Search results

The boom in live streaming has intensified competition among streamers for viewers' gifts, which makes it meaningful to study the factors that affect the viewers’ gifting behavior. Given the emotional attachment between streamers and viewers, the authors set out to elucidate a new driver on viewer gifting: expressions of the streamer. This research aims to explore the impact of streamer emotions on the viewer gifting behaviors, including free and paid gifting. The loyalty level of the viewers is also introduced as a moderating factor to investigate the heterogeneous effect of streamer emotions on gifting behavior.

The dataset the authors collected consists of two parts, including 1809.69 h of live streaming videos and 358,002 gift giving records. Combined with deep learning methods and regression analysis, the authors performed empirical tests on the 81,110 valid samples. Several robustness checks were also conducted to ensure the reliability of main results.

The empirical results show that streamer emotions do have effects on viewers' free and paid gifting behavior. The authors’ findings show that positive streamer expressions, such as happiness and surprise, have a positive influence on viewer gifting behavior. However, some negative expressions, like sadness, can also have a positive impact. Moreover, the authors discovered that higher viewer loyalty amplifies the positive effect of streamer emotions and reduces the negative effect.

This research contributes to the study about streamer emotions and viewers' consumption behavior, which extends the application of emotion as social information model (EASI model) in the live streaming setting. The authors carefully divide the gifting behavior into two types: free and paid, and study how these two types are affected by streamer emotions. Besides, these effects are analyzed within viewers of different loyalty levels. This study offers practical emotion management strategies for streamers and live streaming platforms to gain more economic profits.

The main aim of the present research is to investigate the role of sense of calling (SOC) in the relationships between human resource practices (HRP), turnover intention (TI) and employee satisfaction (ES) in the hotel industry. Moreover, it also examines whether the relationships between these variables are different for city and resort hotel employees.

Quantitative research was employed to test the proposed hypotheses, and the survey technique was used to collect data. The participants of the research consist of 432 hotel employees who work in Antalya, Turkey. The hypotheses were tested using the partial least squares structural equation modeling (PLS–SEM) analyses.

The results of the analysis have shown that SOC significantly impacts the ES and TI, and HRP could be a new antecedent of SOC. On the other hand, while ES is positively affected by HRP, the SOC has a moderating role in the relationship between these variables.

Hotel authorities' understanding of the SOC’s role in employee attitudes and behavior would be a significant factor not only in retaining them but also in reducing employee turnover. They should consider increasing employees' SOC by focusing on HRP.

Besides being one of the few studies that have discussed SOC in the hospitality literature, the current study also contributes by examining the role of SOC in the relationships between HRP, ES and TI. It also adds value to the calling literature by revealing whether the relationship between these variables differs for city and resort hotel employees.

Despite the opportunities of digital twins (DTs) for smart buildings, limited research has been conducted regarding the facility management stage, and this is explained by the high complexity of accurately representing and modelling the physics behind the DTs process. This study thus organises and consolidates the fragmented literature on DTs implementation for smart buildings at the facility management stage by exploring the enablers, applications and challenges and examining the interrelationships amongst them.

A systematic literature review approach is adopted to analyse and synthesise the existing literature relating to the subject topic.

The study revealed six main categories of enablers of DTs for smart building at the facility management stage, namely perception technologies, network technologies, storage technologies, application technologies, knowledge-building and design processes. Three substantial categories of DTs application for smart buildings were revealed at the facility management stage: efficient operation and service monitoring, efficient building energy management and effective smart building maintenance. Subsequently, the top four major challenges were identified as being “lack of a systematic and comprehensive reference model”, “real-time data integration”, “the complexity and uncertainty nature of real-time data” and “real-time data visualisation”. An integrative framework is finally proposed by examining the interactive relationship amongst the enablers, the applications and the challenges.

The findings could guide facility managers/engineers to fairly understand the enablers, applications and challenges when DTs are being implemented to improve smart building performance and achieve user satisfaction at the facility management stage.

This study contributes to the knowledge body on DTs by extending the scope of the existing studies to identify the enablers and applications of DTs for smart buildings at the facility management stage and the specific challenges.

The purpose of this study is to analyze the influence of the firms' external environment on their export intensity. More specifically, it assesses whether domestic market characteristics such as domestic demand and general export environment related to tradability across borders affect firms' export intensity.

The authors use a sample of 29,266 firms from nine European countries, for the period of 2010–2016, and test several estimation methods (random effects models, Tobit models, and Heckman's selection models).

Results show that external factors such as domestic demand and ease of trade across borders are important determinants of firms' export intensity. Moreover, results reveal that firm's internal characteristics such as age, size and productivity also play an import role.

Studies about the influence of the firms' external environment on firms' export intensity are scarce because most of them are confined to a single country context. In this way, the present study contributes to the body of knowledge on the influence that external factors can have on firms' export performance by analyzing firms from nine European countries, which has important policy implications.

