While generative artificial intelligence (generative AI) is being examined extensively, some issues it epitomizes call for more refined scrutiny and deeper contextualization. Besides the lack of nuanced understanding of art's continuously changing character in discussions about generative AI's cultural impact, one of the notably underexplored aspects is the conceptual and ideological substrate of AI science and industry whose attributes generative AI propagates by fostering the integration of diverse modes of AI-powered artmaking into the mainstream culture and economy. Taking the current turmoil around the generative AI as a pretext, this paper summarizes a broader study of AI's influence on art notions focusing on the confluence of certain foundational concepts in computer science and ideological vectors of the AI industry that transfer into art, culture, and society. This influence merges diverse and sometimes inconsistent but somehow coalescing philosophical premises, technical ideas, and political views, many of which have unfavorable overtones.
عبدالله امانیان, سید محمود رضا مرتضوی, حسن دهقان دهنوی
هدف پژوهش حاضر، تعیین نگرش به تدوین راهبردها در سازمانهای رسانهای با رویکرد نقش رسانهها در بهسازی فرهنگی-اجتماعی است. جامعۀ آماری این پژوهش تعداد ۸ مقالۀ پژوهشی مرتبط با موضوع است که بهصورت هدفمند از چهار پایگاه علمی داخلی مگیران، نورمگز، جهاد دانشگاهی و پرتال جامع علوم انسانی انتخاب شدند. ارزیابی مقالات با روش CASP صورت گرفت و مقالات دارای میانگین امتیاز 45 تأیید شدند. مقالات با روش تحلیل مضمون استقرایی مورد مطالعه قرار گرفتند و با نرمافزار مکسکیودا رمزگذاری گردیدند. در مرحلۀ رمزگذاری باز، 198 رمز شناسایی شد که روایی آنها با روش رمزگذاری مستقل و پایایی آنها با ضریب هولستی (87/0) تأیید شد، سپس در مرحلۀ رمزگذاری محوری با حذف موارد تکراری و ادغام مفاهیم مشابه، 17 مفهوم یکپارچه استخراج گردید و درنهایت، در مرحلۀ رمزگذاری انتخابی، مفاهیم در پنج دستۀ اصلی شامل ضرورتها، زمینهها، پدیدهها، عوامل مداخلهگر و پیامدهای بهسازی فرهنگی-اجتماعی طبقهبندی شدند. الگوی نهایی نشان داد که در نگرش راهبردی به سازمانهای رسانهای بهمنظور بهسازی فرهنگی-اجتماعی مواردی باید مورد توجه قرار بگیرند که از این قرارند: تنظیم راهبردها براساس نیازهای متنوع مخاطبان، پویاسازی راهبردها در جهت مطابقت با محیط، درنظرگرفتن خلق ارزش در تدوین راهبرد، استفاده از الگوی فراگردی در اصلاح راهبرد سازمانی، تقویت تعامل و همکاری بینسازمانی بهویژه ایجاد تعامل و ارتباط آموزشی با سازمانهای اجتماعی، انتظامی و توجه به سطوح کارکردها یا پیامدها در تدوین راهبرد.
Social sciences (General), Organizational behaviour, change and effectiveness. Corporate culture
In a globalized world, cultural elements from diverse origins frequently appear together within a single visual scene. We refer to these as culture mixing scenarios, yet how Large Vision-Language Models (LVLMs) perceive them remains underexplored. We investigate culture mixing as a critical challenge for LVLMs and examine how current models behave when cultural items from multiple regions appear together. To systematically analyze these behaviors, we construct CultureMix, a food Visual Question Answering (VQA) benchmark with 23k diffusion-generated, human-verified culture mixing images across four subtasks: (1) food-only, (2) food+food, (3) food+background, and (4) food+food+background. Evaluating 10 LVLMs, we find consistent failures to preserve individual cultural identities in mixed settings. Models show strong background reliance, with accuracy dropping 14% when cultural backgrounds are added to food-only baselines, and they produce inconsistent predictions for identical foods across different contexts. To address these limitations, we explore three robustness strategies. We find supervised fine-tuning using a diverse culture mixing dataset substantially improve model consistency and reduce background sensitivity. We call for increased attention to culture mixing scenarios as a critical step toward developing LVLMs capable of operating reliably in culturally diverse real-world environments.
