Search results

This paper aims to illustrate the scope and challenges of using computer-aided content analysis in international marketing with the aim to capture consumer sentiments about COVID-19 from multi-lingual tweets.

The study is based on some 35 million original COVID-19-related tweets. The study methodology illustrates the use of supervised machine learning and artificial neural network techniques to conduct extensive information extraction.

The authors identified more than two million tweets from six countries and categorized them into PESTEL (i.e. Political, Economic, Social, Technological, Environmental and Legal) dimensions. The extracted consumer sentiments and associated emotions show substantial differences across countries. Our analyses highlight opportunities and challenges inherent in using multi-lingual online sentiment analysis in international marketing. Based on these insights, several future research directions are proposed.

First, the authors contribute to methodology development in international marketing by providing a “use-case” for computer-aided text mining in a multi-lingual context. Second, the authors add to the knowledge on differences in COVID-19-related consumer sentiments in different countries. Third, the authors provide avenues for future research on the analysis of unstructured multi-media posts.

The main goal of the study is to investigate how same-brand slogans simultaneously in use in two emerging markets, namely Turkey and Russia, differ semantically. The study further examines in what ways the industrial competition structure impacts the semantic slogan design within these two contexts.

The study uses the method of semantic explication that is based on a 19-device taxonomy. This method is applied to 56 slogan pairs in the Turkish and Russian languages launched for the same brands/products across 6 industries.

Results indicate that same-brand slogans differ semantically between Turkey and Russia. Moreover, firms tend to conform to a shared semantic pattern within a given industry, largely depending on the industrial competition structure. While strong local competition (as in the electronics and cleaning products industries in Turkey and in the personal care and beverages industries in Russia) leads firms to use self-reference, international competition (as in the automotive, personal care and beverages industries in Turkey and in the electronics and cleaning products industries in Russia) promotes them to use hyperbole in their slogan design.

Adopting a common semantic pattern within an industry may carry the risk of restricting brand differentiation and consumers' sense of novelty. Furthermore, the inclusion of brand names in slogans may make slogans sound assertive and lead consumers to overreact to the brand.

Unlike many studies exploring different-brand slogans through a syntactic or grammatical lens, this study investigates the semantic features of same-brand slogans launched in two emerging market contexts. It adopts a B2B perspective, unlike many extant studies that often focus on a B2C one.

In this chapter, we contribute to ongoing discussions surrounding decolonising research and teaching in higher education by considering the place of language and linguistic diversity within this decolonising turn. The question we explore is how academic researchers and lecturers can recognise and respect that a move to decolonise will involve engaging with epistemologies expressed in different languages and expressed from diverse worldviews. We take inspiration from the work of linguistic citizenship researchers who make explicit the links between knowledge systems, languages and issues of equality/inequality. In linguistic citizenship, research connections are made between the everyday practice of translanguaging, moving between different linguistic repertoires by multilingual speakers, and transknowledging or the fluid movement between differing systems of knowing. To explore the potential of using the concepts of translanguaging and transknowledging as tools in the task of decolonising higher education research and practice, we discuss in depth two published research studies for critical reflection.

This study aims to explore the evolution of the Islamic capital market (ICM) from the perspective of research publications.

A bibliometric analysis was applied based on selected publications from the Web of Science Core Collection (WoSCC) database from 2000 to 2021. The study adopted VOSviewer software which was developed by Leiden University.

This study has some implications that need urgent action. Firstly, there are some areas that have received little attention among researchers, although they are relevant to the industry, for instance, in fintech and blockchain in ICM. Secondly, the inconsistent frequency of publications in some niche areas may suggest that there are unprecedented events that hinder further research; probably, the researcher may anticipate more information and progress in the industry. Thirdly, the need to strengthen the collaboration between industry and academia to advance research.

This study considered only the WoSCC database. The provider of WoSCC is Clarivate (formerly known as Thomson Reuters), where access to publications is limited to institutional subscribers. The implications of this study are to identify and propose future research trends in the field of ICM.

To the best of the authors’ knowledge, the present study is among the pioneer studies in analysing bibliometric focusing on ICM. Previous research has focused on Islamic finance and banking, and not specifically on ICM. Accordingly, this study sheds light on research gaps in ICM.

The purpose of this paper is to describe a new approach to sentence representation learning leading to text classification using Bidirectional Encoder Representations from Transformers (BERT) embeddings. This work proposes a novel BERT-convolutional neural network (CNN)-based model for sentence representation learning and text classification. The proposed model can be used by industries that work in the area of classification of similarity scores between the texts and sentiments and opinion analysis.

The approach developed is based on the use of the BERT model to provide distinct features from its transformer encoder layers to the CNNs to achieve multi-layer feature fusion. To achieve multi-layer feature fusion, the distinct feature vectors of the last three layers of the BERT are passed to three separate CNN layers to generate a rich feature representation that can be used for extracting the keywords in the sentences. For sentence representation learning and text classification, the proposed model is trained and tested on the Stanford Sentiment Treebank-2 (SST-2) data set for sentiment analysis and the Quora Question Pair (QQP) data set for sentence classification. To obtain benchmark results, a selective training approach has been applied with the proposed model.

