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The purpose of this paper is to study a new approach by combining a multilayer perceptron neural network (MLPNN) algorithm with a GM(1, N) model in order to estimate the development cost of a new type of aircraft.

First, data about developing costs and their influencing factors were collected for several types of Boeing and Airbus aircraft. Second, a GM(1, N) model was constructed to simulate development costs for a civil aircraft. Then, an MLPNN algorithm was added to optimize and revise the simulative and forecasting values. Finally, a combined approach, using both a GM(1, N) model and an MLPNN algorithm was adopted to forecast development costs for new civil aircraft.

The results show that the proposed approach could do the work of cost estimation for new types of aircraft. Rather than using a single model, the combined approach could improve simulative and forecasting accuracy.

Scientific cost estimation could improve management efficiency and promote the success of a new type of civil aircraft development. Considering that China’s civil aircraft research and development is at its very beginning stages, only very limited data could be collected. The development costs for civil aircraft are affected by a series of factors. The approach outlined by this paper could be applied to development cost estimations in China’s civil aircraft industry.

The paper has succeeded by constructing a cost estimation index system and proposing a novel combined cost estimation approach comprised of a GM(1, N) model and an MLPNN. It has undoubtedly contributed to improving the accuracy of cost estimations.

The purpose of this paper is to examine the influences of negative performance feedback on firms' cost behaviors including productive behaviors (i.e. R&D behaviors) and non-productive behaviors (i.e. selling behaviors and business entertainment behaviors), as well as to investigate the roles of ownership types and marketization.

A sample of Chinese manufacturing firms from 2007 to 2018 is analyzed employing multiple regression models.

The results show that negative performance feedback has a positive but not significant effect on R&D behaviors, while its effect on selling behaviors is significantly positive. Meanwhile, there is an inverted U-shaped relationship between negative performance feedback and business entertainment behaviors. Furthermore, when facing a performance dilemma, state-owned enterprises tend to adjust selling behaviors, while nonstate-owned enterprises pay more attention to business entertainment behaviors. In terms of marketization, the firms in high-marketization regions are more likely to adjust their R&D, selling and business entertainment behaviors, while the firms in low-marketization regions are difficult to adjust these cost behaviors.

This study explores the role of negative performance feedback in firms' cost behaviors and provides empirical evidence about the differentiated influences regarding ownership types and marketization.

Integrating insights from existing studies and introducing the behavioral theory of the firm and prospect theory, this study proposes a more inclusive framework that addresses the impacts of negative performance feedback on firms' cost behaviors. This paper deepens the understanding of firms' decision behaviors in the dilemma of performance shortfall.

Building on the value chain model, this study develops a theoretical framework to illustrate the associations between digitalization and firms’ value chain activities from three typical dimensions including R&D investment, manufacturing cost efficiency and marketing cost efficiency. The roles of unabsorbed and absorbed slack resources in their relationships are also examined.

Based upon a large sample of Chinese manufacturing firms from 2016 to 2020 and then employing text mining-based analysis and the multiple regression model, this study empirically tests the effects of digitalization on firms’ value chain activities and how these effects are moderated by slack resources.

The findings show that increase in digitalization leads to an increase in firms’ R&D investment and a decline in manufacturing cost, but results in an increase in marketing cost. In addition, the unabsorbed slack resources and absorbed slack resources play differentiated roles in the associations between digitalization and these value chain activities.

This study contributes to the debate on how digitalization facilitates or hinders the value increment by employing the value chain model and further analyzing the impacts of digitalization on three value chain dimensions: R&D investment, manufacturing cost efficiency and marketing cost efficiency. This study extends the possibility that the influences of digitalization on different value chain dimensions may vary depending on some key contingency factors such as unabsorbed and absorbed slack resources.

This paper aims to examine the effects of bonding temperature, bonding time, bonding pressure and the presence of a Pt catalyst on the bonding strength of Cu/SB/P-Cu/SB/Cu joints by transient liquid phase bonding (TLPB).

TLPB is promising to assemble die-attaching packaging for power devices. In this study, porous Cu (P-Cu) foil with a distinctive porous structure and Sn-58Bi solder (SB) serve as the bonding materials for TLPB under a formic acid atmosphere (FA). The high surface area of P-Cu enables efficient diffusion of the liquid phase of SB, stimulating the wetting, spreading and formation of intermetallic compounds (IMCs).

