Context Of Intelligence Research Articles

Utilizing multiple intelligences as a means to improving claim performance outcomes in project-based organizations

PurposeProject claim performance is often poor with negative consequences for project-based organizations (PBOs). Conflict and emotion are reported to impact organizational claim performance outcomes, yet remains unexplored. This study investigates the multiple intelligence (MI) context from the viewpoint of the claim management office (CMO), with the intent of identifying pathways to improving the emotion-based claim performance of PBOs.Design/methodology/approachThe study utilizes an autoethnographic case study involving a PBO-based general contractor (GC). Multiple intelligence theory (MIT) is applied as the theoretical lens. Method of framework analysis is used in analyzing the data, with reference to claim management office (RFCMO) theory as a main source in making “analytic generalization”.FindingsMultifaceted complexities of MIs are identified, including ambidexterity, actions and relations, stress and conflict, cultural differences and universals, meta-strategic thinking and team diversity. The holistic emotion-based strategies model identifies how PBOs can improve claim performance, specifically by adopting theories confection remedy and controlling emotion outcomes strategies in a closed-loop process. Further, the coherent emotions governance (CEG) framework enables CMOs' to upgrade maturity levels, by integrating the strategies, general and specific measures for improving emotion-based organizational claim performance.Originality/valueThis is a pioneering study in unveiling the complexities of organizational emotions engagement and the application of confection remedy, specifically in a project management context. The antecedents of RFCMO theory are here enriched with MIT and the pool of the supporting people-centered theories, augmented by an autoethnographic case study, in which the linkage between emotion and conflict complexity is laid bare. Further, this study identifies how conflicts in construction industry can be mitigated by way of a discrete three-category theories confection remedy.

Artificial Intelligence: The Doers, the Don’t-ers, and the continuum between

ABSTRACT Session 1: Make My Syllabus Nicer: Exploring Generative AI for Inclusivity and Accessibility in LIS Instruction Rebecca J.Morris (University, of Pittsburgh, School of Computing and Information, US) Though certainly not a magic formula, LIS instructors’ use of generative AI tools can support accessibility and inclusivity in design and adaptation of course materials and communications. LIS faculty may likely know and implement accommodations such as Universal Design for Learning to some extent, but perhaps find it challenging to integrate methods more thoroughly for reasons such as time, number of courses or varied formats of courses, and number of disability accommodations. This session includes teaching tasks: making tone and content of syllabi and assignment instructions more transparent and student-friendly generating performance criteria, checklists, and rubrics for differentiated assignments integrating principles of Universal Design for Learning into course design elements, such as o creating text outlines of lectures to provide multiple means of representation o personalizing assessment options to offer multiple means of action and expression A Community Conversation: The Context, Complexities, Contradictions and Critical Concerns of Artificial Intelligence: Parts I & II This dual presentation offers perspectives on the complexities, contradictions, and critical issues concerning Artificial Intelligence (AI). The IP SIG combines presentations with interactive audience engagement, providing an opportunity for ALISE members and attendees to forge cross-disciplinary ties around the conference themes of “[t]he ways in which information is created, disseminated, and altered” in the current education landscape Session 2: Part 1- The Context, Considerations and Complexities Rhiannon Bettivia (Simmons University School of Library and Information Science, US) Co-Moderator Warren Crichlow (Emerita, York University, US) Anthony W. Dunbar, (Dominican University - School of Information Studies, US) Co-Moderator Theodora Kourkoulou (University of Illinois at Urbana-Champaign, US) Cameron McCarthy (Emerita, University of Illinois at Urbana-Champaign, US) The panel centers innovation as a necessity and change agent in the area of digital and technological literacy. Thus, offering a brief historical discussion of technological developments and their deployment in classrooms, grounding a discussion of AI. Then discusses the dichotomy that AI is either the ultimate positive technological contribution to society or it will move society from a state of techno determinism to techno fatalism. This discussion centers the nuances between the pollyannic and fatalist propositions. Consisting of submissions from LIS educators and practitioners eager to share their experiences using (or not) AI in the classroom, this conversation further shapes approaches to AI and contributes to developing policies and practices at institutional and disciplinary levels. Session 3: Panel 2 - The Contradictions and Critical Concerns Rhiannon Bettivia (Simmons University School of Library and Information Science, US) Moderator Warren Crichlow (Emerita, York University, US) Panelist Theodora Kourkoulou (University of Illinois at Urbana-Champaign, US) Panelist Cameron McCarthy (Emerita, University of Illinois at Urbana-Champaign, US) Panelist Part 2 dives deeper into the pedagogical Contradictions and related Critical Concerns that LIS scholars, instructors, and practitioners are grappling with as AI interrupts and, in many instances, disrupts LIS learning spaces. This session offers an interdisciplinary sensibility.

