Drivers of hunter compliance and satisfaction with mandatory harvest reporting
Abstract
Hunter harvest data are important components of game species management and conservation, and natural resource agencies often use self-reporting mechanisms to collect harvest data. Despite the advantages of self-reporting systems, hunter noncompliance with mandatory reporting remains a challenge for managers. Our objective was to utilize the Theory of planned behavior (TPB) and concepts of organizational trust to understand factors influencing hunter compliance and satisfaction with mandatory harvest reporting. We distributed questionnaires through email channels and postcards mailed to resident deer hunters in Georgia, USA. Structural equation model results from the 1,712 survey responses suggest that TPB constructs (attitudes, perceived behavioral control, social norms), organizational trust, and satisfaction with hunting experience positively influenced satisfaction with mandatory reporting. In contrast, the number of years an individual has been hunting negatively influences satisfaction. Further, we tested a binary logistic regression model to predict potential drivers of compliant behavior with harvest reporting requirements. Our model found that attitudes, social norms, and satisfaction with reporting positively impacted reporting compliance. However, results also indicate that older, more experienced hunters were less likely to report harvests. Our findings suggest that targeting efforts towards increasing trust, positive attitudes, and satisfaction with regulations and regulatory systems among older, more experienced hunters may positively influence compliant behavior. Our study emphasizes the importance of engaging hunters in regulatory decision-making processes and improving agency-hunter relationships to achieve greater regulatory compliance and satisfaction.
README: Drivers of Hunter Compliance and Satisfaction with Mandatory Harvest Reporting
https://doi.org/10.5061/dryad.wm37pvmxx
Description of the data and file structure
We conducted a quantitative, wed-based survey to assess perceptions of white-tailed deer management and harvest reporting from resident deer hunters in Georgia, USA. We distributed the survey to a proportionate random sample of 19,000 resident deer hunters.
Files and variables
File: Data.csv
Description: This file contains responses from the 1,712 survey participants who provided valid responses to the survey. The variables in the dataset coincide with the questions on the survey in chronological order (Supplemental Material: Survey.pdf). Some respondents did not answer all questions on the survey. In these cases, missing responses are indicated by blanks in the dataset. We removed several indirect identifiers (i.e., residents’ demographics) of respondents per Dryad’s instructions to properly anonymize the data.
Variables
- Q1_HuntedDeer: Yes (1) or No (2)
- Q2_Years_Hunted: Continuous variable
- Q3_Huntinpast3years: Yes (1) or No (2)
- Q4_1_Archery: Check all that apply. 1 indicates yes, blank indicates no.
- Q4_2_Primitive_Weapons: Check all that apply. 1 indicates yes, blank indicates no.
- Q4_3_Rifle: Check all that apply. 1 indicates yes, blank indicates no.
- Q5_County_Hunted: 1-159. Each number corresponds to a given county in Georgia, USA.
- Q6_Land_Hunted: Private (1), Public (2), Both (3)
- Q6a_WMA_Hunted: Each number corresponds to a wildlife management area in Georgia, USA.
- Q7_Deer_Hunted: Antlered buck (1), Antlerless deer (2), No preference (3)
- Q8_Harvest_Deer: Yes (1) or No (2)
- Q9_Process_Deer: Self process (1), Meat processor (2), Mix (3), Never harvested a deer (4).
- Q10_1_Trophy: 1 = Very unimportant to 5 = Very important
- Q10_2_Mature_buck: 1 = Very unimportant to 5 = Very important
- Q10_3_Eating: 1 = Very unimportant to 5 = Very important
- Q10_4_Meat: 1 = Very unimportant to 5 = Very important
- Q10_5_Disconnect: 1 = Very unimportant to 5 = Very important
- Q10_6_People: 1 = Very unimportant to 5 = Very important
- Q10_7_Connect_with_others: 1 = Very unimportant to 5 = Very important
