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This paper aims to propose a hybrid method based on polynomial fitting bias self-compensation, grey forward-backward linear prediction (GFBLP) and moving average filter (MAF) for error compensation in micro-electromechanical system gyroscope signal especially under motion state.

The error compensation can be divided into two processes: bias correction and noise reduction. A polynomial drift angle fitting algorithm is used to correct bias before denoising processing. For noise reduction, operation can be taken in two stages: detection and processing. First, sample variances are used to judge motion state. According to the detection results, algorithmic system switches between grey GFBLP and MAF to ensure fast convergence rate and small steady-state error.

Experimental results show that the proposed method can correct bias effectively for practical gyroscope signal, and can eliminate noise effectively for both practical gyroscope signal and synthetic signal, which indicates the effectiveness of the proposed method.

Bias correction and noise reduction are considerations. Noise contained in practical or synthetic signal can be reduced rapidly and effectively, which benefits from the new idea of combination grey GFBLP, MAF and sample variances. And most importantly, it is applicable for signal denoising under arbitrary motion state condition, which is different from other methods where the convergence performance is seldom analyzed.

The purpose of this paper is to use the theoretical perspective of structural inertia as a unique lens to study foreign sequential entry mode choices of multinational firms.

It adopts quantitative analysis of a sample of 121 Chinese publicly listed firms with 564 foreign entry incidents in the 2001-2012 period to test the hypotheses.

The empirical results show that multinational firms have a tendency to adopt the same mode in the subsequent entry as the number of prior entry mode choice of a given type (joint venture (JV) in this study) increases. The results support the theoretical prediction that organizations repeat their past activities due to structural inertia. Moreover, such an inertia effect in foreign sequential entry mode choices becomes stronger for older multinational firms, larger multinational firms and state-owned multinational firms.

Consistent with existing research, this study focuses on the entry mode choice between JV and wholly owned subsidiaries. However, it is better to examine the relationship identified in the study for different types of entry mode choices to assess result generalizability.

It reminds managers of multinational firms that they should be cautious to the influence of structural inertia that can be a barrier to strategic flexibility when they make entry mode choices.

The main contribution of this study resides in introducing structural inertia perspective to help understand the determinants of foreign sequential entry mode choices of multinational firms.

The purpose of this paper is to improve the customer satisfaction by offering online personalized recommendation system.

By employing an innovative associative classification method, this paper is able to predict a customer’s pleasure during the online while-recommending process. Consumers can make an active decision to recommended products. Based on customer’s characteristics, a product will be recommended to the potential buyer if the model predicts that he/she will click to view the product. That is, he/she is satisfied with the recommended product. Finally, the feasibility of the proposed recommendation system is validated through a Taobao shop.

The results of the experimental study clearly show that the online personalized recommendation system maximizes the customer’s satisfaction during the online while-recommending process based on an innovative associative classification method on the basis of consumer initiative decision.

Conventionally, customers are considered as passive recipients of the recommendation system. However, customers are tired of the recommendation system, and they can do nothing sometimes. This paper designs a new recommendation system on the basis of consumer initiative decision. The proposed recommendation system maximizes the customer’s satisfaction during the online while-recommending process.

The purpose of this paper is to investigate the influence of employees’ trait rumination on the variability of their state rumination and the continuing influence on their negative affect at home.

A time-lagged experience sampling method was used for the data collection from full-time employees in the hotel industry. The hypotheses were tested with multilevel modeling using a random coefficient modeling approach.

Hotel employees who are high in trait rumination generally show high levels of state rumination and greater within-person variability in state rumination over time. Additionally, the negative effects of workplace state rumination can last until employees come home and the next day before going to work. Furthermore, employees who are high in trait rumination are more likely to be influenced by state rumination, as they experience more negative affect after arriving home.

Rumination has been shown to decrease hotel employee overall well-being. The findings of this study provide suggestions for remedial measures that can be taken by hotel organizations to help employees address ruminative thinking.

Drawing on response styles and work/family border theories, this study contributes to the rumination literature by considering both trait rumination and state rumination in a broader context. For a comprehensive understanding of the dynamic temporal characteristics of state rumination, this study considers the net intraindividual variability of state rumination as the outcome of trait rumination.

This study aims to examine the relative effectiveness of functional and financial remedies in influencing customers' negative coping responses in the event of a data breach. It also uncovers the different mediating roles played by customers' feelings of anger and fear in the process of data breach recovery. This study thus differs from the literature, which has primarily focused on the impact of financial compensation and apologies for service failures in face-to-face environments.

