Search results

Social media platforms that disseminate scientific information to the public during the COVID-19 pandemic highlighted the importance of the topic of scientific communication. Content creators in the field, as well as researchers who study the impact of scientific information online, are interested in how people react to these information resources and how they judge them. This study aims to devise a framework for extracting large social media datasets and find specific feedback to content delivery, enabling scientific content creators to gain insights into how the public perceives scientific information.

To collect public reactions to scientific information, the study focused on Twitter users who are doctors, researchers, science communicators or representatives of research institutes, and processed their replies for two years from the start of the pandemic. The study aimed in developing a solution powered by topic modeling enhanced by manual validation and other machine learning techniques, such as word embeddings, that is capable of filtering massive social media datasets in search of documents related to reactions to scientific communication. The architecture developed in this paper can be replicated for finding any documents related to niche topics in social media data. As a final step of our framework, we also fine-tuned a large language model to be able to perform the classification task with even more accuracy, forgoing the need of more human validation after the first step.

We provided a framework capable of receiving a large document dataset, and, with the help of with a small degree of human validation at different stages, is able to filter out documents within the corpus that are relevant to a very underrepresented niche theme inside the database, with much higher precision than traditional state-of-the-art machine learning algorithms. Performance was improved even further by the fine-tuning of a large language model based on BERT, which would allow for the use of such model to classify even larger unseen datasets in search of reactions to scientific communication without the need for further manual validation or topic modeling.

The challenges of scientific communication are even higher with the rampant increase of misinformation in social media, and the difficulty of competing in a saturated attention economy of the social media landscape. Our study aimed at creating a solution that could be used by scientific content creators to better locate and understand constructive feedback toward their content and how it is received, which can be hidden as a minor subject between hundreds of thousands of comments. By leveraging an ensemble of techniques ranging from heuristics to state-of-the-art machine learning algorithms, we created a framework that is able to detect texts related to very niche subjects in very large datasets, with just a small amount of examples of texts related to the subject being given as input.

With this tool, scientific content creators can sift through their social media following and quickly understand how to adapt their content to their current user’s needs and standards of content consumption.

This study aimed to find reactions to scientific communication in social media. We applied three methods with human intervention and compared their performance. This study shows for the first time, the topics of interest which were discussed in Brazil during the COVID-19 pandemic.

With luxury brands extending into restaurants and cafés, the definition of luxury consumption no longer refers to tangible products alone but to intangible and authentic experiences as well. Drawing on the Stimulus–Organism–Response framework (SOR) and the costly signalling theory, this study explores the sequential mechanisms of experiences in luxury brands’ restaurants and cafés among Korean and US consumers.

This study distributed online surveys in South Korea and the US and recruited 419 participants (South Korea = 210; US = 209). PLS-SEM and multigroup analysis were used to test the hypotheses.

Perceived quality influenced perceived luxury values positively and led to consumers’ loyalty to both the parent brand (i.e. luxury fashion brand) and extended brand (i.e. luxury restaurants and cafés).

Luxury brands seek to offer their consumers authentic and extraordinary experiences. By merging luxury fashion with gastronomy, these brands can foster synergistic long-term relationships with consumers and enhance their brand equity in the global luxury market. Our results also demonstrated that such expansion contributes to competitive advantages in luxury fashion retailing by increasing their intangible values in addition to their parent luxury fashion brands. Further, in the context of globalisation, this research provides insights into how luxury retailers’ novel approach to the F&B sector can enhance consumers’ loyalty across different cultures and strengthen their global luxury retail strategy.

The Grey System Theory (GST) is an emerging area of research within artificial intelligence. Since its founding in 1982, it has seen a lot of multidisciplinary applications. In just a short period, it has garnered some considerable strengths. Based on the 1987–2021 data collected from the Web of Science (WoS), the current study reports the advancement of the GST.

Research papers utilizing the GST in the fields of economics and education were retrieved from the Web of Science (WoS) platform using a set of predetermined keywords. In the final stage of the process, the papers that underwent analysis were manually chosen, with selection criteria based on the information presented in the titles and abstracts.

