Qiong Yao, Jinxin Liu, Shibin Sheng and Heng Fang
Drawing on the literature of eco-innovation and institutional theory, this research aims to answer two fundamental questions: Does eco-innovation improve or harm firm value in…
Abstract
Purpose
Drawing on the literature of eco-innovation and institutional theory, this research aims to answer two fundamental questions: Does eco-innovation improve or harm firm value in emerging markets? and How institutional environments moderate the relationship between eco-innovation and firm value? We explicate the regulatory, normative and cognitive pillars of institutions, manifested as regulation intensity, environmental agency pressure and public pressure, respectively.
Design/methodology/approach
For this study, a cross-sectional panel data set was assembled from multiple archival sources, including data coded from the corporate annual reports and social responsibility reports, statistical yearbooks, China Stock Market Financial Database (CSMAR) and other secondary sources. A hierarchical regression method was used to test the hypotheses. The data comprised 88 firms sampled over four years. The model using feasible generalized least squares (FGLSs) to control heteroscedasticity in errors due to unobserved heterogeneity was estimated.
Findings
Empirical findings from a data set compiled from multiple archival sources reveal that both eco-product and eco-process innovation negatively relate to firm value. The interactions between eco-innovation and regulation intensity, environmental agency pressure and public pressure are positively related to firm value.
Originality/value
First, this study extends the literature of eco-innovation by investigating the impact of eco-innovation on firm value. Contrary to the conventional anecdotal evidence of the beneficial effect of eco-innovation, it was found that eco-innovation relates negatively to firm value. Second, this study develops and tests an institutional contingent view of eco-innovation by accounting for the moderating role of regulatory, normative and cognitive pressures.
Details
Keywords
Zhaopeng Wang, Yi Wang, Bowei Zhang, Zhan Zhang, Kui Xiao, Junsheng Wu, Qiong Yao, Guojia Ma and Gang Sun
The purpose of this paper is to investigate the influence of the potential of hydrogen (pH) and dissolved oxygen in artificial seawater on the passivation behavior of 316L…
Abstract
Purpose
The purpose of this paper is to investigate the influence of the potential of hydrogen (pH) and dissolved oxygen in artificial seawater on the passivation behavior of 316L stainless steel.
Design/methodology/approach
The corrosion behavior was studied by using electrochemical measurements such as electrochemical impedance spectroscopy and polarization curve. The passive films were characterized with X-ray photoelectron spectroscopy.
Findings
The polarization resistance of the passive film decreases as the pH value drops ascribed to the formation of much more point defects. The donor carrier concentration (ND) in the passive film formed in the deaerated condition is lower than that in aerated conditions. Nevertheless, this phenomenon is the opposite when the pH value is 1 due to the significant decrease of Fe oxides/hydroxides coupled with the stable content of Cr oxides/hydroxides species. In addition, the compositional variation of the passive film also leads to the changes of its semiconductor properties from N-type to bipolar type.
Originality/value
This paper shows the variation of polarization resistance, corrosion potential, passive film composition and semiconductor properties with the pH value and dissolved oxygen. The results can serve as references to the further study on crevice corrosion of 316L in seawater.
Details
Keywords
Fabrício Oliveira Leitão, Ely Laureano Paiva and Karim Marini Thomé
The literature has suggested that capabilities have been used to generate performance and competitive advantage, especially in industries with higher technological dynamism in…
Abstract
Purpose
The literature has suggested that capabilities have been used to generate performance and competitive advantage, especially in industries with higher technological dynamism in developed economies. However, knowledge of the topic still needs to be systematically analyzed in agribusiness. Thus, this article fills this gap because it systematically reviews the literature on agribusiness capabilities and performance, classifies and codifies its characteristics, and determines what is known and what gaps there are in the knowledge regarding these subjects.
Design/methodology/approach
A systematic literature review of agribusiness capabilities and performance was conducted based on Cronin et al. (2008) protocol. Thirty-six articles from the WoS and Scopus databases were identified and analyzed.
Findings
This article identified, classified and coded 12 capabilities agribusiness firms employ to improve performance. This article reveals several gaps regarding capabilities and performance in agribusiness, especially emphasizing commodity products, in addition to studies with fruits and vegetables, milk, eggs, meat, agricultural inputs and biofuels. It was also found that higher-order capabilities are more strongly related to performance than lower-order capabilities, that the performance benefits conferred by capabilities are more evident in developing economies, and that the relationship between capabilities and performance is more robust in agribusinesses with lower levels of technological dynamism.
