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For more than 20 years, the consumer–brand relationship (CBR) subfield has flourished with scores of constructs being employed. We provide an epistemological examination of its 14 most commonly measured relational constructs (e.g. Brand Love, Self-Brand Connection) collected from 767 research articles, reflecting 1,753 scales and approximately 9,200 items. We demonstrate that constructs overlap an average of 43% across all journals and 21% in top journals due to assessing highly similar or synonymous ideas (i.e. jangle). We use a combination of text and cluster analyses to show that measures of allegedly the same construct are polysemic, having an average of 5.3 different meanings (i.e. jingle). The results document in the CBR subfield the types of measurement inconsistencies and ambiguities that have sown confusion and frustration in other academic fields. We discuss the roots of these problems and offer recommendations aimed at helping scholars to improve measurement practices and to limit the presence of jingle and jangle in the CBR subfield. [ABSTRACT FROM AUTHOR]

The goal of the mandatory disclosure of Key Audit Matters (KAMs) in the extended audit report is to provide relevant, client-specific information and thereby add value to the audit report. But mixed results fuel debate about the informativeness of expanded audit reports, particularly the disclosure of KAMs. This study employs text analysis to examine whether and how the information content of KAMs is associated with investor perceptions of financial reporting quality, as proxied by earnings response coefficients. Specifically, using a sample of KAMs disclosed in Taiwanese audit reports (for fiscal years 2016 and 2017), companies with KAMs that contain more client-specific information are perceived as having lower reporting quality. We further note that this association is driven by the risk-related description in KAMs. Additional analyses indicate that our textual measure is associated with actual financial reporting quality, as proxied by abnormal accruals and the likelihood of misstatements. Overall, our results provide evidence supporting the information value of KAMs in the Taiwanese audit market. This study contributes by suggesting the value of the expanded audit report. [ABSTRACT FROM AUTHOR]

This research studies the impact of articles appearing in the top four journals for consumer research— JCR , JMR , JM , and JCP— by examining factors that influence scholarly, public, and practical impact. This conceptually distinguishes between "incongruent novelty" (novelty arising from incongruity between an article and other existing articles) and "recombinant novelty" (novelty based on drawing from more disparate references). Incongruent novelty is decomposed into two components: (1) topic incongruity—incongruity between an article's topic and other existing topics at the time and (2) article incongruity—incongruity between an article and other existing articles on the same primary topic. By integrating topic modeling and word embedding to measure these constructs, we demonstrate varying impacts of types of novelty on three types of impact. Overall, our findings illustrate that topic and article incongruity have effects above and beyond recombinant novelty. For both scholarly and public impact, our results suggest that moderately high topic incongruity has the largest influence, suggesting that researchers should address novel topics. Additionally, scholarly citations are positively affected by lower and higher (vs. moderate) levels of article incongruity, suggesting that researchers either engage in programmatic research or add a new approach to the existing research on a topic. [ABSTRACT FROM AUTHOR]

This article analyzes the nature and temporal change of conceptual contributions in marketing scholarship with two complementary studies. First, based on a computer-aided text analysis of 5,922 articles published in the four major marketing journals between 1990 and 2021, the authors analyze how conceptual contributions have changed over time using the MacInnis (2011) framework. Results indicate that over the past three decades, theorizing efforts have strongly favored "envisioning" and "explicating" at the expense of "relating" and "debating," with this imbalance increasing over time. Second, the authors draw on 48 in-depth interviews with editors, department heads, and authors to validate these patterns and uncover the underlying mechanisms. The findings indicate that a prevalent thought style has developed in the field—defined by the research ideals of novelty, clarity, and quantification—that shapes the collective view of how marketing scholars, in their roles as authors, reviewers, and mentors, can make a valuable contribution to marketing scholarship. This thought style favors envisioning and explicating contributions and disfavors relating and debating contributions. Jointly, the two studies point to several rebalancing options that can reinvigorate relating and debating contributions while preserving the current strengths of the marketing field. [ABSTRACT FROM AUTHOR]

Social media offers brands the ability to gauge consumer reactions to marketing and brand crises. While social media listening has focused on aggregate patterns, consumers differ in how they react to a crisis faced by a particular brand. Analyzing consumer behavior for 39 brands pertaining to 77 brand crises through the lens of consumer posts on brands' Facebook pages, we find that consumers' prior online interactions with the brand and the nature of the brand crisis moderate the language they employ in their posts. Specifically, these factors affect the extent to which consumers express anger and the familiarity of their language. While consumers who have not engaged with the brand previously employ more familiar language and self-referencing following values-related crises compared to consumers who have interacted with the brand, these individuals express more anger after performance-related crises. In contrast, consumers who have previously interacted with the brand express more anger in the wake of values-related crises. We discuss the implications of our findings for brand managers using social media posts as a means of monitoring consumer perceptions. [ABSTRACT FROM AUTHOR]

Topic models are becoming a frequently employed tool in the empirical methods repertoire of information systems and management scholars. Given textual corpora, such as consumer reviews and online discussion forums, researchers and business practitioners often use topic modeling to either explore data in an unsupervised fashion or generate variables of interest for subsequent econometric analysis. However, one important concern stems from the fact that topic models can be notorious for their instability, i.e., the generated results could be inconsistent and irreproducible at different times, even on the same dataset. Therefore, researchers might arrive at potentially unreliable results regarding the theoretical relationships that they are testing or developing. In this paper, we attempt to highlight this problem and suggest a potential approach to addressing it. First, we empirically define and evaluate the stability problem of topic models using four textual datasets. Next, to alleviate the problem and with the goal of extracting actionable insights from textual data, we propose a new method, Stable LDA, which incorporates topical word clusters into the topic model to steer the model inference toward consistent results. We show that the proposed Stable LDA approach can significantly improve model stability while maintaining or even improving the topic model quality. Further, employing two case studies related to an online knowledge community and online consumer reviews, we demonstrate that the variables generated from Stable LDA can lead to more consistent estimations in econometric analyses. We believe that our work can further enhance management scholars' collective toolkit to analyze ever-growing textual data. [ABSTRACT FROM AUTHOR]