The higher bonding temperature decreased strength due to the coarsening of IMCs. The longer bonding time reduced the bonding strength owing to the coarsened Bi and thickened IMC. Applying optimal bonding pressure improved bonding strength, whereas excessive pressure caused damage. The presence of a Pt catalyst enhanced bonding efficiency and strength by facilitating reduction–oxidation reactions and oxide film removal.

Overall, this study demonstrates the feasibility of low-temperature TLPB for Cu/SB/P-Cu/SB/Cu joints and provides insights into optimizing bonding strength for the interconnecting materials in the applications of power devices.

The purpose of this research is to understand the effects of self‐monitoring, dining companions and industry segments on the usage of alternate currencies while dining out.

An experimental design using frequent consumers of restaurant services is being used with a scenario approach with a sample size of 471.

Results indicate that self‐monitoring has significant impact on consumers' choice for alternate currencies. In addition, the type of dining companion (boss vs friend vs alone) has significant affect on usage of alternate currencies. Industry segments were not found be a significant factor in making usage of alternate currencies. For high self‐monitoring individuals, the preferences for currency usages are more likely influenced by the image delivered by the currency than for low self‐monitors. Consumers who dine with a friend or alone are more likely to prefer to pay with frequent usage points‐only (as opposed to dollars‐only) than consumers who dine with the boss. This result indicates that the dining companion is an important determinant in preferring the alternative currency, frequent usage points. Since frequent usage points are a signal of price discount, consumers do not want to make an impression of “being cheap” on the higher‐status dining companion (e.g. boss) by using frequent usage points for their dining experiences. On the other hand, if consumers dine with a friend or alone, they are more likely to reap the financial rewards of paying with frequent usage points without regard to the impression it creates.

These findings have significant implications for the restaurants marketers and managers. Implementation of frequent diners program may be affected significantly by self‐monitoring characteristics and nature of dining companions.

This study extends the understanding of individual differences associated with currency preference by examining the effects of self‐monitoring and impression management on consumer preferences for currency usage. Identifying the characteristics of consumers using the different currency options is critical for the foodservice industry.

This study aimed to analyze existing problems in the dissemination and management of emergency information on social media platforms, improve social media users' experience regarding such information, increase the efficiency of emergency information dissemination and curb the spread of misinformation.

In this study, the emergency information quality on social media platforms was examined. Based on the evaluation principles of the quality of mature information, social media information characteristics and the rules of emergency information dissemination, combined with relevant academic research results, an index to evaluate the quality of emergency information on social media was constructed. In addition, the authors have introduced cloud theory as an information quality evaluation method and used social media users' emotional characteristics to assess information quality evaluation results. A comprehensive system for evaluating emergency information quality, including indexes, methods and detection strategies was established. Based on a comprehensive system, a case study was conducted on the forest fires in Sichuan Province and the African swine fever events as reported on the Zhihu platform. In accordance with the results of the case study, the authors expanded the research and introduced the emotional characteristics of social media users as an independent evaluation dimension to evaluate the quality of emergency information on social media.

The comprehensive system's effectiveness was verified through the case study. Further, it was found that users' emotional characteristics (reflected in their information behavior) are inconsistent with their evaluation of websites' information quality regarding major emergencies. Integrating users' emotional characteristics into the information evaluation system can enhance its effectiveness following major emergencies.

