Search results

This study aims to use event system theory and job demands–resources (JD-R) model to examine the double-edged sword effect of event strength of Beijing Winter Olympics (BWO) on volunteers’ in-role performance and proactive behavior as mediated by work overload and meaningfulness.

The sample included 193 volunteer–leader dyads working at one BWO venue. The authors conducted a multitime and multisource study using Mplus 8 to examine the hypotheses.

Volunteers’ perceived BWO event strength was positively related to work overload, which reduced in-role performance and proactive behavior. Volunteers’ perceived BWO event strength was also positively related to work meaningfulness, which promoted in-role performance and proactive behavior. Perceived organizational support served as a moderator by mitigating the positive relationship between perceived BWO event strength and work overload; however, it did not strengthen the positive relationship between perceived BWO event strength and work meaningfulness.

This study developed a comprehensive model of how BWO event strength affected volunteers’ performance from the perspective of event system theory and the JD-R model, which enriches theoretical application and research in the context of mega sport events.

本研究运用事件系统理论和工作需求-资源(JD-R)模型, 探讨了2022年北京冬奥会（BWO）事件强度通过工作负荷和工作意义感知进而对志愿者角色内绩效和主动性行为的双刃剑效应。

我们在冬奥会场所之一实施了多时点、多来源的收集数据的方式, 最终样本包括193份志愿者-领导配对数据, 并使用Mplus 8来检验我们的假设。

志愿者感知的BWO事件强度与工作负荷呈正相关, 进而会降低他们的角色内绩效和主动性行为。志愿者感知的BWO事件强度与工作意义感也呈正相关, 进而会促进他们的角色内绩效和主动性行为。感知的组织支持通过弱化BWO事件强度与工作负荷之间的正相关关系, 进而调节了志愿者的角色内绩效和主动性行为; 然而, 感知的组织支持并没有强化BWO事件强度与工作意义感之间的正相关关系。

本研究结合事件系统理论和JD-R模型, 发展了一个BWO事件强度对志愿者绩效表现影响的综合模型, 丰富了有关大型体育赛事志愿者的理论应用和研究。

Este estudio utiliza la teoría del sistema de eventos y el modelo de demandas de trabajo-recursos (JD-R) para examinar el efecto de doble filo de la fuerza del evento de los Juegos Olímpicos de Invierno de Pekín (BWO) sobre el rendimiento en el rol y el comportamiento proactivo de los voluntarios, mediado por la sobrecarga laboral y la significatividad del trabajo.

Nuestra muestra incluyó 193 díadas de voluntarios-líderes que trabajaban en una sede de BWO. Realizamos un estudio multitemporal y multifuente utilizando Mplus 8 para examinar nuestras hipótesis.

La intensidad percibida por los voluntarios en los eventos de BWO también se relacionó positivamente con la significación del trabajo, que promovió el rendimiento en el rol y el comportamiento proactivo. Además, el apoyo organizativo percibido sirvió como moderador al mitigar la relación positiva entre la intensidad percibida del evento BWO y la sobrecarga de trabajo; sin embargo, no reforzó la relación positiva entre la intensidad percibida del evento BWO y la significatividad del trabajo.

Este estudio desarrolló un modelo integral de cómo la fuerza del evento BWO afectaba al rendimiento de los voluntarios desde la perspectiva de la teoría del sistema de eventos y el modelo JD-R, lo que enriquece la aplicación teórica y la investigación en el contexto de los megaeventos deportivos.

Defining and validating a map of related technologies is critical for managers, investors and inventors. Because of the increase in the applications of and demand for semiconductor lasers, analyzing the technological position of developers has become increasingly critical. Therefore, the purpose of this study is to adopt the technological position analysis to identify mainstream technologies and developments relevant to semiconductor lasers.

Correspondence analysis and k-means cluster analysis, which are data mining techniques, are used to reveal strategic groups of major competitors in the semiconductor laser market according to their Patent Cooperation Treaty (PCT) patent applications.

The results of this study reveal that PCT patent applications are generally obtained for masers, optical elements, semiconductor devices and methods for measuring and that technology developers have varying technological positions.