Natural language processing (NLP) has been widely used in quantitative finance, but traditional methods often struggle to capture rich narratives in corporate disclosures, leaving potentially informative signals under-explored. Large language models (LLMs) offer a promising alternative due to their ability to extract nuanced semantics. In this paper, we ask whether semantic signals extracted by LLMs from corporate disclosures predict alpha, defined as abnormal returns beyond broad market movements and common risk factors. We introduce a simple framework, LLM as extractor, embedding as ruler, which extracts context-aware, metric-focused textual spans and quantifies semantic changes across consecutive disclosure periods using embedding-based similarity. This allows us to measure the degree of metric shifting -- how much firms move away from previously emphasized metrics, referred as moving targets. In experiments with portfolio and cross-sectional regression tests against a recent NER-based baseline, our method achieves more than twice the risk-adjusted alpha and shows significantly stronger predictive power. Qualitative analysis suggests that these gains stem from preserving contextual qualifiers and filtering out non-metric terms that keyword-based approaches often miss.
Economic behavior is shaped not only by quantitative information but also by the narratives through which such information is communicated and interpreted (Shiller, 2017). I show that narratives extracted from earnings calls significantly improve the prediction of both realized earnings and analyst expectations. To uncover the underlying mechanisms, I introduce a novel text-morphing methodology in which large language models generate counterfactual transcripts that systematically vary topical emphasis (the prevailing narrative) while holding quantitative content fixed. This framework allows me to precisely measure how analysts under- and over-react to specific narrative dimensions. The results reveal systematic biases: analysts over-react to sentiment (optimism) and under-react to narratives of risk and uncertainty. Overall, the analysis offers a granular perspective on the mechanisms of expectation formation through the competing narratives embedded in corporate communication.
Imene Kolli, Ario Saeid Vaghefi, Chiara Colesanti Senni
et al.
InfluenceMap's LobbyMap Platform monitors the climate policy engagement of over 500 companies and 250 industry associations, assessing each entity's support or opposition to science-based policy pathways for achieving the Paris Agreement's goal of limiting global warming to 1.5°C. Although InfluenceMap has made progress with automating key elements of the analytical workflow, a significant portion of the assessment remains manual, making it time- and labor-intensive and susceptible to human error. We propose an AI-assisted framework to accelerate the monitoring of corporate climate policy engagement by leveraging Retrieval-Augmented Generation to automate the most time-intensive extraction of relevant evidence from large-scale textual data. Our evaluation shows that a combination of layout-aware parsing, the Nomic embedding model, and few-shot prompting strategies yields the best performance in extracting and classifying evidence from multilingual corporate documents. We conclude that while the automated RAG system effectively accelerates evidence extraction, the nuanced nature of the analysis necessitates a human-in-the-loop approach where the technology augments, rather than replaces, expert judgment to ensure accuracy.
Chowdhury Shahriar Muzammel, Maria Spichkova, James Harland
Requirements Engineering (RE) is one of the most interaction-intensive phases of software development. This means that RE activities might be especially impacted by stakeholders' national culture. Software development projects increasingly have a very diverse range of stakeholders. To future-proof RE activities, we need to help RE practitioners avoid misunderstandings and conflicts that might arise from not understanding potential Cultural Influences (CIs). Moreover, an awareness of CIs supports diversity and inclusion in the IT profession. Bangladesh has a growing IT sector with some unique socio-cultural characteristics, and has been largely overlooked in this research field. In this study, we aim to investigate how the RE process is adopted in the context of Bangladeshi culture and what cultural influences impact overall RE activities.
The Global Change Analysis Model (GCAM) simulates complex interactions between the coupled Earth and human systems, providing valuable insights into the co-evolution of land, water, and energy sectors under different future scenarios. Understanding the sensitivities and drivers of this multisectoral system can lead to more robust understanding of the different pathways to particular outcomes. The interactions and complexity of the coupled human-Earth systems make GCAM simulations costly to run at scale - a requirement for large ensemble experiments which explore uncertainty in model parameters and outputs. A differentiable emulator with similar predictive power, but greater efficiency, could provide novel scenario discovery and analysis of GCAM and its outputs, requiring fewer runs of GCAM. As a first use case, we train a neural network on an existing large ensemble that explores a range of GCAM inputs related to different relative contributions of energy production sources, with a focus on wind and solar. We complement this existing ensemble with interpolated input values and a wider selection of outputs, predicting 22,528 GCAM outputs across time, sectors, and regions. We report a median $R^2$ score of 0.998 for the emulator's predictions and an $R^2$ score of 0.812 for its input-output sensitivity.