Originality/value
This paper contributes to the debate about agribusiness capabilities and performance in three aspects. First, it systematically reviews the literature on these subjects; second, it classifies and codifies agribusiness capabilities and performance characteristics; third, it provides a research agenda on the theme.
Details
Keywords
Qiong Wang, Zeng-Lai Xu and Zhihong Cheng
The precise and sensitive methods for authentication and differentiation of natural and synthetic indigo dyes are required for assurance of textile safety and public health. This…
Abstract
Purpose
The precise and sensitive methods for authentication and differentiation of natural and synthetic indigo dyes are required for assurance of textile safety and public health. This study aims to develop a fast and simple method to distinguish natural indigo from synthetic one.
Design/methodology/approach
A static headspace gas chromatography-mass spectrometry (GC-MS) method was developed for identification of natural and synthetic indigo samples. Natural indigo samples prepared from three different plants and synthetic indigo samples from three famous manufacturers in China, were involved in this study, along with some nonindigo blue samples (such as direct blue, active blue and neutral blue). The yarns and fabrics dyed with natural and synthetic indigo were also analyzed by the GC-MS method.
Findings
High levels of aniline (21.87%–71.59%) or N-methylaniline (25.26%–38.73%) were detected only in synthetic indigo samples (1 g) using the static headspace GC-MS method. The yarns and fabrics dyed with the synthetic indigo were also detected with residual aniline (0.47%–14.86%) or N-methylaniline (6.59%–40.93%).
Originality/value
The results clearly demonstrated that aniline or N-methylaniline can be used a diagnostic marker for distinguishing natural indigo from synthetic indigo. The proposed static headspace GC-MS method is a rapid, simple and convenient approach for differentiation of natural and synthetic indigo, as well as for the yarns and fabrics dyed with synthetic indigo.
Details
Keywords
Xiao Yun Lu, Hecheng Li and Qiong Hao
Consistency and consensus are two important research issues in group decision-making (GDM). Considering some drawbacks associated with these two issues in existing GDM methods…
Abstract
Purpose
Consistency and consensus are two important research issues in group decision-making (GDM). Considering some drawbacks associated with these two issues in existing GDM methods with intuitionistic multiplicative preference relations (IMPRs), a new GDM method with complete IMPRs (CIMPRs) and incomplete IMPRs (ICIMPRs) is proposed in this paper.
Design/methodology/approach
A mathematically programming model is constructed to judge the consistency of CIMPRs. For the unacceptably consistent CIMPRs, a consistency-driven optimization model is constructed to improve the consistency level. Meanwhile, a consistency-driven optimization model is constructed to supplement the missing values and improve the consistency level of the ICIMPRs. As to GDM with CIMPRs, first, a mathematically programming model is built to obtain the experts' weights, after that a consensus-driven optimization model is constructed to improve the consensus level of CIMPRs, and finally, the group priority weights of alternatives are obtained by an intuitionistic fuzzy programming model.
Findings
The case analysis of the international exchange doctoral student selection problem shows the effectiveness and applicability of this GDM method with CIMPRs and ICIMPRs.
Originality/value
First, a novel consistency definition of CIMPRs is presented. Then, a consistency-driven optimization model is constructed, which supplements the missing values and improves the consistency level of ICIMPRs simultaneously. Therefore, this model greatly improves the efficiency of consistency improving. Experts' weights determination method considering the subjective and objective information is proposed. The priority weights of alternatives are determined by an intuitionistic fuzzy (IF) programming model considering the risk preference of experts, so the method determining priority weights is more flexible and agile. Based on the above theoretical basis, a new GDM method with CIMPRs and ICIMPRs is proposed in this paper.
Details
Keywords
Binghai Zhou and Qiong Wu
The extensive applications of the industrial robots have made the optimization of assembly lines more complicated. The purpose of this paper is to develop a balancing method of…
Abstract
Purpose
The extensive applications of the industrial robots have made the optimization of assembly lines more complicated. The purpose of this paper is to develop a balancing method of both workstation time and station area to improve the efficiency and productivity of the robotic assembly lines. A tradeoff was made between two conflicting objective functions, minimizing the number of workstations and minimizing the area of each workstation.
