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1 – 10 of 279Yunfei Xing, Yuming He and Justin Z. Zhang
The coronavirus disease 2019 (COVID-19) pandemic caused significant disruption to the global labor market, resulting in a rapid transition toward remote work, e-commerce and…
Abstract
Purpose
The coronavirus disease 2019 (COVID-19) pandemic caused significant disruption to the global labor market, resulting in a rapid transition toward remote work, e-commerce and workforce automation. This shift has sparked a considerable amount of public discussion. This study aims to explore the online public's sentiment toward remote work amid the pandemic.
Design/methodology/approach
Based on justice theory, this paper examines user-generated content on social media platforms, particularly Twitter, to gain insight into public opinion and discourse surrounding remote work during the COVID-19 pandemic. Employing content analysis techniques such as sentiment analysis, text clustering and evolutionary analysis, this study aims to identify prevalent topics, temporal patterns and instances of sentiment polarization in tweets.
Findings
Results show that people with positive opinions focus mainly on personal interests, while others focus on the interests of the company and society; people's subjectivities are higher when they express extremely negative or extremely positive emotions. Distributive justice and interactional justice are distinguishable with a high degree of differentiation in the cluster map.
Originality/value
Previous research has inadequately addressed public apprehensions about remote work during emergencies, particularly from a justice-based perspective. This study seeks to fill this gap by examining how justice theory can shed light on the public's views regarding corporate policy-making during emergencies. The results of this study provide valuable insights and guidance for managing public opinion during such events.
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Somipam R. Shimray, Sakshi Tiwari and Chennupati Kodand Ramaiah
The purpose of this study is to examine characteristics of retracted publications from Indian authors and inspect a relationship between journal impact factor (JIF) and the number…
Abstract
Purpose
The purpose of this study is to examine characteristics of retracted publications from Indian authors and inspect a relationship between journal impact factor (JIF) and the number of authors (NoA).
Design/methodology/approach
The authors examined the general characteristics of retracted publications and investigated the correlation between JIF and NoA from Indian authors from January 1, 2017, to December 31, 2022. Data were mined from retraction watch http://retractiondatabase.org/ (n = 1,459) and determined the year of publication, year of retraction, authors, journals, publishers and causes of the retractions. A journal citation report was extracted to gather the JIFs.
Findings
About one-third of retracted papers were published in 2020; 2022 has the highest retraction rate (723); studies with two authors represent about one-third (476) of the published articles; Journal of Ambient Intelligence and Humanized Computing (354) has the highest number of retractions; Springer published the most retracted papers (674); and the majority of the journal (1,133) is indexed in journal citation reports, with impact factor extending from 0.504 to 43.474. Retraction due to legal reasons/legal threats was the most predominant reason for retraction.
Originality/value
This study reflects growth in author collaborations with a surge in the JIF. This study recommends that quick retraction is essential to reduce the adverse effects of faulty research.
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Arzu Şen Kılıç, Can Ünal and Ziynet Ondogan
This study establishes the principles and process steps of a new basic trousers pattern using measurements obtained according to the rules of the anthropometric measurement…
Abstract
Purpose
This study establishes the principles and process steps of a new basic trousers pattern using measurements obtained according to the rules of the anthropometric measurement system. The newly developed pattern-making system in this study will be called the “Anthropometric Measurements Based Pattern Making System” (AnMePa). It is aimed at producing trousers that are more fitting to the body, thanks to this pattern-making system.
Design/methodology/approach
In this research, four pattern-making systems used in many parts of the world were compared with the “Anthropometric Measurements Based Pattern Making System” (AnMePa) with regard to the overall appearance and body fit of trousers prepared according to these systems. 10 virtual mannequins (VM) with different adult female body measurements were created, and trousers patterns were prepared for these mannequins. The trousers’ patterns were made and dressed on the mannequins in a 3D virtual dressing system. The body fit of the virtual garments was evaluated by five experts. The scores given by the experts were evaluated using the fuzzy logic method.
Findings
According to the results, it is seen that the new basic trousers pattern developed by utilizing the anthropometric measurement system, AnMePa, provides the best body fit among the basic trousers patterns created according to the other examined pattern-making systems. The combination of 3D virtual dressing and fuzzy logic in the evaluation of garment body fit is considered an innovative method for the future of fashion design and production.