Although subscription boxes are incredibly popular, box companies often miss out on the benefits of a subscription model. Customers routinely skip boxes, and even when they do not, they often return products from each box. Hoping to avoid these returns, box companies ask customers to preview upcoming boxes, evaluate delivered boxes, and justify skipped boxes. The authors are interested in how such extensive customer participation can discourage skipping or, even better, encourage spending. An analysis of 30,000 apparel box customers' repeated preview, feedback, and purchase behavior reveals that, in addition to whether customers participate, the way in which and when they participate matter, and often in counterproductive ways. Specifically, customer participation with the delivered box drives future purchases, whereas participation before and after the delivered box appears to decrease box opt-in and spending. Further, the double-edged nature of customer participation, especially when such participation involves emotionality, has long-lasting effects, indicating the important role of customer participation dynamics in shaping purchase behavior. [ABSTRACT FROM AUTHOR]

Large-scale text data from public sources, including social media or online platforms, can expand urban planners’ ability to monitor and analyze urban conditions in near real-time. To overcome scalability challenges of manual techniques for qualitative data analysis, researchers and practitioners have turned to computer-automated methods, such as natural language processing (NLP) and deep learning. However, the benefits, challenges, and trade-offs of these methods remain poorly understood. How much meaning can different NLP techniques capture and how do their results compare to traditional manual techniques? Drawing on 90,000 online rental listings in Los Angeles County, this study proposes and compares manual, semi-automated, and fully automated methods for identifying context-informed topics in unstructured, user-generated text data. We find that fully automated methods perform best with more-structured text, but struggle to separate topics in free-flow text and when handling nuanced language. Introducing a manual technique first on a small data set to train a semi-automated method, however, improves accuracy even as the structure of the text degrades. We argue that while fully automated NLP methods are attractive replacements for scaling manual techniques, leveraging the contextual understanding of human expertise alongside efficient computer-based methods like BERT models generates better accuracy without sacrificing scalability.

References to agroecology have gradually started appearing in national and international public policies. We examine the extent to which agroecology is becoming a frame of reference for public action in the French national and regional context. Using textual statistics, we analyzed institutional documents concerning agroecology put out by public policy actors between 2008 and the summer of 2021, and conducted interviews with key actors of the Agroecological Project for France (PAEF, Projet Agroécologique pour la France). Our analysis shows that agroecology invoked in framing texts is reclassified in the vocabulary of triple performance, dominated by the notion of economic performance. A comparative analysis of the national and regional texts reveals mismatches, especially in terms of the level of autonomy of the actors concerned, and in the processes of knowledge transfer and support for changes in agricultural practices. [ABSTRACT FROM AUTHOR]

The extent to which rules in a governing arrangement addresses its local conditions is usually defined in the environmental governance literature as the problem of fit. Actors in governing arrangements with decision‐making authority are capable of choosing and designing specific policy tools in order to address specific policy problems. In this manuscript, we combine insights from the literature on policy design and the literature on common‐pool resource governance to assess the extent to which Nebraska's Natural Resources Districts (NRDs), which were created to provide context‐specific solutions to local water problems, design plans, and programs that fit their social‐ecological contexts. Using semi‐automated text analysis approaches and Qualitative Comparative Analysis, we assess how the design of plans and programs created by NRDs vary, to fit their social‐ecological contexts. Results indicate that the biophysical context plays a role in shaping the content of plans and programs, but that broader top‐down institutional mandates may play an even stronger role in shaping the outputs produced by NRDs. [ABSTRACT FROM AUTHOR]

The Latent Dirichlet Location (LDA) model is a popular method for creating mixed-membership clusters. Despite having been originally developed for text analysis, LDA has been used for a wide range of other applications. We propose a new formulation for the LDA model which incorporates covariates. In this model, a negative binomial regression is embedded within LDA, enabling straight-forward interpretation of the regression coefficients and the analysis of the quantity of cluster-specific elements in each sampling units (instead of the analysis being focused on modeling the proportion of each cluster, as in Structural Topic Models). We use slice sampling within a Gibbs sampling algorithm to estimate model parameters. We rely on simulations to show how our algorithm is able to successfully retrieve the true parameter values and the ability to make predictions for the abundance matrix using the information given by the covariates. The model is illustrated using real data sets from three different areas: text-mining of Coronavirus articles, analysis of grocery shopping baskets, and ecology of tree species on Barro Colorado Island (Panama). This model allows the identification of mixed-membership clusters in discrete data and provides inference on the relationship between covariates and the abundance of these clusters. HIGHLIGHTS: We propose a new formulation for the Latent Dirichlet Allocation (LDA) model which incorporates covariates. Our extension enables a straight-forward interpretation of the regression coefficients and the analysis of the quantity of cluster-specific elements in each sampling unit - including the prediction of these quantities in a new sample through covariates. We illustrate the benefits of this formulation using three data sets: text-mining of Coronavirus articles, analysis of grocery shopping baskets, and ecology of tree species in Barro Colorado Island. We provide an R package that enables users to readily apply our model. [ABSTRACT FROM AUTHOR]

The Systems Engineering Research Center (SERC) is a University Affiliated Research Center (UARC) of the US Department of Defense (DoD) formed in 2008 with more than 20 collaborator universities in the United States. Over the last decade, SERC has conducted research with Principal Investigators from universities within the SERC network, as reflected in technical reports (TR). These reports describe detailed information and analysis of the conducted research for every project under SERC support, such as written records of experiments or results of a scientific project. We analyzed the TRs from 2009 to early 2023 to identify research streams, topics, and evolution in systems engineering (SE) research using text mining and network analysis techniques, such as Louvain Community Detection and word similarity. As a result, we identified four major research streams over a decade of research projects, along with insights about topics and the evolution of SE within this time frame. Finally, we distinguished most profile authors and their most significant collaborations and networks. [ABSTRACT FROM AUTHOR]

