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Based on affective events theory, this research attempted to investigate how negative gossip about organizational change drives employees to experience negative emotions and direct their aggression toward customers.

We conducted a scenario-based experiment (Study 1) and a multiwave field survey (Study 2) to test our hypotheses.

The results show that (1) negative emotions mediate the relationship between change-related negative gossip and displaced aggression toward customers; (2) perceived organizational constraints strengthen the relationship between change-related negative gossip and negative emotions; (3) future work self-salience weakens the relationship between change-related negative gossip and negative emotions; and (4) change-related negative gossip has a strengthened (weakened) indirect effect on displaced aggression via negative emotions when employees have high perceived organizational constraints (future work self-salience).

The study expands research on organizational change and displaced aggression and provides practical implications for managing organizational change.

This paper aims to address the safety concerns of path-planning algorithms in dynamic obstacle warehouse environments. It proposes a method that uses improved artificial potential fields (IAPF) as expert knowledge for an improved deep deterministic policy gradient (IDDPG) and designs a hierarchical strategy for robots through obstacle detection methods.

The IAPF algorithm is used as the expert experience of reinforcement learning (RL) to reduce the useless exploration in the early stage of RL training. A strategy-switching mechanism is introduced during training to adapt to various scenarios and overcome challenges related to sparse rewards. Sensor inputs, including light detection and ranging data, are integrated to detect obstacles around waypoints, guiding the robot toward the target point.

Simulation experiments demonstrate that the integrated use of IDDPG and the IAPF method significantly enhances the safety and training efficiency of path planning for mobile robots.

This method enhances safety by applying safety domain judgment rules to improve APF’s security and designing an obstacle detection method for better danger anticipation. It also boosts training efficiency through using IAPF as expert experience for DDPG and the classification storage and sampling design for the RL experience pool. Additionally, adjustments to the actor network’s update frequency expedite convergence.

The purpose of this study is to explore the potential and challenges of biogas utilization as an alternative and sustainable energy source in the Gurage zone of Southern Ethiopia, where traditional energy sources such as firewood and charcoal are widely used.

The study adopts a mixed-methods approach to collect and analyze data from different sources and perspectives. The research collects quantitative data from structured interviews with 200 rural households who use biogas or other energy sources, and qualitative data from key informant interviews and focus group discussions with biogas experts, local authorities and community leaders. Socioeconomic analysis is conducted to assess the importance of biogas in terms of income, expenditure, health and environmental benefits, and a multivariate probit model is used to identify the factors influencing biogas energy adoption among rural households.

The findings indicate that biogas users are more likely to substitute traditional energy sources with biogas for cooking, lighting and heating purposes. The model reveals that age, sex, education level, land size and livestock quantity influence biogas energy adoption, whereas income, distance to market and access to credit do not have a significant effect. The findings also show that biogas users have higher income, lower expenditure, better health and lower greenhouse gas emissions than nonusers.

The study concludes that the socioeconomic impact of biogas varies among households based on location and lifestyle. The study also highlights the need for further research on the technical, institutional and behavioral aspects of biogas utilization in different contexts.

To address the challenges faced by biogas users and their energy choices, such as lack of awareness, maintenance, quality control and affordability, the study suggests exploring biogas energy to meet the diverse needs of cattle owners in different regions. The study also recommends enhancing the capacity of local stakeholders, promoting public–private partnerships, and developing supportive policies and regulations for biogas development in Ethiopia.

The study implies that biogas utilization can contribute to social development by improving the living standards, health status and gender equality of rural households. The study also suggests that biogas utilization can foster social cohesion and empowerment by creating opportunities for collective action, knowledge sharing and income generation among biogas users and their communities.

The study provides a comprehensive and empirical analysis of the socioeconomic landscape of biogas utilization and the determinants of energy choice in the Gurage zone of Southern Ethiopia. The study also offers valuable insights and recommendations for policymakers, practitioners, researchers and other stakeholders involved in biogas development in Ethiopia and other developing countries.

The success of online health communities (OHCs) depends on maintaining long-term relationships with physicians and preventing churn. Even so, the reasons for physician churn are poorly understood. In this study, an empirical model was proposed from a social influence perspective to explore the effects of online social influence and offline social influence on physician churn, as well as the moderating effect of their online returns.

