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The purpose of this paper is to design a unified operational design domain (ODD) monitoring framework for mitigating Safety of the Intended Functionality (SOTIF) risks triggered by vehicles exceeding ODD boundaries in complex traffic scenarios.

A unified model of ODD monitoring is constructed, which consists of three modules: weather condition monitoring for unusual weather conditions, such as rain, snow and fog; vehicle behavior monitoring for abnormal vehicle behavior, such as traffic rule violations; and road condition monitoring for abnormal road conditions, such as road defects, unexpected obstacles and slippery roads. Additionally, the applications of the proposed unified ODD monitoring framework are demonstrated. The practicability and effectiveness of the proposed unified ODD monitoring framework for mitigating SOTIF risk are verified in the applications.

First, the application of weather condition monitoring demonstrates that the autonomous vehicle can make a safe decision based on the performance degradation of Lidar on rainy days using the proposed monitoring framework. Second, the application of vehicle behavior monitoring demonstrates that the autonomous vehicle can properly adhere to traffic rules using the proposed monitoring framework. Third, the application of road condition monitoring demonstrates that the proposed unified ODD monitoring framework enables the ego vehicle to successfully monitor and avoid road defects.

The value of this paper is that the proposed unified ODD monitoring framework establishes a new foundation for monitoring and mitigating SOTIF risks in complex traffic environments.

China’s national carbon market will be officially launched in 2020, when it will become the world’s largest carbon market. However, China’s carbon market is faced with various major challenges. One of the most important challenges is its impact on the social and economic development of arid and semi-arid regions. By simulating the carbon price trends under different economic development and energy consumption levels, this study aims to help the government can plan ahead to formulate various countermeasures to promote the integration of arid and semi-arid regions into the national carbon market.

To achieve this goal, this paper builds a back propagation neural network model, takes the third phase of the European Union Emissions Trading System (EU ETS) as the research object and uses the mean impact value method to screen out the important driving variables of European Union Allowance (EUA) price, including economic development (Stoxx600, Stoxx50, FTSE, CAC40 and DAX), black energy (coal and Brent), clean energy (gas, PV Crystalox Solar and Nordex) and carbon price alternatives Certification Emission Reduction (CER). Finally, this paper sets up six scenarios by combining the above variables to simulate the impact of different economic development and energy consumption levels on carbon price trends.

Under the control of the unchanged CER price level, economic development, black energy and clean energy development will all have a certain impact on the EUA price trends. When economic development, black energy consumption and clean energy development are on the rise, the EUA price level will increase. When the three types of variables show a downward trend, except for the sluggish development of clean energy, which will cause the EUA price to rise sharply, the EUA price trend will also decline accordingly in the remaining scenarios.

On the one hand, this paper incorporates driving factors of carbon price into the construction of carbon price prediction system, which not only has higher prediction accuracy but also can simulate the long-term price trend. On the other hand, this paper uses scenario simulation to show the size, direction and duration of the impact of economic development, black energy consumption and clean energy development on carbon prices in a more intuitive way.

In this study, the authors develop a path model and investigate the effect of pandemic-oriented customer mistreatment on service sabotage through the lens of self-presentation theory. Moreover, the authors question the role of service climate as a moderator of the relationship between service sabotage and service performance.

Data were collected via a survey of 165 F&B frontline employees in restaurants in Iran. The hypotheses are examined using confirmatory factor analysis, structural equation modeling and ordinary least squares regression.

The findings reveal that POCM has a substantial and positive effect on service sabotage, and service climate mitigates the effect of service sabotage on service performance.

The study introduces and conceptually defines the term POCM. Furthermore, the authors apply the self-presentation theory as the overarching theory to explain underlying conditions in customer mistreatment and service sabotage. Moreover, although prior literature has described the saboteur–customer relationship as a one-line interaction, this study contributes to employee sabotage as a multi-linear transaction.

In this study, the authors identify new perspectives on the dark side of hospitality services in crises, such as the COVID-19 pandemic. The authors argue that pandemic-induced changes are essential not simply because they change customers’ moods and lower their patience threshold, but they further provoke ostentatious behaviors in saboteur–customer relations. These findings shed new light on the literature and provide managerial implications for enhancing hospitality performance.

The paper debunks Peter P. Li’s assertion that Yin-Yang is superior to any other cognitive frames or logical systems for paradox research. The purpose of this paper is to alert the Chinese indigenous management researchers to the danger of Chinese exceptionalism and over-confidence.

To show that Peter P. Li’s assertion is doubtful, the authors identify the flaws in his analysis.

The authors find that there are three serious flaws in Peter P. Li’s analysis. First, there are four defects in the typology of cognitive frames he built in order to compare Yin-Yang with the others. Second, his understanding of dialectics in general and Hegelian dialectics in particular is flawed. And finally, without resorting to Yin-Yang, many scholars can develop theories that are equivalent to those derived from Yin-Yang.

Due to the page limit, this paper only focuses on arguing that Yin-Yang is not superior to other cognitive frames or logical systems without going one step further to explain in which situations Yin-Yang are valuable and might be more suitable than others for helping us understand some research issues.