The study identifies prominent authors, institutions, publications and journals closely associated with the subject. In terms of authors, two major clusters are identified around Liu SF and Wang ZX, while the institution with the highest number of publications is Nanjing University of Aeronautics and Astronautics. Moreover, significant keywords, trends and research directions have been extracted and analyzed. Additionally, the study highlights the regions where the theory holds substantial influence.

The study is subject to certain limitations stemming from factors such as the language employed in the chosen literature, the papers included within the Web of Science (WoS) database, the designation of works categorized as “articles” in the database, the specific selection of keywords and keyword combinations, and the meticulous manual process employed for paper selection. While the manual selection process itself is not inherently limiting, it demands a greater investment of time and meticulous attention, contributing to the overall limitations of the study.

The significance of the study extends not only to scholars and practitioners but also to readers who observe the development of emerging scientific disciplines.

The analysis of trends revealed a growing emphasis on the application of GST in diverse domains, including supply chain management, manufacturing and economic development. Notably, the emergence of COVID-19 as a new research focal point among GST scholars is evident. The heightened interest in COVID-19 can be attributed to its global impact across various academic disciplines. However, it is improbable that this interest will persist in the long term, as the pandemic is gradually brought under control.

Drawing on the reciprocity principle of social exchange theory situated within Service-dominant Logic, this study aims to examine how customers’ perception of knowledge sharing in co-production, their inherent scepticism and prosocial orientation relate to their willingness to co-create and provide feedback on services. The authors also explored the interplay between these factors to identify conditions in configurations comprising scepticism, which may help navigate its adverse effects.

The authors surveyed 556 online and offline mobile payment service users. They used a combination of partial least squares structural equation modelling (PLS-SEM) to assess the relationships among variables, and fuzzy-set qualitative comparative analysis (fsQCA) to identify configurations associated with feedback behaviour.

The study determined that customer perception of co-production knowledge sharing is positively associated with willingness to co-create and feedback behaviour. Additionally, prosocial orientation positively affects this relationship, while scepticism has an adverse effect. Willingness to co-create mediates the relationship between customer perception of co-production knowledge sharing and feedback behaviour. The fsQCA findings revealed configurations for potentially navigating doubts regarding feedback. To encourage valuable customer feedback, businesses may consider promoting a collaborative and supportive atmosphere, emphasising shared advantages or building trust even among hesitant and doubtful individuals.

This study uniquely examines how both prosocial tendencies and scepticism relate to customer feedback behaviour in co-creation by using a hybrid PLS-SEM/fsQCA approach to identify co-existing conditions in configurations comprising scepticism that may help navigate its adverse effects and leverage customer feedback for business improvement.

Drawing on the tenets of institutional theory, the purpose of this study is to examine the impact of Confucianism on technology for social good, while also considering the moderating influence of extrinsic informal institutions (foreign culture) and intrinsic formal institutions (property rights).

This study constructs a comprehensive database comprising 9,759 firm-year observations in China by using a sample of Chinese A-share listed firms from 2016 to 2020. Subsequently, the hypotheses are examined and confirmed, with the validity of the results being upheld even after conducting endogenous and robustness tests.

The findings of this study offer robust and consistent evidence supporting the notion that Confucianism positively affects technology for social good through both incentive effect and normative effect. Moreover, this positive influence is particularly prominent in organizations with limited exposure to foreign culture and in nonstate-owned enterprises.

The findings contribute to the literature by fostering a deep understanding of technology for social good and Confucianism research, and further provide a nuanced picture of the role of foreign culture and property rights in the process of technology for social good in China.

This study leverages an enhanced expectation confirmation model of information technology continuance to explore the factors that shape the continuance intention toward cloud computing services.

Data were collected from personal cloud computing service users in Malaysia, resulting in a total of 567 usable responses. The research used partial least squares structural equation modeling (PLS-SEM) for comparative analysis and model selection based on predictive accuracy.

The study findings demonstrate that perceived benefits, ease of use, usefulness, risk and trust play crucial roles as antecedents to user confirmation and satisfaction, thereby influencing the intention for continuous service usage. Notably, the hypotheses formulated for the study received robust support, with 13 out of 14 anticipated relationships proving statistically significant.