Despite the rapid development of large language models (LLMs) for the Korean language, there remains an obvious lack of benchmark datasets that test the requisite Korean cultural and linguistic knowledge. Because many existing Korean benchmark datasets are derived from the English counterparts through translation, they often overlook the different cultural contexts. For the few benchmark datasets that are sourced from Korean data capturing cultural knowledge, only narrow tasks such as bias and hate speech detection are offered. To address this gap, we introduce a benchmark of Cultural and Linguistic Intelligence in Korean (CLIcK), a dataset comprising 1,995 QA pairs. CLIcK sources its data from official Korean exams and textbooks, partitioning the questions into eleven categories under the two main categories of language and culture. For each instance in CLIcK, we provide fine-grained annotation of which cultural and linguistic knowledge is required to answer the question correctly. Using CLIcK, we test 13 language models to assess their performance. Our evaluation uncovers insights into their performances across the categories, as well as the diverse factors affecting their comprehension. CLIcK offers the first large-scale comprehensive Korean-centric analysis of LLMs' proficiency in Korean culture and language.
Bases of discrimination are also dimensions of diversity. However, not every dimension of diversity automatically becomes a ground of discrimination. Thus, this paper aims to identify the dimensions of diversity which are also bases for discrimination in a workplace. As different-collar workers may have different perceptions and experiences, this study aims to compare blue-, grey-, and white-collar workers. The study adopted an exploratory approach, and semi-structured interviews were conducted with eleven workers. The questions were based on a diversity wheel which includes a wide range of diversity dimensions. The findings show that any dimension of diversity can also be a basis for discrimination, with peer-to-peer discrimination being the most prevalent among blue- and grey-collars. This research contributes to a more comprehensive understanding of discrimination, as many existing papers on diversity only cover a few dimensions or bases. Additionally, this study sheds light on an under-researched area of discrimination among blue- and grey-collar workers.
Management. Industrial management, Organizational behaviour, change and effectiveness. Corporate culture
The recent spread of cloud services has enabled many companies to take advantage of them. Nevertheless, the main concern about the adoption of cloud services remains the lack of transparency perceived by customers regarding security and privacy. To overcome this issue, Cloud Service Certifications (CSCs) have emerged as an effective solution to increase the level of trust in cloud services, possibly based on continuous auditing to monitor and evaluate the security of cloud services on an ongoing basis. Continuous auditing can be easily implemented for technical aspects, while organizational aspects can be challenging due to their generic nature and varying policies between service providers. In this paper, we propose an approach to facilitate the automatic assessment of organizational evidence, such as that extracted from security policy documents. The evidence extraction process is based on Natural Language Processing (NLP) techniques, in particular on Question Answering (QA). The implemented prototype provides promising results on an annotated dataset, since it is capable to retrieve the correct answer for more than half of the tested metrics. This prototype can be helpful for Cloud Service Providers (CSPs) to automate the auditing of textual policy documents and to help in reducing the time required by auditors to check policy documents.
Wenxuan Wang, Wenxiang Jiao, Jingyuan Huang
et al.
This paper identifies a cultural dominance issue within large language models (LLMs) due to the predominant use of English data in model training (e.g., ChatGPT). LLMs often provide inappropriate English-culture-related answers that are not relevant to the expected culture when users ask in non-English languages. To systematically evaluate the cultural dominance issue, we build a benchmark of concrete (e.g., holidays and songs) and abstract (e.g., values and opinions) cultural objects. Empirical results show that the representative GPT models suffer from the culture dominance problem, where GPT-4 is the most affected while text-davinci-003 suffers the least from this problem. Our study emphasizes the need to critically examine cultural dominance and ethical consideration in their development and deployment. We show that two straightforward methods in model development (i.e., pretraining on more diverse data) and deployment (e.g., culture-aware prompting) can significantly mitigate the cultural dominance issue in LLMs.
