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The purpose of this paper is to observe consumers’ attitudes toward and intentions to purchase green products on social media and to explore the relationships among social media marketing, perceived consumer effectiveness (PCE), product knowledge, subjective norms, perceived behavioral control, price consciousness and attitudes toward and intentions to purchase green products. In addition, this research attempts to further understand these relationships in different consumer groups.

A questionnaire survey method was used to collect data from consumers in China. The Amos 22.0 software package was used to conduct the data analysis.

The empirical results suggest that attitude, subjective norms and perceived behavioral control positively affect purchase intentions, while price consciousness negatively affects purchase intentions. Product knowledge positively affects consumers’ attitudes and purchase intentions, and PCE positively affects consumers’ attitudes. As expected, social media marketing positively affects subjective norms, product knowledge and PCE and negatively affects price consciousness. However, there is no significant relationship between PCE and purchase intentions. According to the results of multigroup structural equation modeling analysis, the effects differ significantly among different consumer groups.

This study enriches the research about the factors that influence consumers’ purchases of green products in emerging countries in the social media marketing context.

This research aims to conduct a comprehensive conceptual model and empirical validation of the integration of negative (ego-centric) and positive (altruistic and ego-centric) drivers of green buying based on social dilemma theory and psychological egoism theory.

Data were collected using a self-administered questionnaire survey and analyzed with hierarchical regression analysis.

The results indicate that moral obligations, green self-identity, environmental concern and social pressure are positively related to green purchase intention, while perceived cost of green purchasing and price sensitivity are negatively. Meanwhile, social pressure positively moderates the relationship between price sensitivity and consumers' green purchasing intention, but negatively moderates the relationship between perceived cost of green purchasing and consumers' green purchasing intention.

First, this study is a comprehensive model of the concept and empirical validation of the integration of negative (ego-centric) and positive (altruistic and ego-centric) drivers of green buying. Specially, this study emphasizes the neglected importance of positive ego-centric factors of green consumer behavior. Second, this study explicitly incorporated the moderating effect of social pressure in the context of China. Since green buying is a social dilemma, the changes in this social dilemma after being affected by social pressure under the Chinese collectivist culture are also worth noting. Third, little is known about what motivates green consumption in emerging economies, and this has been hampered by a lack of cultural knowledge, conceptual richness and behavioral research that critically analyses consumer behavior.

This paper aims at the problem of surrounding rock excavation damage zone of tunneling in the rich water region, this paper aims to propose a new seepage-stress-damage coupling model and studied the numerical algorithm. This reflects the characteristics of rock damage evolution, accompanied by plastic flow deformation and multi-field interaction.

First of all, rock elastoplastic damage constitutive model based on the Drucker–Prager criterion is established, the fully implicit return mapping algorithm is adopted to realize the numerical solution. Second, based on the relation between damage variation and permeability coefficient, the rock stress-seepage-damage model and multi-field coupling solving iterative method are presented. Finally, using the C++ language compiled the corresponding programs and simulated tunnel engineering in the rich water region.

Results show that difference evolution-based back analysis inversed damage parameters well, at the same time the established coupling model and calculating program have more advantages than general conventional methods. Multiple field coupling effects should be more considered for the design of tunnel support.

The proposed method provides an effective numerical simulation method for the construction of the tunnel and other geotechnical engineering involved underground water problems.

This paper aims to explore the intention of consumers to visit green hotels in China through the theory of planned behavior (TPB) extended by adding perceived consumer effectiveness and environmental concern.

The TPB model is used as the basic theoretical framework but is extended by adding two critical variables. Data is collected from 324 respondents by using a self-administered questionnaire survey and analyzed with the assistance of structural equation modeling.

The empirical results show that perceived consumer effectiveness and environmental concern have positive effects on the attitude and the intention of consumers to visit green hotels, and that perceived consumer effectiveness has the largest effect. However, in China, the impact of environmental concern on the intention of consumers to visit green hotels is relatively limited, and the subjective norm has a strong effect. In addition, this research verifies the usefulness of the extended TPB model in understanding the intention of consumers in green hotels industry in China. This is evidenced by a comparison of the initial TPB model with the extended TPB model where the explanatory power has improved from 65 to 68 per cent.

This research contributes to TPB theory by addressing certain gaps in the literature regarding the intention of consumers to visit green hotels in China. Furthermore, considering the limitations of the TPB model, two pro-social variables, namely, perceived consumer effectiveness and environmental concern, are incorporated into the TPB model to better understand the intention of consumers to visit green hotels. Also, this research addresses the gap that studies on the role of perceived consumer effectiveness are limited in the tourism and hospitality management literature. The results indicate that perceived consumer effectiveness has the largest influence on the intention of consumers to visit green hotels.

The purpose of this paper is to apply an extended technology acceptance model to examine the medical data analyst’s intention to use medical big data processing technique.

Questionnaire survey method was used to collect data from 293 medical data analysts and analyzed with the assistance of structural equation modeling.

The results indicate that the perceived usefulness, social influence and attitude are important to the intention to use medical big data processing technique, and the direct effect of perceived usefulness on intention to use is greater than social influence and attitude. The perceived usefulness is influenced by perceived ease of use. Attitude is influenced by perceived usefulness, and attitude acts as a mediator between perceived usefulness and usage intention. Unexpectedly, attitude is not influenced by perceived ease of use and social influence.