This study presents a novel investigation into the continuance of personal cloud computing services among Malaysian users, contributing to a deeper understanding of user intention dynamics in this context. Integrating an expanded ECM-IT framework offers a comprehensive analytical approach that simultaneously considers information systems and non-IS factors. Additionally, the study underscores the significance of predictive capability assessment using the PLSpredict method, providing a valuable benchmark for model selection in future research.

The purpose of this study is to examine the impact of customers' environmental concern on corporate green innovation and its underlying mechanisms.

This study empirically examines the impact of customer environmental concern on corporate green innovation using 967 company-customer-year observations of Chinese A-share listed companies over the period 2012–2022.

The empirical results show that customer environmental concern significantly enhances corporate green innovation. Furthermore, executive environmental awareness and research and development (R&D) investment play a partial mediating role in this relationship. The heterogeneity analysis reveals that state-owned customers, customers located in the same province with the corporate and the intellectual property model cities contribute to strengthening this relationship. Moreover, corporate performance analysis shows that customer environmental concern can significantly increase corporate financial performance and sustainable performance.

This study innovatively proposes a measure of customer environmental concern and examines its impact on corporate green innovation and its underlying mechanisms. In addition, this study also proposes some insights for policymakers.

Humanoid social robots (HSRs) are an innovative technology revitalizing various service sectors, such as the hospitality industry. However, limited research has explored how anthropomorphic features of HSRs influence user satisfaction with the services delivered by HSRs. To address this, a research model was proposed to evaluate how three distinct anthropomorphic features: appearance, voice and response, impact the perceived values (i.e. utilitarian, social and hedonic values) of HSRs, which, in turn, influence user satisfaction.

Data from an online survey of hotel customers was utilized to test the research model (N = 509).

The results indicated that appearance, voice, and response affect perceived utilitarian, hedonic and social values differently. The response feature of HSRs demonstrated the strongest impact on perceived utilitarian, social and hedonic values. In addition, voice affected all three perceived values, while appearance only affected perceived utilitarian and social values. Furthermore, perceived utilitarian, hedonic and social values showed positive impacts on user satisfaction, with hedonic value being the most influential factor.

This study contributes to the literature on HSRs and anthropomorphism by explaining how different anthropomorphic features affect users’ value perceptions and user satisfaction with HSR services by utilizing the stimulus-organism-response (SOR) framework.

Be that as it may, BC is computationally costly, has restricted versatility and brings about critical transmission capacity upward and postpones, those seems not to be fit with Internet of Things (IoT) setting. A lightweight scalable blockchain (LSB) which is improved toward IoT necessities is suggested by the authors and investigates LSB within brilliant house setup like an agent model to enable more extensive IoT apps. Less asset gadgets inside brilliant house advantage via any unified chief which lays out common units for correspondence also cycles generally approaching and active solicitations.

Federated learning and blockchain (BC) have drawn in huge consideration due to the unchanging property and the relevant safety measure and protection benefits. FL and IoT safety measures’ difficulties can be conquered possibly by BC.

LSB accomplishes fragmentation through shaping any overlaid web with more asset gadgets mutually deal with a public BC and federated learning which assures complete protection also security.

This overlaid is coordinated as without error bunches and reduces extra efforts, also batch leader will be with answer to handle commonly known BCs. LSB joins some of advancements which also includes computations related to lesser weighing agreement, optimal belief also throughput regulatory body.

This study aims to investigate the effects of candidates' perceptions of cybervetting – the evaluation of social media profiles by employers – on the perceived attractiveness and prestige of organizations and the role of gender as a moderator in these relationships. Three perceptions – perceived injustice, privacy invasion and surface validity – are used to evaluate candidates' attitudes about cybervetting.

The sample is comprised of third- and fourth-year undergraduate students at a university in Ankara, Türkiye. A survey technique was used to collect research data. The survey form was prepared on an online platform.

Survey results revealed that negative perceptions of cybervetting significantly affected perceived organizational attractiveness, prestige and intention to pursue the organization. Still, no significant difference was found between the women and men groups in this effect. Men candidates are more concerned about the validity and fairness of cybervetting.

The research's findings are anticipated to shed significant light on how cybervetting is conceptualized, specifically whether feelings of injustice, privacy invasion and validity constitute core components of cybervetting. Besides, the findings are expected to reveal whether candidates' attitudes toward cybervetting affect their perceptions regarding the general attractiveness and prestige of the organizations.