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This research paper is a literature review of the existing building retrofitting process. It proposes studying the functional, technical, and organizational issues of the green retrofit process. The purpose of this paper is to expand the domain of design framework for retrofitting existing buildings.

The paper provides a review of the model-based design process from enrollment to evaluation stages representing the green retrofitting process in selected publications. The paper opted to review the Green Retrofit Design (GRD) process model for achieving a systematic design model of GRD development in the future.

Functional and maintenance issues are mainly for new buildings, also in the field for renovation and demolishing. Publications also show that environmental, social, and technical issues are often examined separately in the decision process of GRD. Papers in the facility management scale would concentrate more on organization/legal issues. Publications with questionnaire design are devoted to the usage on life-cycle assessment on existing building, but not yet on the stakeholder management and design process and related issues.

The achievement of the study is to provide a new framework of design approach that is significant to the theoretical research, education, communication, and practical works in terms of GRD development.

The paper not only achieves a specific sequence of practical approaches, including awareness of problems, conceptual development, and design embodiment, to meet design objectives, but also conforms to academic practice-based research of creative design taking on GRD practice.

With the growing global emphasis on environmental, social and governance (ESG) criteria, it is crucial to investigate the factors that influence individuals' intentions to invest in ESG and to understand the underlying mechanisms at play. This study constructs a theoretical model, grounded in the Fogg behavioral model (FBM), and explores the mediating role of ESG investment attitudes in shaping individuals' ESG investment behaviors.

A survey was conducted among ESG investors and potential ESG investors in China, resulting in 613 valid responses regarding ESG investment. The partial least squares structural equation modeling (PLS-SEM) approach was utilized to evaluate the proposed model and test the hypotheses.

The results reveal that future orientation, ESG investment bias and perceived ESG investment performance are significant determinants of ESG investment intentions, with attitude playing a partially mediating role. Furthermore, government support moderates the relationship between perceived ESG investment performance and investment intention.

This study expands the application of the FBM to the context of ESG investment and introduces a novel conceptual framework for understanding ESG investment behavior. The findings provide valuable insights for enterprises and institutions involved in ESG investment, aiding them in identifying and targeting potential investors more effectively. Additionally, the study offers a foundation for policymakers to devise strategies that promote sustainable development.

Hospitality workers are emotional labor workers because they must display appropriate emotions to their customers to provide outstanding service. Emotional intelligence (EI) helps employees regulate their emotions and display appropriate emotions, and hence should help hospitality workers provide outstanding service. However, the strength of the relationship between EI and hospitality workers’ job performance substantially varied across studies. Hence, the purpose of the present study is to clarify the mixed findings and to examine if EI can improve hospitality workers’ job performance.

A meta-analysis was performed to investigate the relationship between EI and hospitality workers’ job performance as well as the moderators which condition this relationship.

The present meta-analysis indicated that EI is positively related to hospitality workers’ job performance (ρ̅̂ = 0.54); the relationship between EI and hospitality workers’ job performance is stronger when the percentage of married subjects is low and in feminine cultures; and this relationship does not differ between male-dominated and female-dominated studies, across educational levels, between collectivistic and individualistic cultures, between low and high power distance cultures and between low and high uncertainty avoidance cultures.

This study uncovers theoretically important moderators that contribute to cross-cultural research, work–family literature and gender-related literature in hospitality research.

The present study builds a theoretical foundation and performs a meta-analysis to elucidate the relationship between EI and hospitality workers’ job performance and to identify the moderators which condition this relationship.

The purpose of our research is to explore the role of employee AI identity in influencing employee proactive behavior and its boundary conditions in AI workplace.

Based on the IT identity theory and motivation theory, our research discusses the effects of employee AI identity on employee proactive behavior and regarded the proactive work intention as a mediating variable. Meanwhile, we considered organization inducement as a boundary condition and discussed the moderating effects of it and its two sub-dimensions (development rewards and material rewards). Data were collected from 326 employees and partial least squares structural equation modeling was used to analyzed and draw the conclusions.

Findings showed that employee AI identity significantly affects employee proactive behavior, in which the proactive work intention play a mediating role. Moreover, three subdimensions (relatedness, emotional energy, dependence) of employee AI identity have different effects on formation of employee AI identity. And organization inducement acts as a positive moderating role, development rewards and material rewards play different roles in the formation of organization inducements.

Our research explores the different paths that influence employee proactive behavior and their boundary moderation, while analyzing the results of these influences in different subdimensions, deepening the research on employee AI identity and organization inducement. Our research is conducive to the development of the identity theory and organizational behavior research and provide suggestions for managers to improve their organizational management level.

The objective of this study was to analyse the influencing factors of citizens' dissatisfaction with government services during the COVID-19 pandemic to help government departments identify problems in the service process and possible countermeasures.

The authors first used cosine interesting pattern mining (CIPM) to analyse citizens' complaints in different periods of the pandemic. Second, the potential evaluation indices of customer satisfaction were extracted from the hotline business system through a hypothesis analysis and modelled using multiple regression analysis. During the index transformation and standardization process, a machine-learning algorithm of clustering and emotion analysis was adopted. Finally, the authors used the random forest algorithm to evaluate the importance of the indicators and obtain the indicators more important to citizen satisfaction.

The authors found that the complaint topic, appeal time, urgency of citizens' complaints, citizens' emotions, level of detail in the case record, and processing timeliness and efficiency significantly influenced citizens' satisfaction. When the government addresses complaints in a more standardized and efficient manner, citizens are more satisfied.

During the pandemic, government departments should be more patient with citizens, increase the speed of the case circulation and shorten the processing period of appeals. Staff should record appeals in a more standardized manner, highlighting themes and prioritizing urgent cases to appease citizens and relieve their anxiety.

Grey modeling technique is an important element of grey system theory, and academic articles applied to agricultural science research have been published since 1985, proving the broad applicability and effectiveness of the technique from different aspects and providing a new means to solve agricultural science problems. The analysis of the connotation and trend of the application of grey modeling technique in agricultural science research contributes to the enrichment of grey technique and the development of agricultural science in multiple dimensions.

Based on the relevant literature selected from China National Knowledge Infrastructure, the Web of Science, SpiScholar and other databases in the past 37 years (1985–2021), this paper firstly applied the bibliometric method to quantitatively visualize and systematically analyze the trend of publication, productive author, productive institution, and highly cited literature. Then, the literature is combed by the application of different grey modeling techniques in agricultural science research, and the literature research progress is systematically analyzed.

The results show that grey model technology has broad prospects in the field of agricultural science research. Agricultural universities and research institutes are the main research forces in the application of grey model technology in agricultural science research, and have certain inheritance. The application of grey model technology in agricultural science research has wide applicability and precise practicability.

By analyzing and summarizing the application trend of grey model technology in agricultural science research, the research hotspot, research frontier and valuable research directions of grey model technology in agricultural science research can be more clearly grasped.