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This paper investigates how two characteristics of language, abstractness vs concreteness and narrativity, influence user engagement in communication exercises on innovation targeted to the general audience. The proposed conceptual model suggests that innovation fits well with more abstract language because of the association of innovation with imagination and distal construal. Moreover, communication of innovation may benefit from greater adherence to the narrativity arc, that is, early staging, increasing plot progression and climax optimal point. These effects are moderated by content variety and emotional tone, respectively.

Based on a Latent Dirichlet allocation (LDA) application on a sample of 3225 TED Talks transcripts, the authors identify 287 TED Talks on innovation, and then applied econometric analyses to test the hypotheses on the effects of abstractness vs concreteness and narrativity on engagement, and on the moderation effects of content variety and emotional tone.

The authors found that abstractness (vs concreteness) and narrativity have positive effects on engagement. These two effects are stronger with higher content variety and more positive emotional tone, respectively.

This paper extends the literature on communication of innovation, linguistics and text analysis by evaluating the roles of abstractness vs concreteness and narrativity in shaping appreciation of innovation.

This paper reports conceptual and empirical analyses on innovation dissemination through a popular medium – TED Talks – and applies modern text analysis algorithms to test hypotheses on the effects of two pivotal dimensions of language on user engagement.

Sarcasm is a linguistic expression that usually carries the opposite meaning of what is being said by words, thus making it difficult for machines to discover the actual meaning. It is mainly distinguished by the inflection with which it is spoken, with an undercurrent of irony, and is largely dependent on context, which makes it a difficult task for computational analysis. Moreover, sarcasm expresses negative sentiments using positive words, allowing it to easily confuse sentiment analysis models. This paper aims to demonstrate the task of sarcasm detection using the approach of machine and deep learning.

For the purpose of sarcasm detection, machine and deep learning models were used on a data set consisting of 1.3 million social media comments, including both sarcastic and non-sarcastic comments. The data set was pre-processed using natural language processing methods, and additional features were extracted and analysed. Several machine learning models, including logistic regression, ridge regression, linear support vector and support vector machines, along with two deep learning models based on bidirectional long short-term memory and one bidirectional encoder representations from transformers (BERT)-based model, were implemented, evaluated and compared.

The performance of machine and deep learning models was compared in the task of sarcasm detection, and possible ways of improvement were discussed. Deep learning models showed more promise, performance-wise, for this type of task. Specifically, a state-of-the-art model in natural language processing, namely, BERT-based model, outperformed other machine and deep learning models.

This study compared the performance of the various machine and deep learning models in the task of sarcasm detection using the data set of 1.3 million comments from social media.

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.

The application of artificial intelligence chatbots is an emerging trend in educational technology studies for its multi-faceted advantages. However, the existing studies rarely take a perspective of educational technology application to evaluate the application of chatbots to educational contexts. This study aims to bridge the research gap by taking an educational perspective to review the existing literature on artificial intelligence chatbots.

This study combines bibliometric analysis and citation network analysis: a bibliometric analysis through visualization of keyword, authors, organizations and countries and a citation network analysis based on literature clustering.

Educational applications of chatbots are still rising in post-COVID-19 learning environments. Popular research issues on this topic include technological advancements, students’ perception of chatbots and effectiveness of chatbots in different educational contexts. Originating from similar technological and theoretical foundations, chatbots are primarily applied to language education, educational services (such as information counseling and automated grading), health-care education and medical training. Diversifying application contexts demonstrate specific purposes for using chatbots in education but are confronted with some common challenges. Multi-faceted factors can influence the effectiveness and acceptance of chatbots in education. This study provides an extended framework to facilitate extending artificial intelligence chatbot applications in education.

The authors have to acknowledge that this study is subjected to some limitations. First, the literature search was based on the core collection on Web of Science, which did not include some existing studies. Second, this bibliometric analysis only included studies published in English. Third, due to the limitation in technological expertise, the authors could not comprehensively interpret the implications of some studies reporting technological advancements. However, this study intended to establish its research significance by summarizing and evaluating the effectiveness of artificial intelligence chatbots from an educational perspective.