Behavioral and Psychosocial Dynamics of Engagement: The Digital Divide in Artificial Intelligence [AI]-Driven Sports Podcasts.

The digital divide, particularly within the context of Artificial Intelligence (AI) sport podcasts, presents significant behavioral and psychosocial challenges for student engagement. This study examines the disparities in access to and proficiency with Information Communication Technologies (ICTs) across different demographic groups, focusing on gender, age, and religious level. The advent of the commercial web has heightened the significance of these divides, as the first-level digital divide concerns access to the internet, while the second-level digital divide pertains to the ability to use technology proficiently. The existing literature has consistently highlighted persistent inequalities in these areas, which significantly impact the extent to which students from various backgrounds can engage with AI sport podcasts effectively. Understanding these dynamics is crucial for developing strategies to bridge the gap and ensure equitable access to digital learning resources.

Effectiveness of Multimedia Package on Student-teachers’ Achievement in Information and Communication Technology (ICT) in Education Course

In the present context of artificial intelligence, student-teachers need to understand technological applications in education and incorporate them into their classroom instruction. Technological applications help in connecting and collaborating beyond geographical boundaries. The student-teachers should be equipped with the knowledge and understanding of the benefits of incorporating technology into their teaching practices and it should be the focus of teacher-training to equip them with the necessary skills to utilize these applications in their practice. The ICT in Education course was introduced in one of the University's pre-service teacher education programs in India to facilitate student-teachers to gain the ICT skills needed to integrate it into teaching. This study demonstrates the effectiveness of the multimedia package on student-teachers at the pre-service level based on empirical evidence. Some of the findings suggest that the use of the multimedia package not only helps student-teachers identify and use resources effectively but also delivers the content meaningfully. The Multimedia packages could serve as a tool for educators to deliver subject content efficiently.

Disowning the past: an assassination, complex transnational influences, and the generational rift within the Iranian communist movement

ABSTRACT An elaborate sting operation by Iranian security (SAVAK) centred on Abbas Shahriyari, an able SAVAK secret agent and a Tudeh Party member, to entrap underground leftist activists in the 1960s unwittingly unleashed a complex transnational web involving Iranian opposition and Soviet intelligence. The People’s Fadai Guerrillas (PFG) eventually assassinated Shahriyari and vastly propagated this operation through an extensive communiqué. The assassination marked the decisive, and highly symbolic, rejection of the legacy of the Tudeh Party by the rising Marxist PFG that came from an entirely younger generation, but it unwittingly also exposed in retrospect the complex web of actors drawn into SAVAK’s sting operations. This paper situates this assassination in its historical context and quadrangular web of Tudeh Party, PFG, Soviet intelligence, and SAVAK. It offers a close reading of PFG’s extensive communication about this assassination as the break within Iran’s communist movement.

Influencing Factors and Mechanisms of Value Co-Creation in Artificial Intelligence-Driven Human Resource Management: A System Dynamics Simulation

In the era of artificial intelligence, human resource management has undergone significant changes compared to traditional approaches regarding value creation methods and influencing factors. This research aims to utilize grounded theory to comprehensively explore the influencing factors of value co-creation in enterprise human resource management within the context of artificial intelligence. Additionally, this research seeks to capture the dynamic relationships, causal links, and evolutionary patterns among the various elements within the system by constructing a system dynamics model. The results indicated that (1) Environmental factors primarily play a regulatory role, organizational factors serve a supportive role, and participant factors act as the driving force in influencing value co-creation in human resource management. (2) In the context of artificial intelligence, both hardware infrastructure and software capabilities can significantly impact value co-creation in human resource management. This research complements current research on the influencing factors of value co-creation in enterprise human resource management. It offers new perspectives and frameworks for the theoretical development and practical application of value co-creation in this area, supporting companies in effectively managing and developing value co-creation in human resource management.