Despite numerous studies that adopt text analysis in tourism and hospitality published in the past decade, a comprehensive review of this topic is still lacking. This study endeavors to bridge this gap by conducting a systematic review of text analysis research published in the top five journals in tourism and hospitality between 2013 and 2023. From a thematic analysis perspective, this paper provides an elaborate description of how text data can be utilized for prediction and understanding purposes. Specifically, the prediction scenarios encompass hotels, destinations, and restaurants, while the understanding aspect focuses on tourists, firms, destinations, and academic researches within this domain. From a methodological perspective, this paper offers an in-depth exploration of data sources, tools or methodologies, processing procedures, and commonly utilized metrics in text analysis. The paper concludes by proposing a summary framework for text analysis research while highlighting future research trends, opportunities, and challenges in this domain. [ABSTRACT FROM AUTHOR]

Kübler-Ross's five-stage model of death and dying—denial, anger, bargaining, depression, and acceptance—is one of the most popular theoretical models to come out of the 20th century. How did an obscure theory of the dying process come to dominate our understanding of emotional processes altogether? Building on previous work in the sociology of knowledge, I analyze the diffusion and institutionalization of Kübler-Ross's five-stage model in scientific and journalistic fields. Specifically, I analyze all 3216 citations of Kübler-Ross in the New York Times and the Web of Science database using qualitative and quantitative text analysis. I demonstrate how early scientific interest and commercial promotion led to adoption in popular culture, and document how the five-stage model expanded to cover everything from rent prices to COVID-19. I also argue that renewed interest in Kübler-Ross's work may signal contemporary attempts to mine the tradition for meaningful understandings of death and dying. [ABSTRACT FROM AUTHOR]

Policy agendas are a well-studied institutional level phenomenon that capture the set of policy issues that an institution pays attention to over time. They are emergent in nature in that individual behaviour shapes institutional level outcomes when policy makers allocate attention to policy issues. To examine the link between individual-level actions and system-level outcomes we introduce the concept of the agenda-setting constellation, defined as a group of policy makers paying attention to a set of policy issues. Taking the European Central Bank as a case study, and using a combination of text-analysis and networks-analysis techniques, we demonstrate how these meso-level structures shape the evolving policy agenda. We then examine the roles of personal experience, institutional constraints, and policy context in driving agenda-setter constellation membership. Our results show the value of studying policy agendas as networked processes and the key role that agenda-setter constellations play in driving policy agenda dynamics. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to examine the relationship between the readability of annual reports and corporate performance in Chinese listed firms. Design/methodology/approach: This research examined the annual report readability factors of Chinese listed companies by using a textual analysis method using Python to extract the text from the annual reports, convert it into numerical form to facilitate statistical analysis and then merge the results with data from the Chinese stock market to explain the impact on corporate performance and predict future earnings in the Chinese financial markets from 2008 to 2021. Findings: Study findings indicate that firms with better financial reporting readability are more profitable, incur lower agency costs and have low earnings in the Chinese stock markets when readability is low (i.e. more complexity and length of annual reports). It was also found that when a listed company has a good performance, it prefers to use a short space to explain its operating and financial status. More generally, the means of the report length are short, and accounting terms are used less frequently; in the case of a poor company, the annual report is particularly long and accounting terms are more frequently used. In the context of the COVID-19 crisis, this study served as a proxy measure of returns prior to the announcement of the COVID-19 pandemic. In addition, an instrumental variable approach is used, which helps results to remain robust and control for fixed effects and potential endogeneity problems. Research limitations/implications: Although this study's results cannot be generalised globally because of their limited scope, they can still be generalised across non-English speaking countries. Thus, future cross-country research is encouraged to examine the textual analysis of financial reports across those countries. Practical implications: This study conveys two messages to investors and policymakers within the Chinese market. First, investors ought to pay greater attention to the nonfinancial information contained in annual reports to improve the accuracy of their predictions regarding future firm performance. Second, Chinese policymakers are encouraged to instate a policy for the use of plain English in annual reports to make them more readable by international investors. Originality/value: This study contributes to the paucity of research that examines English-written annual reports in non-English speaking countries by examining the readability of annual reports in the Chinese market. [ABSTRACT FROM AUTHOR]

We study the Latent Dirichlet Allocation model, a popular Bayesian algorithm for text analysis. We show that the model's parameters are not identified, which suggests that the choice of prior matters. We characterize the range of values that the posterior mean of a given functional of the model's parameters can attain in response to a change in the prior, and we suggest two algorithms that report this range. Both of our algorithms rely on obtaining multiple Nonnegative Matrix Factorizations of either the posterior draws of the corpus' population term‐document frequency matrix or of its maximum likelihood estimator. The key idea is to maximize/minimize the functional of interest over all these nonnegative matrix factorizations. To illustrate the applicability of our results, we revisit recent work studying the effects of increased transparency on the communication structure of monetary policy discussions in the United States. [ABSTRACT FROM AUTHOR]

As the executor and agent of China's environmental policy, local governments' environmental concern reflects local governments' determination in environmental governance. To figure out how the strengthening environmental concerns affect enterprises' long-term activities, this study focuses on pharmaceutical manufacturing enterprises due to the enormous and complex composition of emissions. We apply bag of words to summarize relevant environmental words from the annual work reports in local governments to measure environmental concern. The empirical results of the OLS method reveal that the increasing environmental concerns of local governments did decrease the growth rate of chemical oxygen demand (COD) emission authentically. At the same time, it will inhibit the research and experimental development (R&D) activity intensity, but promote production efficiency of pharmaceutical manufacturing enterprises. After that, we discuss the heterogeneity of enterprise ownership, corporate social responsibility and regional regulatory strength of enterprises. Overall, we conclude that environmental concern did reduce COD emission and promote production efficiency, but it also has negative spillover effects. The novel contribution of this paper is that it enriches the trade-off between strengthening environmental compliance costs and long-term production and innovation activities. These results indicate that pharmaceutical manufacturing enterprises prioritize optimizing existing production processes instead of adopting efficient technology when complying with stricter environmental regulation. The reduction of R&D activities may pose risks to the long-term sustainable development of enterprises. [ABSTRACT FROM AUTHOR]

