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This research establishes a suitable casting model for magnesium alloy wheel. The casting of the wheel is an element that must be considered in the design of the wheel. Casting is an important basic process and technology in the field of machinery which is widely used in production, transportation, national defense, social life and other aspects. Computer numerical simulation of the casting process can shorten the product manufacturing cycle, reduce product costs, reduce casting defects and ensure product quality. The casting material in this study is AZ91 magnesium alloy used for wheel lightweight.

Lightweight research of automobile is a significant trend, and people are paying attention to the lightweight design of automobiles. Higher requirement was proposed on design and casting performance of the wheel which is an important part of lightweighting vehicle. In order to achieve better quality, the parametric studies of alloy wheel and casting are necessary. This research designs a new model of automobile wheel, to ensure energy efficiency, the wheels must be as lightweight as possible, using magnesium alloy material for lightweight.

Analysis of casting process is a very complex issue. This research based on finite element theory and actual production, designed reasonable casting model, instant filling and solidification data were obtained. Aiming at reducing casting defects, process improvement of casting riser structure was designed. On the basis of the foundation, it has important guiding significance for actual foundry production.

This research establish a suitable casting model for magnesium alloy wheel. Aiming at reducing casting defects, process improvement of casting riser structure were designed.

This study aims to help businesses cope with consumers' no-show behaviour from a multistage perspective. It specifically identifies no-show reasons at each stage of appointment services and proposes the corresponding coping strategies.

By focusing on an outpatient appointment service, we interviewed 921 no-show patients to extract no-show reasons, invited 18 hospital managers to propose coping strategies for these reasons using a Delphi method and evaluated the proposed strategies based on EDAS (Evaluation based on Distance from Average Solution).

The results reveal ten reasons for no-show behaviour (i.e. system service quality, overuse, did not know the appointment, self-judgment, forget, waiting time, lateness, uncontrollable problems, time conflict and service coordination), which have nine coping strategy themes (i.e. prepayment, system intelligence, target, subjective norm, system integration, ease of navigation, reminder, confirmation and cancellation). We classify the ten reasons and nine themes into scheduling, waiting and execution stages of an appointment service.

This study provides a package of coping strategies for no-show behaviour to deal with no-show reasons at each appointment service stage. It also extends the research in pre-service management through appointment services.

Smart card-based E-payment systems are receiving increasing attention as the number of implementations is witnessed on the rise globally. Understanding of user adoption behavior of E-payment systems that employ smart card technology becomes a research area that is of particular value and interest to both IS researchers and professionals. However, research interest focuses mostly on why a smart card-based E-payment system results in a failure or how the system could have grown into a success. This signals the fact that researchers have not had much opportunity to critically review a smart card-based E-payment system that has gained wide support and overcome the hurdle of critical mass adoption. The Octopus in Hong Kong has provided a rare opportunity for investigating smart card-based E-payment system because of its unprecedented success. This research seeks to thoroughly analyze the Octopus from technology adoption behavior perspectives.

Cultural impacts on adoption behavior are one of the key areas that this research posits to investigate. Since the present research is conducted in Hong Kong where a majority of population is Chinese ethnicity and yet is westernized in a number of aspects, assuming that users in Hong Kong are characterized by eastern or western culture is less useful. Explicit cultural characteristics at individual level are tapped into here instead of applying generalization of cultural beliefs to users to more accurately reflect cultural bias. In this vein, the technology acceptance model (TAM) is adapted, extended, and tested for its applicability cross-culturally in Hong Kong on the Octopus. Four cultural dimensions developed by Hofstede are included in this study, namely uncertainty avoidance, masculinity, individualism, and Confucian Dynamism (long-term orientation), to explore their influence on usage behavior through the mediation of perceived usefulness.

TAM is also integrated with the innovation diffusion theory (IDT) to borrow two constructs in relation to innovative characteristics, namely relative advantage and compatibility, in order to enhance the explanatory power of the proposed research model. Besides, the normative accountability of the research model is strengthened by embracing two social influences, namely subjective norm and image. As the last antecedent to perceived usefulness, prior experience serves to bring in the time variation factor to allow level of prior experience to exert both direct and moderating effects on perceived usefulness.

