•The influence of context on the perception of fragranced laundry products by a trained sensory panel was investigated.•Limited contextual effects were observed in the study.•Trained sensory ...panellists’ scoring of fragrance attributes were fairly robust regardless of the context the fabric conditioners were assessed in.•One possible explanation for the findings is that panellist training removes the influence of the external environment on product perception.
Extensive research has been carried out to investigate the effect of context (such as the external environment) on product perception, with previous studies focusing on food or beverage products using naïve consumers. However, it is still unclear whether context affects assessments made by trained panellists when testing fragranced stimuli. Options for conducting sensory testing outside of traditional sensory booths would allow for greater ecological validity and enable sensory testing to become more agile and flexible, with this need becoming even more important as a result of the Covid-19 pandemic. The current research set out to establish whether context has an effect on the perception of fragranced products using a trained sensory panel by testing 5 laundry market products under different context environments (No Context, Imagine, Visual Cue, Priming, Immersive). Statistically significant contextual effects were only observed for two out of the eleven attributes tested (Overall intensity and Herbal). Additional analyses using PCA showed that the same products were grouped together, whereas groupings by context were not observed. These findings suggest that perception of fragranced laundry products is not significantly impacted by different contexts when assessed by trained panellists. One possible explanation is that panellist training removes any influence of the external environment on product perception. Future studies could explore whether other contextual parameters have a greater influence on fragrance perception for trained panellists.
•Li-ion battery operating environments are essentially considered for EV main power sources.•EIS curve is selected as features considered battery degradation and external environment.•Original image ...as well as the recurrence plot and 3-points method are additionally applied.•CNN-based classification model and results for 26 external environment cases are presented.•The model performance and stability was verified with the proposed evaluation metric.
Various electric mobilities are being developed that use Lithium (Li)-ion batteries as the primary power source for target applications. Especially, with the widespread adoption of electric vehicle (EV), the significance of battery management system (BMS) which are closely related to the stable operation of Li-ion batteries, is also increasing. Diagnosing the external environment is essential because the aging pattern of the battery varies depending on the environment condition in which the battery is exposed and can cause abnormal failures. This study presents an approach for classifying the external environment using electrochemical impedance spectroscopy (EIS) as an indicator, which is closely related to the internal chemical characteristics of the batteries. Recurrence plot (RP) algorithm is applied to improve the performance of convolution neural network (CNN) used as classification model as well as original EIS image. According to the frequency at which the main parameters of the Randles circuit model are derived, an additional dataset is constructed based on the selected 3-points. This paper presents the results of the classification from the external environment based on the three methods and derives a statistical evaluation metric to prove the stability and performance of the model. Moreover, it is also suggested that the appropriate method can be selected based on the desired cost by deriving the detailed model loss and accuracy for the training epoch, which is a trade-off relationship for each method.
Assessing potential disruptiveness of innovations is an important but challenging task for incumbents. However, the extant literature focuses only on technological and marketplace aspects, and most ...of the documented methods tend to be case specific. In this study, we present a multidimensional measurement framework to assess the disruptive potential of product innovations. The framework is designed based on the concept that the nature of disruptive innovations is multidimensional. Three aspects are considered, i.e., technological features, marketplace dynamics and external environment. Ten indicators of the three categories are proposed and then connected based on the conceptual and literature analysis. Three innovations, namely, WeChat (successful), Modularised Mobile Phone (failed) and Virtual Reality/Augmented Reality (ongoing), are selected as case studies. A panel of industrial experts with PhD degree in engineering is surveyed. The survey results are calculated and analysed according to the framework and then compared against the developments of the innovations. We also check the robustness of this framework by surveying other groups of people, and the results are nearly identical to the previous findings. This study enables a systematic assessment of disruptive potential of innovations using the framework, providing insights for decisions in product launch and resource allocation.
•We construct a quantitative framework for measuring disruptive potential.•Three aspects included in the framework: technological, marketplace and environment.•Internal connectivity of the indicators are exploited and verified in the survey.•Success of WeChat and failure of modularised mobile phone are well explained.•Capturing some niche markets could fulfil the disruptive potential of VR/AR.
The purpose of the study is to develop theoretical and methodological foundations for the development of tools for assessing the level of financial and technical efficiency of oil and gas companies ...and identifying reserves for improving efficiency based on the influence of external and internal factors. The theoretical basis of the research consists of the authors' works devoted to the problems of assessing financial efficiency, technical efficiency, innovation, market and environmental efficiency of companies. Modeling was carried out on the basis of tools for building DEA models, logistic regression, methods of correlation and regression analysis using the functionality of the software product R. The data of 7 companies with the largest revenue in the oil and gas industry by the end of 2020 and macroeconomic indicators of the financial and economic market of the Russian Federation are also components of the information base of the study. The scientific novelty of the research lies in the development of theoretical provisions and methodological tools for evaluating the effectiveness of companies in effective identification by assessing financial and technical efficiency, developing models of the phenomenon and forecasting the effectiveness of companies in the oil and gas industry.
Large-scale clean energy deployment and energy consumption electrification are important measures for China to respond to severe climate challenges and achieve carbon neutrality goals, and the ...development of lithium-ion battery storage technology is essential to enable clean energy transition. Using three-stage DEA and Tobit model, this paper evaluated the real technological innovation efficiency (TIE) of China's lithium-ion battery listed enterprises (CLBLEs) during 2009–2018, and explored how external environment and enterprise management factors influence the TIE. The results demonstrate that there exist inefficiency problems of the TIE of CLBLEs due to the diseconomies of scale. The average TIE of CLBLEs is low, at 0.39. The stated-owned enterprises, large-scale enterprises, and downstream enterprises have the highest TIE, and mainly concentrated in eastern region. For external environmental factors, regional economic level and government subsidy promote TIE. While for internal enterprise management factors, firm size, financial leverage, profitability, and employee quality significantly promote TIE. Thus, for policy makers, strengthening the implementation of industrial support policies, enhancing industrial agglomeration, and promoting the sharing of innovative resources are conducive to improving the scale efficiency of CLBLEs. In addition, improving management capabilities and personnel composition is also an important direction to promote the TIE of CLBLEs.
•A three-stage DEA model was used to evaluate the real TIE of CLBLEs.•Using the SFA model to examine the impact of external environmental factors on TIE.•Using the Tobit model to analyze the impact of internal management factors on TIE.•The average TIE of CLBLEs is relatively low at 0.39 caused by diseconomies of scale.•REL, SUB, SIZ, LEV, ROE and EDU can effectively improve the real TIE of CLBLEs.
The economic stability of a country, such as Pakistan is dependent on the construction of mega-projects, such as the China-Pakistan Economic Corridor (CPEC). However, certain external factors and ...project characteristics may delay the construction of infrastructure projects; scholars have not investigated the development of CPEC from this perspective. In addition, the COVID-19 outbreak has hindered CPEC initiatives. This analysis will examine the effect of external environment factors on CPEC, and benchmark the project's effects on economic stability through CPEC's development by incorporating 523 samples obtained from employees of various CPEC projects. Structural equation modeling was used to analyze all hypotheses proposed here on AMOS 21.0 tools. According to the findings of this study, the CPEC external environment and project benchmark characteristics have a negative effect on the construction of CPEC development. Furthermore, the development of CPEC is found to have a significant effect on economic stability. However, fear of COVID-19 has weakened the relationship between CPEC development and economic stability. Finally, we also discuss the implications and limitations of the study.
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