هدف از پژوهش حاضر، بررسی نقش احساس امنیت در صنعت گردشگری ورزشی است. این پژوهش ازنظر هدف، کاربردی و ازنظر ماهیت، توصیفی و ازنوع پیمایشی است. جامعۀ آماری مورد بررسی، شامل حدود 78000 تماشاگر مسابقۀ دو تیم فوتبال استقلال و تراکتورسازی تبریز در ورزشگاه آزادی در نیمفصل اول لیگ (97- 98) میشود که با استفاده از فرمول کوکران، تعداد 384 نفر از آنها بهروش نمونهگیری تصادفی ساده انتخاب شدند. بهمنظور جمعآوری دادهها از دو پرسشنامۀ محققساخته، مربوط به احساس امنیت و حضور مجدد گردشگران ورزشی بهره برده شد که روایی صوری و محتوایی آن توسط 12 نفر از اساتید و صاحبنظران تأیید شد؛ علاوهبر این، ضریب آلفای کرونباخ برای بررسی پایایی این پرسشنامهها بهترتیب 82/0 و 84/0 بهدست آمد. بهمنظور تحلیل دادهها، آزمونهای کولموگروف-اسمیرنوف، همبستگی پیرسون، رگرسیون خطی ساده و چندگانه مورد استفاده قرار گرفتند. یافتهها نشان داد که برقراری امنیت بعد از برگزاری، قبل از برگزاری و درحین برگزاری مسابقه بهترتیب با مقادیر ضریب بتای 382/0، 312/0 و 248/0 در این رویداد ورزشی دارای بیشترین تأثیر بر حضور مجدد گردشگران ورزشی بودند (05/0 ˂ p).
Social sciences (General), Organizational behaviour, change and effectiveness. Corporate culture
In recent years, organizations operate in a dynamic, unpredictable and competitive business environment, and therefore the ability to react quickly and adapt to change, in other words, achieve agility, in such an environment has become essential. Agility requires organizations to use knowledge management capabilities in order to gain a sustainable competitive advantage and improve accountability. Accordingly, the purpose of this study is to investigate the effect of knowledge management capabilities on organizational agility by considering the mediating role of strategic flexibility and organizational learning in knowledge-based companies of Bushehr Science and Technology Park. This research is applied in terms of purpose and descriptive-survey in terms of data collection method. The statistical population of the study was the experts of 142 knowledge-based companies that using the Cochran limited society formula, 208 people were selected based on the judgment-purposeful sampling method. Data were collected using a Likert-based questionnaire. The results of data analysis using the partial least squares approach indicated that knowledge management capabilities have a significant effect on organizational agility through strategic flexibility and organizational learning.
Mohammed F. Allehyani, Mohamed Abuagreb, Brian K. Johnson
Inverter-based resources (IBR) have been widely studied for their advantages on the current power systems. This increase in the penetration of renewable energy has raised some concerns about the stability of the existing grid. Historically, power systems are dominated by synchronous generators that can easily react to system instability due to high inertia and damping characteristics. However, with IBR, the control of the inverter plays a crucial role in contributing to the system stability and enhancing the functionality of the inverters. One of these novel control methods is droop control. Droop characteristics are used to control voltage, frequency, and active and reactive power. This paper presents the impact of frequency droop damping on system frequency, real power, and the rate of change of frequency with distributed energy resources. Also, battery sizing is suggested based on the results. The results also show the need for optimal selection for the frequency droop damping to fulfill the appropriate battery size in terms of cost and performance. The simulations are carried out in an electromagnetic transient program (EMTP)
Using a comprehensive sample of 2,585 bankruptcies from 1990 to 2019, we benchmark the performance of various machine learning models in predicting financial distress of publicly traded U.S. firms. We find that gradient boosted trees outperform other models in one-year-ahead forecasts. Variable permutation tests show that excess stock returns, idiosyncratic risk, and relative size are the more important variables for predictions. Textual features derived from corporate filings do not improve performance materially. In a credit competition model that accounts for the asymmetric cost of default misclassification, the survival random forest is able to capture large dollar profits.
IT security outsourcing is the process of contracting a third-party security service provider to perform, the full or partial IT security functions of an organization. Little is known about the factors influencing organizational decisions in outsourcing such a critical function. Our review of the research and practice literature identified several managerial factors and legal factors. We found research in IT security outsourcing to be immature and the focus areas not addressing the critical issues facing industry practice. We therefore present a research agenda consisting of fifteen questions to address five key gaps relating to knowledge of IT security outsourcing, specifically effectiveness of the outcome, lived experience of the practice, the temporal dimension, multi-stakeholder perspectives, and the impact on IT security practices, particularly agility in incident response.
