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Advances in material agency driven by artificial intelligence (AI) have facilitated breakthroughs in material adaptivity enabling smart objects to autonomously provide individualized smart services, which makes smart objects act as social actors embedded in the real world. However, little is known about how material adaptivity fosters the infusion use of smart objects to maximize the value of smart services in customers' lives. This study examines the underlying mechanism of material adaptivity (task and social adaptivity) on AI infusion use, drawing on the theoretical lens of social embeddedness.

This study adopted partial least squares structural equation modeling (PLS-SEM), mediating tests, path comparison tests and polynomial modeling to analyze the proposed research model and hypotheses.

The results supported the proposed research model and hypotheses, except for the hypothesis of the comparative effects on infusion use. Besides, the results of mediating tests suggested the different roles of social embeddedness in the impacts of task and social adaptivity on infusion use. The post hoc analysis based on polynomial modeling provided a possible explanation for the unsupported hypothesis, suggesting the nonlinear differences in the underlying influencing mechanisms of instrumental and relational embeddedness on infusion use.

The formation mechanisms of AI infusion use based on material adaptivity and social embeddedness help to develop the business strategies that enable smart objects as social actors to exert a key role in users' daily lives, in turn realizing the social and economic value of AI.

This study advances the theoretical research on material adaptivity, updates the information system (IS) research on infusion use and identifies the bridging role of social embeddedness of smart objects as agentic social actors in the AI context.

This study aims to apply the affective events theory and psychological contract theory to investigate how job satisfaction and psychological safety mediate the effect of the behavioral integrity of supervisors on the organizational commitment of employees.

A questionnaire survey was conducted using purposive sampling. In total, 500 questionnaire copies were distributed, and 453 responses were collected, of which 441 were valid (valid response rate = 88.2%).

The behavioral integrity of supervisors has a direct negative effect on organizational commitment but significant positive effects on job satisfaction and psychological safety, and job satisfaction and psychological safety have significant positive effects on organizational commitment. Job satisfaction and psychological safety have significant mediating effects on the association between the behavioral integrity of supervisors and the organizational commitment of employees.

Leaders and top management should “practice what they preach,” integrate honesty into organizational culture through training and establish a code of conduct to ensure that employees uphold their commitments. Companies should establish appropriate disciplinary systems and norms related to work and other aspects of organizational culture; they should also establish fair, just and open assessment systems to minimize the gap between their employees’ actual and expected earnings.

This study is the first to simultaneously consider the mediating effects of job satisfaction and psychological safety on the association between behavioral integrity and organizational commitment.

This paper aims to explore the effect of teacher–student collaboration on academic innovation in universities in different stages of collaboration.

Based on collaboration life cycle, this paper divided teacher–student collaboration into initial, growth and mature stages to explore how teacher–student collaboration affects academic innovation.

Collecting data from National Science Foundation of China, the empirical analysis found that collaboration increases the publication of local (Chinese) papers at all stages. However, teacher–student collaboration did not significantly improve the publication of international (English) papers in the initial stage. In the growth stage, teacher–student collaboration has a U-shaped effect on publishing English papers, while its relationship is positive in the mature stage.

The results offer suggestions for teachers and students to choose suitable partners and also provide some implications for improving academic innovation.

This paper constructed a model in which the effect of teacher–student collaboration on academic innovation in universities was established.

Due to the limitation of experimental conditions and budget, fatigue data of mechanical components are often scarce in practical engineering, which leads to low reliability of fatigue data and reduces the accuracy of fatigue life prediction. Therefore, this study aims to expand the available fatigue data and verify its reliability, enabling the achievement of life prediction analysis at different stress levels.

First, the principle of fatigue life probability percentiles consistency and the perturbation optimization technique is used to realize the equivalent conversion of small samples fatigue life test data at different stress levels. Meanwhile, checking failure model by fitting the goodness of fit test and proposing a Monte Carlo method based on the data distribution characteristics and a numerical simulation strategy of directional sampling is used to extend equivalent data. Furthermore, the relationship between effective stress and characteristic life is analyzed using a combination of the Weibull distribution and the Stromeyer equation. An iterative sequence is established to obtain predicted life.