This paper implies that we should not blindly believe that the Chinese way of thinking and acting is superior to other people’s. Chinese people should be open-minded in the globalized era, not only promoting their own culture but also appreciating and learning from other cultures.

The reduction of cultural exceptionalism and ethnocentrism can make cross-cultural communication and interaction smoother.

This paper is a rigorous critique on the “Yin-Yang being superior” assertion of Peter P. Li.

Sarcasm is a linguistic expression that usually carries the opposite meaning of what is being said by words, thus making it difficult for machines to discover the actual meaning. It is mainly distinguished by the inflection with which it is spoken, with an undercurrent of irony, and is largely dependent on context, which makes it a difficult task for computational analysis. Moreover, sarcasm expresses negative sentiments using positive words, allowing it to easily confuse sentiment analysis models. This paper aims to demonstrate the task of sarcasm detection using the approach of machine and deep learning.

For the purpose of sarcasm detection, machine and deep learning models were used on a data set consisting of 1.3 million social media comments, including both sarcastic and non-sarcastic comments. The data set was pre-processed using natural language processing methods, and additional features were extracted and analysed. Several machine learning models, including logistic regression, ridge regression, linear support vector and support vector machines, along with two deep learning models based on bidirectional long short-term memory and one bidirectional encoder representations from transformers (BERT)-based model, were implemented, evaluated and compared.

The performance of machine and deep learning models was compared in the task of sarcasm detection, and possible ways of improvement were discussed. Deep learning models showed more promise, performance-wise, for this type of task. Specifically, a state-of-the-art model in natural language processing, namely, BERT-based model, outperformed other machine and deep learning models.

This study compared the performance of the various machine and deep learning models in the task of sarcasm detection using the data set of 1.3 million comments from social media.

This article aims to describe the thinking behind MASSE, a project in Finland that helps address the fragmentation of care and patient journey disruptions for long-term care. It outlines the conceptualization of an information technology (IT)-assisted solution and presents preliminary findings and research problems in this ongoing project.

The project employs a service engineering and design science approach with the objective of addressing chronic and multimorbid patients in specialized multiprovider environments. It does this by applying information and communication technologies and organizational design. The project has been a cocreative effort with ongoing interviews and workshops with various stakeholders to inform the conceptualization of a solution, an intermediary step before the implementation phase.

Patient journey disruptions occur when caregivers do not know what to do in specific situations. A potential solution is a virtual care operator (VCO) with a personalized patient card that would enable service ecosystem actors to integrate and coordinate their tasks. This article presents the basic design principles of such a solution.

Conceptual ideas and preliminary results only indicative.

Systemic integration efforts like those ongoing in Finland can benefit from the VCO concept encouraging a more collaborative way of thinking about integrative solutions and opening up new avenues of research on business implications and ecosystem strategies.

The VCO concept answers to the continuity of care, the rising costs of health care and the growing numbers of patients with chronic disease and multimorbidity whose care remains fragmented and uncoordinated.

Taking an ecosystem approach to care integration and addressing interoperability issues are on the cutting edge of healthcare system transformation.

This paper aims to examine the association between green innovation and the cost of equity in China. This study relies on the investors’ base perspective and shareholders’ perceived risk perspective to investigate the relation between green innovation and the cost of equity in China.

The paper uses firm-fixed effect regression for a sample of Chinese public companies for the period 2008–2018.

The authors find a negative relationship between green innovation and the cost of equity capital. This negative association is found to be more pronounced for less financially constrained firms, during periods of high economic policy uncertainty, and for firms with a strong internal control environment. Finally, the paper shows that the negative association became more pronounced after the passage of the Environmental Protection Law of China in 2012. The results remain robust to possible endogeneity concerns.

This study contributes to the green innovation literature by documenting that shareholders favorably view firms implementing green innovation policies. The study also has policy implications for Chinese regulators in improving the green credit policy.

This study investigates how social interaction-oriented content in broadcasters' live speech affects broadcast viewers' purchasing and gift-giving behaviors and how broadcaster popularity moderates social interaction-oriented content's effect on the two different behaviors in live-streaming commerce.

A research model was proposed and empirically tested using a panel data set collected from 537 live streams via Douyin (the Chinese version of TikTok), one of the most popular live broadcast platforms in China. A fixed-effects negative binomial regression model was used to examine the proposed research model.

This study's results show that social interaction-oriented content in broadcasters' live speech has an inverted U-shaped relationship with broadcast viewers' purchasing behavior and shares a positive linear relationship with viewers' gift-giving behavior. Furthermore, broadcaster popularity significantly moderates the effect of social interaction-oriented content on viewers' purchasing and gift-giving behaviors.

This research enriches the literature on live-streaming commerce by investigating how social interaction-oriented content in broadcasters' live speech affects broadcast viewers' product-purchasing and gift-giving behaviors from the perspective of broadcast viewers' attention. Moreover, this study provides some practical guidelines for developing live speech content in the live-streaming commerce context.