Organizations devote substantial resources to coordination, yet which tasks actually require it for correctness remains unclear. The problem is acute in multi-agent AI systems, where coordination overhead is directly measurable and routinely exceeds the cost of the work itself. However, distributed systems theory provides a precise answer: coordination is necessary if and only if a task is non-monotonic, meaning new information can invalidate prior conclusions. Here we show that a classic taxonomy of organizational interdependence maps onto the monotonicity criterion, yielding a decision rule and a measure of avoidable overhead (the Coordination Tax). Multi-agent simulations confirm both predictions. We classify 65 enterprise workflows and find that 48 (74%) are monotonic, then replicate on 13,417 occupational tasks from the O*NET database (42% monotonic). These classification rates imply that 24-57% of coordination spending is unnecessary for correctness.
Matthias Huemmer, Theophile Shyiramunda, Michelle J. Cummings-Koether
This paper proposes a rigorous framework to examine the two-way relationship between artificial intelligence (AI), human cognition, problem-solving, and cultural adaptation across academic and business settings. It addresses a key gap by asking how AI reshapes cognitive processes and organizational norms, and how cultural values and institutional contexts shape AI adoption, trust, and use over time. We employ a three-wave longitudinal design that tracks AI knowledge, perceived competence, trust trajectories, and cultural responses. Participants span academic institutions and diverse firms, enabling contextual comparison. A dynamic sample continuous, intermittent, and wave-specific respondents mirrors real organizational variability and strengthens ecological validity. Methodologically, the study integrates quantitative longitudinal modeling with qualitative thematic analysis to capture temporal, structural, and cultural patterns in AI uptake. We trace AI acculturation through phases of initial resistance, exploratory adoption, and cultural embedding, revealing distinctive trust curves and problem-solving strategies by context: academic environments tend to collaborative, deliberative integration; business environments prioritize performance, speed, and measurable outcomes. Framing adoption as bidirectional challenges deterministic views: AI both reflects and reconfigures norms, decision-making, and cognitive engagement. As the first comparative longitudinal study of its kind, this work advances methodological rigor and offers actionable foundations for human-centred, culturally responsive AI strategies-supporting evidence-based policies, training, and governance that align cognitive performance, organizational goals, and ethical commitments.
Drawing on 1,178 safety and reliability papers from 9,439 generative AI papers (January 2020 - March 2025), we compare research outputs of leading AI companies (Anthropic, Google DeepMind, Meta, Microsoft, and OpenAI) and AI universities (CMU, MIT, NYU, Stanford, UC Berkeley, and University of Washington). We find that corporate AI research increasingly concentrates on pre-deployment areas -- model alignment and testing & evaluation -- while attention to deployment-stage issues such as model bias has waned. Significant research gaps exist in high-risk deployment domains, including healthcare, finance, misinformation, persuasive and addictive features, hallucinations, and copyright. Without improved observability into deployed AI, growing corporate concentration could deepen knowledge deficits. We recommend expanding external researcher access to deployment data and systematic observability of in-market AI behaviors.
The advanced reasoning capabilities of Large Reasoning Models enable them to thoroughly understand and apply safety policies through deliberate thought processes, thereby improving the models' safety. Beyond safety, these models must also be able to reflect the diverse range of human values across various cultures. This paper presents the Cultural Norm-based Cultural Alignment (CNCA) framework, which enables models to leverage their powerful reasoning ability to align with cultural norms. Specifically, we propose three methods to automatically mine cultural norms from limited survey data and explore ways to effectively utilize these norms for improving cultural alignment. Two alignment paradigms are examined: an in-context alignment method, where cultural norms are explicitly integrated into the user context, and a fine-tuning-based method, which internalizes norms through enhanced Chain-of-Thought training data. Comprehensive experiments demonstrate the effectiveness of these methods, highlighting that models with stronger reasoning capabilities benefit more from cultural norm mining and utilization. Our findings emphasize the potential for reasoning models to better reflect diverse human values through culturally informed alignment strategies.
