Forgiveness from Emotion Fit: Emotional Frame, Consumer Emotion, and Feeling-Right in Consumer Decision to Forgive

Three studies examine an emotion fit effect in the crisis communication, namely, the interaction between emotional frames of guilt and shame and consumer emotions of anger and fear on consumer forgiveness. Guilt-framing communication results in higher forgiveness than shame-framing for angry consumers, whereas shame-framing communication results in higher forgiveness than guilt-framing for fearful consumers. These effects are driven by consumers’ accessible regulatory foci associated with anger/fear and guilt/shame. Specifically, feelings of anger activate a promotion focus that is represented by guilt frames, while feelings of fear activate a prevention focus that is enacted by shame frames. Compared with emotion nonfit (i.e., anger to shame and fear to guilt), emotion fit (i.e., anger to guilt and fear to shame) facilitates greater feeling-right and consumer forgiveness. The findings offer novel insights for extant literature on emotion, crisis communication, and regulatory focus theory, as well as practical suggestions regarding the emotional frames

Prosumer-to-customer exchange in the sharing economy:Evidence from the P2P accommodation context

Numerous prosumers who share their spare resources have contributed significantly to sharing economy development in recent years. Existing research on the sharing economy has primarily focused on the service demand side of consumers, thus neglecting the service supply side of individual prosumers. Understanding of the service exchange between prosumers and customers in the peer-to-peer (P2P) sharing economy remains limited. Drawing on the motivation, opportunity, and ability (MOA) model and social exchange theory, we developed a conceptual framework to explore how prosumers' service attributes influence consumers in a P2P accommodation sharing context. Using 313 questionnaires and 112 paired objective data points from prosumers in one popular P2P accommodation platform (i.e., Xiaozhu.com), this research found that prosumers' economic motivation, service flexibility, and service knowledge level have distinct effects on consumers' transactional based and relational-based participation. We also found a moderating role of prosumers' shared property management on these effects

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Driving Factor Analysis and Forecasting of CO2 Emissions from Power Output in China Using Scenario Analysis and CSCWOA-ELM Method

With power consumption increasing in China, the CO2 emissions from electricity pose a serious threat to the environment. Therefore, it is of great significance to explore the influencing factors of power CO2 emissions, which is conducive to sustainable economic development. Taking the characteristics of power generation, transmission and consumption into consideration, the grey relational analysis method (GRA) is adopted to select 11 influencing factors, which are further converted into 5 main factors by hierarchical clustering analysis (HCA). According to the possible variation tendency of each factor, 48 development scenarios are set up from 2018–2025, and then an extreme learning machine optimized by whale algorithm based on chaotic sine cosine operator (CSCWOA-ELM) is established to predict the power CO2 emissions respectively. The results show that gross domestic product (GDP) has the greatest impact on the CO2 emissions from power output, of which the average contribution rate is 1.28%. Similarly, power structure and living consumption level also have an enormous influence, with average contribution rates over 0.6%. Eventually, the analysis made in this study can provide valuable policy implications for power CO2 emissions reduction, which can be regarded as a reference for China’s 14th Five-Year development plan in the future