The main purpose of this study was to provide a unified framework of key stakeholder action-oriented approaches to extended producer responsibility based on behavioral estimates of consumers. For Twitter data, descriptive analysis and content analysis (thematic modeling) were performed after reviewing the literature. In addition, a new text analysis framework was employed to analyze extended producer responsibility reports. A total of 2,561,216 tweets and 708 documents related to producer responsibilities were analyzed. According to descriptive analysis, tweets related to North American countries represent about 35 percent of Twitter activity, while tweets related to African countries represent about 5 percent. A further consideration is the amount of attention paid by policymakers to issues related to mobile phones and environmental concerns among young people (about 5% in the 10–20 age group and 15% in the 20–30 age group). Consumer participation is significantly influenced by social factors such as education, awareness, and trust. In addition, consumers are increasingly concerned about data security and privacy. Also, cost, economic benefits, proximity, and convenience influenced consumer participation. The second part of the analysis revealed that producers are attending to enhance the economic benefits of recovery, develop infrastructure (for consumer convenience), ensure data security and privacy for participants, and increase knowledge and awareness about recycling and recovery. The developed countries also work with producers to increase engagement, while developing countries are trying to overcome information transparency, a lack of economic potential, and public awareness. [ABSTRACT FROM AUTHOR]

Research on disease outbreak prediction has suddenly received an enormous interest owing to the COVID-19 pandemic. Natural language processing using user-generated text data has proven to be quite effective for the same. Disease outbreaks that occur frequently can be easily predicted, but novel disease outbreaks are difficult to predict. This review work attempts to summarize the research concerning disease outbreaks and the use of datasets such as news headlines, tweets, and search engine queries using natural language processing techniques. Existing state-of-the-art systems have been analytically discussed with their contributions and limitations. This work is an insight into the existing research in the domain of disease outbreak prediction. A total of 146 articles were reviewed in this study, and results show that news and Twitter datasets are being used most to predict disease outbreaks. This research underlines the fact that numerous works are available in the literature based on specific outbreak-related Internet-sourced text data, viz. news, tweets, and search engine queries. However, this becomes a limitation for any disease outbreak prediction system as it can predict only specific disease outbreaks and motivates the development of systems capable of disease outbreak prediction without any bias. [ABSTRACT FROM AUTHOR]

With the concern of rainbow marketing, sexual minority travelers have become highly vigilant when evaluating hotels' LGBT-friendly marketing with their authenticity. It is important to scrutinize how sexual minority guests interpret brand authenticity toward LGBT-friendly hotels and form personal meanings during their stay. For this reason, this study proposes brand authenticity as a starting point for authenticity formation, which leads to existential authenticity and brand loyalty. Confirmatory factor analysis and PROCESS analysis were used to analyze data collected from sexual minority guests who visited LGBT-friendly hotels within the last 2 years. The findings indicate that (1) brand authenticity is an antecedent to brand loyalty and (2) the relationship between brand authenticity and brand loyalty is mediated by existential (intrapersonal and interpersonal) authenticity. Thus, in building on the multifaceted concept of existential authenticity, this study adds new theoretical insight into hotel marketing and management strategies. [ABSTRACT FROM AUTHOR]

European integration is at risk of becoming stuck in a 'politics' trap: Diverging national EU politicisation inhibits joint agreements which fuels public debates further. What can break this vicious circle? We argue that large and symmetrical exogenous shocks may reduce the divergence of national public EU debates to then study how the COVID-19 and Ukraine crises have altered mediatised EU portrayals across 753,435 articles from 228 major online news sites in the 27 EU member states during the 2018–2023 period. We find that the Covid and especially the Ukraine shock led to higher convergence in the public salience of the EU and the issue areas associated with this while partially also muting domestic party presence. However, we note that these effects appear short-lived. Large exogenous shocks thus do not lift the politicisation constraints on European integration permanently but offer at least windows of opportunity that may facilitate intergovernmental compromise. [ABSTRACT FROM AUTHOR]

For the longest time, the gold standard in preparing spoken language corpora for text analysis in psychology was using human transcription. However, such standard comes at extensive cost, and creates barriers to quantitative spoken language analysis that recent advances in speech-to-text technology could address. The current study quantifies the accuracy of AI-generated transcripts compared to human-corrected transcripts across younger (n = 100) and older (n = 92) adults and two spoken language tasks. Further, it evaluates the validity of Linguistic Inquiry and Word Count (LIWC)-features extracted from these two kinds of transcripts, as well as transcripts specifically prepared for LIWC analyses via tagging. We find that overall, AI-generated transcripts are highly accurate with a word error rate of 2.50% to 3.36%, albeit being slightly less accurate for younger compared to older adults. LIWC features extracted from either transcripts are highly correlated, while the tagging procedure significantly alters filler word categories. Based on these results, automatic speech-to-text appears to be ready for psychological language research when using spoken language tasks in relatively quiet environments, unless filler words are of interest to researchers. [ABSTRACT FROM AUTHOR]

Road traffic accidents are the leading causes of injuries and fatalities. Understanding the traffic accident occurrence pattern and its contributing factors are prerequisites for effective traffic safety management. The paper proposes a deep learning approach for traffic accident recognition and information extraction from online Chinese news to extract and organize traffic accidents automatically. The approach consists of three modules, including automated news collection, news classification, and traffic accident information extraction. The automated news collection module crawls news from online sources, cleans and organizes it into a general news database with different categories of news. The news classification module robustly recognizes the traffic accident news from all types of news by fusing the sentence‐wise and context‐wise semantic news information. The accident information extraction module extracts the key attributes of traffic accidents (e.g. causes, times, locations) from news text using the SoftLexicon‐BiLSTM‐CRF method. The proposed approach is validated by comparing it with state‐of‐the‐art text mining methods using Chinese news data crawled online. The results show that the approach can achieve a high information extraction performance in terms of precision, recall, and F1‐score. It improves the performance of the best benchmark model (BiLSTM‐CRF) by 18.8% in precision and 12.08% in F1‐score. In addition, the potential value of the automatically extracted accident data is illustrated from online news in complementing traditional authority accident data to drive more effective traffic safety management in practice. [ABSTRACT FROM AUTHOR]

This study uses Natural Language Processing (NLP) to analyze 110 articles from the Scopus database (2018–2023). Employing TextBlob for preprocessing and sentiment analysis, and Latent Dirichlet Allocation (LDA) for topic modeling, the research explores evolving themes in smart library abstracts. Key findings highlight a shift toward user-centricity, technological integration, and emerging concepts like Internet of Things (IoT) and Artificial Intelligence (AI). Sentiment analysis shows a generally positive outlook for this evolution. The study provides valuable insights for researchers and practitioners, guiding future innovation and service design in smart libraries. [ABSTRACT FROM AUTHOR]

