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Due to the vital role of frugal innovation (FI) for firms in developing and emerging economies, the purpose of this study is to investigate the effect of transformational leadership (TL) and knowledge sharing (KS) on FI of firms in supply chains. This study aims to bring deeper insight on the correlation among the constructs by exploring and analyzing the moderating effect of collaborative culture (CC) in the KS-FI relationship.

The paper used the quantitative approach and structural equation modeling (SEM) to test the relationship among the latent factors in the proposed research model using data collected from 381 participants in 116 manufacturing and service firms.

The findings revealed that TL might be an important precursor to induce significant influences on FI directly or indirectly through its effect on active and passive KS processes in supply chains. In addition, the paper highlights the moderating role of CC in strengthening the impact of KS processes on FI.

The paper provides a valuable understanding and novel approach for managers to improve firms' FI capability through leadership practice and KS processes. The research findings support the idea that FI capability of firms in developing and emerging nations will be significantly enhanced if leaders are interested in practicing TL style and fostering a climate of collaboration within organization.

This study contributes to bridging research gaps in the literature and advancing the insights of how TL directly and indirectly fosters FI via mediating roles of active and passive KS processes in supply chains under the climate of collaboration.

This study aims to investigate multilingual representation on public signs in the High City tourist destination in Abha, Saudi Arabia. It also reveals the linguistic strategies used in such representation.

This exploratory qualitative study used purposive sampling to analyze bottom-up public signs collected from the target tourist destination. A preliminary analysis was conducted for a more in-depth qualitative analysis of every sign. An Excel database was used to provide a general description and a preliminary reading of the strategies before using an in-depth analysis of every sign.

The study revealed that monolingualism (Arabic or English) and bilingualism (Arabic and English) represented the High City as a tourist destination where the signs served information and symbolic functions. No single multilingual sign was found. Certain linguistic strategies were used on the public signs, including politeness, transliteration, hybrid representation, personification and fragmentary. Some tourist-oriented strategies, such as the crisis communication strategy, are still missing.

These findings indicate that this tourist destination still targets local and regional visitors, and its linguistic landscape (LL) needs further consideration in terms of internationalization and targeting international visitors. This study implies that bilingual Arabic and English tourist destinations are potential domains for translation students and English language learners.

This study has focused on the LL of a newly established tourist destination in Saudi Arabia. It has shed light on the nuanced representations and strategies used through public signage. It contributes to understanding how linguistic elements can shape tourists’ perceptions and experiences.

COVID-19 is a global event affecting supply chain operations and human health. With COVID-19, many issues in business models, business processes and supply chains, especially in the manufacturing industry, have had to change. The ability to analyze supply chain performances and ensure circularity in supply chains has become one of the factors whose importance has increased rapidly with COVID-19. Therefore, it aims to determine which supply chain performance criteria come to the fore for the company under consideration to accelerate the transformation into high performance and circularity in supply chains.

In this study, a new circular-SCOR model is proposed, and 17 supply chain performance measurement criteria are prioritized for a manufacturing company in the context of circular economy principles during COVID-19 by using stepwise weight assessment ratio analysis and analytical hierarchy process method, separately.

As a result, for both methods, in the case study discussed, the demand fulfillment rate is determined as the most prominent criterion in line with the circular economy principles in the COVID-19 period in manufacturing supply chains.

It is expected that this study will contribute to managers and policy makers as it addresses the “new normal” that started after COVID-19 and the criteria to be considered in supply chain performance measurement and emphasizes the need to adopt circular supply chains, especially in manufacturing industries.

This study examines the performance effect of working capital for a large sample of Indian manufacturing firms in light of supply chain disruption, i.e. the COVID-19 pandemic.

This study is based on secondary data collected from the Prowess database on Indian manufacturing firms listed on the Bombay Stock Exchange (BSE) 500. Panel data regression analyses are used to estimate all models. Moreover, this study has employed robust standard errors to consider for heteroscedasticity concerns.

