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This paper aims to investigate the anti-biocorrosion performance and mechanism of the Cu-bearing carbon steel in the environment containing sulfate-reducing bacterial (SRB).

The biocorrosion behavior of specimens with Cu concentration of 0 Wt.%, 0.1 Wt.%, 0.3 Wt.% and 0.6 Wt.% were investigated by immersion test in SRB solution. By examining the prepared cross-section of the biofilm using focused ion beam microscopy, SRB distribution, bacterial morphology, biofilm structure and composition were determined. The ion selectivity of the biofilm was also obtained by membrane potential measurement. Moreover, the anti-biocorrosion performance of the Cu-bearing carbon steel pipeline was tested in a shale gas field in Chongqing, China.

Both the results of the laboratory test and shale gas field test indicate that Cu-bearing carbon steel possesses obvious resistance to microbiologically influenced corrosion (MIC). The SRB, corrosion rate and pitting depth decreased dramatically with Cu concentration in the substrate. The local acidification caused by hydrolyze of ferric ion coming from SRB metabolism and furtherly aggravated by anion selectivity biofilm promoted the pitting corrosion. Anti-biocorrosion of Cu-bearing carbon steel was attributed to the accumulation of Cu compounds in the biofilm and the weaker anion selectivity of the biofilm. This research results provide an approach to the development of economical antibacterial metallic material.

MIC occurs extensively and has become one of the most frequent reasons for corrosion-induced failure in the oil and gas industry. In this study, Cu-bearing carbon steel was obtained by Cu addition in carbon steel and possessed excellent anti-biocorrosion property both in the laboratory and shale gas field. This study provides an approach to the development of an economical antibacterial carbon steel pipeline to resist MIC.

In recent years, counterproductive knowledge behavior (CKB) and its types have received increasing interest in knowledge management as the degree of knowledge sharing and innovation in enterprises continues to increase. A rapidly growing number of studies have shed light on the important antecedents and consequences of employees’ CKB. However, the various labels, conceptualizations and operationalizations of CKB have fragmented this body of research. This study aims to systematically integrate the effects of the six types of organizational characteristics on CKB and further draws more general conclusions based on the results of previous studies.

Based on a survey of 103 effect values responsible for 52 CKB samples, the authors use the ABC theory to explore the effects of the six types of organizational characteristics on CKB. Moderator analysis were performed to resolve inconsistencies in empirical studies and understand the contexts under which CKB has the strongest or weakest effect.

The results showed that task interdependence and a positive organizational atmosphere, in general, negatively affect employees’ CKB in the moderation analysis. In contrast, workplace discomfort, negative organizational atmosphere, internal competition and time pressure positively and partly affect employees’ CKB. The direction and magnitude of these effects were affected by emotional factors, knowledge personnel types and sample sources. Discussing the theoretical, methodological and practical implications of these findings can offer a guiding framework for future research.

Better control of employees’ CKB is not achieved by adjusting organizational characteristics alone but by combining personal characteristics and mood changes with it to balance organizational characteristics and CKB. Furthermore, the large-sample joint study integrated the conceptual definition of CKB. The multivariate data study provided more reliable conclusions and a solid theoretical foundation for CKB research areas.

This study aims to investigate the effects of project-level external knowledge search breadth and search depth on the innovation performance of open innovation (OI) projects in large firms; it further considers these effects mediated by two forms of control mechanisms (process and outcome control) when the level of project complexity and the two stages of a project – early (problem definition) and late (solution development) – are taken into account.

Based on a survey of 187 managers responsible for an OI project, the authors use theory on behavioral-based control mechanisms to explore whether the effect of external knowledge search breadth and depth on OI performance is contingent on having the right levels of control mechanisms in place.

The results showed that the control mechanism mediates the relationship between external knowledge search breadth and depth and OI project performance. Furthermore, project complexity is an important moderator of these effects, especially for outcome control.

