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To understand the mechanisms underlying social media discontinuance behavior, this study explores factors affecting social media discontinuance behavior from the perspective of social cognitive theory (SCT).

Based on SCT, this study puts forward a theoretical model incorporating habit, excessive use and negative emotions to predict social media discontinuance behavior. The proposed research model was empirically tested with 465 responses collected from WeChat users in China via an online survey. WeChat is one of the most popular social media in China. However, WeChat also faces the challenges of reduced or terminated usage among its users. Partial least squares structural equation modeling (PLS-SEM) technique was used to analyze the data.

The research results in this study show that habit exerts a negative effect on social media discontinuance behavior, while exhaustion and regret have positive influences. In addition, habit positively affects excessive use, which further leads to negative emotions of social media exhaustion and regret. Moreover, gender moderates the relationship between habit and social media discontinuance behavior.

This study adds to the literature of information system (IS) use lifecycle by investigating user behavioral changes regarding a transition from habituated to excessive use and further to discontinuance behavior. This study also helps elucidate the complex role of habit by explaining social media discontinuance from the social cognitive view. Furthermore, this study advances the current understanding of gender difference in social media discontinuance in the Chinese context. The study also offers insights to practitioners on how to prevent individuals from discontinuing their use of social media.

Based on cognitive appraisal theory of stress, this study develops an integrated model to examine the double-edged sword effect and boundary conditions of the impact of organizational crisis on employee behavior.

This study collected 672 employees’ data through three stages of longitudinal follow-up. Hierarchical regression analysis and SPSS macro process were used to test the hypotheses.

The paper finds that organizational crisis induces unethical pro-organizational behavior through enhanced job insecurity and foster taking charge by stimulating career calling. Employee resilience negatively moderates the relationship between organizational crisis and job insecurity as well as the indirect effects of organizational crisis on unethical pro-organizational behavior through job insecurity. Conversely, it positively moderates the association between organizational crisis and career calling and the indirect effects on taking charge through career calling.

This study not only expands the research on the mechanisms of organizational crisis' effects on employees' behaviors but also provides practical guidance for corporate managers on how to respond to organizational crisis.

The following insights are available to organizations and managers: first, this study confirms that organizational crisis can be perceived as threatening stressors that create job insecurity, which in turn leads to pro-organizational unethical behavior. Therefore, managers in organizational crisis should focus on stress regulation and guidance, pay timely attention to changes in the mindset of employees to reduce job insecurity, and strictly prohibit unethical pro-organizational behavior. They should promptly calm and control the atmosphere of panic and anxiety in the organization, do a good job of coordinating the division of labor, reduce personnel conflicts and contradictions, create a good organizational climate and reduce employees' sense of stress and negative perceptions of organizational crisis, thus reducing job insecurity and being able to meet the challenges in a better state. Secondly, this study confirms that employees also perceive organizational crisis as challenges and develop career calling, which in turn inspires proactive change behaviors. This suggests that managers in organizational crisis should promote the positive perception of organizational crisis as challenge, stimulate the career calling of employees in organizational crisis and call on and encourage employees to actively adopt taking charge. Therefore, managers should promptly give employees work affirmation, rewards and punishments, enhance the sense of participation and intrinsic motivation of subordinates, improve self-efficacy and self-confidence levels, effectively reduce the negative perception of organizational crisis, awaken positive psychological energy within individuals, increase their sense of belonging to the organization and thus, increase employees' awareness of the positive challenges of organizational crisis, stimulate employees' career calling through positive and optimistic beneficial pressure drive them to lead the corresponding changes in the crisis. Finally, this study confirms that employees' own resilience can change the double-edged sword effect of organizational crisis. Employees with high resilience are more likely to see organizational crisis as challenge and are thus more likely to develop career calling and are more inclined to initiate change, while employees with low resilience are more likely to see organizational crisis as threat, are more negatively affected by them, develop greater job insecurity and are, thus, more inclined to commit unethical pro-organizational behaviors. This reflects the fact that organizations should constantly cultivate employees' resilience and enhance their cognitive toughness at the same time. For instance, the organization can regularly use promotional lectures and scenarios to help leaders and employees establish corporate ethics, strengthen moral beliefs and correctly understand the nature of unethical affinity behavior. Managers should encourage and advocate positive and correct behaviors such as overcoming difficulties, positive innovation and positive suggestions to promote the sustainable and healthy development of the organization.

The results of this study can increase the organization’s understanding of the negative effects of crisis, help the organization take measures to manage and guide the employees in organizational crisis, more effective and targeted functional changes within the organization, reduce stress damage and improve the efficiency and effectiveness of crisis management. It is also beneficial to improve competitiveness and foresight in the organization’s industry and enhance organizations and employees’ resilience.

This study explores the double-edged sword effect of organizational crisis on employees’ behavior from the perspective of the cognitive appraisal theory of stress, which theoretically opens up a new research perspective, enriches the research in the fields of organizational crisis and taking charge, pro-organizational unethical behavior relationship and practically provides theoretical guidance for enterprises and managers on how to effectively respond to organizational crisis from the employees, which is of great practical significance.