Originality/value
In the developed AnMePa, unlike the existing pattern-making systems, values that can be associated with the body measurements of individuals in a way that could be suitable for each community were used instead of constant values in the pattern-making process. Furthermore, the integration of 3D virtual fitting and fuzzy logic in assessing garment fit is considered a pioneering approach with significant implications for the future landscape of fashion design and production.
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Larelle Chapple, Lien Duong and Thu Phuong Truong
The purpose of this research note is to investigate the drivers and market reaction to firms’ decision to release general COVID-19-related announcements and to withdraw earnings…
Abstract
Purpose
The purpose of this research note is to investigate the drivers and market reaction to firms’ decision to release general COVID-19-related announcements and to withdraw earnings forecasts and dividends during the COVID-19 pandemic in the continuous disclosure environment of Australia.
Design/methodology/approach
The authors first tracked the market reaction of all firms in the Australian Securities Exchange All Ordinaries, Top 300, Top 200 and Top 100 indices during the early period of the COVID-19 pandemic between 1 January and 21 September 2020. The authors then focus the investigation on the incidence of firms deciding to withdraw earnings forecasts and dividends and how the market responded to these incidences during that period.
Findings
The market reacted negatively during the March/April 2020 period but then bounced back to the pre-March 2020 level. The market reaction is mainly driven by three industries, including consumer discretionary, health care and utilities. Firms in industry sectors such as consumer discretionary, materials, health care and information technology contribute to the highest percentage of COVID-19 announcements. It is interesting to document that firms issuing COVID-19 announcements and withdrawing earnings forecasts and dividends tend to be larger firms with stronger financial performance and higher financial leverage. Regarding the stock market reaction, while the market generally reacted positively to COVID-19-related announcements, the decision to withdraw earnings forecasts and dividends is significantly regarded as bad news.
Originality/value
The COVID-19 pandemic has provided a unique natural event to examine firms’ disclosure behaviour in the continuous disclosure environment of Australia during this period of extreme uncertainty. The incidences of earnings forecasts and dividend withdrawals are mainly driven by larger, better performing and higher leverage firms in the consumer discretionary, health care, materials and information technology industry sectors. The market generally reacted favourably to COVID-19-related announcements, despite a significant stock price drop during the March/April 2020 period. The findings provide important regulatory and practical implications.
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Tarun Jaiswal, Manju Pandey and Priyanka Tripathi
The purpose of this study is to investigate and demonstrate the advancements achieved in the field of chest X-ray image captioning through the utilization of dynamic convolutional…
Abstract
Purpose
The purpose of this study is to investigate and demonstrate the advancements achieved in the field of chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Typical convolutional neural networks (CNNs) are unable to capture both local and global contextual information effectively and apply a uniform operation to all pixels in an image. To address this, we propose an innovative approach that integrates a dynamic convolution operation at the encoder stage, improving image encoding quality and disease detection. In addition, a decoder based on the gated recurrent unit (GRU) is used for language modeling, and an attention network is incorporated to enhance consistency. This novel combination allows for improved feature extraction, mimicking the expertise of radiologists by selectively focusing on important areas and producing coherent captions with valuable clinical information.
Design/methodology/approach
In this study, we have presented a new report generation approach that utilizes dynamic convolution applied Resnet-101 (DyCNN) as an encoder (Verelst and Tuytelaars, 2019) and GRU as a decoder (Dey and Salemt, 2017; Pan et al., 2020), along with an attention network (see Figure 1). This integration innovatively extends the capabilities of image encoding and sequential caption generation, representing a shift from conventional CNN architectures. With its ability to dynamically adapt receptive fields, the DyCNN excels at capturing features of varying scales within the CXR images. This dynamic adaptability significantly enhances the granularity of feature extraction, enabling precise representation of localized abnormalities and structural intricacies. By incorporating this flexibility into the encoding process, our model can distil meaningful and contextually rich features from the radiographic data. While the attention mechanism enables the model to selectively focus on different regions of the image during caption generation. The attention mechanism enhances the report generation process by allowing the model to assign different importance weights to different regions of the image, mimicking human perception. In parallel, the GRU-based decoder adds a critical dimension to the process by ensuring a smooth, sequential generation of captions.
