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1 – 10 of 260
Article
Publication date: 22 March 2024

Rachana Jaiswal, Shashank Gupta and Aviral Kumar Tiwari

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Practical implications

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.

Social implications

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.

Originality/value

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.

Details

Management Research Review, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2040-8269

Keywords

Article
Publication date: 8 March 2024

Juan Shi

Users' voluntary forwarding behavior opens a new avenue for companies to promote their brands and products on social networking sites (SNS). However, research on voluntary…

Abstract

Purpose

Users' voluntary forwarding behavior opens a new avenue for companies to promote their brands and products on social networking sites (SNS). However, research on voluntary information disseminators is limited. This paper aims to bring an in-depth understanding of voluntary disseminators by answering the following questions: (1) What is the underlying mechanism by which some users are more enthusiastic to voluntarily forward content of interest? (2) How to identify them? We propose a theoretical model based on the Elaboration-Likelihood Model (ELM) and examine three types of factors that moderate the effect of preference matching on individual forwarding behavior, including personal characteristics, tweet characteristics and sender–receiver relationships.

Design/methodology/approach

Via Twitter API, we randomly crawled 1967 Twitter users' data to validate the conceptual framework. Each user’s original tweets and retweeted tweets, profile data such as the number of followers and followees and verification status were obtained. The final corpus contains 163,554 data points composed of 1,634 valid twitterers' retweeting behavior. Tweets produced by these core users' followees were also crawled. These data points constitute an unbalanced panel data and we employ different models — fixed-effects, random-effects and pooled logit models — to test the moderation effects. The robustness test shows consistency among these different models.

Findings

Preference matching significantly affects users' forwarding behavior, implying that SNS users are more likely to share contents that align with their preferences. In addition, we find that popular users with lots of followers, heavy SNS users who author tweets or forward other-sourced tweets more frequently and users who tend to produce longer original contents are more enthusiastic to disseminate contents of interest. Furthermore, interaction strength has a positive moderating effect on the relationship between preference matching and individuals' forwarding decisions, suggesting that users are more likely to disseminate content of interest when it comes from strong ties. However, the moderating effect of perceived affinity is significantly negative, indicating that an online community of individuals with many common friends is not an ideal place to engage individuals in sharing information.

Originality/value

This work brings about a deep understanding of users' voluntary forwarding behavior of content of interest. To the best of our knowledge, the current study is the first to examine (1) the underlying mechanism by which some users are more likely to voluntarily forward content of interest; and (2) how to identify these potential voluntary disseminators. By extending the ELM, we examine the moderating effect of tweet characteristics, sender–receiver relationships as well as personal characteristics. Our research findings provide practical guidelines for enterprises and government institutions to choose voluntary endorsers when trying to engage individuals in information dissemination on SNS.

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 5 January 2023

Qingqing Zhou

With the rapid development of social media, the occurrence and evolution of emergency events are often accompanied by massive users' expressions. The fine-grained analysis on…

Abstract

Purpose

With the rapid development of social media, the occurrence and evolution of emergency events are often accompanied by massive users' expressions. The fine-grained analysis on users' expressions can provide accurate and reliable information for event processing. Hence, 2,003,814 expressions on a major malignant emergency event were mined from multiple dimensions in this paper.

Design/methodology/approach

This paper conducted finer-grained analysis on users' online expressions in an emergency event. Specifically, the authors firstly selected a major emergency event as the research object and collected the event-related user expressions that lasted nearly two years to describe the dynamic evolution trend of the event. Then, users' expression preferences were identified by detecting anomic expressions, classifying sentiment tendencies and extracting topics in expressions. Finally, the authors measured the explicit and implicit impacts of different expression preferences and obtained relations between the differential expression preferences.

Findings

Experimental results showed that users have both short- and long-term attention to emergency events. Their enthusiasm for discussing the event will be quickly dispelled and easily aroused. Meanwhile, most users prefer to make rational and normative expressions of events, and the expression topics are diversified. In addition, compared with anomic negative expressions, anomic expressions in positive sentiments are more common. In conclusion, the integration of multi-dimensional analysis results of users' expression preferences (including discussion heat, preference impacts and preference relations) is an effective means to support emergency event processing.

Originality/value

To the best of the authors' knowledge, it is the first research to conduct in-depth and fine-grained analysis of user expression in emergencies, so as to get in-detail and multi-dimensional characteristics of users' online expressions for supporting event processing.