- Q10_8_Solitude: 1 = Very unimportant to 5 = Very important
- Q11_Quality_of_hunting: 1 = Very poor to 5 = Very good
- Q12_Sat_Hunt_Exp: 1 = Very unsatisfied to 5 = Very important
- Q13_Purchase_License: 1 = Very unlikely to 5 = Very likely. 6 = N/A
- Q14_GAWRD: Yes (1) or No (2)
- Q15_1_Trust_1: 1 = Strongly disagree to 5 = Strongly agree
- Q15_2_Trust_2: 1 = Strongly disagree to 5 = Strongly agree
- Q15_3_Trust_3: 1 = Strongly disagree to 5 = Strongly agree
- Q15_4_Trust_4: 1 = Strongly disagree to 5 = Strongly agree
- Q15_5_Fair_1: 1 = Strongly disagree to 5 = Strongly agree
- Q15_6_Fair_2: 1 = Strongly disagree to 5 = Strongly agree
- Q15_7_Fair_3: 1 = Strongly disagree to 5 = Strongly agree
- Q15_9_Competence_1: 1 = Strongly disagree to 5 = Strongly agree
- Q15_10_Competence_2: 1 = Strongly disagree to 5 = Strongly agree
- Q15_11_Competence_3: 1 = Strongly disagree to 5 = Strongly agree
- Q15_12_Voice_1: 1 = Strongly disagree to 5 = Strongly agree
- Q15_13_Voice_2: 1 = Strongly disagree to 5 = Strongly agree
- Q15_8_Voice_3: 1 = Strongly disagree to 5 = Strongly agree
- Q16_GGC: Yes (1) or No (2)
- Q17_Reported_Harvest: Have not harvested deer (1), Yes (2), No (3), Sometimes (4), I don't know (5)
- Q17_1_No_Report_Forgot: 1 indicates yes, blank is no
- Q17a_2_No_Report_Couldnotcompelte: 1 indicates yes, blank is no
- Q17a_3_No_Report_Deadline_Passed: 1 indicates yes, blank is no
- Q17a_4_Other: 1 indicates yes, blank is no
- Q18_Reporting_Method: Outdoors GA (1), Online (2), Telephone (3)
- Q19_1_Attitudes_1: 1 = Strongly disagree to 5 = Strongly agree
- Q19_2_Attitudes_2: 1 = Strongly disagree to 5 = Strongly agree
- Q19_3_Attitudes_3: 1 = Strongly disagree to 5 = Strongly agree
- Q19_4_Attitudes_4: 1 = Strongly disagree to 5 = Strongly agree
- Q20_1_PBC_1: 1 = Strongly disagree to 5 = Strongly agree
- Q20_2_PBC_2: 1 = Strongly disagree to 5 = Strongly agree
- Q20_3_PBC_3: 1 = Strongly disagree to 5 = Strongly agree
- Q21_1_SN_1: 1 = Strongly disagree to 5 = Strongly agree
- Q21_2_SN_2: 1 = Strongly disagree to 5 = Strongly agree
- Q21_3_SN_3: 1 = Strongly disagree to 5 = Strongly agree
- Q22_1_Importance_1: 1 = Very unimportant to 5 = Very important
- Q22_2_Importance_2: 1 = Very unimportant to 5 = Very important
- Q22_3_Importance_3: 1 = Very unimportant to 5 = Very important
- Q22_4_Importance_4: 1 = Very unimportant to 5 = Very important
- Q22_5_Importance_5: 1 = Very unimportant to 5 = Very important
- Q22_6_Importance_6: 1 = Very unimportant to 5 = Very important
- Q22_7_Importance_7: 1 = Very unimportant to 5 = Very important
- Q22_8_Importance_8: 1 = Very unimportant to 5 = Very important
- Q22_9_Importance_9: 1 = Very unimportant to 5 = Very important
- Q22_10_Importance_10: 1 = Very unimportant to 5 = Very important
- Q23_1_Performance_1: 1 = Very poor to 5 = Very good
- Q23_2_Performance_2: 1 = Very poor to 5 = Very good
- Q23_3_Performance_3: 1 = Very poor to 5 = Very good
- Q23_4_Performance_4: 1 = Very poor to 5 = Very good
- Q23_5_Performance_5: 1 = Very poor to 5 = Very good
- Q23_6_Performance_6: 1 = Very poor to 5 = Very good
- Q23_7_Performance_7: 1 = Very poor to 5 = Very good
- Q23_8_Performance_8: 1 = Very poor to 5 = Very good
- Q23_9_Performance_9: 1 = Very poor to 5 = Very good
- Q23_10_Performance_10: 1 = Very poor to 5 = Very good
- Q24_GGC_Satisfaction: 1 = Very unsatisfied to 5 = Very satisfied
- Q25_1_Hours_Hunted_Willing: 1 = Very unwilling to 5 = Very willing
- Q25_2_Days_Hunted_Willing: 1 = Very unwilling to 5 = Very willing
- Q25_3_Deer_Seen_Willing: 1 = Very unwilling to 5 = Very willing
- Q25_4_Deer_Seen_Sex_Age_Willing: 1 = Very unwilling to 5 = Very willing
- Q26_1_Hours_Hunted_Confidence: 1 = Very unconfident to 5 = Very confident
- Q26_2_Days_Hunted_Confidence: 1 = Very unconfident to 5 = Very confident
- Q26_3_Deer_Seen_Confidence: 1 = Very unconfident to 5 = Very confident
- Q26_4_Deer_Seen_Sex_Age_Confidence: 1 = Very unconfident to 5 = Very confident
Code/software
You can view the data in Microsoft Excel. We used SPSS for the binary logistic regression models and the AMOS software for the structural equation models.