This research examines the medical data analyst’s intention to use medical big data processing technique and provides several implications for using medical big data processing technique.

The purpose of this study is to introduce the concept of big data and provide a comprehensive overview to readers to understand big data application framework in libraries.

The authors first used the text analysis and inductive analysis method to understand the concept of big data, summarize the challenges and opportunities of applying big data in libraries and further propose the big data application framework in libraries. Then they used questionnaire survey method to collect data from librarians to assess the feasibility of applying big data application framework in libraries.

The challenges of applying big data in libraries mainly include data accuracy, data reduction and compression, data confidentiality and security and big data processing system and technology. The opportunities of applying big data in libraries mainly include enrich the library database, enhance the skills of librarians, promote interlibrary loan service and provide personalized knowledge service. Big data application framework in libraries can be considered from five dimensions: human resource, literature resource, technology support, service innovation and infrastructure construction. Most libraries think that the big data application framework is feasible and tend to apply big data application framework. The main obstacles to prevent them from applying big data application framework is the human resource and information technology level.

This research offers several implications and practical solutions for libraries to apply big data application framework.

The purpose of this paper is to identify the drivers of manufacturing transformation and upgrading and examine their influencing paths and effects. The study also dynamically evaluates and compares the achievements of the manufacturing transformation and upgrading of various economies.

This paper establishes the mechanism model of manufacturing transformation and upgrading based on the partial least squares approach to structural equation modeling. With the help of a mechanism model, the study carries out empirical analysis using panel data from the G20 for 2005–2015.

Innovation, measured by innovation input and innovation output, has the largest positive effect on manufacturing transformation and upgrading. Economic development has a negative direct effect, as well as positive indirect effects. Its total effect is positive but small. Economic openness has no significantly direct effect, but has significantly positive indirect effects. Its total effect is significantly positive, but is the smallest among drivers. Developed economies have relatively higher but decreasing achievements, whereas emerging economies have lower but increasing achievements and maintain the catch-up trend toward developed economies.

Few macro empirical studies examine the drivers and achievements of manufacturing transformation and upgrading. A need has arisen for evidence-based research and policies to guide governments in their efforts to promote manufacturing transformation and upgrading. This may be the first macro-level study to empirically analyze the drivers of manufacturing transformation and upgrading, and their influencing paths as well as achievements.

This paper aims to investigate the relation between technological innovation modes and their impact on eco-efficiency of industrial enterprises in China.

This paper first constructs a model to evaluate and measure the eco-efficiency of industrial enterprises in China from 2006 to 2010. Second, this paper compares the role of technological innovation modes – specifically, domestic independent innovation, foreign technology import and domestic technology transfer – in improving eco-efficiency of industrial enterprises in the Eastern, Central and Western regions of China by logarithmic regression.

The study finds that domestic independent innovation has a positive significant influence in improving eco-efficiency of industrial enterprises in the Eastern region; domestic technology transfer has a positive significant role in the Central region; and foreign technology import and domestic technology transfer positively affect the Western region.

This paper is the first to identify the role of technological innovation modes in improving eco-efficiency. The findings can help enterprises in the three regions adopt the most effective technological innovation mode. In addition, the results provide valuable insights into policy development to improve China’s overall eco-efficiency and to balance economic and industrial development among the three regions.

Aerospace assembly demands high drilling position accuracy for fastener holes. Hole position error correction is a key issue to meet the required hole position accuracy. This paper aims to propose a combined hole position error correction method to achieve high positioning accuracy.

The bilinear interpolation surface function based on the shape of the aerospace structure is capable of dealing with position error of non-gravity deformation. A gravity deformation model is developed based on mechanics theory to efficiently correct deformation error caused by gravity. Moreover, three solution strategies of the average, least-squares and genetic optimization algorithms are used to solve the coefficients in the gravity deformation model to further improve position accuracy and efficiency.

Experimental validation shows that the combined position error correction method proposed in this paper significantly reduces the position errors of fastener holes from 1.106 to 0.123 mm. The total position error is reduced by 43.49% compared with the traditional mechanics theory method.

The position error correlation method could reach an accuracy of millimeter or submillimeter scale, which may not satisfy higher precision.

The proposed position error correction method has been integrated into the automatic drilling machine to ensure the drilling position accuracy.

The proposed position error method could promote the wide application of automatic drilling and riveting machining system in aerospace industry.

A combined position error correction method and the complete roadmap for error compensation are proposed. The position accuracy of fastener holes is reduced stably below 0.2 mm, which can fulfill the requirements of aero-structural assembly.

Aircraft structures are mainly connected by riveting joints, whose quality and mechanical performance are directly determined by vertical accuracy of riveting holes. This paper proposed a combined vertical accuracy compensation method for drilling and riveting of aircraft panels with great variable curvatures.

The vertical accuracy compensation method combines online and offline compensation categories in a robot riveting and drilling system. The former category based on laser ranging is aimed to correct the vertical error between actual and theoretical riveting positions, and the latter based on model curvature is used to correct the vertical error caused by the approximate plane fitting in variable-curvature panels.

The vertical accuracy compensation method is applied in an automatic robot drilling and riveting system. The result reveals that the vertical accuracy error of drilling and riveting is within 0.4°, which meets the requirements of the vertical accuracy in aircraft assembly.

The proposed method is suitable for improving the vertical accuracy of drilling and riveting on panels or skins of aerospace products with great variable curvatures without introducing extra measuring sensors.