Two scenario-based experiments were conducted to empirically validate the model. The authors received 302 copies of the questionnaire, of which 269 were valid.

This study finds that functional remedies are more effective than financial remedies when sensitive information has been compromised, but there is no significant difference between the effectiveness of the two remedies when nonsensitive information has been compromised. In addition, functional remedies influence negative coping behaviors directly and indirectly; the indirect effect is achieved through the reduction of fear and anger. Contrary to the authors' expectation, financial remedies do not have a direct effect on negative coping behaviors; they can indirectly affect negative coping behaviors by reducing anger but do not affect negative coping behaviors by reducing fear.

This study provides key insights into how to manage customer reactions in the event of a data breach, suggesting the use of carefully designed recovery strategies. Companies must attend to customers' specific emotional responses to manage their negative coping behaviors.

This study extends the limited literature on data breach recovery actions by investigating the different effectiveness of functional and financial remedies in the event of a data breach. It also uncovers how functional and financial recovery strategies affect customers' negative coping behaviors by revealing the different mediating effects of fear and anger.

This paper studies the location-inventory problem (LIP) in pallet pooling systems to improve resource utilization and save logistics costs, which is a new extension of the classical LIP and also an application of the LIP in pallet pooling systems.

A mixed-integer linear programming is established, considering the location problem of pallet pooling centers (PPCs) with multi-level capacity, multi-period inventory management and bi-directional logistics. Owing to the computational complexity of the problem, a hybrid genetic algorithm (GA) is then proposed, where three local searching strategies are designed to improve the problem-solving efficiency. Lastly, numerical experiments are carried out to validate the feasibility of the established model and the efficiency of the proposed algorithm.

The results of numerical experiments show that (1) the proposed model can obtain the integrated optimal solution of the location problem and inventory management, which is better than the two-stage model and the model with single-level capacity; (2) the total cost and network structure are sensitive to the number of PPCs, the unit inventory cost, the proportion of repairable pallets and the fixed transportation cost and (3) the proposed hybrid GA shows good performance in terms of solution quality and computational time.

The established model extends the classical LIP by considering more practical factors, and the proposed algorithm provides support for solving large-scale problems. In addition, this study can also offer valuable decision support for managers in pallet pooling systems.

Grassland in Qinghai as the main type of ecosystem in this region is located in arid and semi-arid areas. The ecosystem is fragile and sensitive to climate change. Grassland ecosystem not only provides essential ecological and life functions for human society but also plays a vital role in mitigating and adapting to climate change. The empirical literature on grassland ecosystem services value (ESV) does not consider the impact of climate change and regional economic development level factors, which prevents policymakers from making appropriate decisions. This paper aims to analyze the influencing factors of grassland ESV assessment, and, based on the meta-prediction model, account the grassland ESV in Qinghai province.

To understand the value of grassland ecosystem services in China under climate change, this paper used 61 research literature on the evaluation of grassland ESV in China, including a total of 564 value observations to establish a value transfer database. Based on the meta-analysis method, this study has constructed a value transfer model, to evaluate the grassland ESV in Qinghai province, and an interpretation model, which can analyze if the independent variables affect the grassland ESV significantly.

The study finds that the evaluation methods, types of ecosystem service functions, climate change and grassland types can affect the grassland ESV significantly. Based on the meta-regression prediction model to evaluate the grassland ESV in Qinghai is US$1,542.67/ha/year. It indicates several targeted approaches to increase the grassland ESV, and climate change also has a specific impact on the value of grassland ecosystem services.

This study provides a scientific basis for grassland management related to the development of grasslands and ecological compensation, as well as promote the sustainable development of grassland ecosystems.

This paper contributes to the field of grassland ESV assessment in at least three aspects; first, it innovatively introduces the meta-analysis to carry out an integrated analysis of previous research results; second, it includes a broader set of influence variables in the analysis, including meteorological and economic factors; and third, it establishes a methodological basis for the field of grassland ESV accounting.

Based on the examination of the roles of message framing and evidence type, this study made an analysis of the promotion methods of intention and information need towards HPV vaccination.

The study conducted a 2 (gain-framed messages vs loss-framed messages) × 2 (statistical evidence vs narrative evidence) quasi-experimental design built upon theories of message framing and evidence type. This experiment recruited college students who were not vaccinated against HPV as participants. The analysis of variance (ANOVA), the analysis of covariance (ANCOVA), and the independent sample T-test were used to test the hypotheses.

