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Intent detection and slot filling are two important tasks in question comprehension of a question answering system. This study aims to build a joint task model with some generalization ability and benchmark its performance over other neural network models mentioned in this paper.

This study used a deep-learning-based approach for the joint modeling of question intent detection and slot filling. Meanwhile, the internal cell structure of the long short-term memory (LSTM) network was improved. Furthermore, the dataset Computer Science Literature Question (CSLQ) was constructed based on the Science and Technology Knowledge Graph. The datasets Airline Travel Information Systems, Snips (a natural language processing dataset of the consumer intent engine collected by Snips) and CSLQ were used for the empirical analysis. The accuracy of intent detection and F1 score of slot filling, as well as the semantic accuracy of sentences, were compared for several models.

The results showed that the proposed model outperformed all other benchmark methods, especially for the CSLQ dataset. This proves that the design of this study improved the comprehensive performance and generalization ability of the model to some extent.

This study contributes to the understanding of question sentences in a specific domain. LSTM was improved, and a computer literature domain dataset was constructed herein. This will lay the data and model foundation for the future construction of a computer literature question answering system.

This paper aims to present a kinematics analysis method and statics based control of the continuum robot with mortise and tenon joints to achieve better control performance of the robot.

The kinematics model is derived by the geometric analysis method under the piecewise constant curvature assumption, and the workspace and dexterity of the proposed robot are analyzed to optimize its structure parameters. Moreover, the statics model is established by the principle of virtual work, which is used to analyze the mapping relationship between the bending deformation and the applied forces/torques. To improve the control accuracy of the robot, a model-based controller is put forward.

Results of the experiments verify the feasibility of the proposed continuum structure and the correctness of the established model and the control method. The force deviation between the theoretical value and the actual value is relatively small, and the mean value of the deviation between the driving forces is only 0.46 N, which verify the established statics model and the controller.

The proposed model and motion controller can realize its accurate bending control with a few deviations, which can be used as the reference for the motion planning and dynamic model of the continuum robot.

The objective of this study is to investigate how the coronavirus disease 2019 (COVID-19) pandemic affects firms' financial management in China's manufacturing sector. In addition, the authors analyze the changes in various financial indicators before and during the COVID-19 pandemic. Further, the authors make a cross-country comparison of the COVID-19's impact on financial management between China and Romania.

The study uses the balanced panel data of 2,272 manufacturing listed companies from 2019 to 2020, and applies the t-test method and multiple regression method.

The results show that firms' financial performance in most manufacturing sub-sectors decreased during the observed period. In addition, the authors find that equity financing, proper liquidity management and an expanded firm scale can improve firms' financial performance. The authors further compare the results with the Romanian results, and find that the negative impact of debt-to-equity ratio on firms' financial performance in Romania is greater than that in China and the positive impact of financial autonomy ratio and working capital ratios is greater in China than that in Romania.

The findings can help corporate managers make the best financial management decision in response to crisis.

This study is one of the pioneers that analyze how manufacturing companies carried out their financial management during the COVID-19 crisis in the Chinese context, and provides a cross-country analysis of corporate financial management practices in China and Romania.

Transformational leadership (TFL) has been identified by a number of studies as a positive force for business success. However, few studies have explicitly examined its influence on the cooperative employment relationship, such as frontline employees' voice in the workplace. Thus, this study conducts an empirical analysis of dual-level (i.e. group-focused and individual-focused) TFL and its effect on frontline employees' cooperative voice.

This empirical study is based on data from a survey of 468 full-time frontline workers from 38 hotels in China.

The empirical results revealed that both individual-level and group-level TFL are significantly associated with frontline employees' cooperative voice. Person–Organization value congruence and Person–Supervisor value congruence both act as mediators in the linkage between group-level TFL and employees' cooperative voice behaviors. The partial mediating role of Demand–Ability congruence on the relationship between individual-level TFL and cooperative voice is also established.

First, this study investigates the dual-level effects of TFL on the cooperative voice behavior of frontline employees. Second, this study explores whether three dimensions of value congruence mediate the influence of TFL on the voice behaviors of employees.