Details

Aslib Journal of Information Management, vol. 76 no. 2
Type: Research Article
ISSN: 2050-3806

Keywords

Article
Publication date: 8 March 2024

Peter Madzik, Lukas Falat, Luay Jum’a, Mária Vrábliková and Dominik Zimon

The set of 2,509 documents related to the human-centric aspect of manufacturing were retrieved from Scopus database and systmatically analyzed. Using an unsupervised machine…

46

Abstract

Purpose

The set of 2,509 documents related to the human-centric aspect of manufacturing were retrieved from Scopus database and systmatically analyzed. Using an unsupervised machine learning approach based on Latent Dirichlet Allocation we were able to identify latent topics related to human-centric aspect of Industry 5.0.

Design/methodology/approach

This study aims to create a scientific map of the human-centric aspect of manufacturing and thus provide a systematic framework for further research development of Industry 5.0.

Findings

In this study a 140 unique research topics were identified, 19 of which had sufficient research impact and research interest so that we could mark them as the most significant. In addition to the most significant topics, this study contains a detailed analysis of their development and points out their connections.

Originality/value

Industry 5.0 has three pillars – human-centric, sustainable, and resilient. The sustainable and resilient aspect of manufacturing has been the subject of many studies in the past. The human-centric aspect of such a systematic description and deep analysis of latent topics is currently just passing through.

Details

European Journal of Innovation Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1460-1060

Keywords

Article
Publication date: 5 March 2024

Sana Ramzan and Mark Lokanan

This study aims to objectively synthesize the volume of accounting literature on financial statement fraud (FSF) using a systematic literature review research method (SLRRM). This…

Abstract

Purpose

This study aims to objectively synthesize the volume of accounting literature on financial statement fraud (FSF) using a systematic literature review research method (SLRRM). This paper analyzes the vast FSF literature based on inclusion and exclusion criteria. These criteria filter articles that are present in the accounting fraud domain and are published in peer-reviewed quality journals based on Australian Business Deans Council (ABDC) journal ranking. Lastly, a reverse search, analyzing the articles' abstracts, further narrows the search to 88 peer-reviewed articles. After examining these 88 articles, the results imply that the current literature is shifting from traditional statistical approaches towards computational methods, specifically machine learning (ML), for predicting and detecting FSF. This evolution of the literature is influenced by the impact of micro and macro variables on FSF and the inadequacy of audit procedures to detect red flags of fraud. The findings also concluded that A* peer-reviewed journals accepted articles that showed a complete picture of performance measures of computational techniques in their results. Therefore, this paper contributes to the literature by providing insights to researchers about why ML articles on fraud do not make it to top accounting journals and which computational techniques are the best algorithms for predicting and detecting FSF.

Design/methodology/approach

This paper chronicles the cluster of narratives surrounding the inadequacy of current accounting and auditing practices in preventing and detecting Financial Statement Fraud. The primary objective of this study is to objectively synthesize the volume of accounting literature on financial statement fraud. More specifically, this study will conduct a systematic literature review (SLR) to examine the evolution of financial statement fraud research and the emergence of new computational techniques to detect fraud in the accounting and finance literature.

Findings

The storyline of this study illustrates how the literature has evolved from conventional fraud detection mechanisms to computational techniques such as artificial intelligence (AI) and machine learning (ML). The findings also concluded that A* peer-reviewed journals accepted articles that showed a complete picture of performance measures of computational techniques in their results. Therefore, this paper contributes to the literature by providing insights to researchers about why ML articles on fraud do not make it to top accounting journals and which computational techniques are the best algorithms for predicting and detecting FSF.

Originality/value

This paper contributes to the literature by providing insights to researchers about why the evolution of accounting fraud literature from traditional statistical methods to machine learning algorithms in fraud detection and prediction.

Details

Journal of Accounting Literature, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0737-4607

Keywords

Article
Publication date: 15 February 2024

Xinyu Liu, Kun Ma, Ke Ji, Zhenxiang Chen and Bo Yang

Propaganda is a prevalent technique used in social media to intentionally express opinions or actions with the aim of manipulating or deceiving users. Existing methods for…

Abstract

Purpose

Propaganda is a prevalent technique used in social media to intentionally express opinions or actions with the aim of manipulating or deceiving users. Existing methods for propaganda detection primarily focus on capturing language features within its content. However, these methods tend to overlook the information presented within the external news environment from which propaganda news originated and spread. This news environment reflects recent mainstream media opinions and public attention and contains language characteristics of non-propaganda news. Therefore, the authors have proposed a graph-based multi-information integration network with an external news environment (abbreviated as G-MINE) for propaganda detection.