Findings
The findings of this study highlight the significant advancements achieved in chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Experiments conducted using the IU-Chest X-ray datasets showed that the proposed model outperformed other state-of-the-art approaches. The model achieved notable scores, including a BLEU_1 score of 0.591, a BLEU_2 score of 0.347, a BLEU_3 score of 0.277 and a BLEU_4 score of 0.155. These results highlight the efficiency and efficacy of the model in producing precise radiology reports, enhancing image interpretation and clinical decision-making.
Originality/value
This work is the first of its kind, which employs DyCNN as an encoder to extract features from CXR images. In addition, GRU as the decoder for language modeling was utilized and the attention mechanisms into the model architecture were incorporated.
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Devesh Kumar, Gunjan Soni, Yigit Kazancoglu and Ajay Pal Singh Rathore
This research aims to update the literature about the importance of reliability in supply chain (SC) and to find out the SC determinants.
Abstract
Purpose
This research aims to update the literature about the importance of reliability in supply chain (SC) and to find out the SC determinants.
Design/methodology/approach
This research surveys while contributing to the academic grasp of supply chain reliability (SCR) concepts. The study found 45 peer-reviewed publications using a structured survey technique with a four-step filtering process. The filtering process includes data reduction processes such as an evaluation of abstract and conclusion. The filtered study focuses on SCR and its determinants.
Findings
One of the major findings is that most of the study has focused on mathematical and conceptual studies. Also, this study provides the answer to a question like how can reliability be better accepted and evolved within the SC after finding the determinants of SCR.
Originality/value
The observed methodological gap in understanding and development of SCR was identified and classified into three categories: mathematical, conceptual and empirical studies (case studies and survey’s mainly). This research will aid academics in developing and understanding the determinants of SCR.
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Ravita Kharb, Charu Shri and Neha Saini
The objective is to develop an empirical model estimating the relationship and interaction amongst the factors affecting and enhancing green finance (GF) in developing economies…
Abstract
Purpose
The objective is to develop an empirical model estimating the relationship and interaction amongst the factors affecting and enhancing green finance (GF) in developing economies like India.
Design/methodology/approach
Around nine growth-accelerating enablers of green financing were found through literature and unstructured interviews and analysed using the total interpretive structural modelling (TISM) method. The hierarchical link between each factor is established using TISM, and further to evaluate the driver-dependent relationship the Matriced’ Impacts Croises Appliquee Aaun Classement (MICMAC) approach is utilised.
Findings
The findings demonstrate an interrelationship between growth-accelerating factors, where the political environment and information and communication technology (ICT), have minimal dependency but a strong driving force. Political environment and ICT are found as strategic-level factors lying at the bottom of the model driving towards the dependent variables. The government should focus on enacting effective policies such as the green credit guarantee scheme and carbon credit and establishing a regulatory framework to enhance green financing.
Research limitations/implications
This study examines the literature to generalise the findings and focus on the primary motivators for developing green financing. To increase green financial activity, practitioners must concentrate on aspects with significant driving forces. Furthermore, it makes organisations more profitable, efficient and competitive and promotes long-term growth.
Originality/value
The study is the first in the literature which identifies the growth-accelerating factors of green financing using the TISM and MICMAC-based hierarchical models.
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Tahani Hakami, Omar Sabri, Bassam Al-Shargabi, Mohd Mohid Rahmat and Osama Nashat Attia
This study aims to examine the present condition of blockchain technology (BT) applications in auditing by analyzing journal publications on the topic to acquire a better…
Abstract
Purpose
This study aims to examine the present condition of blockchain technology (BT) applications in auditing by analyzing journal publications on the topic to acquire a better understanding of the field.
Design/methodology/approach
This study makes use of the Bibliometric Analysis method and gathered 725 papers from the Web of Science and Scopus databases in the management and accounting, business, financial, economic and social science, as well as decision sciences fields from 2017 to 2021 using the R-Package Bibliometrix Analysis “biblioshiny”.