Ontology in Hybrid Intelligence: A Concise Literature Review

In the context of the constant evolution and proliferation of AI technology, hybrid intelligence is gaining popularity in reference to a balanced coexistence between human and artificial intelligence. The term has been extensively used over the past two decades to define models of intelligence involving more than one technology. This paper aims to provide (i) a concise and focused overview of the adoption of ontology in the broad context of hybrid intelligence regardless of its definition and (ii) a critical discussion on the possible role of ontology to reduce the gap between human and artificial intelligence within hybrid-intelligent systems, as well as (iii) the identification of possible future research directions in the field. Alongside the typical benefits provided by the effective use of ontologies at a conceptual level, the conducted analysis has highlighted a significant contribution of ontology to improving quality and accuracy, as well as a more specific role to enable extended interoperability, system engineering and explainable/transparent systems. Additionally, an application-oriented analysis has shown a significant role in present systems (70+% of cases) and, potentially, in future systems. However, despite the relatively consistent number of papers on the topic, a proper holistic discussion on the establishment of the next generation of hybrid-intelligent environments with a balanced co-existence of human and artificial intelligence is fundamentally missed in the literature. Last but not the least, there is currently a relatively low explicit focus on automatic reasoning and inference in hybrid-intelligent systems.

How do employees form initial trust in artificial intelligence: hard to explain but leaders help

This study experimentally investigates initial trust formation in the organizational context of an artificial intelligence (AI) system in human resource management (HRM). Drawing on social exchange theory and leader‐member exchange theory, we identify factors that contribute to initial trust in AI through cognitive and affective processing from the perspective of employees in the Chinese context. An online survey (N = 426) was conducted with a 2 (explanation of AI: without vs with) × 2 (trust in leaders: low vs high) design. Our findings demonstrate that initial trust plays a crucial role in AI adoption, and a trustworthy leader increases employees' AI trust and intention to adopt. Providing AI's benefits and risks moderates initial trust and the pathway to adoption. Moreover, familiarity with AI's application in HRM and organizational collectivism is also beneficial. Our findings suggest that organizations should prioritize cultivating initial trust in AI with employee‐oriented strategies, including trusted leadership and supportive training resources.

Perceptions of Collaboration between Virtual and Human Teachers in Online Education

This study delves into the perceptions of teachers regarding the collaboration between virtual and human teachers in online education. It is situated within the broader context of artificial intelligence (AI) in education, with a particular focus on Generative Artificial Intelligence (Gen AI) and its potential to transform online learning. This study aims to explore the possibilities of Virtual Humans (VH) as humanized Gen AI entities. More specifically, in this presentation, we study the perceptions of online teachers of d-teach online school regarding VH. This study employs a survey that combines the Agent Persona Instrument (API-R) and the Technology Acceptance Model (TAM) to measure the participants' perceptions with respect to using a VH as a digital co-teacher. The primary research objective is thus to understand how these stakeholders perceive VH roles and effectiveness in supporting the learning process. Follow up interviews are planned to deepen our insights on how real teachers think about virtual humans as a teacher. Although specific results are pending at this stage, the study promises to provide valuable insights into teachers' perspectives. Beyond the confines of the school, this research has the potential to benefit global education and training by enhancing the quality, efficiency, accessibility, and inclusivity of online education.

Construction of Smart Learning Spaces in Private Universities' Accounting Programs under the Dual Perspective of Digital Intelligence and Industry-education Integration

This study explores the construction model of smart learning spaces in private universities' accounting programs within the dual context of digital intelligence and industry-education integration. Utilizing literature review and theoretical analysis, the research systematically examines the current applications of digital intelligence in education and the impact of industry-education integration on educational reform. Initially, the concept of smart learning spaces is defined, and their application value in accounting programs is analyzed. Subsequently, the study investigates how digital intelligence enhances the smart, personalized, and interactive aspects of learning spaces, and how industry-education integration effectively aligns educational content with industrial needs. Finally, based on constructivist and ecosystem theories, a theoretical model of smart learning spaces is constructed, along with implementation strategies. The findings indicate that the construction of smart learning spaces effectively improves the teaching quality and students' professional abilities in accounting programs, providing new insights and methods for the reform and development of private universities' accounting programs.

AI 추천서비스 기반 상품구매 경험자의 자기결정성이 소비만족 및 재구매의도에 미치는 영향

Background/Objec tives: This study explores the relationship between consumption satisfaction and repurchase intention within the context of artificial intelligence(Al) recommendation services, using three self- determination factors(autonomy, competence, relatedness). It further investigates the moderating influence of consumers' gender and age on these relationships. Methods: : An online survey was administered to consumers who had purchased products based on Al recommendation services within the past year. Data from 200 respondents were analyzed using SPSS and AMOS software. Results: 1) Autonomy and competence in self-determination had a significant positive effect on consumption satisfaction. 2) Self-determination did not significantly affect repurchase intentions. 3) Consumption satisfaction exhibited a positive effect on repurchase intention. 4) Age was found to moderate the relationship between self-determination, consumption satisfaction, and repurchase intention, with significant effects observed across age groups, except for individuals in their 20s. 5) Gender was found to have no significant moderating effect m the relationship between self-determination, consumption satisfaction, and repurchase intention. Conclusion/Implications: This study enriches the academic discourse on AI services by examining consumers' perceptions within the online shopping field in terms of self- determination. It elucidates the impact of self-determination on consumption satisfaction and repurchase intention. It also underscores the significance of consumers' competence in shaping the online shopping environment, offering practical insights for service enhancement.