Through position analysis, this study identifies the technological focuses of different manufacturers to obtain information that can guide the allocation of research and development resources.

This study aims to investigate the influence of supervisor knowledge sharing on the task performance and the overall evaluation of employees in the context of supervisor-subordinate relationships, a prevalent yet underexplored mode of knowledge transfer.

Drawing on the social exchange perspective, we propose a framework integrating supervisors’ self-sacrificial leadership and employees’ supervisor-directed organizational citizenship behavior (OCB-S) as critical drivers of efficient knowledge transfer. By bridging micro- and macro-level organizational behavior, this study addresses a key research gap and provides a holistic understanding of factors that enhance knowledge transfer within organizations. To empirically test the hypotheses proposed in our study, we employed the PROCESS macro Model 7 to validate the moderated mediation model and conducted bootstrapping analyses to confirm the statistical significance of the predicted relationships.

This study offers insights into the micro-processes underlying interpersonal knowledge transfers within supervisor-subordinate relationships. It highlights the significance of self-sacrificial leadership and OCB-S in facilitating effective knowledge sharing, ultimately influencing the task performance and the overall evaluation of employees.

This study contributes to the existing literature on knowledge management by exploring the understudied area of knowledge sharing within supervisor-subordinate relationships. It provides a framework that integrates leadership and OCB as key factors influencing knowledge transfer efficiency. The findings offer practical implications for organizations seeking to optimize knowledge management practices, leadership development and performance appraisal processes.

People have utilized artificial intelligence (AI) reading assistants for study. This tool assists readers in summarizing the content of a book. However, the crucial factor in summarizing book content lies in the quality of the content by generative AI, as this quality affects readers’ willingness to use AI tools as reading aids. This study expands the acceptance architecture for artificially intelligent device use (AIDUA), integrates the concept of generative AI quality and proposes a new model for users’ continuous use of generative AI reading assistants.

This study employed a quantitative approach. A total of 362 respondents were from Taiwan. This study used partial least squares structural equation modeling (PLS-SEM) to validate, aiming to identify factors influencing users’ continued adoption of AI reading assistants.

The results show that the quality of AI-generated content and readability significantly influence users’ performance expectations and effort expectancy. However, credibility and representationalness have different effects, impacting effort expectancy but not performance expectancy. These findings underscore the critical role of generative AI quality in shaping user expectations and their continued use of AI reading assistants.

This research is of great significance in examining the quality of generative AI. It establishes a theoretical framework applicable to future research, enabling industry players to understand better the pivotal role of generative AI quality in the operation of information services. And focus on using AI reading assistants, describing the specific use of AI for specific tasks.

This study aims to explore the impact of artificial intelligence-powered personalised recommendations (AI-PPRs) on user engagement, browsing behaviour and purchase intentions on TikTok (Douyin in China), focusing on how these recommendations affect user satisfaction and purchase intention, while also addressing potential privacy concerns. In addition, the research investigates the influence of AI-recommended product presentation, timing and placement, as well as social factors such as key opinion leaders’ (KOLs) influence on consumer decision-making.

Using the expectancy-value theory and the stimulus-organism-response model, this research used a qualitative methodology through interviews with Douyin users to explore their experiences and perceptions of AI-PPRs.

The findings indicate that Douyin’s proactive “push” mechanism of AI-PPRs enhances user engagement by effortlessly integrating product discovery into the entertainment experience. Content-driven AI-PPRs align with user preferences, decrease search time and increase satisfaction and purchase intentions through engaging short videos and live streaming. However, privacy concerns emerge when personalisation is perceived as excessively intrusive, leading to negative emotions and avoidance behaviours. Recommendation timing and cultural context significantly influence receptiveness, with inappropriate timing (e.g. during holidays) causing negative reactions. Technical challenges, such as network issues during live streaming, negatively impact user experience and engagement. Content quality is crucial, and poor or irrelevant content leads to negative perceptions and disengagement. While KOLs face scepticism due to perceived commercialisation, endorsements from trusted figures and authentic influencers are better received. Innovative payment methods, like “Douyin Monthly Payment”, enhance financial flexibility and promote customer loyalty. This study highlights the need to balance personalisation with privacy, emphasising the importance of content quality and authenticity in influencer marketing. For businesses using AI-PPRs, maintaining this balance is essential for preserving trust and sustaining consumer engagement and loyalty.

