Search results

This study aims to explore the impacts of service industry employees’ AI awareness on their voice behavior while also considering the dual mediating roles of voice efficacy and job insecurity, as well as the moderating role of trait competitiveness.

The sample comprises data from a two-wave longitudinal survey of 203 employees in the service sector. This study examined all the hypotheses using Mplus 8.0.

This study confirms that service sector employees’ AI awareness has significant negative effects on both promotive and prohibitive voice behaviors. Voice efficacy can mediate the negative impact of AI awareness on promotive voice. Both voice efficacy and job insecurity can mediate the negative impact of AI awareness on prohibitive voice. Furthermore, employees’ trait competitiveness can weaken the negative impact of employees’ AI awareness on their voice efficacy.

Managers should first investigate employees’ AI awareness and then adopt targeted managerial strategies to promote their voice behavior.

This study contributes to the literature related to the consequences of AI awareness by linking AI awareness to employee voice behavior. Furthermore, this study deepens our understanding of how AI awareness affects employee voice behavior by proposing voice efficacy (i.e. the efficacy pathway) and job insecurity (i.e. the safety pathway) as key mediating mechanisms. Moreover, this study advances our understanding of when AI awareness influences employee voice behavior by identifying the moderating role of trait competitiveness.

The increasing popularity of live-streaming commerce has provided a new opportunity for e-retailers to boost sales. This study integrated signaling theory and social exchange theory to investigate how streamer- and product-centered signals influence customers’ likelihood of making an impulsive purchase in the live-streaming commerce context.

An online survey was designed and distributed to the target respondents in China using purposive sampling. A total of 735 valid responses were analyzed with partial least square structural equation modeling (PLS-SEM).

Both streamer-centered signals, i.e. streamer credibility and streamer interaction quality, were discovered to significantly influence product-centered signal, i.e. product information quality. Additionally, streamer interaction quality was found to have a significant impact on streamer credibility. Furthermore, it was observed that customer engagement played a significant mediating role in the relationship between product information quality and impulsive buying tendency. Moreover, the paths between product information quality and customer engagement, as well as the connection between engagement and impulsive buying tendency, were found to be moderated by guanxi orientation.

Despite the prevalence of impulsive purchases in live-streaming commerce, few studies have empirically investigated the impact of streamer and product signals on influencing customers’ impulsive purchase decisions. Consequently, to the best of our knowledge, this study distinguishes itself by offering empirical insights into how streamers use reciprocating relationship mechanisms to communicate signals that facilitate impulsive purchase decisions.

The purpose of this study is to reveal the significant contribution of MXene on enhancing tribological properties and to obtain the influence mechanism of various factors on friction characteristics of rolling bearing under extreme conditions.

Under extreme working conditions, the friction characteristics of rolling bearings directly determine the safety and reliability of the transmission system. In this study, MXene is added to the origin lubricating grease (OLG) of rolling bearing to enhance their friction characteristics. Then, the effects of inner ring speed, radial load, grease filling volume and other factors on the friction coefficient of rolling bearing are analyzed using the Taguchi method.

The results indicate that the ranking of various factors affecting the friction coefficient is: radial load, inner ring speed, MXene additive content in grease and grease filling volume. Especially, the radial load and inner ring speed exhibit extremely significant effects, while the MXene additive content in grease (P < 0.05) has a significant influence on the friction coefficient of rolling bearing. The optimal condition for rolling bearing lubricated with MXene additives lubricating grease (MALG) achieves the lowest friction coefficient of 0.0049 under 1,000 rpm, 9 kN and 35% grease filling volume.

This study could offer reference solution for utilizing MXene nano-lubrication to fufill the demands of precision, heavy-load, or long-lifespan bearings. Furthermore, the lubrication approach has the potential to be expanded into aerospace, defense, and various industrial fields, thereby significantly promoting its practial engineering applications.

In many instances, the data exhibits periodic and trend characteristics. However, indices like the Digital Economy Development Index (DEDI), which pertains to science, technology, policy and economy, may occasionally display erratic behaviors due to external influences. Thus, to address the unique attributes of the digital economy, this study integrates the principle of information prioritization with nonlinear processing techniques to accurately forecast rapid and anomalous data.

The proposed method utilizes the new information priority GM(1,1) model alongside an optimized BP neural network model achieved through the gradient descent technique (GD-BP). Initially, the provincial Digital Economic Development Index (DEDI) is derived using the entropy weight approach. Subsequently, the original GM(1,1) time response equation undergoes alteration of the initial value, and the time parameter is fine-tuned using Particle Swarm Optimization (PSO). Next, the GD-BP model addresses the residual error. Ultimately, the prediction outcome of the grey combination forecasting model (GCFM) is derived by merging the findings from both the NIPGM(1,1) model and the GD-BP approach.

Using the DEDI of Jiangsu Province as a case study, researchers demonstrate the effectiveness of the grey combination forecasting model. This model achieves a mean absolute percentage error of 0.33%, outperforming other forecasting methods.