Design/methodology/approach
This research proposes an optimal method for balancing robotic assembly lines with space consideration and reducing robot changeover and area for tools and fixtures to further minimize assembly line area and cycle time. Due to the NP-hard nature of the considered problem, an improved multi-objective immune clonal selection algorithm is proposed to solve this constrained multi-objective optimization problem, and a special coding scheme is designed for the problem. To enhance the performance of the algorithm, several strategies including elite strategy and global search are introduced.
Findings
A set of instances of different problem scales are optimized and the results are compared with two other high-performing multi-objective algorithms to evaluate the efficiency and superiority of the proposed algorithm. It is found that the proposed method can efficiently solve the real-world size case of time and space robotic assembly line balancing problems.
Originality/value
For the first time in the robotic assembly line balancing problems, an assignment-based tool area and a sequence-based changeover time are took into consideration. Furthermore, a mathematical model with bi-objective functions of minimizing the number of workstations and area of each station was developed. To solve the proposed problem, an improved multi-objective immune clonal selection algorithm was proposed and a special coding scheme is designed.
Details
Keywords
Binghai Zhou and Qiong Wu
The balancing of robotic weld assembly lines has a significant influence on achievable production efficiency. This paper aims to investigate the most suitable way to assign both…
Abstract
Purpose
The balancing of robotic weld assembly lines has a significant influence on achievable production efficiency. This paper aims to investigate the most suitable way to assign both assembly tasks and type of robots to every workstation, and present an optimal method of robotic weld assembly line balancing (ALB) problems with the additional concern of changeover times. An industrial case of a robotic weld assembly line problem is investigated with an objective of minimizing cycle time of workstations.
Design/methodology/approach
This research proposes an optimal method for balancing robotic weld assembly lines. To solve the problem, a low bound of cycle time of workstations is built, and on account of the non-deterministic polynomial-time (NP)-hard nature of ALB problem (ALBP), a genetic algorithm (GA) with the mechanism of simulated annealing (SA), as well as self-adaption procedure, was proposed to overcome the inferior capability of GA in aspect of local search.
Findings
Theory analysis and simulation experiments on an industrial case of a car body welding assembly line are conducted in this paper. Satisfactory results show that the performance of GA is enhanced owing to the mechanism of SA, and the proposed method can efficiently solve the real-world size case of robotic weld ALBPs with changeover times.
Research limitations/implications
The additional consideration of tool changing has very realistic significance in manufacturing. Furthermore, this research work could be modified and applied to other ALBPs, such as worker ALBPs considering tool-changeover times.
Originality/value
For the first time in the robotic weld ALBPs, the fixtures’ (tools’) changeover times are considered. Furthermore, a mathematical model with an objective function of minimizing cycle time of workstations was developed. To solve the proposed problem, a GA with the mechanism of SA was put forth to overcome the inferior capability of GA in the aspect of local search.
Details
Keywords
Yuanyuan Liu, Fan Zhang, Bin Li, Pingqing Liu, Shuzhen Liu and Qiong Sun
This study reveals the trigger of innovative behavior from the perspective of intrinsic and extrinsic spiritual inspiration and provides a new research idea for the formation…
Abstract
Purpose
This study reveals the trigger of innovative behavior from the perspective of intrinsic and extrinsic spiritual inspiration and provides a new research idea for the formation mechanism of innovative behavior. The purpose of this study is to provide certain guidance and implications for enterprises to cultivate and enhance employees’ innovative behavior.
Design/methodology/approach
We conducted three studies, collected multi-source data (N = 1,175) from different countries longitudinally, as well as used hierarchical regression analysis and fuzzy-set quantitative comparative analysis to verify the theoretical model.
Findings
According to the findings, both spiritual leadership and career calling have a positive impact on employees’ innovative behavior through the mediating effect of autonomous motivation and the moderating effect of person-vocation fit.
Originality/value
Innovative behavior is the positive professional pursuit of employees, which is difficult to form without the motivation of spiritual factors. Spirituality is a complex concept that contains intrinsic and extrinsic spiritual factors, both of which could stimulate employees’ innovative behavior. Although many discussions have been held on this topic in recent years, little attention has been paid simultaneously to the motivating effects of the two perspectives. Drawn from self-determination theory, this study explores the mechanisms of two spiritual motivation paths (i.e. the intrinsic and extrinsic spiritual motivation paths) in the improvement of employees’ innovative behavior.