A better OI project’s performance is not achieved by external knowledge search breadth and depth alone, but by building process and outcome control mechanism on it to balance knowledge sharing and protecting tension. Furthermore, Outcome control is only helpful with less complex OI projects.

The purpose of this paper is to examine the impact of consumers’ attitudes towards organic food on online impulse buying behaviour as well as the moderating effect of three website features (visual, information and navigation design) on this relationship.

Survey data were collected via an online survey using social media platforms. A total of 653 online questionnaires were collected (response rate = 72.5 per cent) and analysed by applying exploratory and confirmatory factor analyses. The proposed hypotheses were tested through structural equation modelling.

Social media forums, ratings and reviews shape Chinese consumers’ attitudes towards organic food and positively influence their online impulse buying in this market. Website features are critical for disseminating information on organic food. Informative webpages featuring product quality and certification have a greater moderating effect on purchase. Information cues such as nutritional content; production and processing methods, and environmentally friendliness also influence consumers’ attitudes and thus impulse buying decisions.

Marketers should reconsider their tactics for dealing with modern consumers, as webpages should be user-friendly and visually appealing with a social learning mechanism to drive organic food consumption.

This study bridges a gap in the literature on social commerce initiatives for developing consumers’ attitudes towards organic food and online impulse buying. Further, it proposes measures that can enhance organic consumption and contributes to the literature on the importance of social factors, resulting in enhanced knowledge on the online impulse buying of organic food.

Credit spread change is a key issue for investors to earn the excess return from corporate bonds. Generally, there are two kinds of different effects that support the changing of credit spread: asset allocation effect and credit risk change effect. The existing literature based on US data supports credit spread change is driven by credit risk change; however, this issue based on Chinese market data has not been investigated clearly. This paper seeks to address this issue.

The authors adopt Markov regime switching model to investigate the changing mode of the credit spread in the Chinese bond market. They select three kinds of variables: the credit risk variables, the asset allocation variables and the liquidity condition variables. With ML estimators, the authors can find the changing mode and further they study the relationship between the regime switching and some observed variables, such as macro economy variables and turnover of stock market.

The authors find it is driven by asset allocation effect in most time and by credit risk change only in shorter period. Empirical results show that the switching of dominance from one effect to another isn't closely related with macro‐economy variables, but related with the turnover of stock market.

These results indicate that in China the credit risk of corporate bonds is relatively low and the corporate bonds are still auxiliary asset for investors.

In this paper, the authors have the following two contributions: first, they discuss the asset allocation effect in the Chinese bond market and introduce the stock market variables and bank credit variable to describe the asset allocation effect; second, based on Chinese bond market data, they find different findings from the existing literature about US and European bond markets, showing that the changing of credit spread is mostly related with asset allocation effect but not credit risk change.

The purpose of this paper is to unravel the complex linkages between properties of knowledge, firm's absorptive capacities, and innovation performance in China small and medium‐sized enterprises (SMEs).

The authors utilize moderated multiple regression (MMR) to explore the complex relationships by using the empirical data of Chinese information technology and electro‐communication firms during 2005‐2008.

The results show that only a few knowledge properties have negative effects on innovation performance. Most knowledge properties have a positive effect on innovation. The results also claim that the relationship between properties of knowledge and innovation performance is more pronounced when the firm has higher absorptive capacity.

The study indicates that properties of knowledge and absorptive capacity are two inseparable determinants for innovation performance, and absorptive capacity moderates the relationship between properties of knowledge and innovation performance.

The purpose of this paper is to propose a new four-node membrane element model with bending modification based on the equilibrium principle of element nodal internal forces and bending moments for the application of the one-step algorithm for bus rollover collision. And it can be concluded whether the proposed four-node membrane element model has practical value in engineering application or not.

Based on the equilibrium principle of element nodal internal forces and bending moments, the paper puts forward a four-node membrane element model with bending modification. A case study on the rollover of a typical bus body section is carried out by using the one-step algorithm for bus rollover collision to verify the effectiveness of the proposed element model.