The resulting research model is analyzed by partial least squares (PLS)-based Structural Equation Modeling (SEM) approach. The research findings reveal that all cultural dimensions demonstrate direct effect on perceived usefulness though the influence of uncertainty avoidance is found marginally significant. Other constructs on innovative characteristics and social influences are validated to be significant as hypothesized. Prior experience does indeed significantly moderate the two influences that perceived usefulness receives from relative advantage and compatibility, respectively. The research model has demonstrated convincing explanatory power and so may be employed for further studies in other contexts. In particular, cultural effects play a key role in contributing to the uniqueness of the model, enabling it to be an effective tool to help critically understand increasingly internationalized IS system development and implementation efforts. This research also suggests several practical implications in view of the findings that could better inform managerial decisions for designing, implementing, or promoting smart card-based E-payment system.

The purpose of this paper is to solve the abrupt deterioration of lubricant performance in high-temperature conditions.

Three silver pyrazolyl methyl pyridine complexes with different morphologies were synthesized. A four-ball tribometer was used to assess the tribological characteristics as an additive for pentaerythritol oleate both independently and compound with 1-hexyl-3-methylimidazolium bis(trifluoromethane sulfonyl)imide.

The results showed that when silver complexes and ionic liquids (IL) act independently, sheet silver complex 1 and rod silver complex 2 exhibit good lubricating performance; the optimal antifriction concentration of the ILs is 0.25 Wt.%. The tribological results of the compounds additive of ILs and silver complexes indicate that the wear scar diameter of compound 1 decreased by 16.914%, the wear volume reduced by 7.44% and the lubrication effect surpassed that of the two substances individually; rod compound 2 exhibited an antagonistic effect, intensifying wear; compound 3’s lubrication effect fell between that of the two individual components.

The compound of sheet silver complexes and ILs effectively solves the agglomeration problem of micro/nano lubricant additives. When the interface fails, self-repair is completed, improving the stability and antiwear performance of the lubricating oil.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2024-0128

This study aims to examine if the participation of live-stream influencers (LSIs) affects tipping frequency on live streaming platforms, and further investigate the mediating and moderating mechanisms.

Quasi-experiment and difference-in-differences models are used for data analysis. Propensity score matching is used to address potential unobservable endogeneity.

Real-time live streaming data reveal that LSIs’ participation significantly improves tipping frequency in live streaming rooms. Also, more users are attracted to the live streaming rooms and more users become active in participation. Additionally, the positive impact of LSIs’ participation is enhanced in the live streaming rooms with a greater number of relationship links between users.

The findings clarify the new role of influencers and reveal the mechanisms on how LSIs benefit the platforms.

The findings offer novel insights into implementing influencer marketing to interactive social media platforms, by encouraging influencer participation, user relationship building and influencer network growth.

This study highlights the value of LSIs for interactive social media platforms in terms of organic growth, revenue generation and cost reduction.

The purpose of this paper is to empirically test a research model that incorporated antecedents of praise feedback behaviour (fear of confrontation and incentive for reducing nuisance costs), praise feedback behaviour (deliberatively praise feedback, casual praise feedback, and true compliment feedback) and consequences (trust and repurchase intention).

A structural equation model was employed to test the relationships of the research model using survey data collected from 398 Taobao consumers.

The results showed that fear of confrontation and incentive for reducing nuisance costs had a significant positive influence on deliberatively praise feedback and true compliment feedback, respectively, and both antecedents had a significant positive influence on casual praise feedback of consumers. It also showed that trust was influenced negatively by deliberatively praise feedback, and positively by casual praise feedback and true compliment feedback. Meanwhile, deliberatively praise feedback and true compliment feedback were found to have negative and positive influences on repurchase intention, respectively.

This research was a pilot study to identify a three-dimension conceptualization of praise feedback behaviour from the perspective of customer satisfaction, and to understand positive review bias from the perspective of input processes.

This study aims to reveal the room-temperature effect of a superconducting gravimeter prototype, which will guide its subsequent optimization to improve its gravimetric measurement accuracy.

Without leveling, the prototype output signal, tilt data and room temperature were measured under steady operating conditions. After analyzing the correlations of the three data sets, the residuals of the prototype’s output signal were compensated using the tilt data and the geodynamic effects (ocean tide loading, atmospheric loading and the gravitational effect of polar motion) were then corrected.