Details
Keywords
Qiong Jia, Ying Zhu, Rui Xu, Yubin Zhang and Yihua Zhao
Abundant studies of outpatient visits apply traditional recurrent neural network (RNN) approaches; more recent methods, such as the deep long short-term memory (DLSTM) model, have…
Abstract
Purpose
Abundant studies of outpatient visits apply traditional recurrent neural network (RNN) approaches; more recent methods, such as the deep long short-term memory (DLSTM) model, have yet to be implemented in efforts to forecast key hospital data. Therefore, the current study aims to reports on an application of the DLSTM model to forecast multiple streams of healthcare data.
Design/methodology/approach
As the most advanced machine learning (ML) method, static and dynamic DLSTM models aim to forecast time-series data, such as daily patient visits. With a comparative analysis conducted in a high-level, urban Chinese hospital, this study tests the proposed DLSTM model against several widely used time-series analyses as reference models.
Findings
The empirical results show that the static DLSTM approach outperforms seasonal autoregressive integrated moving averages (SARIMA), single and multiple RNN, deep gated recurrent units (DGRU), traditional long short-term memory (LSTM) and dynamic DLSTM, with smaller mean absolute, root mean square, mean absolute percentage and root mean square percentage errors (RMSPE). In particular, static DLSTM outperforms all other models for predicting daily patient visits, the number of daily medical examinations and prescriptions.
Practical implications
With these results, hospitals can achieve more precise predictions of outpatient visits, medical examinations and prescriptions, which can inform hospitals' construction plans and increase the efficiency with which the hospitals manage relevant information.
Originality/value
To address a persistent gap in smart hospital and ML literature, this study offers evidence of the best forecasting models with a comparative analysis. The study extends predictive methods for forecasting patient visits, medical examinations and prescriptions and advances insights into smart hospitals by testing a state-of-the-art, deep learning neural network method.
Details
Keywords
Jinhuan Tang, Qiong Wu and Kun Wang
Intelligent new energy vehicles (INEVs) are becoming the competitive hotspot for the automobile industry. The major purpose of this study is to determine how to increase…
Abstract
Purpose
Intelligent new energy vehicles (INEVs) are becoming the competitive hotspot for the automobile industry. The major purpose of this study is to determine how to increase innovation efficiency through knowledge sharing and technology spill between new energy vehicle (NEV) enterprises and technology enterprises. This will help to improve the core competence of the automobile industry in China. Also, it serves as a guide for the growth of other strategic.
Design/methodology/approach
The authors construct a tripartite evolutionary game model to study the cross-border cooperative innovation problem. Firstly, the payment matrix of NEV enterprise, technology enterprise and government is established, and the expected revenue of each participant is determined. Then, the replication dynamic equations and evolutionary stability strategies are analyzed. Finally, the theoretical research is validated through numerical simulation.
Findings
Results showed that: (1) An optimal range of revenue distribution coefficient exists in the cross-border cooperation. (2) Factors like research and development (R&D) success rate, subsidies, resource and technology complementarity, and vehicles intelligence positively influence the evolution towards cooperative strategies. (3) Factors like technology spillover risk cost inhibit the evolution towards cooperative strategies. To be specific, when the technology spillover risk cost is greater than 2.5, two enterprises are inclined to choose independent R&D, and the government chooses to provide subsidy.
Research limitations/implications
The research perspective and theoretical analysis are helpful to further explore the cross-border cooperation of the intelligent automobile industry. The findings suggest that the government can optimize the subsidy policy according to the R&D capability and resource allocation of automobile industry. Moreover, measures are needed to reduce the risk of technology spillovers to encourage enterprise to collaborate and innovate. The results can provide reference for enterprises’ strategic choice and government’s policy making.
Originality/value
The INEV industry has become an important development direction of the global automobile industry. However, there is limited research on cross-border cooperation of INEV industry. Hence, authors construct a tripartite evolutionary game model involving NEV enterprise, technology enterprise and the government, and explore the relationship of cooperation and competition among players in the INEV industry, which provides a new perspective for the development of the INEV industry.