Design/methodology/approach

G-MINE is proposed to comprise four parts: textual information extraction module, external news environment perception module, multi-information integration module and classifier. Specifically, the external news environment perception module and multi-information integration module extract and integrate the popularity and novelty into the textual information and capture the high-order complementary information between them.

Findings

G-MINE achieves state-of-the-art performance on both the TSHP-17, Qprop and the PTC data sets, with an accuracy of 98.24%, 90.59% and 97.44%, respectively.

Originality/value

An external news environment perception module is proposed to capture the popularity and novelty information, and a multi-information integration module is proposed to effectively fuse them with the textual information.

Details

International Journal of Web Information Systems, vol. 20 no. 2
Type: Research Article
ISSN: 1744-0084

Keywords

Article
Publication date: 28 July 2023

Viriya Taecharungroj and Ioana S. Stoica

The purpose of this paper is to examine and compare the in situ place experiences of people in Luton and Darlington.

Abstract

Purpose

The purpose of this paper is to examine and compare the in situ place experiences of people in Luton and Darlington.

Design/methodology/approach

The study used 109,998 geotagged tweets from Luton and Darlington between 2020 and 2022 and conducted topic modelling using latent Dirichlet allocation. Lexicons were created using GPT-4 to evaluate the eight dimensions of place experience for each topic.

Findings

The study found that Darlington had higher counts in the sensorial, behavioural, designed and mundane dimensions of place experience than Luton. Conversely, Luton had a higher prevalence of the affective and intellectual dimensions, attributed to political and faith-related tweets.

Originality/value

The study introduces a novel approach that uses AI-generated lexicons for place experience. These lexicons cover four facets, two intentions and two intensities of place experience, enabling detection of words from any domain. This approach can be useful not only for town and destination brand managers but also for researchers in any field.

Details

Journal of Place Management and Development, vol. 17 no. 1
Type: Research Article
ISSN: 1753-8335

Keywords

Article
Publication date: 5 June 2023

Ali Daei, Seyed Mahmood Zanjirchi, Seyed Habibolah Mirghafoori and Alireza Naser Sadrabadi

The varying nature of the competitive environment of small- and medium-sized enterprises (SMEs), contributing significantly to gross domestic product in most countries, has made…

Abstract

Purpose

The varying nature of the competitive environment of small- and medium-sized enterprises (SMEs), contributing significantly to gross domestic product in most countries, has made their moving toward internationalization and global competition unavoidable in such a way that the life cycle of research in this area is experiencing a period of rapid growth. This study aims to evaluate the status of research on SME internationalization based on bibliographic records retrieved from the Web of Science Core Collection and Scopus.

Design/methodology/approach

Using a scientometric analysis, reviewing the important points and the boundaries of research on SME internationalization as well as practicing co-occurrence and burst detection analysis.

Findings

Through a rigorous examination of the crucial points and boundaries within the realm of SMEs internationalization research, coupled with an analysis of co-occurrence and burst detection techniques to detect contemporary hotbed topics, this study has uncovered that the predominant focus of current discourse centers around the areas of networks and networking, as well as internationalization models and entry into the global arena. Moreover, it gives insight that future investigations will shift toward enhancing SME internationalization performance, while simultaneously prioritizing the expeditiousness of their entrance into international markets. The insights garnered from this inquiry are expected to facilitate salient contributions to future literature in this area, thereby advancing our understanding of these complex phenomena.

Practical implications

The trend of the research in this field can be useful for enthusiasts. In this context, the life cycle of research on SME internationalization has been drawn that shows the period of research growth of publications is almost between 2005 and 2023, and the saturation will be approximately from 2023 to 2035. The top researching SME internationalization in the world have been occurred in the USA, England, Canada, Sweden countries and in Department of Management, Department of Marketing, School of Management, Faculty of Management Studies institutions. Also, most of the research has been published in Journal of International Business Studies, International Business Review and Strategic Management Journal.

Originality/value

This study accordingly provided a valuable perspective for future research in this line.

Details

Competitiveness Review: An International Business Journal , vol. 34 no. 2
Type: Research Article
ISSN: 1059-5422

Keywords

Abstract

Details

The Impact of ChatGPT on Higher Education
Type: Book
ISBN: 978-1-83797-648-5

Open Access
Article
Publication date: 31 July 2023

Daniel Šandor and Marina Bagić Babac

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…

2599

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

Information Discovery and Delivery, vol. 52 no. 2
Type: Research Article
ISSN: 2398-6247

Keywords

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