First of all, due to the limited data access, it is impossible to obtain a more comprehensive dataset related to the DEDI of Jiangsu Province. Secondly, according to the test results of the GCFM from 2011 to 2020 and the forecasting results from 2021 to 2023, it can be seen that the results of the GCFM are consistent with the actual development situation, but it cannot guarantee the correctness of the long-term forecasting, so the combination forecasting model is only suitable for short-term forecasting.

This article proposes a grey combination prediction model based on the principles of new information priority and nonlinear processing.

With the development of data mining technology, diverse and broader domain knowledge can be extracted automatically. However, the research on applying knowledge mapping and data visualization techniques to genealogical data is limited. This paper aims to fill this research gap by providing a systematic framework and process guidance for practitioners seeking to uncover hidden knowledge from genealogy.

Based on a literature review of genealogy's current knowledge reasoning research, the authors constructed an integrated framework for knowledge inference and visualization application using a knowledge graph. Additionally, the authors applied this framework in a case study using “Manchu Clan Genealogy” as the data source.

The case study shows that the proposed framework can effectively decompose and reconstruct genealogy. It demonstrates the reasoning, discovery, and web visualization application process of implicit information in genealogy. It enhances the effective utilization of Manchu genealogy resources by highlighting the intricate relationships among people, places, and time entities.

This study proposed a framework for genealogy knowledge reasoning and visual analysis utilizing a knowledge graph, including five dimensions: the target layer, the resource layer, the data layer, the inference layer, and the application layer. It helps to gather the scattered genealogy information and establish a data network with semantic correlations while establishing reasoning rules to enable inference discovery and visualization of hidden relationships.

On the premise of verifying whether the platformization organization of DEEs is born, this work aims to explore the evolutionary process of the organizational structure of digital entrepreneurial enterprises (DEEs) and to further reveal the drivers of organizational structure evolution from the perspective of data resources.

The authors use a longitudinal two-case approach to analyze rich archival and interview data from two DEEs in China.

The findings reveal that the organizational structure of DEEs evolves from hierarchy, network and flatlization to platformization, that the drivers of evolution include building data flow channels, removing barriers of data flow and forming data rules. Meanwhile, the coordination devices in this process have gradually evolved from hierarchy to standard operating procedures, shared culture, norms, etc. to achieve a balance between commercial and creative success.

This work develops a framework for the evolution of organizational structure of DEEs from organization design theory lens and provide some management insights into the development of DEEs.

This study uncovers interdependent mechanisms triggered by excessive use of mobile phones which lower the performance of individuals in business organizations. The quantum of cognitive and attentional fluctuations caused by Nomophobia-induced impulsive use of mobile phone that degrades job performance is the focus of this study for suggesting realistic regulatory measures. Similarly, the threshold of allowable smartphone use was determined as a foundation to strike balance between adverse psycho-behavioral implications of blanket ban policy and the cognitive overload of unregulated mobile phone usage.

Adopting the quasi-experimental design, a sample of 159 individuals working in six different organizations was tested under dissimilar conditions using a variety of experimental interventions. Participants were subjected to different intensity of planned interruptions inciting responses through sets of short message services (SMS), messages on Whatsapp, X (formerly twitter), Instagram and emails. The main data obtained from the experiment comprised 636 test performances and 5,724 reactive responses on smartphones along with 642 video recordings as supplementary evidence.

The analysis of data revealed five underlying inter-related mechanisms impacting performance of individuals, i.e. slow-down of cognitive processing, increased temptation for peripheral activities/side scrolling, widened lag between focus and refocus, depletion of short-term working memory and reduced attention span. The strength of relationships between the mechanisms and intensity of Nomophobia significantly varied with the experimental interventions. Based on the identified mechanisms, organizations were suggested certain regulatory measures to minimize negative effects of Nomophobia-induced impulsive mobile phone usage.

The study is based on a comparatively smaller sample size (total 159, 27 from each of the six organizations). Though sufficient, yet, the sample size could have been a little larger.

The blanket ban policy adopted by organizations for regulating use of mobile phone has been found to adversely affect performance more than the excessive use of mobile phone. Using mobile phone thrice an hour (1–2 min each) was found to have negligible effects on job performance. Allowing use of mobile phones at workplace (except in highly hazardous areas) can reduce stress, anxiety and depression caused by Nomophobia.

To meeting social need, workers may not be denied the facility of mobile phone at workplace, except highly hazardous areas to allow them to remain connected and accessible. This study suggests viable measures to regulate use of mobile phones without depriving them of this vital facility.

The study is unique as it is based on experimental data, contrary to most of the studies relying on self-report methods of data collection. The mechanisms which degrade performance of workers due to excessive use of mobile phones (induced by Nomophobia) have not been explored and how the impact is propagated to the performance of workers is not known. This study has identified the five mechanisms and based on the mechanisms has suggested measures for the organizations to regulate the use of mobile phones in the organizations. This study has found that use of mobile phone thrice in an hour (1–2 min each) affects performance of individuals negligibly. Organizations adopting a blanket ban policy on use of mobile phone increase stress of workers (Nomophobia) that is more harmful for job performance.

