Search for: All records

Online surveys are a popular method for collecting data in the social sciences. Despite its cost-effectiveness, concerns regarding the legitimacy of data from online surveys are increasing. One such concern is fraudulent responses or “spam” by malicious agents intentionally deceiving the survey process to gain monetary incentives or sway research results. The research costs of “spam”—their influence on research conclusions and their threat to scientific integrity—are not well understood. Here we show the differences in financial and research costs of spam using data from an online survey of transportation workers that was cleaned using a stringent battery of spam detection techniques that utilized commercially available features and a custom spam detection algorithm. We found that we would have wasted about 73% of our budget on incentivizing spammers if we had stopped data collection upon reaching the intended sample size. We also found significant differences in research conclusions related to the relationships between key organizational constructs, including affective commitment, job satisfaction, and turnover intention, between subsamples with and without spam. Our results demonstrate that researchers who are unaware of spam or do not adequately clean their data may spend substantially more monetary and human resources, as well as derive misleading conclusions. This study highlights the importance of survey researchers being cognizant of spam responses and employing robust spam detection techniques to ensure the scientific integrity of non-probability online survey research. 

This exploratory sequential mixed-methods paper explores the relationship between gig and taxi drivers’ perceptions of autonomous vehicles (AVs) and their continuance intentions. Drawing from the Career Construction Model of Adaptation, we examined the relationship between drivers’ expectations about AV-related job changes and their intentions to stay or leave their driving role upon the integration of AVs. In Study 1, we collected qualitative data from gig and taxi drivers (N= 69) in 24 focus groups. In Study 2, we administered a survey to gig and taxi drivers (N= 496). The thematic analysis in Study 1 revealed how drivers expected the onset of AVs to positively and negatively impact their job (changes to work stress, safety, job enjoyment, etc.). These expectations influenced their decisions to remain in or leave their jobs. Multivariate regression in Study 2 showed that multiple factors identified in Study 1 were related to continuance intentions, with some being “retention factors” (related to intentions to stay) and others being “turnover factors” (related to intentions to leave). Our findings contribute to the evolving discourse on the impact of new technologies on continuance intentions by offering theoretical and practical implications in careers and vocational behavior. 

Abstract This study presents a novel low‐temperature, combustion synthesis (CS) approach for the rapid production of belite‐rich cements. CS leverages the exothermic heat released from the combustion of biofuels such as lignin and/or biomass, intermixed with pelletized limestone and quartz. At an imposed furnace temperature of ∼700°C (as opposed to 1200°C–1300°C required in a conventional kiln), the source materials are rapidly transformed to belite, resulting in energy, emissions, and economic benefits. This work explores the influence of various process parameters, viz., fuel types and contents, airflow rate, porosity, holding temperature, and holding time on the efficiency of CS‐based belite synthesis. Through careful optimization of these parameters, including through machine learning‐based methods, >90% belite content is obtained in the synthesized pellets. Advanced analytical tests show that the belite produced from CS closely resembles that obtained from traditional high‐temperature processing. CS has the potential to significantly reduce energy consumption and emissions associated with belite production, and to accelerate the synthesis process by three‐to‐four times as compared to the conventional method.