The results (N = 300) indicate that (1) Loss-framed messages will lead to a more favorable intention towards HPV vaccination than gain-framed messages. (2) Statistical evidence will lead to a more explicit information need than narrative evidence. (3) Message framing and evidence type will interact and (a) for statistical evidence, loss-framed messages will lead to a more favorable intention towards HPV vaccination than gain-framed messages and (b) for narrative evidence, gain-framed messages will lead to a more favorable intention towards HPV vaccination than loss-framed messages. (4) Message framing and evidence type will interact and (a) for loss-framed messages, statistical evidence will stimulate more explicit information need of HPV vaccination than narrative evidence and (b) for gain-framed messages, narrative evidence will stimulate more explicit information need of HPV vaccination than statistical evidence.

This paper can help to further understand the important roles of message framing and evidence type in health behavior promotion. The study contributes to the literature on how health information can be well organized to serve the public health communication and further enhance the health information service.

Driven by the dual-carbon target and the widespread digital transformation, leveraging digital technology (DT) to facilitate sustainable, green and high-quality development in heavy-polluting industries has emerged as a pivotal and timely research focus. However, existing studies diverge in their perspectives on whether DT’s impact on green innovation is synergistic or leads to a crowding-out effect. In pursuit of optimizing the synergy between DT and green innovation, this paper aims to investigate the mechanisms that can be harnessed to render DT a more constructive force in advancing green innovation.

Drawing from the theoretical framework of resource orchestration, the authors offer a comprehensive elucidation of how DT intricately influences the green innovation efficiency of enterprises. Given the intricate interplay within the synergistic relationship between DT and green innovation, the authors use the fuzzy-set qualitative comparative analysis method to explore diverse configurations of antecedent conditions leading to optimal solutions. This approach transcends conventional linear thinking to provide a more nuanced understanding of the complex dynamics involved.

The findings reveal that antecedent configurations fostering high green innovation efficiency actually differ across various stages. First, there are three distinct configuration patterns that can enhance the green technology research and development (R&D) efficiency of enterprises, namely, digitally driven resource integration (RI), digitally driven resource synergy (RSy) and high resource orchestration capability. Then, the authors also identify three configuration patterns that can bolster the high green achievement transfer efficiency of enterprises, including a digitally optimized resource portfolio, digitally driven RSy and efficient RI. The findings not only contribute to advancing the resource orchestration theory in the digital ecosystem but also provide empirical evidence and practical insights to support the sustainable development of green innovation.

The findings can offer valuable insights for enterprise managers, providing decision-making guidance on effectively harnessing the innovation-driven value of internal and external resources through resource restructuring, bundling and leveraging, whether with or without the support of DT.

The research findings contribute to heavy-polluting enterprises addressing the paradoxical tensions between digital transformation and resource constraints under environmental regulatory pressures. It aims to facilitate the simultaneous achievement of environmental and commercial success by enhancing their green innovation capabilities, ultimately leading to sustainability across profit and the environment.

Compared with previous literature, this research introduces a distinctive theoretical perspective, the resource orchestration view, to shed light on the paradoxical relationship on resource-occupancy between DT application and green innovation. It unveils the “black box” of how digitalization impacts green innovation efficiency from a more dynamic resource-based perspective. While most studies regard green innovation activities as a whole, this study delves into the impact of digitalization on green innovation within the distinct realms of green technology R&D and green achievement transfer, taking into account a two-stage value chain perspective. Finally, in contrast to previous literature that predominantly analyzes influence mechanisms through linear impact, the authors use configuration analysis to intricately unravel the complex influences arising from various combinatorial relationships of digitalization and resource orchestration behaviors on green innovation efficiency.

The purpose of this paper is to improve the corrosion resistance of the 6061-Al alloy as the battery pack material for electric vehicles, and the nano-SiC/MAO composite coating was prepared.

The corrosion resistance of coatings was evaluated by the global electrochemical test, and the local electrochemical impedance spectroscopy (LEIS) was used to study the local corrosion mechanism. The laser confocal microscope, scanning electron microscope and X-ray diffractometer (XRD) were used to characterise coatings.

Results showed that the impedance of nano-SiC/MAO coating was 1–2 times higher than MAO coating, and the main corrosion product was Al(OH)₃. LEIS results showed that the impedance of the nano-SiC/MAO coating was two times higher than the MAO coating. The defective SiC/Micro-arc oxidation coating still had high corrosion resistance compared to the MAO coating.

The physical model of the local corrosion mechanism for SiC/MAO composite coating in “cavity-fracture collapse” mode was proposed.