Beyond demographic identities and motivation to learn: The effect of organizational identification on diversity training outcomes

There is a pressing need for better explanations of diversity training effectiveness so that organizations can administer training programs that facilitate positive intergroup interactions. In this paper, we consider the unique predictive effect of organizational identification on diversity training outcomes beyond the effects of the traditional predictors of demographic-based identities and motivation to learn across two samples of employees involved in diversity-related training at their employing organizations. Organizational identification predicted unique variance in voluntary participation in diversity training, diversity training-related knowledge application, motivation to transfer diversity training, and diversity training-related organizational citizenship behavior intentions. Research and practitioner implications are discussed based on our findings.     

Disentangling horizontal pay dispersion: Experimental evidence

Although horizontal pay dispersion has been explored extensively using cross‐sectional field methods, it has received little attention using the control available through experimental designs. Many of the questions relevant to pay dispersion research can be addressed by taking an individual‐level experimental approach because this allows for clean separation of pay policies and individual effects. In this paper, we hypothesize both the motivation‐based and affect‐based effects of pay dispersion policies and test our hypotheses with a sample of over 400 participants in a real‐pay, real‐effort experiment. Results of the experiment provide support that two pay dispersion‐related pay policies, performance‐based pay dispersion and allocation criteria, have unique effects. Whereas motivation and performance effects are direct, pay satisfaction and interest in continuing work effects are the result of interactions, incorporating the performance level of workers. In follow‐up analyses, we find evidence that the temporal nature of responses to pay dispersion should be incorporated into future studies of pay dispersion.     

Gender and career success: A typology and analysis of dual paradigms

Despite women's advancements in the workplace, gender inequality persists. We classify and test two frameworks used to explain gender differences in career success: unequal attributes and unequal effects. The unequal attributes framework suggests that gender is related to other attributes, which result in unequal career outcomes for men and women (i.e., a mediated effect). The unequal effects framework suggests that even when men and women share the same attribute or circumstances, they are rewarded differently, such that individual attributes have unequal effects on career outcomes for men and women (i.e., a moderated effect). We collected survey data from a gender‐balanced sample of 394 business school alumni. Using structural equation modeling to test the unequal attributes framework, we found that work hours, career orientation, having a nonemployed spouse, and working in a predominantly female job were unequal attributes that explained gender differences in career success. Using multigroup path analysis to examine unequal effects, we found that being agentic, married, having children at home, and working in a predominantly female workplace had unequal effects in relation to career success for men and women. We find support for both models across three categories of career success antecedents (i.e., personal, family, and job attributes).     

Restoration of rivers used for timber floating: effects on riparian plant diversity.

Fluvial processes such as flooding and sediment deposition play a crucial role in structuring riparian plant communities. In rivers throughout the world, these processes have been altered by channelization and other anthropogenic stresses. Yet despite increasing awareness of the need to restore natural flow regimes for the preservation of riparian biodiversity, few studies have examined the effects of river restoration on riparian ecosystems. In this study, we examined the effects of restoration in the Ume River system, northern Sweden, where tributaries were channelized to facilitate timber floating in the 19th and early 20th centuries. Restoration at these sites involved the use of heavy machinery to replace instream boulders and remove floatway structures that had previously lined stream banks and cut off secondary channels. We compared riparian plant communities along channelized stream reaches with those along reaches that had been restored 3-10 years prior to observation. Species richness and evenness were significantly increased at restored sites, as were floodplain inundation frequencies. These findings demonstrate how river restoration and associated changes in fluvial disturbance regimes can enhance riparian biodiversity. Given that riparian ecosystems tend to support a disproportionate share of regional species pools, these findings have potentially broad implications for biodiversity conservation at regional or landscape scales.     

Photochemical reduction of hexavalent chromium in glycerol-containing solutions

Hexavalent chromium [Cr(VI)] in the form of potassium dichromate was photochemically reduced to trivalent chromium [Cr(III)] in aqueous solutions containing glycerol. This reaction occurred rapidly during irradiation with either unfiltered sunlight or a UVA-emitting light source. Photochemical reduction of Cr(VI) was pH-dependent and did not occur in dilute solutions of sodium hydroxide. In acidified solutions, the reduction occurred at elevated rates and at lower concentrations of glycerol. This reaction was found to be dependent on the unsubstituted alcohol groups of glycerol since alpha-phosphoglycerol and beta-phosphoglycerol did not support the photochemical reduction of Cr(VI). These findings suggest that glycerol or related polyols can be used for the remediation of hexavalent (toxic) chromium at contaminated environmental sites.     