Anti-epidemic Emergency Projects (AEEPs) have unique characteristics such as a short construction period, high-quality requirements, complex construction environment, many construction participants and many uncertain affecting factors. The purpose of this paper was to propose the establishment method for the Construction Community (CC) of AEEPs (CC-AEEPs) by analyzing the management features of AEEPs, to establish the method of the Social Network Analysis (SNA) for CC-AEEPs, and to apply this method and the framework to Wuhan Huoshenshan Hospital for their verification.

According to the CC theory, this paper explored the member composition and the establishment method for CC-AEEPs. The optimal management factors of CC-AEEPs were proposed by combining the management features of AEEPs and the SNA method for CC-AEEPs was further established. Finally, the applicability of the method was verified through a case study, and some countermeasures for the CC-AEEP social networks were proposed.

The establishment of CC-AEEPs is an important guarantee to complete AEEPs with top speed and high quality. Ten types of CC-AEEP members all played different but irreplaceable roles in cooperative construction, among which the Government, the Contractor, and the Supervisor had outstanding performances. The SNA method could effectively analyze the complexity and cooperative relationship among the members in four aspects. The case study of Huoshenshan Hospital validated the important role of CC-AEEP and its social network in the AEEP research providing beneficial enlightenment for the cooperative optimization path of the AEEP construction participants.

The new establishment method for CC-AEEPs was proposed from the perspective of “cooperation among human, society, and engineering” according to the theories of the engineering sociology and the CC. In this paper, the SNA method was applied to the research on the AEEP construction for the first time and the SNA method for CC-AEEPs was purposed. The optimal management factors of CC-AEEPs and the expansion path of the CC-AEEP social networks were proposed according to the whole-process tracking of AEEPs in Wuhan.

This study aims to examine the relationship between polychronicity, job autonomy, perceived workload, work–family conflict and high work demand on the health-care employee turnover intention during the COVID-19 pandemic.

The authors conducted quantitative research in private hospitals using a self-administered questionnaire, and 264 respondents participated. The authors also used an analysis of moment structures to determine the relationship between independent and moderating variables.

The results show a significant positive relationship between polychronicity, job autonomy, perceived workload, work–family conflict and high work demand, affecting turnover intention. This study also found the moderating effect of high work demand on work–family conflict and turnover intention.

This research was limited to hospitals in Bahrain during the COVID-19 pandemic. Nevertheless, the findings highlight the factors associated with health-care employee turnover intention and only five factors were identified.

This study enhances the theoretical and practical effects of turnover intention. The results provide a competitive benchmark for hospital managers, administrators and governing bodies of employee retention.

It advances economics and management theory by enhancing the understanding of health-care employees’ turnover intention in Bahrain. It serves as a basis for future large-scale studies to test or refine existing theories.

To the best of the authors’ knowledge, this study is the first to adopt extrinsic variables in self-determination theory to measure the turnover intention of health-care employees. However, using resources in a crisis can be applied to any disaster.

The purpose of this study was to comprehend the current state of green supply chain management (GSCM) and its drivers and enablers in the pharmaceutical supply chains (PSCs).

This study developed a model consisting of the factors green drivers, green enablers and green practices. For this study, data were collected from 166 supply chain heads from 124 pharmaceutical companies operating in India. The data was empirically analysed using SmartPLS3 software.

The study revealed that in Indian PSCs, public pressure, supplier pressure and competitor pressure were the major drivers for GSCM practices and coercive pressures such as pressure from government and regulatory bodies were insignificant drivers. The results indicated that the influence of green drivers and green enablers altered with supply chain flow. Green drivers indicated higher influence at the upstream of PSCs whereas the influence of green enablers was higher at the downstream of PSCs. Furthermore, the study revealed that the factor “green enablers” was a good mediator for the relationship between “green drivers” and “GSCM practices” in PSCs.

This study was an extension work with the addition of external green drivers to the self-induced organizational activities. Further investigations can be done by including the “green barriers” construct in the conceptual model.