This study identifies the publication trends of artificial intelligence chatbots in educational contexts. It bridges the research gap caused by previous neglection of treating educational contexts as an interconnected whole which can demonstrate its characteristics. It identifies the major application contexts of artificial intelligence chatbots in education and encouraged further extending of applications. It also proposes an extended framework to consider that covers three critical components of technological integration in education when future researchers and instructors apply artificial intelligence chatbots to new educational contexts.

The professional experiences of teachers of languages and cultures, along with the learning experiences of their students, are embedded in educational contexts, which themselves are informed, and constrained, by national language policies. This study aims to explore 51 English-as-a-foreign-language (EFL) secondary teachers’ perceptions of Web-based technology use to enhance students’ cultural awareness in Chile. Specifically, the study investigated teachers’ use of Web-based resources for cultural awareness, culture content and technology-based tasks, as well as perceived challenges in implementing technology-enhanced language and culture learning.

The study adopted a mixed-method research design combining online questionnaires and interviews as data collection tools. Results were analyzed through the use of descriptive statistics and content analysis.

The teachers in this study emphasized reflection in their classrooms but did not take a critical approach. Their approach to culture was limited to a “country-specific” view, and technology-enhanced activities accentuated differences rather than promoting meaningful intercultural exchange. Challenges to the successful implementation of technology-enhanced language and culture learning included a somewhat out-of-date theoretical approach to intercultural learning in the national curriculum, a nationwide approach to professional development that lacks a focus on critical reflection and inadequate support for effective use of technologies in schools.

The study highlights the importance of periodically revising a country’s EFL language policies, communication methods, support mechanisms and implementation factors to ensure classroom integration of language, culture and technology education.

This paper explores the tension between macro-level national policy and teachers’ perspectives on their classroom practice, including the contextualized limitations of implementing national policy at the micro level.

This study aims to examine the interaction effects of chatbots’ language style and customers’ decision-making journey stage on customer’s service encounter satisfaction and the mediating role of customer perception of emotional support and informational support using the construal level theory and social support theory as conceptual frameworks.

This study used a scenario-based experiment with a 2 (chatbot’s language style: abstract language vs concrete language) × 2 (decision-making journey stage: informational stage vs transactional stage) between-subjects design.

Findings show that during the informational stage, chatbots that use abstract language style exert a strong influence on service encounter satisfaction through emotional support. During the transactional stage, chatbots that use concrete language style exert a strong impact on service encounter satisfaction through informational support.

Findings provide some suggestions for improving customer–chatbot interaction quality during online service encounters.

This study offers a novel perspective on customer interaction experience with chatbots by investigating the chatbot’s language styles at different decision-making journey stages.

Industry 4.0 defines the application of digital technologies on business infrastructure and processes. With the increasing need to take into account the social and environmental impact of technologies, the concept of Society 5.0 has been proposed to restore the centrality of humans in the proper utilization of technology for the exploitation of innovation opportunities. Despite the identification of humans, resilience and sustainability as the key dimensions of Society 5.0, the definition of the key factors that can enable Innovation in the light of 5.0 principles has not been yet assessed.

An SLR, followed by a content analysis of results and a clustering of the main topics, is performed to (1) identify the key domains and dimensions of the Industry 5.0 paradigm; (2) understand their impact on Innovation 5.0; (3) discuss and reflect on the resulting implications for research, managerial practices and the policy-making process.

The findings allow the elaboration of a multileveled framework to redefine Innovation through the 5.0 paradigm by advancing the need to integrate ICT and technology (Industry 5.0) with the human-centric, social and knowledge-based dimensions (Society 5.0).

The study detects guidelines for managers, entrepreneurs and policy-makers in the adoption of effective strategies to promote human resources and knowledge management for the attainment of multiple innovation outcomes (from technological to data-driven and societal innovation).