This study contributes valuable insights to the field by unravelling the intricate dynamics between AI-PPRs, user preferences and social influences. The findings provide practical implications for companies aiming to optimise personalised recommendation algorithms and enhance user engagement, thereby facilitating business growth in the dynamic short video e-commerce market.

This study aims to use psychological contract theory to explore the substantial impact of illegitimate tasks on the well-being of hospitality employees. It also examines the mediating role of psychological contract breach and the moderating role of employee emotional intelligence within the proposed moderated mediation model.

Data were collected from 338 hospitality employees in Guangdong, China, and the hypotheses were examined through hierarchical regression analysis.

The results revealed that illegitimate tasks could negatively impact employee well-being through psychological contract breach. Furthermore, employee emotional intelligence was confirmed as a critical moderator that buffers the negative impact of illegitimate tasks on psychological contract breach.

This research offers novel insights into the dynamics affecting hospitality employee well-being by framing illegitimate tasks within psychological contract theory. It identifies the critical conditions under which employees’ well-being is impacted, thus broadening the understanding of employee–organization interactions.

To answer the questions: what roles windows of opportunity act in the catchup process of latecomers, what strategies latecomer enterprises should adopt to size windows of opportunity to catch-up with incumbents even going beyond?

This paper studies the catch-up history of the Chinese mobile phone industry and proposes a sectoral innovation system under scenario of technology paradigm shifts. Then a history-friendly simulation model and counterfactual analysis are conducted to learn how different windows of opportunity and catch-up strategies influence the catch-up performance of latecomers.

Results show latecomers can catch up with technology ability by utilizing technology window and path-creating strategy. However, catching up with the market is not guaranteed. Demand window can help latecomers to catch up with market as it increases their survival rates, different sized windows benefit different strategies. However, it also enlarges incumbents' scale effect. Without technology window technology catch up is not guaranteed. Two windows have combination effects. Demand window affects the “degree” of change in survival rates, while the technology window affects the “speed” of change. Demand window provides security; technology window provides the possibility of a breakthrough for technology ability.

The findings of this paper provide theoretical guidance for latecomer enterprises to choose appropriate catch-up strategies to seize different opportunity windows.

This paper emphasizes the abrupt change of industrial innovation system caused by technology paradigm shifts, which makes up for the shortcomings of previous researches on industrial innovation system which either studied the influence of static factors or based on the influence of continuous changes.

This study introduces a novel approach to conducting importance-performance analysis (IPA) and importance-performance competitor analysis (IPCA) by utilizing online reviews as an alternative to traditional survey-based marketing research.

In order to conduct IPA and IPCA utilizing online reviews, the following three steps were executed: (1) Extract key attributes of the product/service with latent Dirichlet allocation (LDA) topic modeling. (2) Measure the importance of each attribute with keyword analysis. (3) Measure the performance of each attribute with KoBERT-based sentiment analysis.

By utilizing LDA, we were able to identify significant attributes identified by real users’ reviews. The approach of evaluating attribute importance using keyword metrics offers the advantage of straightforward computations, reducing computational expenses and facilitating intuitive metric assessments. The evaluation of BERT-based performance involved adapting pre-trained language models to the specific analysis domain, resulting in substantial time savings without compromising accuracy, ultimately bolstering the dependability of the metrics. Lastly, the case study’s findings indicate a growing emphasis on the aesthetic aspects of mobile banking apps in South Korea while highlighting the pressing need for enhancements in critical areas such as stability and security, which are particularly pertinent to the finance industry.