asma nikakhtar, Ebadollah Baneshi, vahid makizadeh
The purpose of this research was to investigate the key leadership competencies of female entrepreneurs in Hormozgan Province, Iran. The research was applied and descriptive in terms of its purpose, and qualitative and quantitative in terms of data collection. The statistical population in the first part included all domestic and foreign research related to the leadership competencies of female entrepreneurs in reputable scientific databases. Using a systematic review method, 172 articles in the field of leadership and entrepreneurship of women were initially identified, and finally 32 articles in Persian and English were reviewed over a ten-year period (2012 to 2022). In the second part, through interviews with 25 female entrepreneurs in Hormozgan Province who were selected using purposive sampling, the key leadership competencies of women were finalized. Based on the research findings, these competencies can be categorized into the key axes of strategic competencies, commitment competencies, technical competencies, innovation competencies, analytical/cognitive competencies, communication/interpersonal competencies, and opportunity competencies.
Michael E. Todhunter, Sheikh Jubair, Ruchika Verma
et al.
Cultured meat has the potential to provide a complementary meat industry with reduced environmental, ethical, and health impacts. However, major technological challenges remain which require time- and resource-intensive research and development efforts. Machine learning has the potential to accelerate cultured meat technology by streamlining experiments, predicting optimal results, and reducing experimentation time and resources. However, the use of machine learning in cultured meat is in its infancy. This review covers the work available to date on the use of machine learning in cultured meat and explores future possibilities. We address four major areas of cultured meat research and development: establishing cell lines, cell culture media design, microscopy and image analysis, and bioprocessing and food processing optimization. This review aims to provide the foundation necessary for both cultured meat and machine learning scientists to identify research opportunities at the intersection between cultured meat and machine learning.
Muhammad Farid Adilazuarda, Sagnik Mukherjee, Pradhyumna Lavania
et al.
We present a survey of more than 90 recent papers that aim to study cultural representation and inclusion in large language models (LLMs). We observe that none of the studies explicitly define "culture, which is a complex, multifaceted concept; instead, they probe the models on some specially designed datasets which represent certain aspects of "culture". We call these aspects the proxies of culture, and organize them across two dimensions of demographic and semantic proxies. We also categorize the probing methods employed. Our analysis indicates that only certain aspects of ``culture,'' such as values and objectives, have been studied, leaving several other interesting and important facets, especially the multitude of semantic domains (Thompson et al., 2020) and aboutness (Hershcovich et al., 2022), unexplored. Two other crucial gaps are the lack of robustness of probing techniques and situated studies on the impact of cultural mis- and under-representation in LLM-based applications.
Corporate social responsibility encourages companies to integrate social and environmental concerns into their activities and their relations with stakeholders. It encompasses all actions aimed at the social good, above and beyond corporate interests and legal requirements. Various international organizations, authors and researchers have explored the notion of CSR and proposed a range of definitions reflecting their perspectives on the concept. In Morocco, although Moroccan companies are not overwhelmingly embracing CSR, several factors are encouraging them to integrate the CSR approach not only into their discourse, but also into their strategies. The CGEM is actively involved in promoting CSR within Moroccan companies, awarding the "CGEM Label for CSR" to companies that meet the criteria set out in the CSR Charter. The process of labeling Moroccan companies is in full expansion. The graphs presented in this article are broken down according to several criteria, such as company size, sector of activity and listing on the Casablanca Stock Exchange, in order to provide an overview of CSR-labeled companies in Morocco. The approach adopted for this article is a qualitative one aimed at presenting, firstly, the different definitions of the CSR concept and its evolution over time. In this way, the study focuses on the Moroccan context to dissect and analyze the state of progress of CSR integration in Morocco and the various efforts made by the CGEM to implement it. According to the data, 124 Moroccan companies have been awarded the CSR label. For a label in existence since 2006, this figure reflects a certain reluctance on the part of Moroccan companies to fully implement the CSR approach in their strategies. Nevertheless, Morocco is in a transitional phase, marked by the gradual adoption of various socially responsible practices.
Productive interactions between diverse users and language technologies require outputs from the latter to be culturally relevant and sensitive. Prior works have evaluated models' knowledge of cultural norms, values, and artifacts, without considering how this knowledge manifests in downstream applications. In this work, we focus on extrinsic evaluation of cultural competence in two text generation tasks, open-ended question answering and story generation. We quantitatively and qualitatively evaluate model outputs when an explicit cue of culture, specifically nationality, is perturbed in the prompts. Although we find that model outputs do vary when varying nationalities and feature culturally relevant words, we also find weak correlations between text similarity of outputs for different countries and the cultural values of these countries. Finally, we discuss important considerations in designing comprehensive evaluation of cultural competence in user-facing tasks.