This paper aims to analyze the implications of orality for management practices in a developing country such as Iran.

This paper relies on the seminal theory of Walter Ong (1982) and a leading line of anthropological research to analyze the implications of orality/literacy for management practices in Iran. The authors first define orality and literacy as distinct modes of communication and examine their conceptual properties. Then, the authors draw on the existing literature to analyze the five main management functions impacted by orality.

The analyses suggest that the predominance of orality in Iran is associated with a wide range of management practices, including short-term or unstructured planning, spontaneous decision-making, fluid organizational structure, the prevalence of interpersonal relations, authoritarian and traditional leadership and behavior-based controlling mechanisms.

While most studies have focused on the impacts of cultural dimensions and economic variables, this paper offers a novel approach to analyzing management practices. More specifically, the paper suggests that in addition to the implications of cultural dimensions and economic variables, the mode of communication, namely, orality/literacy, could have significant implications for management practices.

The study aims to explore digital transformation from the viewpoint of human resource management to uncover possible threads of relationship using bibliometric analysis. It also aims to identify the trending research themes within the domains of digital transformation (DT) and human resource management (HRM) collectively.

The research employs a mix of quantitative bibliometric techniques and qualitative content analysis. A corpus of 227 articles retrieved from the Scopus database was analyzed using the R-based Biblioshiny and VOS viewer.

The study shows publication trends, influential authors, leading journals, highly productive institutions, and, countries in the domain of DT and HRM. Co-citation and co-occurrence analysis was undertaken to identify the research clusters, depicting trending research themes that extensively dominate the research under this domain.

This study will serve as a ready reckoner for academicians and business leaders, giving them useful insights to make their road towards digital transformation less challenging with the assistance of human capital.

This study is one of the initial efforts to quantitatively synthesize the results of earlier publications using bibliometric techniques in the domain of DT and HRM together. It will aid researchers in locating research gaps and filling those gaps in the future.

This research paper aimed to investigate the affordances of using virtual reality (VR) in teaching culture among in-service teachers of teaching Persian to speakers of other languages (TPSOL) in Iran.

This qualitative case study, conducted at two Iranian universities, used purposeful sampling to select 34 eligible in-service Persian teachers from a pool of 73. Data collection used an open-ended questionnaire and interviews.

Before the TPSOL in-service training workshop, teachers expressed their reservations regarding the use of VR to teach culture in TPSOL courses. The emerged themes were “skepticism toward effectiveness,” “practicality concerns,” “limited awareness of VR applications,” “technological apprehension” and “prevalence of traditional teaching paradigms.” During the post-workshop interview, it was discovered that the teachers’ perceptions of VR in teaching culture had undergone a positive shift. The workshop generated emergent themes that reflected positive perceptions and affordances for using VR to teach culture in TPSOL, including “enhanced cultural immersion,” “increased student engagement,” “simulation of authentic cultural experiences,” and “facilitation of interactive learning environments.”

One primary limitation is the lack of prior experience with VR for teaching practices in real-world classrooms among the participants. While the study aimed to explore the potential of VR in enhancing pedagogical approaches, the absence of participants with prior exposure to VR in educational contexts may impact the generalizability of the findings to a broader population. Additionally, the study faced practical constraints, such as the unavailability of sufficient facilities in the workshop. As a result, the instructor had to project the VR cont7ent on a monitor, potentially diverging from the immersive nature of true VR experiences. These limitations offer opportunities for future research to refine methodologies and gain a more comprehensive understanding of the implications of integrating VR into teaching practices.

Extensive research has been conducted on the effectiveness of VR in language education. However, there is a significant gap in research on TPSOL, which is considered a less commonly taught language. This study aims to address this gap by exploring the use of VR in the TPSOL through the lenses of in-service teachers. As part of a larger investigation, this qualitative inquiry focuses on the perceptions of in-service teachers about VR, with a particular emphasis on the cultural understanding of the Persian language.