Estimation of (in)efficiency became a popular practice that witnessed applications in virtually any sector of the economy over the last few decades. Many different models were deployed for such endeavors, with Stochastic Frontier Analysis (SFA) models dominating the econometric literature. Among the most popular variants of SFA are Aigner, Lovell, and Schmidt (1977), which launched the literature, and Kumbhakar, Ghosh, and McGuckin (1991), which pioneered the branch taking account of the (in)efficiency term via the so-called environmental variables or determinants of inefficiency. Focusing on these two prominent approaches in SFA, the goal of this chapter is to try to understand the production inefficiency of public hospitals in Queensland. While doing so, a recognized yet often overlooked phenomenon emerges where possible dramatic differences (and consequently very different policy implications) can be derived from different models, even within one paradigm of SFA models. This emphasizes the importance of exploring many alternative models, and scrutinizing their assumptions, before drawing policy implications, especially when such implications may substantially affect people’s lives, as is the case in the hospital sector.

Abnormal vibrations often occur in the liquid oxygen kerosene transmission pipelines of rocket engines, which seriously threaten their safety. Improper handling can result in failed rocket launches and significant economic losses. Therefore, this paper aims to examine vibrations in transmission pipelines.

In this study, a three-dimensional high-pressure pipeline model composed of corrugated pipes, multi-section bent pipes, and other auxiliary structures was established. The fluid–solid coupling method was used to analyse vibration characteristics of the pipeline under various external excitations. The simulation results were visualised using MATLAB, and their validity was verified via a thermal test.

In this study, the vibration mechanism of a complex high-pressure pipeline was examined via a visualisation method. The results showed that the low-frequency vibration of the pipe was caused by fluid self-excited pressure pulsation, whereas the vibration of the engine system caused a high-frequency vibration of the pipeline. The excitation of external pressure pulses did not significantly affect the vibrations of the pipelines. The visualisation results indicated that the severe vibration position of the pipeline thermal test is mainly concentrated between the inlet and outlet and between the two bellows.

The results of this study aid in understanding the causes of abnormal vibrations in rocket engine pipelines.

The causes of different vibration frequencies in the complex pipelines of rocket engines and the propagation characteristics of external vibration excitation were obtained.

We study the potential effects of pension underfunding on corporate investment, financial constraints and improved employee bonding using 10 Pacific-Basin countries (including the United States, Australia, and eight Asian countries) at heterogeneous economic development stages and different regulatory environments. We document that corporate pensions are significantly underfunded in most countries of our sample in the period of 2001–2017, when interest rates were ultralow in most countries. In addition, firms from countries with stronger employee protection and more generous retirement benefits tend to show higher levels of underfunding in their defined benefit (DB) pension plans. To the extent of pension underfunding imposing constraints on corporate investment, we find that firms in these countries can face more constraints on investment when their pension is underfunded.

The paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of clustered ground objects and noisy backgrounds. Recent research typically leverages larger volume models to achieve advanced performance. However, the operating environments of remote sensing commonly cannot provide unconstrained computational and storage resources. It requires lightweight algorithms with exceptional generalization capabilities.

This study introduces an efficient knowledge distillation (KD) method to build a lightweight yet precise convolutional neural network (CNN) classifier. This method also aims to substantially decrease the training time expenses commonly linked with traditional KD techniques. This approach entails extensive alterations to both the model training framework and the distillation process, each tailored to the unique characteristics of RSIs. In particular, this study establishes a robust ensemble teacher by independently training two CNN models using a customized, efficient training algorithm. Following this, this study modifies a KD loss function to mitigate the suppression of non-target category predictions, which are essential for capturing the inter- and intra-similarity of RSIs.

This study validated the student model, termed KD-enhanced network (KDE-Net), obtained through the KD process on three benchmark RSI data sets. The KDE-Net surpasses 42 other state-of-the-art methods in the literature published from 2020 to 2023. Compared to the top-ranked method’s performance on the challenging NWPU45 data set, KDE-Net demonstrated a noticeable 0.4% increase in overall accuracy with a significant 88% reduction in parameters. Meanwhile, this study’s reformed KD framework significantly enhances the knowledge transfer speed by at least three times.

This study illustrates that the logit-based KD technique can effectively develop lightweight CNN classifiers for RSI classification without substantial sacrifices in computation and storage costs. Compared to neural architecture search or other methods aiming to provide lightweight solutions, this study’s KDE-Net, based on the inherent characteristics of RSIs, is currently more efficient in constructing accurate yet lightweight classifiers for RSI classification.

We attempt to construct a grey three-way conflict analysis model with constraints to deal with correlated conflict problems with uncertain information.