Cultural accumulation drives the open-ended and diverse progress in capabilities spanning human history. It builds an expanding body of knowledge and skills by combining individual exploration with inter-generational information transmission. Despite its widespread success among humans, the capacity for artificial learning agents to accumulate culture remains under-explored. In particular, approaches to reinforcement learning typically strive for improvements over only a single lifetime. Generational algorithms that do exist fail to capture the open-ended, emergent nature of cultural accumulation, which allows individuals to trade-off innovation and imitation. Building on the previously demonstrated ability for reinforcement learning agents to perform social learning, we find that training setups which balance this with independent learning give rise to cultural accumulation. These accumulating agents outperform those trained for a single lifetime with the same cumulative experience. We explore this accumulation by constructing two models under two distinct notions of a generation: episodic generations, in which accumulation occurs via in-context learning and train-time generations, in which accumulation occurs via in-weights learning. In-context and in-weights cultural accumulation can be interpreted as analogous to knowledge and skill accumulation, respectively. To the best of our knowledge, this work is the first to present general models that achieve emergent cultural accumulation in reinforcement learning, opening up new avenues towards more open-ended learning systems, as well as presenting new opportunities for modelling human culture.
Large Language Models (LLMs) and Text-To-Image (T2I) models have demonstrated the ability to generate compelling text and visual stories. However, their outputs are predominantly aligned with the sensibilities of the Global North, often resulting in an outsider's gaze on other cultures. As a result, non-Western communities have to put extra effort into generating culturally specific stories. To address this challenge, we developed a visual storytelling tool called Kahani that generates culturally grounded visual stories for non-Western cultures. Our tool leverages off-the-shelf models GPT-4 Turbo and Stable Diffusion XL (SDXL). By using Chain of Thought (CoT) and T2I prompting techniques, we capture the cultural context from user's prompt and generate vivid descriptions of the characters and scene compositions. To evaluate the effectiveness of Kahani, we conducted a comparative user study with ChatGPT-4 (with DALL-E3) in which participants from different regions of India compared the cultural relevance of stories generated by the two tools. The results of the qualitative and quantitative analysis performed in the user study show that Kahani's visual stories are more culturally nuanced than those generated by ChatGPT-4. In 27 out of 36 comparisons, Kahani outperformed or was on par with ChatGPT-4, effectively capturing cultural nuances and incorporating more Culturally Specific Items (CSI), validating its ability to generate culturally grounded visual stories.
Crowdsourcing markets are expanding worldwide, but often feature standardized interfaces that ignore the cultural diversity of their workers, negatively impacting their well-being and productivity. To transform these workplace dynamics, this paper proposes creating culturally-aware workplace tools, specifically designed to adapt to the cultural dimensions of monochronic and polychronic work styles. We illustrate this approach with "CultureFit," a tool that we engineered based on extensive research in Chronemics and culture theories. To study and evaluate our tool in the real world, we conducted a field experiment with 55 workers from 24 different countries. Our field experiment revealed that CultureFit significantly improved the earnings of workers from cultural backgrounds often overlooked in design. Our study is among the pioneering efforts to examine culturally aware digital labor interventions. It also provides access to a dataset with over two million data points on culture and digital work, which can be leveraged for future research in this emerging field. The paper concludes by discussing the importance and future possibilities of incorporating cultural insights into the design of tools for digital labor.
Each enterprise operates in a different way and has a different work system and culture. So, each enterprise has different requirements, action plans, and organizational systems. In order for an organization to have better functionality and to be innovative and effective, it is important to analyze the system of operation, the duties assigned to employees, and the organizational climate. This will help to examine what affects the functionality of the enterprise and the efficiency of the employees. Hence, it is important to know what the organizational climate is and how to study it. In this context, the chapter will explore the most common dimensions of organizational climate and understand their correlations with organizational culture in the broadest sense.
Yasaman Modaresi, Mir Ali Seyd Naghavi, Habib Roodsaz
et al.