This research connected the institutional theory with the resource-based view to understanding the role of green drivers and green enablers in greening PSC. For managers and regulators, this study provided clear insights on how to approach different supply chain drivers and enablers to maximize the impact of GSCM practices. Moreover, the findings of this study helped in incorporating knowledge about GSCM into a framework that can be used for defining strategies for setting up a sustainable PSC for pharmaceutical products leading to the development and growth of the pharmaceuticals industry.

Most of the earlier studies on GSCM were at the firm level and some studies were at the supply chain level. To further enhance the scope of GSCM theory, this study was conducted at two different levels, first at the complete supply chain level and second at three different levels: upstream, firm-level and downstream.

This study aims to examine the current state of academic research pertaining to the role played by artificial intelligence (AI) in the achievement of a critical sustainable development goal (SDG) – poverty alleviation and describe the field’s development by identifying themes, trends, roadblocks and promising areas for the future.

The authors analysed a corpus of 253 studies collected from the Scopus database to examine the current state of the academic literature using bibliometric methods.

This paper identifies and analyses key trends in the evolution of this domain. Further, the paper distils the extant literature to unpack the intermediary mechanisms through which AI and related technologies help tackle the critical global issue of poverty.

The corpus of literature used for the analysis is limited to English language studies from the Scopus database. The paper contributes to the extant research on AI for social good, and more broadly to the research on the value of emerging technologies such as AI.

Policymakers and government agencies will get an understanding of how technological interventions such as AI can help achieve critical SDGs such as poverty alleviation (SDG-1).

The primary focus of this paper is on the role of AI-related technological interventions to achieve a significant social objective – poverty alleviation.

To the best of the authors’ knowledge, this is the first study to conduct a comprehensive bibliometric analysis of a critical research domain such as AI and poverty alleviation.

Identifying the criteria that effectively drive innovation in universities is critical to assessing their innovation maturity level, and hence, planning for the improvements required to reach a target level. This paper aims to propose a three-phase approach to develop a multidimensional maturity assessment framework used by university decision-makers to determine their level of innovation readiness.

First, a systematic collection of evaluation criteria from the literature is conducted. The results are mapped into different categories in a hierarchical and multidimensional way, and validated by experts. The second phase aims to identify the critical factors and their priorities, which are determined using analytic network process (ANP). To facilitate that, a panel of thirteen experts is formed and questionnaires are sent to rank the importance of the criteria and their elements. Finally, a maturity assessment tool is developed to complement the framework, allowing decision-makers to determine the level of innovation maturity with respect to each dimension and the overall position.

Results revealed three clusters, eight criteria and 26 subcriteria related to innovation in universities. The findings about the relative importance of the various attributes are reflected in the developed assessment tool and taken into consideration in the maturity indices computation approach.

To the best of the authors’ knowledge, this is the first attempt to develop a comprehensive list of innovation success drivers in universities and to use this list to design an innovation maturity assessment framework

Green hospitality studies have not adequately focused on the diffusion of eco-innovative hotels amongst visitors. This study aims to fill this gap by identifying green hotel attributes that influence visitors’ adoption of eco-friendly hotel and their intentions to partake in green initiatives.

The paper uses a mixed-method approach to explore the drivers of customers’ green hotel adoption and consumption. In the qualitative phase, data were collected via 20 open-ended interviews and analyzed to derive a measurement scale. The scale was then tested through a survey comprising 500 respondents using structural equation modelling.

The study results elucidate how guests’ visit intentions and green consumption behavior is built through their perception of newness and uniqueness of eco-innovative attributes. Findings shed light on how green hotel’s sustainable communication and corporate social responsibility outreach efforts positively influence guest visit intentions.

Study results reveal perceived eco-innovativeness as an important antecedent of visit intentions. Based on guest’s preferences, green hotels striving to increase its visitors’ base could begin by expanding their eco-innovative attributes.

Contrasting previous studies that have exclusively used the theory of planned behavior constructs, this study argues that diffusion of innovation constructs also offer valuable insights into guests’ visit intentions. While existing studies have covered limited number of eco-innovative attributes, this study adds to the literature by presenting a comprehensive set of attributes including trustworthiness of communication and observability of its social impacts.