A qualitative study of college students' perceptions of risky driving and social influences

Objectives: Young adults and teens are documented as the riskiest drivers on the road, and newer issues such as texting and driving are a growing concern. This study sought to determine the risk perceptions of young adults regarding various driving behaviors, their past experiences, how their social circles are structured, and how this might affect their driving. Methods: This study conducted qualitative research with 25 college undergraduate students to determine their peer and social influences regarding distracted driving. Data were analyzed and related to the health belief model and past research on social influence. Results: Though most participants felt that their behaviors were set after learning to drive, they were, in fact, quite susceptible to the influence of those in their social circles (e.g., fear of judgment and accountability) and, more broadly, to social norms. Texting and driving was the largest and most topical distracted driving issue and was also identified as very difficult to stop due to perceived barriers and the idea that intervening is rude. Participants identified low perceived susceptibility and severity (perceived threat) for a number of risky driving behaviors, including texting and driving. Conclusions: Training is needed to encourage people to intervene and speak up regarding behaviors other than drinking and driving, and cues to action and campaigns should target intervention to increase self-efficacy, as well as norms, susceptibility, and common rationalizations for risky behavior.     

Near-term forecasts of stream temperature using deep learning and data assimilation in support of management decisions

Deep learning (DL) models are increasingly used to make accurate hindcasts of management-relevant variables, but they are less commonly used in forecasting applications. Data assimilation (DA) can be used for forecasts to leverage real-time observations, where the difference between model predictions and observations today is used to adjust the model to make better predictions tomorrow. In this use case, we developed a process-guided DL and DA approach to make 7-day probabilistic forecasts of daily maximum water temperature in the Delaware River Basin in support of water management decisions. Our modeling system produced forecasts of daily maximum water temperature with an average root mean squared error (RMSE) from 1.1 to 1.4°C for 1-day-ahead and 1.4 to 1.9°C for 7-day-ahead forecasts across all sites. The DA algorithm marginally improved forecast performance when compared with forecasts produced using the process-guided DL model alone (0%–14% lower RMSE with the DA algorithm). Across all sites and lead times, 65%–82% of observations were within 90% forecast confidence intervals, which allowed managers to anticipate probability of exceedances of ecologically relevant thresholds and aid in decisions about releasing reservoir water downstream. The flexibility of DL models shows promise for forecasting other important environmental variables and aid in decision-making.     

Biological impacts of ocean acidification: a postgraduate perspective on research priorities

Research into the effects of ocean acidification (OA) on marine organisms has greatly increased during the past decade, as realization of the potential dramatic impacts has grown. Studies have revealed the multifarious responses of organisms to OA conditions, indicating a high level of intra- and interspecific variation in species’ ability to accommodate these alterations. If we are to provide policy makers with sound, scientific input regarding the expected consequences of OA, we need a broader understanding of these predicted changes. As a group of 20 multi-disciplinary postgraduate students from around the globe, with a study focus on OA, we are a strong representation of ‘next generation’ scientists in this field. In this unique cumulative paper, we review knowledge gaps in terms of assessing the biological impacts of OA, outlining directions for future research.     

Eliciting expert knowledge in conservation science

Expert knowledge is used widely in the science and practice of conservation because of the complexity of problems, relative lack of data, and the imminent nature of many conservation decisions. Expert knowledge is substantive information on a particular topic that is not widely known by others. An expert is someone who holds this knowledge and who is often deferred to in its interpretation. We refer to predictions by experts of what may happen in a particular context as expert judgments. In general, an expert-elicitation approach consists of five steps: deciding how information will be used, determining what to elicit, designing the elicitation process, performing the elicitation, and translating the elicited information into quantitative statements that can be used in a model or directly to make decisions. This last step is known as encoding. Some of the considerations in eliciting expert knowledge include determining how to work with multiple experts and how to combine multiple judgments, minimizing bias in the elicited information, and verifying the accuracy of expert information. We highlight structured elicitation techniques that, if adopted, will improve the accuracy and information content of expert judgment and ensure uncertainty is captured accurately. We suggest four aspects of an expert elicitation exercise be examined to determine its comprehensiveness and effectiveness: study design and context, elicitation design, elicitation method, and elicitation output. Just as the reliability of empirical data depends on the rigor with which it was acquired so too does that of expert knowledge.