In today's digital world, organizations are looking for changes in appearance, thinking, and performance to remain competitive. Digital transformation is a tremendous transformation that cannot be avoided, and organizations must prepare themselves to face it. This research aims to identify the soft components of digital transformation that are very important and vital and to design the resulting model. The statistical population of this research in the first part was the existing articles in this field, which were selected by purposeful sampling. In the second part, the statistical population was formed by the managers and experts of the Ministry of Communication and Information Technology, and 14 people were selected by the purposeful sampling method and through the snowball method. The tool used in this study was MAXQDA software. First, using the theme analysis of the background of the research, the desired components were extracted and used in the design of semi-structured interview questions. The research findings identified six overarching themes, 44 organizing themes, and 185 basic themes of soft digital transformation, and the resulting network of themes has been drawn. IntroductionOver the past few decades, global industries have faced technological changes that, in addition to creating opportunities such as greater flexibility, reactivity, and product customization, have created various challenges such as change. Fast technology has also increased complexity and changed customer expectations and legal requirements (Rachinger et al., 2018). Digital transformation injects digital technology into all aspects of an organization, including its business processes and culture (Techbeacon, 2020). Transformation in the digital age is not related to technology alone and is more related to organizational agility. This means that organizational culture plays a fundamental role in the digital transformation of businesses (Shirazi et al., 2021). In response to the question of why digital transformation has become a necessity in today's world, it is enough to mention that the implementation of digital transformation can guarantee the organization's survival or, in a better case, stabilize the organization's market share.Moreover, it can increase organizations' market share and profit margin ideally. Therefore, the issue of digital transformation is the issue of survival and life. Digital transformation is of undeniable importance for organizations in today's world. Most of the research carried out in the field of digital transformation has either dealt with the issue of digital transformation in general and especially the hard part of it, which is related to the technology and infrastructure of this transformation, or separately and not in a single research. have examined the soft components of digital transformation; Therefore, there is no comprehensive research that specifically deals with the soft part of digital transformation; The scientific gap felt by the researchers of this research was, and the purpose of the current research is to identify the soft components of digital transformation using the opinions of experts from the Ministry of Communications and Information Technology and the lack of a comprehensive framework for the soft part of digital transformation. Conceptual framework design is the result of them.Research QuestionsWhat are the soft components of digital transformation in the Ministry of Communications and Information Technology?What is the conceptual framework for the soft components of digital transformation using the opinions of experts from the Ministry of Communications and Information Technology? Literature Review Theoretical foundations of researchDigital transformation: To avoid making a mistake in the definition of digital transformation, we must note that digital transformation should not be considered a magic bean that grows quickly and results beyond expectations in a short period (Shami Zanjani, 2017). In some cases, a superficial understanding of the nature of digital transformation reduces it to technological changes, and the deep processes of activity structure transformation are ignored (Rabinovich et al., 2020). Digital transformation is an endless change and is not similar to projects such as the electronicization an organization's processes Because these projects will end. However, digital transformation is a continuous improvement in the organization's processes, products, and services (Hosseini-Nasab et al., 2021).Soft components of digital transformation: Digital transformation consists of two complex parts (technology, infrastructure, Etc.) and soft (people, culture, Etc.). Digital transformation is not only about the implementation of information technology solutions but it should be seen in a broader context as "organizational change," "cultural transformation," and "moving towards a customer-oriented approach" (Verina. & Titko, 2019). Digitization and digitization are fundamentally related to technology, but digital transformation is not (Bloomberg, 2018). Digital transformation can be defined as a cultural, organizational, and operational change in the organization through the intelligent integration of digital technologies, processes, and competencies step by step in all functions and at every level with a strategic approach (Tonina Yaneva, 2022). Digital transformation does not mean decorating the organization with new technologies. However, digital transformation also includes people and culture, and neglecting them causes digital transformation not to be appropriately implemented and its benefits. It will not be given to organizations (Rislana Kanya, 2020). Therefore, the soft part of digital transformation includes everything related to humans. The degree of success of companies in recognizing and considering the influence of culture determines whether cultural factors act as supporting or opposing