On the SST-2 data set, the proposed model achieved an accuracy of 92.90%, whereas, on the QQP data set, it achieved an accuracy of 91.51%. For other evaluation metrics such as precision, recall and F1 Score, the results obtained are overwhelming. The results with the proposed model are 1.17%–1.2% better as compared to the original BERT model on the SST-2 and QQP data sets.

The novelty of the proposed model lies in the multi-layer feature fusion between the last three layers of the BERT model with CNN layers and the selective training approach based on gated pruning to achieve benchmark results.

This research was developed to identify artificial intelligence (AI) opportunities and challenges in higher education.

This qualitative research was developed using the six-step thematic analysis method (Braun and Clark, 2006). Participants in this study were AI PhD students from Tehran University in 2022–2023. Purposive sampling was used to select the participants; a total of 15 AI PhD students, who were experts in this field, were selected and interviews were conducted.

The authors considered the opportunities that AI creates for higher education in eight secondary subthemes (for faculty members, for students, in the teaching and learning process, for assessment, the development of educational structures, the development of research structures, the development of management structures and the development of academic culture). Correspondingly, The authors identified and categorized the challenges that AI creates for higher education.

Concerning the intended research, several limitations are significant. First, the statistical population was limited, and only people with characteristics such as being PhD students, studying at Tehran University and being experts in AI could be considered the statistical population. Second, caution should be exercised when generalizing the results due to the limited statistical population (PhD students from Tehran University). Third, the problem of accessing some students due to their participation in research grants, academic immigration, etc.

The innovation of the current research is that the authors identified the opportunities and challenges that AI creates for higher education at different levels. The findings of this study also contribute to the enrichment of existing knowledge in the field regarding the effects of AI on the future of higher education, as researchers need more understanding of AI developments in the future of higher education.

In recent times, the growing use of electronic word of mouth (eWOM) has attracted consumers, organizations and marketers alike. The objective of this study is to summarize and compare the current mass of eWOM research published in leading hospitality and tourism journals with research published in the other fields of both business and management.

This study uses multiple bibliometric analysis methods, including citation, co-citation, keyword and co-word analysis. It compares various assessments of eWOM research published in 399 selected business publications and 398 selected hospitality/tourism publications (ABDC A and above and ABS 3 and above) between 2003 and 2021.

The co-citation analysis identified three thematic areas under each of the domains, i.e. in the hospitality/tourism field, the three themes included eWOM and behavior; eWOM and social media; and eWOM as a marketing tool. Similarly, under the business field (encompasses remaining business and management subdisciplines), the three themes are eWOM and sales, eWOM quality and attributes; and eWOM, information and consumer. Additionally, the word and co-word analysis mapped the comparative evolution of research in these two fields. The study advocates more research focusing on less researched platforms using diverse data, recommender systems adoption and application of eWOM in the business to business (B2B) context.

This study summarizes the overall theoretical and conceptual structure of eWOM research in both business and hospitality/tourism fields; based upon which, several recommendations for future research are proposed.

By comparing the developments in the specialized hospitality/tourism sector with broader management literature using multiple, complementary techniques, this study brings out important insights for hospitality/tourism researchers.

In this chapter, Mousumi De presents the principles and implications of CRT in the context of Asian and Asian American experiences including the perspective, features, strategies, and new directions on how to facilitate the preparation of teacher candidates and work with all teachers to understand the complexity of the Asian and Asian American identity, their racialized experiences, and their sociohistorical, transnational contexts that continue to influence their lived experiences. This chapter highlights the important issues and challenges facing Asians and Asian Americans that have been camouflaged by their stereotypical treatment as model minorities. It also shares the work of many scholars on approaches for promoting diversity and inclusion, such as implementing anti-racist, anti-oppressive, and inclusive history curricula, cultural citizenship education, teaching for social justice, and culturally responsive and culturally sustaining teaching for addressing the marginalization of Asians and Asian Americans.

The purpose of this paper is to critically examine the agri-business/small and medium investment schemes (AGSMEIS) in Nigeria. Its special focus is to investigate the impact of the AGSMEIS on youth entrepreneurship development in Nigeria.

This paper adopts a survey research technique, aimed at gathering information from a representative sample of the population, as it is essentially cross-sectional, describing and interpreting the current situation. A total of 1,200 respondents were sampled across the six geopolitical zones of Nigeria.

The results from the use of a combined propensity score matching (PSM) and logit model indicate that AGSMEIS initiative generates significance gains in empowering youths in enterprise development, and if enhanced will help many young people become entrepreneurs.

This suggests that AGSMEIS initiative can facilitate youth's access to credit and help them become owners of small and medium enterprises.

It implies that investing in young people for small and medium enterprises could bring Nigeria into the modern economy and lift sub-Saharan Africa out of poverty.

This research adds to the literature on youth entrepreneurship development’s debate in developing countries. It concludes that targeting the young people in AGSMEIS should form the foundation of public policy for entrepreneurship, poverty alleviation, and economic development.