For the simulation of bus rollover collision, the computational accuracy can be guaranteed, meanwhile, the calculated amount is much smaller than the shell element, and computational efficiency is improved significantly.

The proposed four-node membrane element model is used for the simulation of bus rollover collision for the first time. It holds the advantage of high computational efficiency of membrane element, and the computational accuracy is improved as well. In conclusion, it has some practical value in engineering application.

This paper aims to report on the bibliometric research trends on the application of machine learning/intelligent systems in the prediction of food contamination and the surveillance of foodborne diseases.

In this study, Web of Science (WoS) core collection database was used to retrieve publications from the year 1996–2021. Document types were classified according to country of origin, journals, citation and key research areas. The bibliometric parameters were analyzed using VOSviewer version 1.6.15 to visualize the international collaboration networks, citation density and link strength.

A total of 516 articles across 6 document types were extracted with an average h-index of 51 from 10,570 citations. The leading journal in publications was Science of the Total Environment (3.6%) by Elsevier and the International Journal of Food Microbiology (2.5%). The United States of America (USA) (24%) followed by the People's Republic of China (17.2%) were the most influential countries in terms of publications. The top-cited articles in this study focused on themes such as contamination from packaging materials and on the strategies for preventing chemical contaminants in the food chain.

This report is significant because the public health field requires innovative strategies in forecasting foodborne disease outbreaks to advance effective interventions. Therefore, more collaboration need to be fostered, especially in developing nations regarding food safety research.

Can Chinese social media data (SMD) be used as an alternative to traditional surveys used to understand tourists' visitation of attractions in Chinese cities? The purpose of this paper is to explore this question.

Popular tourism SMD sources in China, such as Ctrip, Weibo and Dazhong Dianping (DZDP), were used as data source, and the relationships between these sources and traditional data sources were studied with statistical methods. Data from Shanghai were used in this study since it is rich in tourism resources and developed in information.

A systematic research method was followed and led to the following conclusions: There were positive correlations for attraction visitation between Chinese SMD and traditional survey data; Chinese SMD source could temporally indicate visits to Shanghai tourist attractions; Ctrip SMD generally performed less well than Weibo or DZDP, and different SMD performed differently depending on the specific attractions and time units in the visitation calculation process; and factors including visitation, distance from the city center and the grade of attractions might affect the prediction performance based on data from the SMD. The findings suggest that Chinese SMD could be used as a cost-efficient and reliable proxy for traditional survey data to predict Chinese attraction visitation.

This study applies and improves the methods of SMD reliability in attraction use studies, supplies the gap for premise, basis and foundation for the large amounts of tourism researches using SMD in China and could promote and inspire more efficient and advanced measures in tourism management and urban development.

The purpose of this paper is to develop an approach to measuring the performance of motor vehicle manufacturers (MVMs) from economic and environmental (E&E) perspectives.

Eight measures are identified for benchmarking the performance from E&E perspectives. A new company performance index I_MVM is constructed to quantitatively generate the historical data of MVMs’ company performance. Autoregressive integrated moving average (ARIMA) models are built to generate the forecast data of the I_MVM. The minimum Akaike information criteria value is used to identify the model of the best fit. Forecast accuracy of the ARIMA models is tested by the mean absolute percentage error.

The construction of the index I_MVM is benchmarked against three frameworks by six benchmark metrics. The I_MVM satisfies all of its applicable metrics while the three frameworks are incapable to satisfy their applicable metrics. Out of 15, 4 MVMs are excluded for benchmarking future performance due to their non-stationary time series data. Based on the forecast I_MVM data, GM is the best performer among the 15 samples in the FY2018.

This research highlights the environmental perspective during vehicles’ production. The development of this approach is based on publicly available data and transparent about the methods it used. The data out of the approach can benefit stakeholders with insights by benchmarking the historical performance of MVMs as well as their future performance.