forces for the organization's digital transformation (Tuukkanen et al., 2022). Empirical foundations of researchMuch research has been conducted in the field of digital transformation. The subject of the present research, which is to identify the soft components of digital transformation and present the model resulting from them, has been investigated and analyzed, such as the research done by Shirazi et al. (2021), with the title of presenting a roadmap for the implementation of the organizational culture required for digital transformation with a hybrid approach, the findings of the research showed that the roadmap for implementing the organizational culture for digital transformation includes three layers of infrastructure. It includes four primary categories, the layer of cultural processes includes three main categories, and the layer of goals includes six main categories. The research conducted by Konopik et al. (2022), titled Mastering Digital Transformation through Organizational Capabilities: A Conceptual Framework. This research led to organizational identifications for digital transformation, including strategy and ecosystem, innovation thinking, digital transformation technologies, data, operations, organizational design, and digital transformation leadership.MethodologyIn terms of its purpose, this research is considered a developmental-applied type of study. It was conducted with the qualitative thematic analysis method in both parts of the analysis (research background and interview). The software used in this research was MAXQDA2020. The statistical population of this research is divided into two parts. At first, the studies and research conducted around the research subject were collected and examined. The statistical population of the interview section of the research consisted of managers, experts, and employees of the Ministry of Communications and Information Technology of Iran who were familiar with the field of digital transformation. Sampling in both sections was purposeful and judgmental. ResultsThe results of the theme analysis of the investigated research, based on the frequency of the codes, showed that the resulting soft digital transformation model consists of 4 comprehensive themes of culture, organization manager, digital leader, and skills, 21 organizing themes, and 116 basic themes. In the analysis of the theme of the background of the research, four overarching themes were identified with the names of the organization manager, digital leadership, culture, and skills, along with their organizing and basic themes. In the theme analysis section of the interviews, the results included 185 basic themes, 44 organizing themes, and six comprehensive themes, including culture, organizational manager, digital leader, human capital, digital innovation, and digital strategy.. DiscussionBy comparing the theme analysis in two parts (research background and interview with experts), it can be seen that in the interview with experts, the overarching themes of organization manager, digital leadership, and culture remained unchanged, and only the themes of the organizer and their base had changed; The overarching theme of skills was removed and replaced by the overarching theme of human capital, and finally, the overarching themes of digital innovation and digital strategy were added. ConclusionDigital transformation is not only buying new digital technologies and equipping the organization with them; digital transformation means the organization's complete transformation. In this process, they must change from the organization's thinking to the duties of individual employees. Digital transformation is not an isolated effort but a set of actions that must be appropriately managed and coordinated. One of the most important influencing parameters in the success of digital transformation is digital culture. The degree of success of companies in recognizing and considering the influence of culture determines whether cultural factors act as supporting or opposing forces for the organization's digital transformation.Along with the importance of digital culture for organizations that seek to achieve digital transformation, it is very important to pay attention to human capital. The key element in the success of digital transformation is the people who lead, accept, and implement this transformation, that is, the organization's human capital (Haydn Shaughnessy, 2018). How to carry out digital transformation is one of the challenging tasks of senior management of organizations (Mahmood et al., 2019). The turbulent and constantly changing digital environment forces managers to adopt decisions and strategies significantly faster than necessary (Nadkarni & Prügl, 2020). Several studies have also defined digital leadership as a critical skill that leaders must possess to carry out digital transformation. Digital leadership is a fast-paced, collaborative, and team-oriented approach with a strong focus on innovation, where a digital leader can use new methods and tools to solve complex problems and maintain business continuity (Oberer & Erkollar, 2018). The starting point of digital transformation is a digital business strategy that aims to create differential value using digital resources in composition and implementation (Goerzig & Bauernhansl, 2018). Therefore, digital transformation is driven by strategy, not technology (Bumann & Peter, 2019). Today's competitive world is a world of innovations that have come into existence with the help of new digital technologies. Digital transformation requires a change of focus and includes innovation in technology and modification of institutional culture to ensure the evolution of digital transformation (Abad-Segura et al., 2020). For highly digital organizations, understanding and managing digital innovations is of particular importance.
Karl J. Friston, Tommaso Salvatori, Takuya Isomura
et al.