Modular capacity units enable rapid reconfiguration, providing tactical flexibility to efficiently meet customer demand during disruptions and ensuring sustainability. Moreover, the Physical Internet (PI) enhances the potential of modular capacity in addressing efficiency, sustainability, and resilience challenges. To evaluate the sustainability and resilience advantages of the PI-enabled reconfigurable modular system (PI-M system), this paper studies a PI-enabled sustainable and resilient production-routing problem with modular capacity.

We develop a multi-objective optimization model to assess the sustainability and resilience benefits of combining PI and modular capacity in a chemical industry case study. A hybrid solution approach, combining the augmented e-constraint method, construction heuristic, and hybrid adaptive large neighborhood search, is developed.

The experimental results reveal that the proposed solution approach is capable of obtaining better solutions than the Gurobi and the existing heuristic in a shorter running time. Moreover, compared with the traditional system, the PI only and traditional with modular capacity systems, PI-M system has significant advantages in both sustainability and resilience.

To the best of our knowledge, this study is the first to integrate the PI and modular capacity and investigate sustainability and resilience in the production-routing problem.

To ensure the motion attitude and stable contact force of massage robot working on unknown human tissue environment, this study aims to propose a robotic system for autonomous massage path planning and stable interaction control.

First, back region extraction and acupoint recognition based on deep learning is proposed, which provides a basis for determining the working area and path points of the robot. Second, to realize the standard approach and movement trajectory of the expert massage, 3D reconstruction and path planning of the massage area are performed, and normal vectors are calculated to control the normal orientation of robot-end. Finally, to cope with the soft and hard changes of human tissue state and body movement, an adaptive force tracking control strategy is presented to compensate the uncertainty of environmental position and tissue hardness online.

Improved network model can accomplish the acupoint recognition task with a large accuracy and integrate the point cloud to generate massage trajectories adapted to the shape of the human body. Experimental results show that the adaptive force tracking control can obtain a relatively smooth force, and the error is basically within ± 0.2 N during the online experiment.

This paper incorporates deep learning, 3D reconstruction and impedance control, the robot can understand the shape features of the massage area and adapt its planning massage path to carry out a stable and safe force tracking control during dynamic robot–human contact.

Utilising componential theory of creativity the study aims to examines the roles of green transformational leadership, organizational culture and green mindfulness as antecedents of green creativity.

A three-wave data collection method was used to collect data from the 304 hotel employees belonging to hotels located in the tourist’s location of India. The study used PROCESS macro to test the hypothesized model.

The results found a significant serial mediating effect of green organizational culture and green mindfulness for strengthening the association between green transformational leadership and green creativity.

The study establishes that a transformational leadership can bring about a much-needed green turnaround and thus makes significant practical contribution. As customers are becoming environmentally conscious, the industry can translate the green practices and motivate their subordinates by exhibiting the environmentally conscious behaviour and exhibit the same in their actions at work.

The current research work expands the body of literature on green transformational leadership and green creativity nexus in tourism and hospitality industry by exploring the boundary condition that increases the strength of this relationship.

Despite its devastating nature, the COVID-19 pandemic has also catalyzed a substantial surge in the adoption and integration of technological tools within economies, exerting a profound influence on the dissemination of information among participants in stock markets. Consequently, this present study delves into the ramifications of post-pandemic dynamics on stock market behavior. It also examines the relationship between investors' sentiments, underlying behavioral drivers and their collective impact on global stock markets.

Drawing upon data spanning from 2012 to 2023 and encompassing major world indices classified by Morgan Stanley Capital International’s (MSCI) market and regional taxonomy, this study employs a threshold regression model. This model effectively distinguishes the thresholds within these influential factors. To evaluate the statistical significance of variances across these thresholds, a Wald coefficient analysis was applied.

The empirical results highlighted the substantive role that investors' sentiments and behavioral determinants play in shaping the predictability of returns on a global scale. However, their influence on developed economies and the continents of America appears comparatively lower compared with the Asia–Pacific markets. Similarly, the regions characterized by a more pronounced influence of behavioral factors seem to reduce their reliance on these factors in the post-pandemic landscape and vice versa. Interestingly, the post COVID-19 technological advancements also appear to exert a lesser impact on developed nations.

This study pioneers the investigation of these contextual dissimilarities, thereby charting new avenues for subsequent research studies. These insights shed valuable light on the contextualized nexus between technology, societal dynamics, behavioral biases and their collective impact on stock markets. Furthermore, the study's revelations offer a unique vantage point for addressing market inefficiencies by pinpointing the pivotal factors driving such behavioral patterns.

This study aims to construct a sentiment series generation method for danmu comments based on deep learning, and explore the features of sentiment series after clustering.

This study consisted of two main parts: danmu comment sentiment series generation and clustering. In the first part, the authors proposed a sentiment classification model based on BERT fine-tuning to quantify danmu comment sentiment polarity. To smooth the sentiment series, they used methods, such as comprehensive weights. In the second part, the shaped-based distance (SBD)-K-shape method was used to cluster the actual collected data.