Findings
The findings revealed that blockchain research in terms of auditing has already increased and started to spark a quick rise in popularity, but is still in its initial phases with important quality though less in quantity. Moreover, the Journal of Emerging Technologies in Accounting is the most prolific journal with 2019 as the highest publication year, with the United States and China as the most cited countries in this field. Furthermore, in this field, there are much research topics involving blockchain, audit and smart contracts; and there is less involving data analytics, governance, hyperledger, distributed ledger and financial reporting. Additionally, Sheldon (2019) and Smith and Castonguay (2020) are the most productive authors in the field in terms of the H-index.
Research limitations/implications
This study has certain limitations such as the fact that it only looked at 105 papers in the domains of finance, business, economics, accounting, management as well as multidisciplinary science. Moreover, the research’s data and dates have an impact on the results dependability. As this is an original topic, fresh studies are anticipated to remain to shine a spotlight on and suggest answers to blockchain’s implications on auditing. Additionally, the period of time was limited to only the last five years, from 2017 to 2021. As a result, extensive study into the topic is required since there is currently a research deficit in the blockchain field in the setting of auditing. So, new research is required to offer new frameworks and understandings for describing the blockchain function in auditing, including processes, techniques, security, as well as timeliness. Investigations in unique circumstances and research employing innovative research methodologies for discovering the new issue would be valuable in acquiring a higher grasp of the complexities faced.
Originality/value
This research contributed to the field by assessing the present state of the art of research on the usage and use of BT in finding research gaps, the audit profession and, most importantly, recommending a future direction for researchers in the subject.
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Kamran Mahroof, Amizan Omar, Emilia Vann Yaroson, Samaila Ado Tenebe, Nripendra P. Rana, Uthayasankar Sivarajah and Vishanth Weerakkody
The purpose of this study is to evaluate food supply chain stakeholders’ intention to use Industry 5.0 (I5.0) drones for cleaner production in food supply chains.
Abstract
Purpose
The purpose of this study is to evaluate food supply chain stakeholders’ intention to use Industry 5.0 (I5.0) drones for cleaner production in food supply chains.
Design/methodology/approach
The authors used a quantitative research design and collected data using an online survey administered to a sample of 264 food supply chain stakeholders in Nigeria. The partial least square structural equation model was conducted to assess the research’s hypothesised relationships.
Findings
The authors provide empirical evidence to support the contributions of I5.0 drones for cleaner production. The findings showed that food supply chain stakeholders are more concerned with the use of I5.0 drones in specific operations, such as reducing plant diseases, which invariably enhances cleaner production. However, there is less inclination to drone adoption if the aim was pollution reduction, predicting seasonal output and addressing workers’ health and safety challenges. The findings outline the need for awareness to promote the use of drones for addressing workers’ hazard challenges and knowledge transfer on the potentials of I5.0 in emerging economies.
Originality/value
To the best of the authors’ knowledge, this study is the first to address I5.0 drones’ adoption using a sustainability model. The authors contribute to existing literature by extending the sustainability model to identify the contributions of drone use in promoting cleaner production through addressing specific system operations. This study addresses the gap by augmenting a sustainability model, suggesting that technology adoption for sustainability is motivated by curbing challenges categorised as drivers and mediators.
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The purpose of this paper is to analyze the mechanism of the role of government subsidies on corporate environmental investment and explore how specific characteristics of firms…
Abstract
Purpose
The purpose of this paper is to analyze the mechanism of the role of government subsidies on corporate environmental investment and explore how specific characteristics of firms affect corporate environmental responsibility.
Design/methodology/approach
This paper examines the relationship between government subsidies and corporate environmental investment and models with a sample of 78,854 industries. The authors measure the corporate environmental investment by the natural logarithm of the volume of waste gas treatment facilities.
Findings
The results show the positive effect of government subsidies on corporate environmental investment. In addition, state ownership positively regulates the relationship between government and corporations, but the relationship between them is negatively regulated by the slack resources.
Practical implications
When people are increasingly concerned about corporate social and environmental responsibility, clarifying the link between government subsidies and corporate environmental investments can help policymakers formulate policies and allocate limited resources.
Originality/value
This study uses the resource-based view as a theoretical framework to reveal the mechanism of action between government subsidies and corporate environmental responsibility, enriching the previous literature that explores the issue based on the legitimacy perspective.
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