Recent advances in theoretical biology suggest that basal cognition and sentient behaviour are emergent properties of in vitro cell cultures and neuronal networks, respectively. Such neuronal networks spontaneously learn structured behaviours in the absence of reward or reinforcement. In this paper, we characterise this kind of self-organisation through the lens of the free energy principle, i.e., as self-evidencing. We do this by first discussing the definitions of reactive and sentient behaviour in the setting of active inference, which describes the behaviour of agents that model the consequences of their actions. We then introduce a formal account of intentional behaviour, that describes agents as driven by a preferred endpoint or goal in latent state-spaces. We then investigate these forms of (reactive, sentient, and intentional) behaviour using simulations. First, we simulate the aforementioned in vitro experiments, in which neuronal cultures spontaneously learn to play Pong, by implementing nested, free energy minimising processes. The simulations are then used to deconstruct the ensuing predictive behaviour, leading to the distinction between merely reactive, sentient, and intentional behaviour, with the latter formalised in terms of inductive planning. This distinction is further studied using simple machine learning benchmarks (navigation in a grid world and the Tower of Hanoi problem), that show how quickly and efficiently adaptive behaviour emerges under an inductive form of active inference.
Within the cultural heritage sector, there has been a growing and concerted effort to consider a critical sociotechnical lens when applying machine learning techniques to digital collections. Though the cultural heritage community has collectively developed an emerging body of work detailing responsible operations for machine learning in libraries and other cultural heritage institutions at the organizational level, there remains a paucity of guidelines created specifically for practitioners embarking on machine learning projects. The manifold stakes and sensitivities involved in applying machine learning to cultural heritage underscore the importance of developing such guidelines. This paper contributes to this need by formulating a detailed checklist with guiding questions and practices that can be employed while developing a machine learning project that utilizes cultural heritage data. I call the resulting checklist the "Collections as ML Data" checklist, which, when completed, can be published with the deliverables of the project. By surveying existing projects, including my own project, Newspaper Navigator, I justify the "Collections as ML Data" checklist and demonstrate how the formulated guiding questions can be employed and operationalized.
During the Covid-19 pandemic, trust has been identified as a key mechanism in pandemic containment. Norway and Finland, two Nordic countries with high trust scores, are cited as best-practice examples. In a qualitative research project on the theoretical construct of caring economics conducted by the author, the deep societal anchoring of trust and integrity has been confirmed in both countries. Based on the empirical example of the Nordic countries, the concept of caring economics emphasizes partnerism and thereby the real wealth of nations. Dugnad/Dugnadsånd, which refers to collective effort, is a trust-based Norwegian type of commons and commoning that can be regarded as an intersection with caring economics. Dugnad/Dugnadsånd integrates the various notions of interpersonal, system, and institutional trust, and thus widely supports mechanisms of pandemic control.
Ethnology. Social and cultural anthropology, Organizational behaviour, change and effectiveness. Corporate culture
The purpose of this study was to design an effective model of virtuous organizations with emphasis on the ، Cooperative Development Bank and using the integrated method (exploratory) in four stages: At first, the documents, platforms and indices of virtuous organizations were extracted, then the Delphi method (41) was used for final compilation of the indices. Factor analysis has been used to identify the components and finally the extraction model has been validated by structural equation modeling. The statistical population in qualitative and quantitative part of research consisted of experts, faculty members and staff and experts of Isfahan Cooperative Development Bank and five cities of Isfahan province (160) that were evaluated using survey technique. Finally, the model of virtuous organizations in Co-operative Development Bank was extracted in the form of 10 components of participatory leadership, core team, managers' commitment and support, structural support, organizational strategies, organizational communication, upstream documents, environmental dynamics and technological infrastructure and 67 indicators and the data collected had a good fit in the model of virtuous organizations.
Background: A community-academic partnership responded to a community-voiced need to address parenting challenges while living in poverty.
Purpose: To evaluate the feasibility and acceptability of a community-based intervention that included positive parenting strategies to build and support healthy families, and behavioral health promotion.
Design: Multi-method pilot study
Findings: Focus group participants (n=11) valued the program highly. There were no changes in participants' (n=36) Perceived Stress Scale scores after program completion (p>0.05, d=0.063).
Conclusions: Participants were highly engaged throughout the program and requested a longer duration of the intervention to continue to build social connectivity.
Ethnology. Social and cultural anthropology, Organizational behaviour, change and effectiveness. Corporate culture