In this article, we develop a methodological approach for organizational research regarding the construction of multidimensional and relational similarity measures by using the vector space model in natural language processing (NLP). Our vector space approach draws on the well-established premise in organizational research that texts provide a window into social reality and allow measuring theory-based constructs (e.g., organizations' self-representations). Using a vector space approach allows capturing the multidimensionality of these theory-based constructs and computing relational similarities between organizational entities (e.g., organizations, their members, and subunits) in social spaces and with their environments, such as the organization itself, industries, or countries. Thus, our methodological approach contributes to the recent trend in organizational research to use the potential inherent in big (textual) data by using NLP. In an example, we provide guidance for organizational scholars by illustrating how they can ensure validity when applying our methodological contribution in concrete research practice. [ABSTRACT FROM AUTHOR]

A key difficulty in the ever-changing cybersecurity scene is the detection of sophisticated cyber-attacks. Because new threats are so much more sophisticated and difficult to detect, traditional tactics typically fail. A new technique to improving cyber-attack detection skills is explored in this study. It uses Generative Adversarial Networks (GANs) and Natural Language Processing (NLP). Using GANs' realistic data generation capabilities, possible attack paths are simulated, creating a strong dataset for training detection systems. At the same time, natural language processing (NLP) methods are used to decipher the mountain of textual information produced by cyberspace, including incident reports, communication patterns, and logs. Our approach is based on building a fake dataset using GANs that mimics the features of advanced cyberattacks. A detection model is then trained using this dataset. Simultaneously, we improve the detection model's capacity to spot intricate and nuanced assault patterns by processing and analysing text-based data using natural language processing approaches. We use a benchmark cybersecurity dataset to test the integrated method. The experimental findings show that our GANNLP based detection system outperforms existing systems, which have an average accuracy of 85.3%, by a wide margin. It achieves a recall of 93.2%, precision of 92.5%, and accuracy of 94.7%. These findings prove that GANs and NLP work well together to identify complex cyberattacks. Finally, GANs and NLP together provide a potent instrument for better cyber-attack detection. A scalable solution that can adapt to the ever-changing nature of cyber threats is offered by this integrated approach, which also increases detection accuracy and efficiency. Improving the models and investigating their use in a real-world cybersecurity setting will be the primary goals of future research. [ABSTRACT FROM AUTHOR]

The field of entrepreneurship has seen remarkable growth, increasing the expectations of academic audiences. Articles need to balance novelty with rigorous methodology, theoretical contributions, social implications, and coherent argumentation to succeed in the publication process. However, navigating these varied and sometimes conflicting expectations to achieve optimal distinctiveness in academic narratives is challenging for authors. To explore how authors can achieve optimal distinctiveness amidst these complex expectations, we studied academic narratives and related editorial decisions of two leading entrepreneurship journals, Entrepreneurship: Theory & Practice (ETP, 4,151 papers) and Small Business Economics Journal (SBEJ, 4,043 papers), using computer-aided text analysis. Our study debunks common assumptions about what makes a successful entrepreneurship paper, providing an empirical basis for understanding actual versus perceived publication requisites. Furthermore, we extend optimal distinctiveness theory by demonstrating that high distinctiveness is not uniformly advantageous, meeting numerous expectations is not necessarily beneficial, and clear language is crucial for complex narratives. Our study underscores that crafting narratives is more nuanced than traditionally believed. Plain English Summary: Getting published in Entrepreneurship Journals: Less is more! How can entrepreneurship scholars increase their chances of getting published? Our study delves into scholarly articles in entrepreneurship journals, investigating which papers are published and which papers are not. We challenge the assumption that authors must fulfil as many expectations as possible and emphasize the importance of addressing specific audience expectations. By analyzing narratives and editorial decisions from Entrepreneurship Theory & Practice (ETP) and Small Business Economics Journal (SBEJ), we uncover the key to publication success: tailoring articles to meet the targeted audience's most pronounced requirements. Focusing on theoretical contributions when submitting one's work to ETP and focusing on empirical contributions when submitting one's work to SBEJ can increase the chances of getting your work published. [ABSTRACT FROM AUTHOR]

In a randomized trial that collects text as an outcome, traditional approaches for assessing treatment impact require that each document first be manually coded for constructs of interest by human raters. An impact analysis can then be conducted to compare treatment and control groups, using the hand-coded scores as a measured outcome. This process is both time and labor-intensive, which creates a persistent barrier for large-scale assessments of text. Furthermore, enriching one's understanding of a found impact on text outcomes via secondary analyses can be difficult without additional scoring efforts. The purpose of this article is to provide a pipeline for using machine-based text analytic and data mining tools to augment traditional text-based impact analysis by analyzing impacts across an array of automatically generated text features. In this way, we can explore what an overall impact signifies in terms of how the text has evolved due to treatment. Through a case study based on a recent field trial in education, we show that machine learning can indeed enrich experimental evaluations of text by providing a more comprehensive and fine-grained picture of the mechanisms that lead to stronger argumentative writing in a first- and second-grade content literacy intervention. Relying exclusively on human scoring, by contrast, is a lost opportunity. Overall, the workflow and analytical strategy we describe can serve as a template for researchers interested in performing their own experimental evaluations of text. [ABSTRACT FROM AUTHOR]

Student evaluations of teaching (SET) have been examined over the years to better understand the student experience, with an increasing portion of literature exploring the presence of implicit bias in SET surveys against minority gender and ethnic lecturers. This study explores free-text comments made by students from a large public university in Australia over the period 2010-2016, using a semi-supervised statistical approach. Data were collected via surveys administered online at the end of each course to every student officially enrolled in that course via the learning management system, and completion of the surveys was voluntary. We build a probabilistic topic model which incorporates student and lecturer characteristics into the topic formation process. We make statistical inference on the effects of gender and cultural or language backgrounds based on the topic and word prevalence probabilities. The results showed clear separation of topics discussed betweenmale and female lecturers. From a gendered perspective, our topic analyses have found that students are significantly more likely to critique female lecturers to improve on structural aspects of the course, as well as aspects of time management and control of the lecturing environment. In comparison, male lecturers were significantlymore likely to be critiqued on specific aspects related to lecture delivery. Lecturers from non-English speaking backgrounds were more likely to be both critiqued and praised for the clarity of their delivery. [ABSTRACT FROM AUTHOR]

