The diverse motivations of citizen scientists: Does conservation emphasis grow as volunteer participation progresses?

Larson, Lincoln R.; Cooper, Caren B.; Futch, Sara; Singh, Devyani; Shipley, Nathan J.; Dale, Kathy; LeBaron, Geoffrey S.; Takekawa, John Y.

doi:10.1016/j.biocon.2020.108428

Cited by 112 publications

(96 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Research specifically on CSVs engaged with the two principal motives of intrinsic and extrinsic and found that (1) both motivational factors are important at several stages (West and Pateman 2016;Land-Zandstra et al 2016;Mueller and Tippins 2011;Rotman et al 2012), (2) multiple motives can drive participation simultaneously (Larson et al 2020;Asah and Blahna 2012;Katz 1960), and (3) motivation(s) to participate may change over time (Ryan et al 2001;Jacobs 2018). Two process models exist to explain CSV motivations and stages of participation.…”

Section: Motivationsmentioning

confidence: 99%

“…Most CS programs are housed at nonprofit organizations; therefore, they may face challenges such as funding and volunteer recruitment and retention (Bloom and Crowder 2020;Alender 2016;Hidayat and Stoecker 2018). CSV recruitment and (sustained) participation are the "forces driving the engine" behind successful CS programs, and while CSV motivations have been examined, an understanding of how motivations are related to and influenced by attributes of an organization is lacking (Larson et al 2020;Phillips et al 2019;Johnson et al 2018;Domroese and Johnson 2017).…”

Section: Introductionmentioning

confidence: 99%

“…The data collected by CSVs are known to be valuable for conservation research efforts (Larson et al 2020) because they are collected on a scale that wouldn't otherwise be possible (e.g., larger data sets, greater spatial coverage) because of the larger number of participants (Davis, Ramirez-Andreotta, and Buxner 2020;Bonney et al 2014;Domroese and Johnson 2017;Riesch, Potter, and Davies 2013). Further, data are comparable in quality to professional data collection.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Motives for Citizen Science Program Participation and the Role of the Organization: Lessons from Water Quality Monitors in Texas

Lopez¹

2021

CSTP

View full text Add to dashboard Cite

Citizen science (CS) programs often question what motivates their volunteers and how volunteer participation can be sustained. Using a case study of citizen scientist volunteers (CSVs) who monitor water quality in Texas, I apply here a novel approach-the Dispositional-Organizational Interactions Framework (DOIF)-that provides a nuanced understanding of CSVs. The DOIF allows for consideration of how dispositional variables, such as sociodemographic characteristics and motivations for participation, may relate to organizational variables (e.g., program efficacy, results, and recognition); both overarching variables relate to indicators of commitment. The purpose of this study is to examine interactions among different aspects of a CS program and CSVs-observations that can improve CSV satisfaction and possibly retention. In a community geography partnership, volunteers of a statewide CS program were surveyed (n = 327). Results of exploratory factor analyses and a series of nonparametric tests indicate the DOIF offers insights into five major motivational factors; it uncovers between-group differences in how CSVs value organizational variables and indicate a commitment to volunteerism. This study contributes to the broader literature by incorporating the role of the organization in assessments of motivations through the creation of a novel framework and through empirical findings. The paper considers implications of results for CS programs and practice, then concludes with suggestions for future research.

show abstract

Section: Motivationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Motives for Citizen Science Program Participation and the Role of the Organization: Lessons from Water Quality Monitors in Texas

Lopez¹

2021

CSTP

View full text Add to dashboard Cite

show abstract

“…In contrast, volunteer-based citizen science does not enjoy the same investment. Participants most often find that their greatest challenge is not enough training resources (Turrini et al 2018;Larson et al 2020). We identify that, despite the lack of resources, data quality issues are the Achilles heel of citizen science projects.…”

Section: Introductionmentioning

confidence: 97%

“…Firstly, the growth and popularity of citizen science present citizens, civic society, and governments with multiple challenges and opportunities. New citizen science projects appear daily (Larson et al 2020). The proliferation of literature in this area is hard to digest: a Google Scholar search using the search terms 'citizen science' and 'data quality' identifies more than 200 articles published in January-February 2020.…”

Section: Introductionmentioning

confidence: 99%

Data Quality in Citizen Science

Balázs¹,

Mooney

Nováková

et al. 2021

The Science of Citizen Science

View full text Add to dashboard Cite

This chapter discusses the broad and complex topic of data quality in citizen science – a contested arena because different projects and stakeholders aspire to different levels of data accuracy. In this chapter, we consider how we ensure the validity and reliability of data generated by citizen scientists and citizen science projects. We show that this is an essential methodological question that has emerged within a highly contested field in recent years. Data quality means different things to different stakeholders. This is no surprise as quality is always a broad spectrum, and nearly 200 terms are in use to describe it, regardless of the approach. We seek to deliver a high-level overview of the main themes and issues in data quality in citizen science, mechanisms to ensure and improve quality, and some conclusions on best practice and ways forwards. We encourage citizen science projects to share insights on their data practice failures. Finally, we show how data quality assurance gives credibility, reputation, and sustainability to citizen science projects.

show abstract

Species data for understanding biodiversity dynamics: The what, where and when of species occurrence data collection

Petersen

Speed

Grøtan

et al. 2021

Ecol Sol and Evidence

View full text Add to dashboard Cite

1. The availability and quantity of observational species occurrence records have greatly increased due to technological advancements and the rise of online portals, such as the Global Biodiversity Information Facility (GBIF), coalescing occurrence records from multiple datasets. It is well-established that such records are biased in time, space and taxonomy, but whether these datasets differ in relation to origin have not been assessed. If biases are specific to different types of datasets, and the relative contribution from these datasets have changed over time, these shifting biases will have implications for interpretations of results and, consequentially, for management and conservation measures. 2. We examined observational GBIF records from Norway to test potential differences in taxonomic, time and land-cover biases between 10 different datasets, with a focus on red-listed and non-native species. 3. The datasets differ in their taxonomic coverage, with datasets dominated by citizen scientist recorders focusing greatly on birds. The number of records has increased over time; in particular, citizen science datasets have had a sharp increase in recent years. 4. The different datasets (including division of the datasets by conservation status) showed differences in geographical coverage. Anthropogenic land covers have more records than would be expected by chance in the majority of cases. Remote areas have fewer records than would be expected, underlining the prevalence of a roadside bias. 5. Accounting for biases in opportunistic species occurrence records need to be a dynamic rather than static process, as the taxonomic and geographical biases have changed over time and differ between datasets, depending on origin and inherent characteristics. Data-collection programmes should be designed to counteract the biases of the specific datasets, and methods to account for the biases in existing data should be developed. When utilizing compiled, open-source data, care must be taken to ensure complementarity between the datasets, both regarding time and space. Incorporating strengths and accounting for biases between datasets can strengthen the integration This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

The diverse motivations of citizen scientists: Does conservation emphasis grow as volunteer participation progresses?

Cited by 112 publications

References 55 publications

Motives for Citizen Science Program Participation and the Role of the Organization: Lessons from Water Quality Monitors in Texas

Motives for Citizen Science Program Participation and the Role of the Organization: Lessons from Water Quality Monitors in Texas

Data Quality in Citizen Science

Species data for understanding biodiversity dynamics: The what, where and when of species occurrence data collection

Contact Info

Product

Resources

About