First, an evaluation index system of emergency information quality on social media about major emergencies was offered. Unlike the commonly available index system for information quality evaluation, this proposed evaluation index system not only accounted for the characteristics of social media, such as massive disordered information, multiple information sources and rapid dissemination, but also for the characteristics of emergency events, such as variability and the absence of precursors. This proposed evaluation index system enhances the pertinence of the information quality evaluation and compensates for the shortcoming that the current research only focuses on evaluating social media information quality in a broad context, but pays insufficient attention to major emergencies. Second, cloud theory was introduced as a method to evaluate the emergency information quality found on social media. Existing research has primarily included the use of traditional statistical methods, which cannot transform numerical values into qualitative concepts effectively. Various indeterminate factors inevitably affect the quality of emergency information on social media platforms, and the traditional methods cannot eliminate this uncertainty in the evaluation process. The method to assess emergency information quality based on cloud theory can effectively compensate for the gaps in the research and improve the accuracy of information quality assessment. Third, the inspection and the dynamic adjustment of assessment results are absent in the research on information quality assessment, and the research has relied principally on the information users' evaluation and has paid insufficient attention to their attitudes and behaviors toward information. Therefore, the authors incorporated users' emotional characteristics into the evaluation of emergency information quality on social media and used them to test the evaluation results so that the results of the information quality assessment not only include the users' explicit attitudes but also their implicit attitudes. This enhances the effectiveness of the information quality assessment system. Finally, through this case study, it was found that an inconsistency exists between user evaluation and user emotional characteristics after major emergencies. The reasons for this phenomenon were explained, and the necessity of integrating user emotional characteristics into information quality assessment was demonstrated. Based on this, the users' emotional characteristics were used as a separate evaluation dimension for assessing the quality of emergency information on social media. Compared with assessing the quality of general information, integrating the user's emotional characteristics into the evaluation index system can lead the evaluation results to include not only the users' cognitive evaluation but also their emotional experience, further enhancing their adaptability.

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.

Reorganising unstructured academic abstracts according to a certain logical structure can help scholars not only extract valid information quickly but also facilitate the faceted search of academic literature. This study aims to build a high-performance model for identifying of the functional structures of unstructured abstracts in the social sciences.

This study first investigated the structuring of abstracts in academic articles in the field of social sciences, using large-scale statistical analyses. Then, the functional structures of sentences in the abstract in a corpus of more than 3.5 million abstracts were identified from sentence classification and sequence tagging by using several models based on either machine learning or a deep learning approach, and the results were compared.

The results demonstrate that the functional structures of sentences in abstracts in social science manuscripts include the background, purpose, methods, results and conclusions. The experimental results show that the bidirectional encoder representation from transformers exhibited the best performance, the overall F1 score of which was 86.23%.

The data set of annotated social science abstract is generated and corresponding models are trained on the basis of the data set, both of which are available on Github (https://github.com/Academic-Abstract-Knowledge-Mining/SSCI_Abstract_Structures_Identification). Based on the optimised model, a Web application for the identification of the functional structures of abstracts and their faceted search in social sciences was constructed to enable rapid and convenient reading, organisation and fine-grained retrieval of academic abstracts.

Human capital is the most important determinant of the Hospitality industry’s success. Executive chefs should be skilled in both management and culinary arts, in addition to ensuring the success of the entire hospitality division. The study aims to understand the competencies of executive chefs in international tourist hotels in Taiwan.

Literature review and behavioral event interviews were conducted with ten executive chefs and executive sous chefs. The modified Delphi method verified the results using 15 experts.

A competency framework was created, with four quadrants – managerial, operational, behaviors and skills – to classify executive chefs’ competencies. Each competency was further divided into sub-competencies – culinary research, emotional control, negotiation skills, job guidance and proactive thinking ability – for 25 items. Quadrants I and II are hard competencies that can be improved through education and training, whereas the third and fourth are soft competencies that require more time for development in workers.

The two-step study developed a competency framework with a practical reference value. The study results could be utilized by human resources managers during their companies’ training, recruitment, selection and promotion.

Besides the Delphi method, a deep behavioral event interview that enabled determining critical competencies was also used to collect data. The results obtained could be used to structure schools’ curricula. Collaborations between the hospitality industry and schools could help develop better curricula and training plans to maximize the availability of educational resources.

This paper aims to investigate the influence of stearate types on the thickening ability, dropping point and fiber structure of greases.

Several greases were prepared from polyolefins and various stearates. The melting point of the stearates and the dropping point of the resultant greases were measured, and the intermolecular binding energies of the thickener and the radial distribution function of the metal–oxygen in the thickener were determined with the aid of molecular simulation. The microstructures of the greases were also analyzed via scanning electron microscopy.

A higher stearate binding energy was found to correlate to a higher dropping point of the resultant greases. The thickening ability of the stearate is related to the group and period of the constituent metal ion. Within a group, greater atomic numbers of the metal were correlated to lower thickening ability. In a period, as the atomic number of the metal increased, the thickening ability was enhanced. The radial distribution functions of metal and oxygen can explain the aggregation of the stearate thickeners in the grease.

This work compared the thickening capacity of several stearates. Guidelines for preparing stearates to tailor the resultant grease are presented.