Based on a database of over 650,000 green public procurement (GPP) contracts, we apply machine learning and text analysis methods to identify GPP. Our study examines the impact of GPP on green innovation using Chinese companies listed on the A-share market. The study has the following findings. (1) GPP significantly promotes green innovation by alleviating financing constraints and incentivizing green practices. (2) Enterprises in the maturity or growth stage, non-heavily polluting firms, and companies in regions with stringent environmental regulations are more likely to benefit from GPP. Additionally, GPP initiatives from local governments, centralized procurement, and university-based programs provide stronger incentives for green innovation. (3) Demand-side GPP and supply-side subsidies for green innovation have coordination effects. Corporate green innovation is positively influenced by the “Demand-side procurement First, Supply-side subsidies Later (DFSL)” and the “Demand-side procurement and Supply-side subsidies Simultaneously (DSS)” policies. [ABSTRACT FROM AUTHOR]

Background: Effective communication between patients and healthcare providers in the emergency department (ED) is challenging due to the dynamic nature of the ED environment. This study aimed to trial a chat service enabling patients in the ED and their family members to ask questions freely, exploring the service's feasibility and user experience. Objectives: To identify the types of needs and inquiries from patients and family members in the ED that could be addressed through the chat service and to assess the user experience of the service. Methods: We enrolled patients and family members aged over 19 years in the ED, providing the chat service for up to 4 h per ED visit. Trained research nurses followed specific guidelines to respond to messages from the participants. After participation, participants were required to complete a survey. Those who agreed also participated in interviews to provide insights on their experiences with the ED chat service. Results: A total of 40 participants (20 patients and 20 family members) sent 305 messages (72 by patients and 233 by family members), with patients sending an average of 3.6 messages and family members 11.7. Research nurses resolved 41.4% of patient inquiries and 70.9% of family member inquiries without further healthcare provider involvement. High usability was reported, with positive feedback on communication with healthcare workers, information accessibility, and emotional support. Conclusions: The ED chat service was found to be feasible and led to positive user experiences for both patients and their family members. [ABSTRACT FROM AUTHOR]

The study of China's geo-relations has received wide attention from the academic world. Chinese diplomatic speeches objectively record the process of China's interaction with other countries and the evolution of its attention to current hotspots, and the study of geo-relations hotspots from China's perspective can be conducted based on this officially released unstructured data. This paper selects Chinese Foreign Ministry speech text data and analyzes the hot issues implied in the text data from the perspective of feature vocabulary and topic coding, and the study shows that: (1) The Asia-Pacific and neighboring countries occupy a primary position in the handling of China's geopolitical relations. (2) In recent years, the geopolitical conflict with the United States and other Western countries has shifted from geo-economic to geo-cultural aspects. (3) In terms of topics, trade and economy are the focus of geopolitical relations between countries in the world, and the role of science, technology and innovation elements in the penetration of great power competition is becoming increasingly prominent. (4) In terms of geostrategy, attention is drawn to the impact of NATO's "eastern concern" on the Asia-Pacific region, and the South Pacific island region is a potential major power geopolitical game area that deserves attention. This paper applies text analysis techniques to study the hot issues of China's geopolitical relations, providing a new perspective for the study of geopolitical relations and a certain reference for the development of China's geopolitical relations. [ABSTRACT FROM AUTHOR]

Using publicly available data from 299 preregistered replications from the social sciences, we found that the language used to describe a study can predict its replicability above and beyond a large set of controls related to the article characteristics, study design and results, author information, and replication effort. To understand why, we analyzed the textual differences between replicable and nonreplicable studies. Our findings suggest that the language in replicable studies is transparent and confident, written in a detailed and complex manner, and generally exhibits markers of truthful communication, possibly demonstrating the researchers' confidence in the study. Nonreplicable studies, however, are vaguely written and have markers of persuasion techniques, such as the use of positivity and clout. Thus, our findings allude to the possibility that authors of nonreplicable studies are more likely to make an effort, through their writing, to persuade readers of their (possibly weaker) results. [ABSTRACT FROM AUTHOR]

Representation is a concept that is constantly undergoing historical-social transformation and is still studied with an interdisciplinary approach today. The reception of literary representation has gained different socio-cultural dimensions and been given different meanings in the historical process. Literary works are cultural products with high representative power, and with these features, they provide important data to researchers in revealing social and cultural meanings. While studies that problematize the forms of literary representation reveal the meaning of the text, they also provide important data in the context of gender through the way male-female relations are reflected. The works of Bosnian Sabit (d. 1712), who is considered a poet who created his own style in classical Turkish literature, produced with different perspectives and approaches, constitute a wide range that is rich in diversity and content. Bosnian Sabit's Derenâme masnavi can be considered as a social and cultural indicator of the period in terms of its content and the way the characters are presented. Sabit is an author who contributes with his original style to the establishment of the traditional Turkish literature with the characters he chose in Derenâme and the way he presents the story. In this study, character representations in the Derenâme masnavi will be tried to be evaluated with a critical and analytical approach. [ABSTRACT FROM AUTHOR]

Although prior research has proposed multiple approaches to reducing anti-immigrant discrimination, less is known about whether priming a shared political ideology works. Integrating a text analysis and a pre-registered survey experiment, we study Hong Kong residents' attitudes toward mainland Chinese immigrants. By employing the structural topic model, we analyze more than 4,000 Hong Kong newspaper articles on mainland immigrants between 2003 and 2020 and show that the political implications of mainland immigration have gained increasing attention in mass media, whereas economic and social concerns have waned in salience. Resonating with this analysis, our survey experiment reveals that when exposed to a vignette priming mainland immigrants' support for democratic values, young adults in Hong Kong are less likely to support restrictive immigration policies for mainland Chinese. We also find that priming immigrants' economic contribution and cultural integration, as well as natives' family history, has limited effects on Hongkongers' immigration attitudes. [ABSTRACT FROM AUTHOR]