The purpose of this study is to explore the interfacial adhesion between superhydrophobic coatings FC-X (X = 1%, 2%, 3%, 4% and 5%) and the concrete substrate, along with the impact of FC-X on the water repellency characteristics of the concrete substrate.

One synthetic step was adopted to prepare novel F-SiO₂ NP hybrid fluororesin coating. The impact of varying mass fractions of F-SiO₂ NPs on the superhydrophobicity of FC-X was analyzed and subsequently confirmed through water contact angle (WCA) measurements. Superhydrophobic coatings were simply applied to the concrete substrate using a one-step spraying method. The interfacial adhesion between FC-X and the concrete substrate was analyzed using tape pasting tests and abrasion resistance measurements. The influence of FC-X on the water repellency of the concrete substrate was investigated through measurements of water absorption, impermeability and electric flux.

FC-4% exhibits excellent superhydrophobicity, with a WCA of 157.5° and a sliding angle of 2.3°. Compared to control sample, FC-X exhibits better properties, including chemical durability, wear resistance, adhesion strength, abrasion resistance, water resistance and impermeability.

This study offers a thorough investigation into the practical implications of enhancing the durability and water repellency of concrete substrates by using superhydrophobic coatings, particularly FC-4%, which demonstrates exceptional superhydrophobicity alongside remarkable chemical durability, wear resistance, adhesion strength, abrasion resistance, water resistance and impermeability.

Through the examination of the interfacial adhesion between FC-X and the concrete substrate, along with an assessment of FC-X’s impact on the water repellency of the concrete, this paper provides valuable insights into the practical application of superhydrophobic coatings in enhancing the durability and performance of concrete materials.

This study aims to explore how to overcome and address the loss of exploratory innovation, thereby achieving greater success in exploratory innovation. This phenomenon of loss occurs when enterprises decrease their investment in and engagement with exploratory innovation, ultimately leading to an insufficient amount of such innovation efforts. Drawing on dynamic capabilities, this study investigates the relationship between organizational foresight and exploratory innovation and examines the moderating role of breakthrough orientation/financial orientation.

This study used survey data collected from 296 Chinese high-tech companies in multiple industries and sectors.

The evidence produced by this study reveals that three elements of organizational foresight (i.e. environmental scanning capabilities, strategic selection capabilities and integrating capabilities) positively influence exploratory innovation. Furthermore, this positive effect is strengthened in the context of a high-breakthrough orientation. Moreover, the relationships among environmental scanning capabilities, strategic selection capabilities and exploratory innovation become weaker as an enterprise’s financial orientation increases, whereas a strong financial orientation does not affect the relationship between integrating capabilities and exploratory innovation.

Ambidexterity is key to successful enterprise innovation. Compared with exploitative innovation, it is by no means easy to engage in exploratory innovation, which is especially important in high-tech companies. While the loss of exploratory innovation has been observed, few empirical studies have explored ways to promote exploratory innovation more effectively. A key research implication of this study pertains to the role of organizational foresight in the improvement of exploratory innovation in the context of high-tech companies.

This paper contributes to the broader literature on exploratory innovation and organizational foresight and provides practical guidance for high-tech companies regarding ways of avoiding the loss of exploratory innovation and becoming more successful at exploratory innovation.

This paper aims to amplify the retrieval and utilization of historical newspapers through the application of semantic organization, all from the vantage point of a fine-grained knowledge element perspective. This endeavor seeks to unlock the latent value embedded within newspaper contents while simultaneously furnishing invaluable guidance within methodological paradigms for research in the humanities domain.

According to the semantic organization process and knowledge element concept, this study proposes a holistic framework, including four pivotal stages: knowledge element description, extraction, association and application. Initially, a semantic description model dedicated to knowledge elements is devised. Subsequently, harnessing the advanced deep learning techniques, the study delves into the realm of entity recognition and relationship extraction. These techniques are instrumental in identifying entities within the historical newspaper contents and capturing the interdependencies that exist among them. Finally, an online platform based on Flask is developed to enable the recognition of entities and relationships within historical newspapers.

This article utilized the Shengjing Times·Changchun Compilation as the datasets for describing, extracting, associating and applying newspapers contents. Regarding knowledge element extraction, the BERT + BS consistently outperforms Bi-LSTM, CRF++ and even BERT in terms of Recall and F1 scores, making it a favorable choice for entity recognition in this context. Particularly noteworthy is the Bi-LSTM-Pro model, which stands out with the highest scores across all metrics, notably achieving an exceptional F1 score in knowledge element relationship recognition.

Historical newspapers transcend their status as mere artifacts, evolving into invaluable reservoirs safeguarding the societal and historical memory. Through semantic organization from a fine-grained knowledge element perspective, it can facilitate semantic retrieval, semantic association, information visualization and knowledge discovery services for historical newspapers. In practice, it can empower researchers to unearth profound insights within the historical and cultural context, broadening the landscape of digital humanities research and practical applications.