The TC4–DT titanium alloy is selected to assess the accuracy and reliability of the proposed method and the results show that predicted life obtained with the proposed method is within the double dispersion band, indicating high accuracy.

The purpose of this study is to provide a reference for the expansion of small sample fatigue test data, verification of data reliability and prediction of fatigue life data. In addition, the proposed method provides a theoretical basis for engineering applications.

This paper aims to present an iterative path-following method with joint limits to solve the problem of large computation cost, movement exceeding joint limits and poor path-following accuracy for the path planning of hyper-redundant snake-like manipulator.

When a desired path is given, new configuration of the snake-like manipulator is obtained through a geometrical approach, then the joints are repositioned through iterations until all the rotation angles satisfy the imposed joint limits. Finally, a new arrangement is obtained through the analytic solution of the inverse kinematics of hyper-redundant manipulator. Finally, simulations and experiments are carried out to analyze the performance of the proposed path-following method.

Simulation results show that the average computation time is 0.1 ms per step for a hyper-redundant manipulator with 12 degrees of freedom, and the deviation in tip position can be kept below 0.02 mm. Experiments show that all the rotation angles are within joint limits.

Currently , the manipulator is working in open-loop, the elasticity of the driving cable will cause positioning error. In future, close-loop control based on real-time attitude detection will be used in in combination with the path-following method to achieve high-precision trajectory tracking.

Through a series of iterative processes, the proposed method can make the manipulator approach the desired path as much as possible within the joint constraints with high precision and less computation time.

Despite carbon reduction commitments, many constraints hinder the delivery of low-carbon buildings (LCBs) in high-rise high-density cities. The collaborative commitment of relevant stakeholders is vital to effectively address and mitigate these constraints. Hence, this study aims to comprehensively explore the required stakeholder collaboration attributes to address and mitigate the “common” constraints of delivering LCBs by focussing on several high-rise high-density cities.

A list of 21 “significant and common” constraints was identified through a systematic literature review followed by a questionnaire survey covering five economies (Hong Kong, Singapore, Australia, Qatar and the UAE). Nineteen influential stakeholders/stakeholder categories were identified through the literature, and their ability to influence the 21 constraints was mapped and identified through a two-round Delphi survey of 15 experienced professionals. The Delphi survey findings were analysed through social network analysis (SNA) methods to assess the stakeholder engagement and collaboration attributes.

The SNA results revealed the ability of stakeholders to influence the constraints, required collaborative stakeholder networks to address the constraints, significance of stakeholders according to the SNA centrality measures, core and periphery stakeholders and individual co-affiliation networks of core stakeholders.

While achieving the planned primary target of exploring stakeholder collaboration and their significance through SNA, this study also presents a useful sequential methodological approach for future researchers to conduct similar studies in different contexts. The findings also provide a foundation for accelerating the delivery of LCBs by strengthening stakeholder collaboration.

Infrastructure financialization plays a critical role in infrastructure development and urban growth around the world. However, on the one hand, the existing research on the infrastructure financialization focuses on qualitative and lacks quantitative country-specific studies. On the other hand, the spatial heterogeneity and influencing factors of infrastructure financialization are ignored. This study takes China as a typical case to identify and analyze the spatial characteristics, development process and impact factors of infrastructure financialization.

To assess the development and characteristics of infrastructure financialization in China, this study constructs an evaluation index of infrastructure financialization based on the infrastructure financialization ratio (IFR). This study then analyzes the evolution process and spatial pattern of China's infrastructure financialization through the spatial analysis method. Furthermore, this study identifies and quantitatively analyzes the influencing factors of infrastructure financialization based on the spatial Dubin model. Finally, this study offers a policy suggestion as a governance response.