A long-standing concern in the study of culture is understanding how culture is distributed, often discussed in terms of cultural "coherence." Cultural diversity, defined as the degree to which people share beliefs or meanings, is one dimension of cultural coherence that has been associated with many social outcomes. This article contributes to this area of research by considering how to measure cultural diversity in text and introducing a simple approach that uses word counts and sets of diversity indices (called "diversity profiles"). Text is useful for social-psychological analysis because as an artifact of individual thought, it provides a way to measure how beliefs and meanings are distributed and made salient across groups. The measurement approach outlined here contrasts to many contemporary computational approaches to measuring culture in text, which employ a relational logic of meaning based on word co-occurrences. While these more sophisticated approaches are well suited to measuring diversity in many instances, I show that there are some cases for which simpler measures based on word counts are ideal. After discussing the measurement of cultural diversity using word counts, I present a computational analysis of interview transcripts of American religious parents discussing the ages at which it is appropriate for children to participate in different practices often considered inappropriate for young children. The analysis points to the homogenizing influence of institutions on discussions of age appropriateness. I conclude by discussing implications for cultural analysis more generally. [ABSTRACT FROM AUTHOR]

Research on lawmaking generally examines why lawmakers enact certain policy ideas. However, most ideas in the United States Congress never become law. Eventually, members stop introducing them altogether. This process, where policies are proposed, not acted on, and no longer advocated for is, by far, the most common legislative outcome. The literature pays it little attention. Yet, how ideas die is important in understanding why some measures stop being realistic alternatives, the importance of policy entrepreneurship, and how policy windows affect the supply of ideas. This paper analyzes why congressional ideas die. I argue and find that the proposals designed to be enacted, especially by legislators with more issue expertise and agenda-setting powers, are less likely to persist across terms. I also show that ideas disappear more often when their sponsors leave Congress, but do not find a similar pattern for when policy windows close and lawmaking conditions worsen. [ABSTRACT FROM AUTHOR]

Most natural language models and tools are restricted to one language, typically English. For researchers in the behavioral sciences investigating languages other than English, and for those researchers who would like to make cross-linguistic comparisons, hardly any computational linguistic tools exist, particularly none for those researchers who lack deep computational linguistic knowledge or programming skills. Yet, for interdisciplinary researchers in a variety of fields, ranging from psycholinguistics, social psychology, cognitive psychology, education, to literary studies, there certainly is a need for such a cross-linguistic tool. In the current paper, we present Lingualyzer (https://lingualyzer.com), an easily accessible tool that analyzes text at three different text levels (sentence, paragraph, document), which includes 351 multidimensional linguistic measures that are available in 41 different languages. This paper gives an overview of Lingualyzer, categorizes its hundreds of measures, demonstrates how it distinguishes itself from other text quantification tools, explains how it can be used, and provides validations. Lingualyzer is freely accessible for scientific purposes using an intuitive and easy-to-use interface. [ABSTRACT FROM AUTHOR]

As part of the War on Terror, the US conducted at least 378 air and drone strikes in Yemen from 2002 to 2023. While primarily targeting members of al-Qaeda in the Arabian Peninsula (AQAP), these strikes have killed over 1,000 people, including more than 125 civilians. This research aims to understand the broader societal impact of US military action as shown on Twitter, now known as 'X'. The authors take a mixed-methods approach, using both qualitative and quantitative data to investigate this impact. They perform word- and tweet-level analyses on the content and sentiment of a subset of Yemeni Twitter users and contrast the content of Yemeni users' tweets about US drone strikes with that of US users' tweets. Finally, the article quantifies the temporal relationship between the frequency and severity of US drone strikes and Twitter activity levels in Yemen, considering the prevalence of Twitter bots. [ABSTRACT FROM AUTHOR]

In the field of stock price forecasting, we are actively seeking to integrate various information to more accurately grasp market dynamics. Although historical stock prices and financial news have been widely used in previous studies, it is relatively rare to find research considering news-based, technical, and chip factors simultaneously and evaluating their combined effect. In this study, we innovatively propose a multi-kernel model that not only combines news-based, technical, and chip factor analysis but also utilizes market data provided by the Taiwan Stock Exchange, including institutional trading situations and stock price technical indicators. The aim is to further enhance the prediction accuracy of stock price dynamics. Based on the frequency of word occurrences, we design a new discriminant index to extract features highly correlated with stock prices from financial news. The empirical results show that our multi-kernel model significantly surpasses the single-kernel model in prediction accuracy. However, we also find that although financial news is somewhat correlated with stock price dynamics, information such as chip factors and stock price technical indicators contribute more significantly in our model. This further validates that our multi-kernel learning algorithm can effectively handle multifaceted data sources and give appropriate weights according to the importance of each data point, thereby enhancing the comprehensiveness of prediction. Through this research, we hope to bring new perspectives and inspirations to the field of stock price forecasting. [ABSTRACT FROM AUTHOR]