The results challenge the current notion of working capital investment and reveal that higher working capital has a positive and significant impact on firm performance. Further, it highlights that Indian manufacturing firms suffered financially post-COVID-19 as they significantly lack the working capital to run day-to-day operations.

This research contributes to the scant literature by examining the association between working capital financing and firm performance in light of the COVID-19 pandemic, representing typical developing economies like India.

This study aims to contribute to the field of computer systems for sustainability research. It proposes a framework for sustainability awareness using online social networks (OSNs) by analyzing major research streams of the current state of knowledge and different bibliometric variables, and identifies a future research agenda in the field.

The preferred reporting items for systematic review and meta-analysis (PRISMA) methodology, content analysis and bibliometric tools were employed to identify, select, collect, synthesize, analyze and evaluate all research published on sustainability awareness using OSNs to provide complete insight into this research area.

This study proposed a framework comprising four categories for sustainability awareness using OSNs. These four categories are: the key factors to success, analysis of existing tools, proposal of new methods, approaches and theoretical frameworks, and case examples. In addition, this study synthesized the future research challenges for each category of the proposed framework.

Fostering sustainability awareness and sustainable behavior using OSNs is a growing area of research that seeks cultural change in society to achieve sustainable development. Through OSNs, people can discover and become aware of the consequences of unsustainable practices and habits in society, and learn how to develop sustainable behavior.

The peer review history for this article is available at https://publons.com/publon/10.1108/OIR

This viewpoint note aims to discuss the challenges and the suggestions of using assistive technologies to improve the accessibility for travellers with disabilities and their companions at heritage cities.

The viewpoint note used the narrative approach which conceptualised accessible tourism, challenges and solutions of using assistive technologies for improving the heritage cities visit experience of travellers with disabilities.

The viewpoint note discussed three challenges (physical, informational and attitudinal) and three approaches of accessible platform, navigation system, and accessible and interactive heritage interpretation solutions for accessible heritage cities.

The viewpoint note presents the expert opinion of authors who investigated the accessibility issue in heritage cities. The first author of this note is a person with disabilities who has been living and studying about experience of travellers with disabilities at many heritage cities. The other author is an expert in heritage tourism.

Grounded in the stakeholder theory and signaling theory, this study aims to broaden the research agenda on environmental, social and governance (ESG) investing by uncovering public sentiments and key themes using Twitter data spanning from 2009 to 2022.

Using various machine learning models for text tonality analysis and topic modeling, this research scrutinizes 1,842,985 Twitter texts to extract prevalent ESG investing trends and gauge their sentiment.

Gibbs Sampling Dirichlet Multinomial Mixture emerges as the optimal topic modeling method, unveiling significant topics such as “Physical risk of climate change,” “Employee Health, Safety and well-being” and “Water management and Scarcity.” RoBERTa, an attention-based model, outperforms other machine learning models in sentiment analysis, revealing a predominantly positive shift in public sentiment toward ESG investing over the past five years.

This study establishes a framework for sentiment analysis and topic modeling on alternative data, offering a foundation for future research. Prospective studies can enhance insights by incorporating data from additional social media platforms like LinkedIn and Facebook.

Leveraging unstructured data on ESG from platforms like Twitter provides a novel avenue to capture company-related information, supplementing traditional self-reported sustainability disclosures. This approach opens new possibilities for understanding a company’s ESG standing.

By shedding light on public perceptions of ESG investing, this research uncovers influential factors that often elude traditional corporate reporting. The findings empower both investors and the general public, aiding managers in refining ESG and management strategies.

This study marks a groundbreaking contribution to scholarly exploration, to the best of the authors’ knowledge, by being the first to analyze unstructured Twitter data in the context of ESG investing, offering unique insights and advancing the understanding of this emerging field.