PurposeThe purpose of this paper is to propose and empirically test a model that examines psychological ownership as an intervening variable between organizational justice and organizational citizenship behaviour drawing on the social exchange theory, equity theory and event mediated model.Design/methodology/approachThe study was based on a cross-sectional research design, with a sample of 301 full-time employees from various information technology organizations in India. Amos software was used to test the validity of the hypothesised model, and PROCESS macro was used to test the mediation of psychological ownership.FindingsThe findings showed that organizational justice impacted both psychological ownership and organizational citizenship behaviour. Furthermore, psychological ownership impacted the organizational citizenship behaviour of employees. The key finding of this study is the partial mediation of psychological ownership in the relationship between organizational justice and organizational citizenship behaviour.Practical implicationsBesides enriching the organizational behaviour literature, the findings of the study offer valuable messages to the organizational leaders in creating sustained competitive advantage through employee behaviours like organizational citizenship behaviour and psychological ownership.Originality/valueEven though the literature reports the impact of organizational justice on organizational citizenship behaviour, the majority of this research is based on a western context. There is little research work done to examine the direct relationship between these variables in a non-western context, especially in an emerging economy like India. This study bridges this research gap and enriches the literature by elucidating how organizational justice impacts organizational citizenship behaviour by evincing the mediating mechanism of psychological ownership. Moreover, this is one of the primary studies that explore the mediating role of psychological ownership in the relationship between organizational justice and organizational citizenship behaviour.
The popular 70:20:10 individual learning model is similarly beguiling the organizational learning world. Like Circe’s warning to Odysseus, some forewarning of its dangers seems prudent. Since the 1990s, scholarly emphasis and practitioner interest in individual learning practice within organizations has shifted from the formal to the informal (Clardy, 2018; McCauley et al., 2013). A popular version of the informal approach claims ‘70%’ of learning arises from in work experiences; ‘20%’ through relationships and ‘10%’ through ‘formal’ training, hence the 70:20:10 ‘rule’ (Clardy, 2018). From researching learning and development (L&D) in UK policing organizations, it is clear this ratio is beguiling its leaders. They are not alone; this seductive ratio has captured leaders imaginations world-wide (Johnson et al., 2018). Its ‘clear toned song’ of apparent simplicity, efficiency and cost effectiveness has led numerous organizations to reduce investment in L&D departments (Clardy, 2018) because if formal training ‘accounts for only 10% of development, why doweneed it?’ (McCauley, 2013; as cited inClardy, 2018, p. 154). The concept of workplace ‘learning’ from experience, peers, and environment is supported by common sense, lived experience and academic literature (e.g.: McCall, 2004). Experiential learning is central to creating organizational culture (Schein, 2009) and it is clear that ‘learning’ takes place irrespective of plan or intent. Consequently there are often differences between what an organization wants its corporate citizens’ behaviours, cognition and knowledge to be and how they actually behave, think and value and share knowledge (Schein, 2009). The siren song of the 70:20:10 ‘rule’ tempts the unwary, promising self-evident truth, efficiency and cost saving. Here Circe might have warned of ‘kíndūnos’ (the ancient Greek word for danger); the exactness of the 70:20:10 ratio suggests caution. Contingent real world phenomena generally defy such precise, immutable and generalized quantification (Pirie, 2015) and it’s unlikely to apply so exactly in any organization—let alone across differing ones. That ‘there is actually no empirical evidence supporting this assumption [the 70:20:10 rule], yet scholars and practitioners frequently quote it as if it is fact’ (DeRue & Myers, 2014, p. 842); it fails to deliver individual development (Johnson et al., 2018); and it has a misleading central assumption that modes of learning are independent, rather than interdependent and need to be considered holistically (McCall, 2010), suggests further caution. Overconfidence in the assumption that unstructured learning approaches automatically deliver capability development (Johnson et al., 2018) leading to their prioritization over other approaches, creating a ‘haphazard process’ (Conger, 1993, p. 46) that insufficiently considers intentionality, accountability and formal evaluation (Day, 2000), suggests that even more caution is necessary. Lastly, even where learners have exposure to knowledge aligned with organizational and professional orthodoxy it competes with other informal knowledge through not wholly rational processes which are subject to contextual, individual and inter-relational influences (Powell et al., 2018); thus its value to and adoption by individuals and groups is less than certain. There is no dispute that informal learning occurs, however what ‘lessons’ are ‘learnt’ is more equivocal; the hazard lies in the potential that this ‘learning’ meets neither corporate requirement, utility or approval, and in some cases may be their antithesis. Circe advised Odysseus to: