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The purpose of this paper is to explore how a perceived ethical climate influences employees’ intention to whistle-blow through internal organizational channels and incorporates the mediating role of organizational identification and moral identity as well as the moderating role of individual risk aversion.

The five proposed hypotheses were tested using hierarchical regression analysis with two waves of data collected in 2016 from 667 employees in Chinese organizations.

The findings indicate that perceived ethical climate had a positive effect on employees’ internal whistle-blowing intention, which was mediated by organizational identification and moral identity. Furthermore, employees’ risk aversion weakened the effect of organizational identification, while the moderating role by moral identity on internal whistle-blowing intention was not validated.

This study explains the psychological mechanism of whistle-blowing intention from the perspective of social identity, which contributes to opening the “black box” of the transmitting processes from the perceived ethical climate to whistle-blowing intention. This study also extends the literature by defining a boundary condition of risk aversion that hinders organizational identification influence on employee whistle-blowing intention.

Large size of aircraft assembly tooling structure and complex measurement environment exist. The laid enhanced reference points (ERS) are subject to a combination of nonuniform temperature fields and measurement errors, resulting in increased measurement registration errors. In view of the nonuniform temperature field and measurement errors affecting the ERS point registration problem, the purpose of this paper is to propose a neural network-based ERS point registration compensation method for large-size measurement fields under a nonuniform temperature field.

The approach is to collect ERS point information and temperature data, normalize the collected data to complete the data structure design and complete the construction of the neural network prediction model by data training. The data learning is performed to complete the prediction model construction, and the prediction model is used to complete the compensation analysis of ERS points. Finally, the algorithm is verified through experiments and engineering practice.

Experimental results show that the proposed neural network-based ERS point prediction and compensation method for nonuniform temperature fields effectively predicts ERS point deformation under nonuniform temperature fields compared with the conventional method. After the compensation analysis, the registration error is effectively reduced to improve registration accuracy. Reducing the combined effect of environmental nonuniform temperature field and measurement error has apparent advantages.

The method reduces the registration error caused by combining a nonuniform temperature field and measurement error. It can be used for aircraft assembly site prediction and registration error compensation analysis, which is essential to improve measurement accuracy further.

This paper aims to offeri novel nano-additives polymer microgels to precisely improve the lubrication properties of titanium alloy under different temperatures.

The surfactant-free emulsion polymerization method was used to prepare Poly(N-isoprolylacrylamide) (PNIPAM) microgels. A ball-on-disk tribometer was applied to investigated the temperature-sensitive lubrication behavior of PNIPAM microgels.

The results show that the as-prepared microgels are ball-like nanoparticles with the size of 50–900 nm. In addition, potassium persulfate as initiator, complete mixing technic and high sodium dodecyl sulfate (SDS) concentration contribute to the formation of microgels with small size. The tribosystem shows a lower friction coefficient with the lubrication of PNIPAM microgels as aqueous solution additives when the temperature exceeding the critical temperature.

This work reports the temperature-sensitive lubrication of PNIPAM microgels. The critical temperature of PNIPAM microgels could be regulated by the addition of SDS in the solutions. This offers an effective and facile strategy for regulation of the critical temperature of smart microgels, which is helpful for the smart lubrication in the future.

The purpose of this paper is to compare the pricing decisions and earning potential of the software supplier and the smart device manufacturer in different software promotion strategies.

Based on game theory, the authors formulate two promotion models, that is, the supplier implements software promotion activities individually (SP model) or outsources the promotion activity to the manufacturer under profit-sharing contract (MP model) when taking different channel power structures into consideration. Besides, in order to test the robustness of the conclusions, the authors also extend the basic model to the following situations: (1) the customers have different price elasticity toward service fee and product price; (2) the revenue sharing contract is employed by the supply chain members; and (3) the manufacturer's product promotion practice is taken into consideration.

The optimal service fee (product price) of the supplier (manufacturer) under SP model is always lower (higher) than that under MP model. Surprisingly, if the supplier is the channel leader and the profit sharing ratio exceeds certain threshold, the manufacturer's profit decreases in profit sharing ratio, which remains robust in three extension models. Moreover, the supply chain's profit in supplier-led game is always lower than that in Nash game irrespective of the promotion strategy in profit sharing context. When revenue sharing contract is adopted, the result holds only when the revenue sharing ratio is relatively low.

The authors originally explore two promotion strategies of the software supplier when taking the channel power structures into considerations, which has not been explored in the literature to the best of the authors' knowledge.

Considering the substantial benefits derived from the use of Building Information Modeling (BIM) in construction projects, governments and its related sectors have introduced mandatory policies requiring the use of BIM. However, little is known about the impact of mandatory policies on BIM-based project performance. Therefore, the purpose of this paper is to provide a systematical understanding on the impact of policy interventions on the implementation practice of innovative technologies.

This paper utilizes the propensity score matching and difference in differences (PSM-DID) method to investigate the impact of policy interventions on BIM-based project performance. Using the panel data collected from 2015 to 2021 in the Hong Kong construction industry, this paper explores the impact of the first mandatory BIM policy on the BIM-based project performance of three key stakeholders.