Most new media research focuses on behavior as a measure of engagement, while the psychological state of being occupied with its content has received little attention. This study examined news engagement beyond pure action observation by exploring young people’s psychological experiences with the news.

The study carried out a digital native’s survey on 212 people (18–28 years). The focus of the survey was on understanding individuals’ engagement with online news using affective and cognitive components. The authors compared the influence of each type of engagement on youth consumption of and attitudes toward online news.

The results of the hierarchical regression analysis showed that affective engagement can be a stronger predictor of online news consumption than cognitive engagement. While affective engagement significantly predicts positive attitudes toward online news, cognitive engagement had no significant effect.

These findings suggest that “engaging the heart” is more influential than “engaging the mind” in drawing young people to the news in today’s information environment. The study thus contributes to the understanding of the cognitive and emotional focus on news content and their importance in shaping young people’s expectations of online news. The findings from this study could have broader implications for future trends in online news consumption.

The purpose of this paper is to expand and analyze deeply customer emotions, concretize the levels of positive or negative emotions with the aim of using machine learning methods, and build a model to identify customer emotions.

The study proposed a customer emotion detection model and data mining method based on the collected dataset, including 80,593 online reviews on agoda.com and booking.com from 2009 to 2022.

By discerning specific emotions expressed in customers' comments, emotion detection, which refers to the process of identifying users' emotional states, assumes a crucial role in evaluating the brand value of a product. The research capitalizes on the vast and diverse data sources available on hotel booking websites, which, despite their richness, remain largely unexplored and unanalyzed. The outcomes of the model, pertaining to the detection and classification of customer emotions based on ratings and reviews into four distinct emotional states, offer a means to address the challenge of determining customer satisfaction regarding their actual service experiences. These findings hold substantial value for businesses operating in this domain, as the findings facilitate the evaluation and formulation of improvement strategies within their business models. The experimental study reveals that the proposed model attains an exact match ratio, precision, and recall rates of up to 81%, 90% and 90%, respectively.

The study has yet to mine real-time data. Prediction results may be influenced because the amount of data collected from the web is insufficient and preprocessing is not completely suppressed. Furthermore, the model in the study was not tested using all algorithms and multi-label classifiers. Future research should build databases to mine data in real-time and collect more data and enhance the current model.

The study's results suggest that the emotion detection models can be applied to the real world to quickly analyze customer feedback. The proposed models enable the identification of customers' emotions, the discovery of customer demand, the enhancement of service, and the general customer experience. The established models can be used by many service sectors to learn more about customer satisfaction with the offered goods and services from customer reviews.

The research paper helps businesses in the hospitality area analyze customer emotions in each specific aspect to ensure customer satisfaction. In addition, managers can come up with appropriate strategies to bring better products and services to society and people. Subsequently, fostering the growth of the hotel tourism sector within the nation, thereby facilitating sustainable economic development on a national scale.

This study developed a customer emotions detection model for detecting and classifying customer ratings and reviews as 4 specific emotions: happy, angry, depressed and hopeful based on online booking hotel websites agoda.com and booking.com that contains 80,593 reviews in Vietnamese. The research results help businesses check and evaluate the quality of their services, thereby offering appropriate improvement strategies to increase customers' satisfaction and demand more effectively.

This paper attempts to develop a simple, static model of tax administration that is capable of explaining the widespread collusive petty tax administration corruption observed in developing countries.

This paper utilizes a positivist research framework and adopts a theoretical method of analysis, although secondary data will also be mentioned to support theoretical arguments whenever it is appropriate to do so.

A high rate of collusive tax corruption is inevitable in developing countries.

The model is static and needs to be extended into a dynamic model.

Traditional enforcement tools such as higher audits or a higher penalty regime against tax evasion do not work. Tax simplification can lessen the incidence of tax corruption.

Fighting tax corruption requires significant changes in the attitudes of taxpayers and tax auditors.

This paper combines the literature on Kantian economics and tax compliance in an innovative fashion.