The results demonstrate that infrastructure financialization effectively promotes the development of infrastructure in China. Second, there are significant spatial differences in China’s infrastructure financialization. Third, many factors affect infrastructure financialization, with government participation having the greatest impact. In addition, over-financialization of infrastructure has the potential to lead to government debt risks, which is a critical challenge the Chinese Government must address. Finally, this study suggests that infrastructure financialization requires more detailed, tailored,and place-specific policy interventions by the government.

This study not only contributes to enriching the knowledge body of global financialization theory but also helps optimize infrastructure investment and financing policies in China and provides peer reference for other developing countries.

The purpose of this study is to propose a research model based on the stimulus-organism-response (S-O-R) model to explore whether media richness and social interaction as environmental feature antecedents to nurses’ learning engagement (LE) can affect their continuance intention of massive open online courses (MOOCs) and task performance.

Sample data for this study were collected from nurses at five university-/medical university-affiliated hospitals in Taiwan. A total of 500 questionnaires were distributed, and 303 (60.6%) usable questionnaires were analyzed using structural equation modeling in this study.

This study proved that nurses’ perceived media richness and social interaction in MOOCs positively influenced their behavioral LE and psychological LE elicited by MOOCs, which jointly caused their continuance intention of MOOCs and, in turn, enhance their task performance. The results support all proposed hypotheses and the research model, respectively, explains 84.3% and 63.7% of the variance in nurses’ continuance intention of MOOCs and task performance.

This study uses the S-O-R model as a theoretical base to frame nurses’ continuance intention of MOOCs and task performance as a series of the internal process, which is affected by environmental stimuli (i.e. media richness and social interaction) and organismic states. Noteworthily, while the S-O-R model has been extensively used in prior literature, little research uses this paradigm to expound nurses’ continuance intention of MOOCs in the work settings. Besides, there is a dearth of evidence on the antecedents of nurses’ task performance in the context of MOOCs. Hence, this study’s empirical evidence contributes significantly to the existing literature on bridging the gap of limited evaluation for the research on the impact of nurses’ MOOCs learning on their task performance in the work settings, which is very scarce in the S-O-R view.

Introduction: India has the 15th-largest domestic natural gas consumption (NGC), critical to sustainable economic growth. Promoting natural gas will have a crucial impact on production in all industries.

Purpose: This research gives an overview of NGC and gross domestic product (GDP) in India from 1990 to 2021 and investigates the association and nature of causality between NGC and GDP in India.

Methodology: For the years 1990 through 2021, we used annual statistics from the NGC and the GDP of India. Both research variables data have been taken from the World Bank Indicator.

Findings: There is no causality and correlation between natural gas and GDP in India.

Practical Implications: Based on the research, the Government of India can create different policies for substituting natural gas for other energy sources to have a healthier impact on a sustainable environment in the short and long term. In the future, researchers can work on environmental degradation and GDP.

This study aims to investigate the corrosion inhibitor effect of migrating corrosion inhibitor (MCI) on Q235 steel in high alkaline environment under cathodic polarization.

This study investigated the electrochemical characteristics of Q235 steel with and without MCI by polarization curve and electrochemical impedance spectroscopy. Besides, the surface composition of Q235 steel under different environments was analyzed by X-ray photoelectron spectroscopy. In addition, the migration characteristic of MCI and the adsorption behavior of MCI under cathodic polarization were studied using Raman spectroscopy.

Diethanolamine (DEA) and N, N-dimethylethanolamine (DMEA) can inhibit the increase of Fe(II) in the oxide film of Q235 steel under cathodic polarization. The adsorption stability of DMEA film was higher under cathodic polarization potential, showing a higher corrosion inhibition ability. The corrosion inhibition mechanism of DEA and DMEA under cathodic polarization potential was proposed.

The MCI has a broad application prospect in the repair of damaged reinforced concrete due to its unique migratory characteristics. The interaction between MCIs, rebar and concrete with different compositions has been studied, but the passivation behavior of the steel interface in the presence of both the migrating electric field and corrosion inhibitors has been neglected. And it was investigated in this paper.