Corporate social responsibility (CSR) has evolved over time into a mature interdisciplinary scientific field. However, there is a lack of research to explore the disciplinary association patterns and diffusion trajectories of scientific knowledge within this field. This research proposes a knowledge tracing framework that combines text analysis based on scientific texts and main path extraction based on citation networks to fill this gap. This framework is implemented in a dataset containing 15,949 papers related to CSR in the social sciences published from 1969 to 2022. This study identifies 19 main research directions within this field and their five growth trends. Combined with disciplinary categories of publication venues, both the disciplinary origins and disciplinary association patterns of these knowledge contents are also revealed. Besides that, three knowledge diffusion paths are obtained, which meticulously reveal the knowledge evolution and paradigm shift. Aside from the methodological novelty and result scarcity, this study also identifies research streams and potential opportunities. To begin with, CSR‐related studies have shown the tendency of microscopic and interdisciplinary. Then, related research methods show the tendency to be complicated and focus on causal logic. On this basis, some future agendas of CSR are also proposed in this work. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to determine stock market reactions to the news about innovations and other types of publications for illiquid stocks. Design/methodology/approach: (1) The authors opt for machine learning techniques and expert analysis and propose their own lexicon of innovations based on the news articles published on the professional website; (2) the dataset consists of the data on 2,000 US companies for 6 years; (3) the text analysis including BERT and Top2 Vec models which are superior to Latent Dirichlet allocation (LDA) in information criteria allows for more accurate evaluation of news sentiment and idea; and (4) furthermore, random forest and gradient boosting were applied to increase validity of results and demonstrate factor importance. Findings: (1) The paper presents theoretical findings adding to signalling theory and efficient market hypothesis for US illiquid stocks; (2) this study suggests that information on product innovations (unlike other types of innovations) has a direct and significant effect on the return of illiquid stocks; (3) the results also give evidence that under uncertainty innovation-related publications do not affect the return of illiquid stocks; and (4) the analysis of the news topics (narratives) demonstrates that only the narrative related to important corporate announcements has a positive impact on the return of illiquid stocks. Originality/value: (1) The authors are the first to conduct a large-scale study of the impact of various information on the return of illiquid stocks; (2) the paper focuses on information on several types of innovations with regard to the return of illiquid stocks; (3) based on Top2 Vec model, this study identifies the key topics-narratives discussed by investors and assesses their impact on the return of illiquid stocks; and (4) as an information source, the authors use the sample comprising a total of 1.4m news articles released on the professional website for investors "Benzinga". [ABSTRACT FROM AUTHOR]

It is essential to ensure that teachers allocate adequate attention to homework evaluation and effectively carry it out in order to successfully implement the "double reduction" policy. From the perspective of teachers' attention allocation, this study employed the NVivo 12 software to conduct text analysis on 39 cases of homework evaluation reform practices in primary and secondary schools in City N, China, from 2017 to 2021. The findings indicate that homework evaluation reform in these schools is a practical problem-oriented behavioral decision, and implementing the "double reduction" policy enhances teachers' attention allocation on homework evaluation. The attention allocation of teachers encompasses multiple aspects, such as determining the purpose of evaluation, setting evaluation content, and selecting evaluation subjects and methods. Following the implementation of the "double reduction" policy, teachers allocate more attention to reducing the homework burden on students. However, certain issues persist in the practices of homework evaluation reform, including inadequate consideration of constraints, an unbalanced content structure, and a lack of process coordination. Therefore, under the "double reduction" policy, it is imperative to improve school incentive systems, enhance teachers' evaluation capabilities, and alleviate their workload. These measures can guide teachers to allocate more attention to homework evaluation, thereby enhancing the efficiency and sustainability of attention allocation and fully realizing the educational function of homework evaluation. [ABSTRACT FROM AUTHOR]

This research adopts a holistic approach to analyze customer reviews in the e-commerce industry by utilizing a combined approach of numerical and text analysis. Specifically, this study integrates univariate, multivariate, and sentiment analysis to gain comprehensive insights into product preferences and customer satisfaction. The methodology includes a detailed examination of univariate distributions to uncover numerical trends in product ratings and preferences. Multivariate distributions are explored to understand the complex relationships between related variables. Sentiment analysis is performed using the Sentiment Intensity Analyzer to categorize reviews into positive, neutral, and negative sentiments. Additionally, N-gram analysis is applied to both recommended and non-recommended reviews to identify key themes, such as dissatisfaction with product size and satisfaction with fit. Logistic regression and naive Bayes models are employed to classify sentiment, with logistic regression achieving high accuracy on both training (91.3%) and validation data (89.2%). This research highlights the significant role of product recommendations as indicators of positive sentiment, while product ratings reveal the complexity in consumer judgment. The study contributes significantly to understanding the dynamics of customer reviews in the e-commerce industry, providing a solid foundation for smarter decision-making to improve customer experience and product quality. [ABSTRACT FROM AUTHOR]

Subsequent analyses of participatory budgeting in Poland emphasise the popularity of environmental issues such as greenery, air quality, and sustainable transport, which should also support objectives in urban climate change adaptation (UCCA). An increasing number of adaptation projects within local standards or ‘green’ civic budgets are often listed among the indicators in urban adaptation plans for Polish cities. However, available research studies and urban evaluation reports show that monitoring participatory budgets poses a methodological challenge. Therefore, the aim of the article is twofold. First, it proposes a method of identifying overlaps of urban climate change adaptation objectives with descriptions of projects submitted to local participatory budgets of the Upper Silesian-Zagłębie Metropolis. For this purpose, a dictionary-based text analysis of project descriptions was performed and critically examined. The second aim was to identify trends in urban climate change adaptation objectives. Thus, the quantitative distribution of coded descriptions was evaluated to investigate how subsequent types of participatory budgets (editions, green vs. standard, implemented vs. non-implemented) differed. The results demonstrate that the available database of participatory budget projects is a rich source of information on local preferences towards issues related to UCCA. To strengthen the objectives of urban climate change adaptation, city administrations can consider additional tools, such as UCCA-related tips/inspiration and targeted calls for projects, as well as supplementing participatory budgets with other deliberative methods. However, collecting precise and calculable data on this topic based on participatory budget databases requires both methodological adjustments and careful validation.

Abstract Road traffic accidents are the leading causes of injuries and fatalities. Understanding the traffic accident occurrence pattern and its contributing factors are prerequisites for effective traffic safety management. The paper proposes a deep learning approach for traffic accident recognition and information extraction from online Chinese news to extract and organize traffic accidents automatically. The approach consists of three modules, including automated news collection, news classification, and traffic accident information extraction. The automated news collection module crawls news from online sources, cleans and organizes it into a general news database with different categories of news. The news classification module robustly recognizes the traffic accident news from all types of news by fusing the sentence‐wise and context‐wise semantic news information. The accident information extraction module extracts the key attributes of traffic accidents (e.g. causes, times, locations) from news text using the SoftLexicon‐BiLSTM‐CRF method. The proposed approach is validated by comparing it with state‐of‐the‐art text mining methods using Chinese news data crawled online. The results show that the approach can achieve a high information extraction performance in terms of precision, recall, and F1‐score. It improves the performance of the best benchmark model (BiLSTM‐CRF) by 18.8% in precision and 12.08% in F1‐score. In addition, the potential value of the automatically extracted accident data is illustrated from online news in complementing traditional authority accident data to drive more effective traffic safety management in practice.