How academic researchers select collaborative research projects: a choice experiment

Evaluating strategies to recruit health researchers to participate in online survey research

BACKGROUND

Engaging researchers as research subjects is key to informing the development of effective and relevant research practices. It is important to understand how best to engage researchers as research subjects.

METHODS

A 24 factorial experiment, as part of a Multiphase Optimization Strategy, was performed to evaluate effects of four recruitment strategy components on participant opening of an emailed survey link and survey completion. Participants were members of three US-based national health research consortia. A stratified simple random sample was used to assign potential survey participants to one of 16 recruitment scenarios. Recruitment strategy components were intended to address both intrinsic and extrinsic sources of motivation, including: $50 gift, $1,000 raffle, altruistic messaging, and egoistic messaging. Multivariable generalized linear regression analyses adjusting for consortium estimated component effects on outcomes. Potential interactions among components were tested. Results are reported as adjusted odds ratios (aOR) with 95% confidence intervals (95% CI).

RESULTS

Surveys were collected from June to December 2023. A total of 418 participants were included from the consortia, with final analytical sample of 400 eligible participants. Out of the final sample, 82% (341) opened the survey link and 35% (147) completed the survey. Altruistic messaging increased the odds of opening the survey (aOR 2.02, 95% CI: 1.35-2.69, p = 0.033), while egoistic messaging significantly reduced the odds of opening the survey (aOR 0.56, 95%CI 0.38-0.75, p = 0.08). The receipt of egoistic messaging increased the odds of completing the survey once opened (aOR 1.81, 95%CI: 1.39-2.23, p < 0.05). There was a significant negative interaction effect between the altruistic appeal and egoistic messaging strategies for survey completion outcome. Monetary incentives did not a have a significant impact on survey completion.

CONCLUSION

Intrinsic motivation is likely to be a greater driver of health researcher participation in survey research than extrinsic motivation. Altruistic and egoistic messaging may differentially impact initial interest and survey completion and when combined may lead to improved rates of recruitment, but not survey completion. Further research is needed to determine how to best optimize message content and whether the effects observed are modified by survey burden.

Fault diagnosis of photovoltaic systems using artificial intelligence: A bibliometric approach

Conventional fault detection methods in photovoltaic systems face limitations when dealing with emerging monitoring systems that produce vast amounts of high-dimensional data across various domains. Accordingly, great interest appears within the international scientific community for the application of artificial intelligence methods, which are seen as a highly promising solution for effectively managing large datasets for detecting faults. In this review, more than 620 papers published since 2010 on artificial intelligence methods for detecting faults in photovoltaic systems are analyzed. To extract major research trends, in particular to detect most promising algorithms and approaches overcoming excessive time calculations, a conventional bibliographic survey would have been extremely difficult to complete. That is why this study proposes to carry out a review with an innovative approach based on a statistical method named Bibliometric and a Expert qualitative content analysis. This methodology consists of three stages. First, a collection of data from databases is carried out with all precautions to achieve a large, robust, high-quality database. Second, multiple bibliometric indicators are chosen based on the objectives to be achieved and analyzed to assess their real impact, such as the quantity and nature of publications, collaborative connections among organizations, researchers, and countries or most cited articles. Finally, the Expert qualitative content analysis carried out by experts identifies the current and emerging research topics that have the greatest impact on fault detection in photovoltaic systems using artificial intelligence.