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Based on the role theory, this study examines whether workplace age discrimination indirectly relates to older workers' bridge employment intentions through work meaningfulness.

Study 1 used two-wave time-lagged survey data from one hundred and seventy nurses (≥45 years old) from the Midwestern United States. Study 2 used three-wave time-lagged survey data from one hundred and eighty-six employees from a wide range of occupations in the United States. The online survey contains various self-reports on workplace age discrimination, work meaningfulness, affective commitment, and bridge employment intentions.

Results in Study 1 found that workplace age discrimination was negatively and indirectly related to older nurses' bridge employment intentions through their experiences of work meaningfulness. Results in Study 2 further confirmed the mediating role of work meaningfulness in the relationship between age discrimination and bridge employment intentions, above and beyond the role of affective commitment.

This study contributes to the research by testing the indirect relationship between workplace age discrimination and older workers' bridge employment intentions through work meaningfulness, further raising our awareness of the importance of social and interpersonal experiences in older workers' preretirement jobs to their late-career development.

Corrosion of carbon steel and copper is a troublesome problem in low hardness cooling water systems. A new kind of water stabiliser containing hydroxy phosphonocarboxylic acid, zinc salts and molybdate has been developed. Its performance has been proved by means of weight loss tests and a static state scale‐inhibiting test method. The mechanism was also studied using polarisation tests, scanning electron microscope examination and XPS analysis. The test results showed that the corrosion rate of carbon steel and copper could be reduced to 0.0136 and 0.0010 mm/a, respectively. A compact film containing P, Mo and Zn was formed on the surface of carbon steel, by means of which the steel was protected.

Based on the theory of planned behavior, this study aims to examine antecedents of older workers' intentions to engage in postretirement work (PRW) and actual planning for PRW.

A cross-sectional survey design was used. About 469 nurses (≥45 years old) from Midwestern United States completed an online survey containing various self-reports on attitude, perceived control, subjective norm, intentions to engage in PRW and actual planning for PRW.

Results found that attitude and subjective norm (but not perceived control) were positively related to older employees' intentions to engage in PRW. Perceived control was positively related to actual planning for PRW. PRW intentions mediated the effects of attitude and subjective norm on older workers' actual planning for PRW. Finally, perceived control enhanced the positive effect of PRW intentions on actual planning for PRW.

This study contributes to the research by testing the application of the theory of planned behavior to the literature on PRW, further increasing our knowledge of the roles of individuals' attitudinal and cognitive factors in predicting older employees' PRW intentions and actual planning for PRW.

The purpose of this paper is to expand the research on workplace mistreatment and its effects on individual employees while taking into account the organizational setting. This cross-level study explores the interaction between the team Civility climate (CC) and individual experience of exclusion and their combined effect on the target’s organization-based self-esteem (OBSE).

In total, 251 individuals nested in 71 teams (mean team size=4.6) completed surveys. A two-way multi-level interaction model was used to test the moderation hypothesis.

The cross-level interaction between CC and exclusion was significant, which means that CC influenced the strength of the relationship between exclusion and OBSE. Specifically, it was found that the higher the group-level civility norms, the stronger the negative relationship between exclusion and OBSE.

The main limitation of this study is its cross-sectional design. All variables were self-reported and collected at one time-point.

The present study contributes to workplace mistreatment literature by using a multi-level design to examine exclusion as a predictor of OBSE and team CC as a cross-level moderator of this relationship.

The authors said their exploratory research question was “does perceived control interact with PRW intentions in influencing actual planning for PRW?”

Participants were nurses from the Midwestern US, and all were 45 years and older. A total of 469 qualified and complete questionnaires were received with an average age of 54.6.

The results showed that attitude and subjective norm were significantly related to PRW planning, but not perceived control. But perceived control correlated with PRW planning. Meanwhile, PRW intentions were positively related to PRW planning. The data also suggested that variables of TPB significantly predict older employees’ PRW intentions above and beyond age, health status and retirement income satisfaction. Finally, CIs indicated that the indirect effects of attitude and subjective norm on actual planning for PRW via PRW intentions were significant.