The remaining residuals after correction may be caused by small tilt variations that are due to the sensor chamber temperature and radiation shield temperature changes. These small tilt variations were submerged in the tilt signal noise. Although the peak-to-peak noise of the tiltmeter does not exceed 15 µrad, it can still produce gravimetric deviations above 60 µGal when the prototype is significantly tilted.

This study analyzes in detail the room-temperature effect of a superconducting gravimeter prototype, introduces the tilt effect of the relative gravimeters to compensate for the gravimetric deviations and emphasizes that the improvement of fine leveling and the accuracy of the tiltmeter are key requirements for the prototype to perform high-accuracy gravity measurement tasks.

Higher education plays an essential role in achieving the United Nations sustainable development goals (SDGs). However, there are only scattered studies on monitoring how universities promote SDGs through their curriculum. The purpose of this study is to investigate the connection of existing common core courses in a university to SDG education. In particular, this study wanted to know how common core courses can be classified by machine-learning approach according to SDGs.

In this report, the authors used machine learning techniques to tag the 166 common core courses in a university with SDGs and then analyzed the results based on visualizations. The training data set comes from the OSDG public community data set which the community had verified. Meanwhile, key descriptions of common core courses had been used for the classification. The study used the multinomial logistic regression algorithm for the classification. Descriptive analysis at course-level, theme-level and curriculum-level had been included to illustrate the proposed approach’s functions.

The results indicate that the machine-learning classification approach can significantly accelerate the SDG classification of courses. However, currently, it cannot replace human classification due to the complexity of the problem and the lack of relevant training data.

The study can achieve a more accurate model training through adopting advanced machine learning algorithms (e.g. deep learning, multioutput multiclass machine learning algorithms); developing a more effective test data set by extracting more relevant information from syllabus and learning materials; expanding the training data set of SDGs that currently have insufficient records (e.g. SDG 12); and replacing the existing training data set from OSDG by authentic education-related documents (such as course syllabus) with SDG classifications. The performance of the algorithm should also be compared to other computer-based and human-based SDG classification approaches for cross-checking the results, with a systematic evaluation framework. Furthermore, the study can be analyzed by circulating results to students and understanding how they would interpret and use the results for choosing courses for studying. Furthermore, the study mainly focused on the classification of topics that are taught in courses but cannot measure the effectiveness of adopted pedagogies, assessment strategies and competency development strategies in courses. The study can also conduct analysis based on assessment tasks and rubrics of courses to see whether the assessment tasks can help students understand and take action on SDGs.

The proposed approach explores the possibility of using machine learning for SDG classifications in scale.

Consumers have pervasively relied on mobile reviews in digital economy. However, little knowledge exists regarding how customers adopt several mobile reviews to make purchasing decisions. With the assistance of reader-response theory, this study investigates how the consistency of product reviews, in terms of their adherence to both other reviews and the prior experience of the customer, affect perceived quality, confirmation of the customer's expectations, the customer's level of trust in the seller and the consequent purchase intention.

Based on a scenario simulation and an online experiment to collect data, the authors employed AMOS to test the proposed hypotheses using survey data collected from 314 customers in Study 1 and 420 consumers in Study 2.

The results indicate that global consistency positively and significantly contributes to confirmation, perceived quality and trust in sellers while sequential inconsistency positively and significantly influences perceived quality. Meanwhile, purchase intention is positively and significantly promoted by confirmation, perceived quality and trust in sellers, and initial valence has some moderating effects on these relationships.

This study contributes to the understanding of how customers apply product reviews to make purchasing decisions from a new angle. It also elucidates the way in which the perceived consistency of product reviews affects how reviewers are perceived and the consequent effect of these perceptions on a customer's purchase intentions.

The COVID-19 pandemic has made contactless services such as those provided by robots increasingly pervasive. Some stores are gradually adopting service robots to sell products, which has not been explored in previous research. This study aims to explore how appearance personification of service robots affects customer decision-making in the product recommendation context.

Based on authentic in-store product recommendation service interactions, an experiment for three simulated scenarios was conducted and data was collected from 338 valid samples.

The results show appearance personification has a positive impact on customer purchase behavior while it has negative impacts on customer decision time and degree of hesitation.

This study not only enriches the literature on application scenarios of service robots but also supplements the literature on various customer decision-making variables in the field of service robots. It provides important practical guidance for designing robots to optimize their impact on customer decision-making.