The filtered sentiment series or curves of the microfilms on the Bilibili website could be divided into four major categories. There is an apparently stable time interval for the first three types of sentiment curves, while the fourth type of sentiment curve shows a clear trend of fluctuation in general. In addition, it was found that “disputed points” or “highlights” are likely to appear at the beginning and the climax of films, resulting in significant changes in the sentiment curves. The clustering results show a significant difference in user participation, with the second type prevailing over others.

Their sentiment classification model based on BERT fine-tuning outperformed the traditional sentiment lexicon method, which provides a reference for using deep learning as well as transfer learning for danmu comment sentiment analysis. The BERT fine-tuning–SBD-K-shape algorithm can weaken the effect of non-regular noise and temporal phase shift of danmu text.

This study aims to investigate the influence of resource slack on the decline of Brazilian companies, with a particular focus on the moderating role of environmental dynamism. The authors specifically examine three types of resource slack: available, potential and recoverable. These represent surplus resources that exceed what is necessary for the organization’s basic operations. The role of environmental dynamism, characterized by rapid changes in customer preferences, technologies and competitive dynamics, is considered as a moderating factor in this relationship.

The authors used data from Brazilian companies spanning from 1997 to 2008. The research sample was composed using the matching-pairs method, which included a group of publicly traded companies that experienced decline (43 companies) and a group that did not (40 companies) within the specified timeframe.

Findings of this study indicate that the presence of available slack, being more liquid resources, decreases the likelihood of organizational decline. Furthermore, the moderation effect of potential resource slack can mitigate decline in companies operating in dynamic industries.

This research provides valuable insights into the impact of slack resources on potential organizational turnarounds. Given the relative scarcity of resources in these companies compared to those in developed countries, whether they be financial, human or technological, the study highlights the unique influence of slack in a less explored institutional environment. This research underscores the importance of examining the decline of Brazilian companies from a broader perspective, emphasizing that decisions regarding resource use can have significant implications on a company’s trajectory, either amplifying or mitigating its decline.

¿Cuál es el impacto del slack de recursos en el declive de grandes empresas brasileñas? Para responder a esta pregunta, hemos probado hipótesis por separado para tres tipos de salck de recursos: disponible, potencial y recuperable. Estos excedentes consisten en recursos en exceso más allá de lo necesario para mantener la organización funcionando.

Desarrollamos un estudio empírico cuantitativo y longitudinal con datos de empresas brasileñas de 1997 a 2008. Adoptamos el método de pares emparejados, componiendo la muestra de investigación con un grupo de empresas cotizadas en bolsa que declinaron (43 empresas) y otro que no declinó (40 empresas) en el período de tiempo.

Encontramos que la disponibilidad de recursos más líquidos reduce la posibilidad de declive. El efecto de moderación en el slack de recursos potenciales para empresas en industrias dinámicas puede mitigar el declive.

Esta investigación contribuye a una mejor comprensión del efecto del excedente en posibles recuperaciones. Extender los estudios de recursos excedentes al contexto de empresas brasileñas mostró la influencia que el excedente ejerce en un ambiente institucional relativamente menos explorado. Ya sea financiero, humano o tecnológico, la escasez de recursos es más pronunciada que en empresas de países desarrollados. Esta investigación llama la atención sobre el hecho de que la declinación de empresas brasileñas se analiza desde una perspectiva más amplia. Las decisiones sobre cómo la empresa usa sus recursos pueden afectar positiva o negativamente la declinación de las empresas, reforzando la importancia de discutir esta relación.

A Qual é o impacto da folga de recursos no declínio de grandes empresas brasileiras? Para responder a essa pergunta, testamos hipóteses separadamente para três tipos de folga de recursos: disponível, potencial e recuperável. Essas folgas consistem em recursos além do necessário para manter a organização funcionando.

Desenvolvemos um estudo empírico quantitativo e longitudinal com dados de empresas brasileiras de 1997 a 2008. Adotamos o método de pares combinados, compondo a amostra de pesquisa com um grupo de empresas de capital aberto que declinaram (43 empresas) e outro que não declinou (40 empresas) no período.

Descobrimos que a disponibilidade de recursos mais líquidos reduz a possibilidade de declínio. O efeito moderador na folga de recursos potenciais para empresas em indústrias dinâmicas pode mitigar o declínio.

Esta pesquisa contribui para uma melhor compreensão do efeito da folga sobre possíveis recuperações. A extensão dos estudos de folgas de recursos para o contexto de empresas brasileiras mostrou a influência que a folga exerce em um ambiente institucional relativamente menos explorado. Seja financeiro, humano ou tecnológico, a escassez de recursos é mais pronunciada do que em empresas de países desenvolvidos. Esta pesquisa chama a atenção para o fato de que o declínio de empresas brasileiras é analisado sob uma perspectiva mais ampla. Decisões sobre como a empresa usa seus recursos podem afetar positiva ou negativamente o declínio das empresas, reforçando a importância de discutir essa relação.