The empirical data of 4,145 physicians from a Chinese OHC, and probit regression models were employed to verify the proposed theoretical model.

The results suggest that physicians' churn intention is influenced by online and offline social influences, and the offline social influence is more powerful. Physicians' reputational and economic returns could weaken the effect of online social influence on churn intention. However, physicians' economic returns could strengthen the effect of offline social influence on churn intention.

This research study is the first attempt to explore physician churn and divides the social influence into online and offline social influences according to the source of social relationship. The findings contribute to the literature on e-Health, user churn and social influence and provide management implications for OHC managers.

This paper aims to select an appropriate contact force model and apply it to the interaction model between the balls and the cage in the rolling bearings to describe the elastic–plastic collision phenomena between the two.

Taking the ball–disk collision mode as an example, several main contact force models were compared and analyzed through simulation and experiment. In addition, based on the consideration of yield strength of materials and initial collision velocity, a variable recovery coefficient model was proposed, and its validity and accuracy were verified by the ball–disk collision experiments. Then, respectively, the Flores model and the Hertz model were applied to the interaction between the balls and the cage, and the dynamics simulation results were compared.

The results indicate that the Flores model has good regression of recovery coefficient, indicating good applicability for both elastic and elastic–plastic contacts and can be applied to the contact collision situations of various materials. Under certain working conditions, there are significant differences in the dynamics results of rolling bearings simulated using the Flores model and Hertz model, respectively.

This paper applies the Flores model with variable recovery coefficients to the dynamics simulation analysis of ball bearings to solve the elastic–plastic collision problem between the rolling elements and the cage that cannot be reasonably handled by the Hertz model.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2024-0138/

Political factors play a crucial role in China's initial public offering (IPO) market due to its distinctive institutional context (i.e. “economic decentralization” and “political centralization”). Given the significant level of IPO underpricing in China, we examine the impact of local political uncertainty (measured by prefecture-level city official turnover rate) on IPO underpricing.

Using 2,259 IPOs of A-share listed companies from 2001 to 2019, we employ a structural equation model (SEM) to examine the channel (voluntarily lower the issuance price vs aftermarket trading) through which political uncertainty affects IPO underpricing. We check the robustness of the results using bootstrap tests, adopting alternative proxies for political uncertainty and IPO underpricing and employing subsample analysis.

Local official turnover-induced political uncertainty increases IPO underpricing by IPO firms voluntarily reducing the issuance price rather than by affecting investor sentiment in aftermarket trading. These relations are stronger in firms with pre-IPO political connections. The effect of political uncertainty on IPO underpricing is also contingent upon the industry and the growth phase of an IPO firm, more pronounced in politically sensitive industries and firms listed on the growth enterprise market board.

Local government officials in China usually have a short tenure and Chinese firms witness significantly severe IPO underpricing. By introducing the SEM model in studying China IPO underpricing, this study identifies the channel through which local government official turnover to political uncertainty on IPO underpricing.

This study aims to extract knowledge of ancient Chinese scientific and technological documents bibliographic summaries (STDBS) and provide the knowledge graph (KG) comprehensively and systematically. By presenting the relationship among content, discipline, and author, this study focuses on providing services for knowledge discovery of ancient Chinese scientific and technological documents.

This study compiles ancient Chinese STDBS and designs a knowledge mining and graph visualization framework. The authors define the summaries' entities, attributes, and relationships for knowledge representation, use deep learning techniques such as BERT-BiLSTM-CRF models and rules for knowledge extraction, unify the representation of entities for knowledge fusion, and use Neo4j and other visualization techniques for KG construction and application. This study presents the generation, distribution, and evolution of ancient Chinese agricultural scientific and technological knowledge in visualization graphs.

The knowledge mining and graph visualization framework is feasible and effective. The BERT-BiLSTM-CRF model has domain adaptability and accuracy. The knowledge generation of ancient Chinese agricultural scientific and technological documents has distinctive time features. The knowledge distribution is uneven and concentrated, mainly concentrated on C1-Planting and cultivation, C2-Silkworm, and C3-Mulberry and water conservancy. The knowledge evolution is apparent, and differentiation and integration coexist.

This study is the first to visually present the knowledge connotation and association of ancient Chinese STDBS. It solves the problems of the lack of in-depth knowledge mining and connotation visualization of ancient Chinese STDBS.