Previous studies have limitations in assessing significance solely based on sentiment scores and review ratings, resulting in an inability to independently measure satisfaction and importance metrics. This research addresses these limitations by introducing a keyword frequency-based importance metric, enhancing the accuracy and suitability of these measurements independently. In the context of performance measurement, this study utilizes pre-trained large language models (LLMs), which provide a cost-effective alternative to previous methods while preserving measurement accuracy. Additionally, this approach demonstrates the potential for industry-wide competitive analysis by enabling comparisons among multiple competitors. Furthermore, the study extends the application of review data-based IPA and IPCA, traditionally used in the tourism sector, to the evaluation of financial mobile applications. This innovation expands the scope of these methodologies, indicating their potential applicability across various industries.

Language styles of online reviews are becoming increasingly important in consumers’ purchase decisions. However, there are inconsistencies in research on the effects of literal and figurative language styles in online reviews on service consumption. Drawing upon construal level theory, this research explores the effects of literal and figurative online reviews on consumers’ word-of-mouth recommendations and their internal mechanisms in the context of service consumption. In addition, this research identifies service types (experience vs credence services) as boundary conditions under which online review language styles play a role.

Three studies are designed to verify the effect of language style in online reviews on consumer word-of-mouth recommendations. Study 1 (N = 195) tests the interaction between construal level and (literal vs figurative) language style on consumers’ word-of-mouth recommendations. Study 2 (N = 191) identifies the depth of information processing as an underlying mechanism. Study 3 (N = 466) examines the boundary condition due to service type. The main methods used are independent sample t-test, ANOVA and bootstrapping.

The results illustrate that (1) consumers at different construal levels prefer online reviews with different language styles, and this can influence their word-of-mouth recommendations: consumers with a low construal level prefer online reviews with a literal language style, while those with a high construal level prefer online reviews with a figurative language style; (2) the depth of information processing plays a mediating role in the above interaction effect and (3) service type serves as a boundary condition such that the preference for literal (vs figurative) language style among low- (vs high-) construal-level consumers holds only for experience services; for credence services, online reviews with a literal language style enhance word-of-mouth recommendations, regardless of consumers’ construal level. The findings shed light on the drivers of word-of-mouth recommendations and provide insights to promote more effective word-of-mouth recommendations.

Drawing upon the construal level theory, this research explores the factors that influence online review language styles on consumer word-of-mouth recommendations and their underlying mechanisms and discusses the moderating effects of different service types (i.e. experience services and trust services). It not only sheds light on the contradictions in the previous literature but also provides new insights for academics and business managers to deepen their understanding of facilitating word-of-mouth recommendations.

This examine the impact of environmental, social and governance (ESG) performance on financial reporting quality (FRQ) before and during COVID-19 in the Egyptian market.

This study uses quarterly data from 2017 to 2021 to draw conclusions, with a sample consisting of 486 firm-year observations for 27 Egyptian companies listed on the Standard and Poor’s/Egyptian Stock Exchange ESG index. This study uses both firms’ ESG scores and the Beneish Model, an earnings detection model, as proxies for FRQ. COVID-19 effects on ESG performance and FRQ were examined by using Pearson’s correlation coefficient and two-stage least squares.

COVID-19 has a significant impact on the link between ESG and FRQ. This implies that corporations with high ESG performance are less likely to manipulate earnings (having a low M-score) and thus provide high FRQ during the COVID-19 pandemic. Moreover, there is a significant positive relationship between firm size, leverage and M-Score, indicating that large firms typically present a high FRQ.

The sample size and data availability are the main research limitations. Additionally, this study only considers the effects of firms’ ESG performance on FRQ during the COVID-19 pandemic. Thus, future research should consider other factors associated with investors’ corporate social responsibility (CSR).

This research has practical implications for market regulators seeking to establish a legislative framework and enhance guidance to mandate managers to provide ESG data and CSR reports appropriate for Egypt and other developing economies in times of crisis.

Promoting the adoption of ESG practices in business, particularly during crises, has the potential to effectively provide high-quality and reliable financial reporting required for investment.

This study aspires to address notable deficiencies in the pertinent literature concerning the relationship between ESG performance and FRQ during COVID-19. To the best of the authors’ knowledge, little is known about how ESG performance changes in response to pandemics in emerging markets. To address this gap, this study examines the effects of COVID-19 on the relationship between ESG performance and FRQ in Egyptian-listed firms from 2017 to 2021.