The subjective BIM performance and BIM return on investment (ROI) have significantly improved after implementing the mandatory BIM policy. The promotion effect of mandatory BIM policy on BIM-based project performance gradually increases over time. Moreover, the promotion effect of mandatory BIM policy on BIM performance shows significant heterogeneity for different stakeholders and organizations of different sizes.

This study examined the impact of policy interventions on BIM-based project performance. The research findings can provide a holistic understanding of the potential implications of innovative mandatory policy in performance improvement and offer some constructive suggestions to policymakers and industry practitioners to promote the penetration of BIM in the construction industry.

The aim of this study was to examine how manufacturing digitalization can be leveraged to promote green innovation in the digital era by investigating the effects of manufacturing digitalization on green process innovation, and thus firm performance. The authors also explored how the role of manufacturing digitalization varies with horizontal information sharing, vertical bottom-up learning and technological modularization.

Five hypotheses were examined by performing regression analyses on survey data from 334 manufacturing firms in China.

Manufacturing digitalization positively affects green process innovation, and thus firm performance. Furthermore, this positive effect is strengthened by horizontal information sharing and technological modularization and weakened by vertical bottom-up learning.

This study extends the literature rooted in the natural-resource-based view by identifying the crucial role of green process innovation and investigating the value of manufacturing digitalization for developing green capabilities in the digital era. It also contributes to this line of research by revealing contingent factors to leverage manufacturing digitalization from the information processing perspective. Furthermore, this study extends information processing theory to the digital context and identifies the interaction of organizational design (vertical bottom-up learning and horizontal information sharing) and digital investment (manufacturing digitalization).

The purpose of the research was to test whether the novel synthesized additive TEAT can be a substitution for the traditional additive ZDDP partly or entirely in mineral oil.

The extreme pressure, antiwear (AW) and friction reducing property of TEAT was examined, the tribological property of the combination of TEAT and ZDDP was also examined. The worn surfaces of the steel balls were observed using a polarizing optical microscope and x‐ray absorption near edge structure spectroscopy.

The results show that: TEAT can enhance the P_B value of the base oil obviously; TEAT shows better AW performance than ZDDP in high load and a wide range of concentration, it shows better friction‐reducing performance than ZDDP in high load and all the concentration tested; TEAT alone can provide a better AW and friction reducing property than ZDDP and the combination of TEAT and ZDDP; and the tribochemical reaction occurs between novel compound and metallic surface, generating sulfur containing layer mainly exists in the form of FeS.

These findings indicate that TEAT presents better tribological properties than ZDDP in a wide range of practical conditions. TEAT may be a substitute for ZDDP in the future.

This paper briefly introduces the concept of model selection, reviews recent development in the area of econometric analysis of model selection and addresses some of the crucial issues that are being faced by researchers in their routine research problems. The paper emphasizes on the importance of model selection, particularly the information criteria and penalty functions based model selection procedures which are useful for economists and finance researchers.

Social technologies can offer a strong means for organizations to manage their information flows and thus make changes on the organizational knowledge sharing, which may then be linked to employees’ productivity and performance enhancements. The purpose of this paper is to predict the impact of using the online social network on employee motivation and employee motivation effects on organizational knowledge sharing.

From employees of tax affairs organization, data are collected. For evaluating the model’s elements, a questionnaire was designed. It was revised by experts with significant experiences. For statistical analysis, SMART-PLS 3.2 is used.

Findings have confirmed the validity of the proposed model. The results also have indicated that online social networks, social trust and social goals have a positive and important impact on employee motivation. Furthermore, obtained results have revealed that organizational knowledge sharing is significantly influenced by employee motivation and organizational commitment.

The study contributes to the literature on organizational knowledge sharing and employee motivation in online social networking sites. Organizations could benefit from this knowledge by accepting that social networking sites must be considered as a critical component of the knowledge sharing, and precise targeting efforts could be directed for those users. Also, it could be exciting to study further factors affecting the development of organizational knowledge sharing in larger organizations.

The paper rises the understanding of what online social networking encompasses and how it can be utilized for the organization. The ideas and discussion are similarly applicable to libraries and may give them new visions into the delivery of social networking applications as part of their facilities to users.

The size of the aircraft tooling structure is huge, and the ambient temperature is difficult to maintain a constant state. Aiming at the influence of current temperature, this paper aims to propose a compensation method for registration error of large-scale measurement fields based on multi-temperature sensors.

In this method, an enhanced reference points (ERS)–temperature regression model is constructed from ERS and temperature data. The ERS offsets compensation model is established by solving the offset through the regression model, and the ERS offset compensation analysis is carried out.

The experimental results show that the proposed registration error compensation algorithm has obvious advantages over traditional methods in reducing the influence of ambient temperature and improving the measurement accuracy by reducing the registration error.

This method reduces registration error caused by the influence of ambient temperature and is used for aircraft measurements in different temperature environments.