Previous studies have shown that people who plan for PRW are more likely to actually do it. However, there have been very few studies on the antecedents of older workers’ actual planning.

The purpose of this paper is to characterize distracted driving by quantifying the response time and response intensity to an emergency stop using the driver’s physiological states.

Field tests with 17 participants were conducted in the connected and automated vehicle test field. All participants were required to prioritize their primary driving tasks while a secondary nondriving task was asked to be executed. Demographic data, vehicle trajectory data and various physiological data were recorded through a biosignalsplux signal data acquisition toolkit, such as electrocardiograph for heart rate, electromyography for muscle strength, electrodermal activity for skin conductance and force-sensing resistor for braking pressure.

This study quantified the psychophysiological responses of the driver who returns to the primary driving task from the secondary nondriving task when an emergency occurs. The results provided a prototype analysis of the time required for making a decision in the context of advanced driver assistance systems or for rebuilding the situational awareness in future automated vehicles when a driver’s take-over maneuver is needed.

The hypothesis is that the secondary task will result in a higher mental workload and a prolonged reaction time. Therefore, the driver states in distracted driving are significantly different than in regular driving, the physiological signal improves measuring the brake response time and distraction levels and brake intensity can be expressed as functions of driver demographics. To the best of the authors’ knowledge, this is the first study using psychophysiological measures to quantify a driver’s response to an emergency stop during distracted driving.

This paper aims to attempt to explore the influence of network position on innovation performance, specifically for companies from a less-developed area in China.

The paper uses a social network analysis method based on the data of high and new tech companies from Jiangxi province, China. It relies on Ucinet 6.212 software for data processing.

The present work, on the one hand, reveals that a high and new tech company from Jiangxi province tends to achieve better innovation performances if it is in the central position of its social network. On the other hand, it indicates that the same type of company from the same area does not always accomplish better innovation performance, even it possesses more structural holes.

The conclusions of this paper suggest that a high and new tech company from a less-developed area in China should build up its position closer to the center when constructing a social network. Meanwhile, the company should also exert more effort into managing its networks, as only building structural holes offers little help with operations besides showing that it has a broad social network.

The purpose of this study is to present a large-scale real-world comparative study using pre-COVID lockdown data versus post-COVID lockdown data on predicting shipment times of therapeutic supplies in e-pharmacy supply chains and show that our proposed methodology is robust to lockdown effects.

The researchers used organic data of over 5.9 million records of therapeutic shipments, with 2.87 million records collected pre-COVID lockdown and 3.03 million records collected post-COVID lockdown. The researchers built various Machine Learning (ML) classifier models on the two datasets, namely, Random Forest (RF), Extra Trees (XRT), Decision Tree (DT), Multi-Layer Perceptron (MLP), XGBoost (XGB), CatBoost (CB), Linear Stochastic Gradient Descent (SGD) and the Linear Naïve Bayes (NB). Then, the researchers stacked these base models and built meta models on top of them. Further, the researchers performed a detailed comparison of the performances of ML models on pre-COVID lockdown and post-COVID lockdown datasets.

The proposed approach attains performance of 93.5% on real-world post-COVID lockdown data and 91.35% on real-world pre-COVID lockdown data. In contrast, the turn-around times (TAT) provided by therapeutic supply logistics providers are 62.91% accurate compared to reality in post-COVID lockdown times and 73.68% accurate compared to reality pre-COVID lockdown times. Hence, it is clear that while the TAT provided by logistics providers has deteriorated in the post-pandemic business climate, the proposed method is robust to handle pandemic lockdown effects on e-pharmacy supply chains.

The implication of the study provides a novel ML-based framework for predicting the shipment times of therapeutics, diagnostics and vaccines, and it is robust to COVID-19 lockdown effects.

E-pharmacy companies can readily adopt the proposed approach to enhance their supply chain management (SCM) capabilities and build resilience during COVID lockdown times.

The present study is one of the first to perform a large-scale real-world comparative analysis on predicting therapeutic supply shipment times in the e-pharmacy supply chain with novel ML ensemble stacking, obtaining robust results in these COVID lockdown times.