In order to address these correlated conflict problems with uncertain information, considering the interactive influence and mutual restraints among agents and portraying their attitudes toward the conflict issues, we utilize grey numbers and three-way decisions to propose a grey three-way conflict analysis model with constraints. Firstly, based on the collected information, we introduced grey theory, calculated the degree of conflict between agents and then analyzed the conflict alliance based on the three-way decision theory. Finally, we designed a feedback mechanism to identify key agents and key conflict issues. A case verifies the effectiveness and practicability of the proposed model.

The results show that the proposed model can portray their attitudes toward conflict issues and effectively extract conflict-related information.

By employing this approach, we can provide the answers to Deja’s fundamental questions regarding Pawlak’s conflict analysis: “what are the underlying causes of conflict?” and “how can a viable consensus strategy be identified?”

Amid rapid technological progress, the construction industry is embracing Construction 4.0, redefining work practices through emerging technologies. However, the implications of Construction 4.0 technologies to enhancing well-being are still poorly understood. Particularly, the challenge lies in selecting technologies that critically contribute to well-being enhancement. Therefore, this study aims to evaluate the implications of Construction 4.0 technologies to enhancing well-being.

A list of Construction 4.0 technologies was identified from a national strategic plan on Construction 4.0, using Malaysia as a case study. Fourteen construction industry experts were selected to evaluate the implications of Construction 4.0 technologies on well-being using fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The expert judgment was measured using linguistic variables that were transformed into fuzzy values. Then, the collected data was analyzed using the following analyses: fuzzy TOPSIS, Pareto, normalization, sensitivity, ranking performance and correlation.

Six Construction 4.0 technologies are critical to enhancing well-being: cloud & real-time collaboration, big data & predictive analytics, Internet of Things, building information modeling, autonomous construction and augmented reality & virtualization. In addition, artificial intelligence and advanced building materials are recommended to be implemented simultaneously as a very strong correlation exists between them.

The novelty of this study lies in a comprehensive understanding of the implications of Construction 4.0 technologies to enhancing well-being. The findings can assist researchers, industry practitioners and policymakers in making well-informed decisions to select Construction 4.0 technologies when targeting the enhancement of the overall well-being of the local construction industry.

Existing studies have paid less attention to the impact of knowledge accumulation on digital transformation and its boundary conditions. Hence, this study aims to investigate the effects of ambidextrous knowledge accumulation on manufacturing digital transformation under the moderation of dynamic capability.

This study divides knowledge accumulation into exploratory and exploitative knowledge accumulation and divides dynamic capability into alliance management capability and new product development capability. To clarify the relationship among ambidextrous knowledge accumulation, dynamic capability and manufacturing digital transformation, the authors collect data from 421 Chinese listed manufacturing enterprises from 2016 to 2020 and perform analysis by multiple hierarchical regression method, heterogeneity test and robustness analysis.

The empirical results show that both exploratory and exploitative knowledge accumulation can significantly promote manufacturing digital transformation. Keeping ambidextrous knowledge accumulation in parallel is more conducive than keeping single-dimensional knowledge accumulation. Besides, dynamic capability positively moderates the relationship between ambidextrous knowledge accumulation and manufacturing digital transformation. Moreover, the heterogeneity test shows that the impact of ambidextrous knowledge accumulation and dynamic capabilities on manufacturing digital transformation varies widely across different industry segments or different regions.

First, this paper shifts attention to the role of ambidextrous knowledge accumulation in manufacturing digital transformation and expands the connotation and extension of knowledge accumulation. Second, this study reveals that dynamic capability is a vital driver of digital transformation, which corroborates the previous findings of dynamic capability as an important driver and contributes to enriching the knowledge management literature. Third, this paper provides a comprehensive micro measurement of ambidextrous knowledge accumulation and digital transformation based on the development characteristics of the digital economy era, which provides a theoretical basis for subsequent research.

Effectively engaging patients is critical for the sustainable development of online health communities (OHCs). Although physicians’ general knowledge-sharing, which is free to the public, represents essential resources of OHCs that have been shown to promote patient engagement, little is known about whether such knowledge-sharing can backfire when superfluous knowledge-sharing is perceived as overwhelming and anxiety-provoking. Thus, this study aims to gain a comprehensive understanding of the role of general knowledge-sharing in OHCs by exploring the spillover effects of the depth and breadth of general knowledge-sharing on patient engagement.

The research model is established based on a knowledge-based view and the literature on knowledge-sharing in OHCs. Then the authors test the research model and associated hypotheses with objective data from a leading OHC.

Although counterintuitive, the findings revealed an inverted U-shape relationship between general knowledge-sharing (depth and breadth of knowledge-sharing) and patient engagement that is positively associated with physicians’ number of patients. Specifically, the positive effects of depth and breadth of general knowledge-sharing increase and then decrease as the quantity of general knowledge-sharing grows. In addition, physicians’ offline and online professional status negatively moderated these curvilinear relationships.

This study further enriches the literature on knowledge-sharing and the operations of OHCs from a novel perspective while also offering significant specific implications for OHCs practitioners.