Search results

This study aims to analyze government hotline text data and generating forecasts could enable the effective detection of public demands and help government departments explore, mitigate and resolve social problems.

In this study, social problems were determined and analyzed by using the time attributes of government hotline data. Social public events with periodicity were quantitatively analyzed via the Prophet model. The Prophet model is decided after running a comparison study with other widely applied time series models. The validation of modeling and forecast was conducted for social events such as travel and educational services, human resources and public health.

The results show that the Prophet algorithm could generate relatively the best performance. Besides, the four types of social events showed obvious trends with periodicities and holidays and have strong interpretable results.

The research could help government departments pay attention to time dependency and periodicity features of the hotline data and be aware of early warnings of social events following periodicity and holidays, enabling them to rationally allocate resources to handle upcoming social events and problems and better promoting the role of the big data structure of government hotline data sets in urban governance innovations.

Advanced big data analysis and machine learning methods are concurrently used to unleash the value of the data generated by government hotline and help devise intelligent applications including automated process management, standard construction and more accurate dispatched orders to build high-quality government service platforms as more widely data-driven methods are in the process.

In this study, based on the influence of the record specifications of texts related to work orders generated by the government hotline, machine learning tools are implemented and compared to optimize classify dispatching tasks by performing exploratory studies on the hotline work order text, including linguistics analysis of text feature processing, new word discovery, text clustering and text classification.

The complexity of the content of the work order is reduced by applying more standardized writing specifications based on combining text grammar numerical features. So, order dispatch success prediction accuracy rate reaches 89.6 per cent after running the LSTM model.

The proposed method can help improve the current dispatching processes run by the government hotline, better guide staff to standardize the writing format of work orders, improve the accuracy of order dispatching and provide innovative support to the current mechanism.

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.

The decision-making for additive manufacturing (AM) process selection is typically applied in the end of the product design stages based upon an already finished design. However, due to unique characteristics of AM processes, the part needs to be designed for the specific AM process. This requires potentially feasible AM techniques to be identified in early design stages. This paper aims to develop such a decision-making methodology that can seamlessly be integrated in the product design stages to facilitate AM process selection and assist product/part design.

The decision-making methodology consists of four elements, namely, initial screening, technical evaluation and selection of feasible AM processes, re-evaluation of the feasible process and production machine selection. Prior to the design phase, the methodology determines whether AM production is suitable based on the given design requirements. As the design progresses, a more accurate process selection in terms of technical and economic viability is performed using the analytic hierarchy process technique. Features that would cause potential manufacturability issues and increased production costs will be identified and modified. Finally, a production machine that is best suited for the finished product design is identified.

The methodology was found to be able to facilitate the design process by enabling designers to identify appropriate AM technique and production machine, which was demonstrated in the case study.

This study addresses the gap between the isolated product design and process selection stages by developing the decision-making methodology that can be integrated in product design stages.

In human history, poverty for most and prosperity for few is the norm. Thus, no theory or practice of common prosperity has been developed. Marxism first formulated the theory of common prosperity, and the classical Marxist authors conducted theoretical exploration on the issue of common prosperity, forming a series of scientific conclusions.

The century-long practical history of the Communist Party of China (CPC) is the great practice of leading the Chinese people in getting rid of poverty, letting some people and regions get rich first and ultimately achieving the goal of common prosperity.

Common prosperity is the great practice of the CPC that leads all Chinese people in building a modern socialist country in an all-round way in the new era.

The path of common prosperity with Chinese characteristics will certainly arise in the process of the great practice of common prosperity with Chinese characteristics. Based on the anti-poverty theory and the “spirit of poverty alleviation” from the battle against poverty with Chinese characteristics, the theory of common prosperity and its spirit with Chinese characteristics will certainly be formed. The above conclusions constitute the basic principles of the theory of common prosperity with Chinese characteristics.

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.

The present investigation aimed to evaluate the influence of envy on the predisposition to innovative behavior, starting from a conceptual model that considers not only the direct influence of envy but its indirect influence through ostracism and alignment with the negative behaviors of superiors.

Using a survey applied to 168 individuals, a conceptual model was developed based on the relationship ignored in the literature between envy and innovative behavior. The model was validated using the multivariate statistical technique of structural equation modeling with partial least squares estimation (Partial least squares structural equation modeling [PLS-SEM]).

The results of the study suggest that envy not only has a direct positive influence on alignment with negative boss behaviors and ostracism, but also an indirect influence on ostracism mediated by alignment with negative boss behaviors. Another important result of the present investigation refers to the negative effect of envy on the predisposition to innovative behavior. The results suggest that the greater the envy, the lower the innovative behavior.

This research provides evidence that envy can act as a barrier to innovation by triggering counterproductive behaviors such as ostracism and a decrease in predisposition to innovative behaviors, either due to innovative individuals prematurely exiting the organization or due to them lessening/dampening their innovativeness to avoid the negative consequences. Given this scenario, it becomes necessary to increase managerial awareness on the subject to manage negative emotions to promote the conditions for organizational innovation.

The present research contributes in both practical and theoretical ways to understanding the effects of envy on the predisposition to innovative behavior. Adding to this, this research represents a conceptual advance by linking envy to innovative behavior, providing a promising avenue for extending the psychological relevance of the envy construct to organizational and management studies, which are generally positive, normative and outcome-oriented.

This study investigates how personality traits influence individuals’ intention to become community group buying (CGB) leaders.

Data include 517 valid questionnaires that are employed to examine the research model and test the hypotheses using partial least squares structural equation modeling.

This study reveals that among the Big Five personality traits, extroversion and neuroticism have more impact on the perceived ease of use and usefulness of social media, and individuals with high levels of these traits are more likely to become CGB leaders. Perceived ease of use only mediates the relationship between agreeableness and CGB leader intention, whereas perceived usefulness mediates the relationships between conscientiousness and CGB leader intention and neuroticism and CGB leader intention.

This study can serve as a catalyst for advancing the exploration of how personality traits and social media affect the intention of being CGB leaders. In addition, the study investigates the mediating effect of social media technology acceptance obtaining valuable insights into how social media affects individuals’ intention to become CGB leaders, expanding the research in this field.

• (1)

  Individuals with extroversion, neuroticism, and conscientiousness personality traits exhibit higher perceived ease of use and usefulness of social media.

• (2)

  Unlike previous research suggested, neurotic individuals appear to be attracted to becoming community group buying (CGB) leaders.

• (3)

  Individuals with high agreeableness are encouraged by ease in pursuing CGB leadership.

• (4)

  Perceived usefulness mediates the relationship between conscientiousness and CGB leadership intention and neuroticism and CGB leader intention.

Individuals with extroversion, neuroticism, and conscientiousness personality traits exhibit higher perceived ease of use and usefulness of social media.

Unlike previous research suggested, neurotic individuals appear to be attracted to becoming community group buying (CGB) leaders.

Individuals with high agreeableness are encouraged by ease in pursuing CGB leadership.

Perceived usefulness mediates the relationship between conscientiousness and CGB leadership intention and neuroticism and CGB leader intention.