This paper aims to provide a secure and efficient pairing protocol for two devices. Due to the large amount of data involving sensitive information transmitted in Internet of Things (IoT) devices, generating a secure shared key between smart devices for secure data sharing becomes essential. However, existing smart devices pairing schemes require longer pairing time and are difficult to resist attacks caused by context, as the secure channel is established based on restricted entropy from physical context.

This paper proposes a fuzzy smart IoT device pairing protocol via speak to microphone, FS2M. In FS2M, the device pairing is realized from the speaking audio of humans in the environment around the devices, which is easily implemented in the vast majority of Internet products. Specifically, to protect the privacy of secret keys and improve efficiency, this paper presents a single-round pairing protocol by adopting a recently published asymmetric fuzzy encapsulation mechanism (AFEM), which allows devices with similar environmental fingerprints to successfully negotiate the shared key. To instantiate AFEM, this paper presents a construction algorithm, the AFEM-ECC, based on elliptic curve cryptography.

This paper analyzes the security of the FS2M and its pairing efficiency with extensive experiments. The results show that the proposed protocol can achieve a secure device pairing between two IoT devices with high efficiency.

In FS2M, a novel cryptographic primitive (i.e., AFEM-ECC) are designed for IoT device pairing by using a new context-environment (i.e., human voice) . The experimental results show that FS2M has a good performance in both communication cost (i.e., 130 KB) and running time (i.e., 10 S).

The study explores how mobile governance affects the administrative burden on older adults, focusing on learning, psychological and compliance costs.

Using attribution theory, the research employs a quantitative research design, utilizing surveys to gather data from 516 older adults across three cities in China: Quzhou, Wuhan and Shanghai. The study examines how intrinsic factors and extrinsic factors of m-government interfaces impact older adults’ administrative burden.

Perceived complexity increases learning, psychological and compliance costs for older adults. Personalization and high-quality information decrease these costs, enhancing user satisfaction. Visual appeal decreases anxiety and psychological costs.

This research links attribution theory with m-government’s administrative burden on older adults, offering new insights into optimizing m-government to serve older adults better.

Advances in digital technologies and the growing number of touch points have had a significant impact on the shopping behaviour of omnichannel customers. Several research papers have explored different facets of omnichannel, but only a few have thoroughly explored the literature on showrooming and webrooming simultaneously. This paper aims to identify the key groups of antecedents influencing customer buying behaviour in omnichannel, under the influence of digital technologies, with a particular focus on showrooming and webrooming.

Our study conducted a systematic literature review to identify the factors influencing customers’ buying behaviour in omnichannel, which have been the subject of academic discussion over the last decade. We finalized 149 articles for the thematic analysis and identified three groups of antecedents: channel-related, product-related and consumer-related with their subgroups.

Under channel-related antecedents, cost and perceived benefits, search convenience, need for interaction and situational circumstances have been identified as major attributes. The expressiveness of the product, product demonstration and search and experienced products have been identified under product-related antecedents, followed by price consciousness, past experiences, perceived risks and shopping motivations as leading attributes under consumer-related antecedents. The study revealed the multifaceted influence of digital technologies on omnichannel buying behaviour. Digital technologies are shaping the antecedents related to channels, products and consumers. Digital technologies simultaneously mediate between antecedents and the selection of a specific path within an omnichannel environment. Showrooming and webrooming should no longer be seen as general concepts. The rise of digital technologies has led to the development of new consumer journey patterns and the blurring of distinctions between showrooming and webrooming. A conceptual framework has been proposed to understand consumers' omnichannel behaviour, having considered the identified antecedents and the role of digital technologies.

This study advances the academic understanding of consumer behaviour in omnichannel under the influence of digital technologies and provides important implications for omnichannel management. With the advancement of digital technologies such as augmented reality and virtual reality, retailers should implement channel integration strategies to bridge the gap between online and offline channels, providing a memorable shopping experience for omnichannel customers.

This study is unique because it identifies and analyses the antecedents of consumer behaviour in omnichannel settings under the influence of digital technologies. It also uncovers new potential combinations of showrooming and webrooming patterns. The proposed framework can help retailers in their future planning of omnichannel strategies.