consumers: Who are they? J. Mark 38, 20–24. doi: 10.2307/1250192
Lever J. and Evans A. (2017). “Corporate social responsibility and farm animal welfare: toward sustainable development in the food industry?,” in Stages of Corporate Social Responsibility: From Ideas to Impacts (Springer, Cham), 205–222. doi: 10.1007/978-3-319-43536-7
Lusk J. L. (2012). The political ideology of food. Food Policy 37, 530–542. doi: 10.1016/j.foodpol.2012.05.002
Malle B. F. (2022). “Attribution theories: How people make sense of behavior,” in Theories in Social Psychology, 2nd Ed (Hoboken, NJ: John Wiley & Sons, Ltd), 93–120. doi: 10.1002/9781394266616.ch4
Mohr L. A., Webb D. J., and Harris K. E. (2001). Do consumers expect companies to be socially responsible? The impact of corporate social responsibility on buying behavior. J. Consum. Aff. 35, 45–72. doi: 10.1111/j.1745-6606.2001.tb00102.x
Mueller H. and Theuvsen L. (2014). “Influences on consumer attitudes toward CSR in agribusiness,” in Proc AAEA/EAAE/CAES Joint Symp. (Montreal, QC, Canada: Social Networks, Social Media and the Economics of Food) doi: 10.22004/ag.econ.166108
Napolitano F., Girolami A., and Braghieri A. (2010). Consumer liking and willingness to pay for high welfare animal-based products. Trends Food Sci. Tech. 21, 537–543. doi: 10.1016/j.tifs.2010.07.012
National Milk Producers Federation (NMPF) (2024). National dary FARM (Farmers assuring responsible management) animal care: reference manual version 5, July 2024 – June 2027. Available online at: https://nationaldairyfarm.com/wp-content/uploads/2024/09/FARM-14787-2023-Animal-Care-Standards-Reference-Manual.pdf (Accessed July 7, 2025).
Panwar R., Han X., and Hansen E. (2010). A demographic examination of societal views regarding corporate social responsibility in the US forest products industry. For Policy Econ. 12, 121–128. doi: 10.1016/j.forpol.2009.09.003
Pérez A. and del Bosque I. R. (2015). How customers construct corporate social responsibility images: Testing the moderating role of demographic characteristics. Bus Res. Q 18, 127–141. doi: 10.1016/j.brq.2014.04
Powers R., Li N., Gibson C., and Irlbeck E. (2020). Consumers’ evaluation of animal welfare labels on poultry products. J. Appl. Commun. 104, 1. doi: 10.4148/1051-0834.2310
Rim H. and Kim S. (2016). Dimensions of corporate social responsibility (CSR) skepticism and their impacts on public evaluations toward CSR. J. Public Relat. Res. 28, 248–267. doi: 10.1080/1062726X.2016.1261702
Robbins J. A., Franks B., Weary D. M., and von Keyserlingk M. A. G. (2016). Awareness of ag-gag laws erodes trust in farmers and increases support for animal welfare regulations. Food Policy 61, 121–125. doi: 10.1016/j.foodpol.2016.02.008
Robbins J., Proudfoot K., Strand E., Hemsworth L., Coleman G., Hemsworth P., et al. (2024). Perceptions of dairy cow–handling situations: A comparison of public and industry samples. J. Dairy Sci. 107, 540–554. doi: 10.3168/jds.2023-23496
Robbins J. A., Roberts C., Weary D. M., Franks B., and von Keyserlingk M. A. G. (2019). Factors influencing public support for dairy tie stall housing in the US. PloS One 14, e0216544. doi: 10.1371/journal.pone.0216544
Robbins J. A., Weary D. M., Schuppli C. A., and von Keyserlingk M. A. G. (2015). Stakeholder views on treating pain due to dehorning dairy calves. Anim. Welf. 24, 399–406. doi: 10.7120/09627286.24.4.399
Rushen J. and de Passillé A. M. (2020). “The importance of good stockmanship and its benefits to animals,” in Improving Animal Welfare: A Practical Approach (Digital Library: CABI), 125–139. doi: 10.1079/9781780644677.0125
Samdahl D. M. and Robertson R. (1989). Social determinants of environmental concern: Specification and test of the model. Environ. Behav. 21, 57–81. doi: 10.1177/0013916589211004
Singhapakdi A., Karande K., Rao C. P., and Vitell S. J. (2001). How important are ethics and social responsibility? A multinational study of marketing professionals. Eur. J. Mark 35, 133–153. doi: 10.1108/03090560110363382
Sørensen J. T. and Fraser D. (2010). On-farm welfare assessment for regulatory purposes: Issues and possible solutions. Livest Sci. 131, 1–7. doi: 10.1016/j.livsci.2010.02.025
Stern P. C., Dietz T., and Kalof L. (1993). Value orientations, gender, and environmental concern. Environ. Behav. 25, 322–348. doi: 10.1177/0013916593255002
Story J. and Neves P. (2015). When corporate social responsibility (CSR) increases performance: exploring the role of intrinsic and extrinsic CSR attribution. Bus Ethics Environ. Responsib. 24, 111–124. doi: 10.1111/beer.12084
Suchyta M. (2021). Environmental values and Americans’ beliefs about farm animal well-being. Agric. Hum. Values 38, 987–1001. doi: 10.1007/s10460-021-10206-0
Tonsor G. T. and Olynk N. J. (2011). Impacts of animal well-being and welfare media on meat demand. J. Agric. Econ. 62, 59–72. doi: 10.1111/j.1477-9552.2010.00266.x
United States Census Bureau (2024). American community survey. Available online at: https://www.census.gov/programs-surveys/acs/ (Accessed July 7, 2025).
Validus Verification Services, LLC (2024). Animal welfare review dairy audit standards. Available online at: https://library.wherefoodcomesfrom.com/documents/validus/Validus_AWR_Dairy_Standards_01_07_2025.pdf (Accessed July 7, 2025).
Vanhonacker F. and Verbeke W. (2013). Public and consumer policies for higher welfare food products: Challenges and opportunities. J. Agric. Environ. Ethics 27, 153–171. doi: 10.1007/s10806-013-9479-2
Vázquez Burguete J. L., Lanero Carrizo A., García Miguélez M. P., and García González J. (2013). Altruism or strategy? A study of attributions of responsibility in business and its impact on the consumer decision making process. Econ. Sociol. 6, 108–122. doi: 10.14254/2071-789X.2013/6-1/9
Vitell S. J., Lumpkin J. R., and Rawwas M. Y. A. (1991). Consumer ethics: An investigation of the ethical beliefs of elderly consumers. J. Bus Ethics 10, 365–375. doi: 10.1007/BF00383238
Vlachos P. A. (2012). Corporate social performance and consumer-retailer emotional attachment: The moderating role of individual traits. Eur. J. Mark 46, 1559–1580. doi: 10.1108/03090561211259989
Weary D. M. and von Keyserlingk M. A. G. (2017). Public concerns about dairy-cow welfare: how should the industry respond? Anim. Prod Sci. 57, 1201–1209. doi: 10.1071/AN16680
Weathers S. T., Caviola L., Scherer L., Pfister S., Fischer B., Bump J. B., et al. (2020). Quantifying the valuation of animal welfare among Americans. J. Agric. Environ. Ethics 33, 261–282. doi: 10.1007/s10806-020-09824-1
Wut T. M. and Ng P. M. L. (2023). Perceived CSR motives, perceived SCR authenticity, and pro-environmental behavior intention: an internal stakeholder perspective. Soc. Responsib. J. 19, 797–811. doi: 10.1108/SRJ-08-2020-0350
Keywords: animal welfare assurance, corporate social responsibility, social sustainability, social license, public perception, animal handling, stockmanship
Citation: Robbins J, Proudfoot K, Hemsworth L, Coleman G, Hemsworth P and Van Os J (2026) Motivational attributions and demographic factors associated with U.S. dairy consumer attitudes toward a hypothetical animal welfare initiative. Front. Anim. Sci. 6:1677465. doi: 10.3389/fanim.2025.1677465
Received: 31 July 2025; Accepted: 28 November 2025; Revised: 20 November 2025;
Published: 05 January 2026.
Edited by:
Jonathan James Cooper, University of Lincoln, United KingdomReviewed by:
Agata Malak-Rawlikowska, Warsaw University of Life Sciences, PolandOsayanmon Wellington Osawe, Teagasc Animal Bioscience Research Centre Grange, Ireland
Copyright © 2026 Robbins, Proudfoot, Hemsworth, Coleman, Hemsworth and Van Os. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Jennifer Van Os, anZhbm9zQHdpc2MuZWR1