Advanced insights through systematic analysis: Mapping future research directions and opportunities for xAI in deep learning and artificial intelligence used in cybersecurity

This paper engages in a comprehensive investigation concerning the application of Explainable Artificial Intelligence (xAI) within the context of deep learning and Artificial Intelligence, with a specific focus on its implications for cybersecurity. Firstly, the paper gives an overview of xAI techniques and their significance and benefits when applied in cybersecurity. Subsequently, the authors methodically delineate their systematic mapping study, which serves as an investigative tool for discerning the potential trajectory of the field. This strategic methodological framework lets one identify the future research directions and opportunities that underlie the integration of xAI within the realm of Deep Learning, Artificial Intelligence, and cybersecurity, which are described in-depth. Then, the paper brings together all the gathered insights from this extensive investigation and closes with final conclusions.

Transferability Analysis of Data-Driven Additive Manufacturing Knowledge: A Case Study Between Powder Bed Fusion and Directed Energy Deposition

Abstract Data-driven research in additive manufacturing (AM) has gained significant success in recent years. This has led to a plethora of scientific literature emerging. The knowledge in these works consists of AM and artificial intelligence (AI) contexts that haven't been mined and formalized in an integrated way. Moreover, no tools or guidelines exist to support data-driven knowledge transfer from one context to another. As a result, data-driven solutions using specific AI techniques are being developed and validated only for specific AM process technologies. There is a potential to exploit the inherent similarities across various AM technologies and adapt the existing solutions from one process or problem to another using AI, such as transfer learning (TL). We propose a three-step knowledge transferability analysis framework in AM to support data-driven AM knowledge transfer. As a prerequisite to transferability analysis, AM knowledge is featured into identified knowledge components. The framework consists of pre-transfer, transfer, and post-transfer steps to accomplish knowledge transfer. A case study is conducted between two flagship metal AM processes: laser powder bed fusion (LPBF) and directed energy deposition (DED). The relatively mature LPBF is the source while the less developed DED is the target. We show successful transfer at different levels of the data-driven solution, including data representation, model architecture, and model parameters. The pipeline of AM knowledge transfer can be automated in the future to allow efficient cross-context or cross-process knowledge exchange.

Impact of misinformation from generative AI on user information processing: How people understand misinformation from generative AI

This study examines the impact of artificial intelligence (AI) on the ways in which users process and respond to misinformation in generative artificial intelligence (GenAI) contexts. Drawing on the heuristic–systematic model and the concept of diagnosticity, our approach examines a cognitive model for processing misinformation in GenAI. The study’s findings revealed that users with a high-heuristic processing mechanism, which affects positive diagnostic perception, were more likely to proactively discern misinformation than users with low-heuristic processing and low-perceived diagnosticity. When exposed to misinformation from GenAI, users’ perceived diagnosticity of misinformation can be accurately predicted by the ways in which they perform heuristic systematic evaluations. With this focus on misinformation processing, this study provides theoretical insights and relevant recommendations for firms to be more resilient in protecting users from the detrimental impacts of misinformation.

Enhanced Audio-Based Open-Source Intelligence Insights using Machine Learning

Nowadays, data collection methods and techniques are increasingly used to address intelligence needs in the sense of training models to predict correct information. Open-source intelligence (OSINT) could now incorporate Machine Learning (ML) by correlating diverse data types, such as text, images, audio, and video. In this research, we focused on an essential yet underdeveloped aspect of OSINT, extracting insights from audio data for military intelligence, especially in Pakistan's defence and focused on developing advanced tools for analyzing the expanding audio data, proposing a novel method to extract perfect information for intelligence purposes, specifically targeting key entities like Location, Rank, Operation, Date, and Weapon in military contexts. First, we developed a unique dataset containing 2000 transcribed sentences with annotations for the mentioned entities using an open-source NER annotator. Then, we trained four customized models using advanced NLP frameworks such as Hugging Face's Transformers (DistilBERT), spaCy, NLTK and Stanford CoreNLP, which are subject of assessment to determine their practical use in intelligence contexts. The selected models were evaluated, which proved that AI-based techniques are crucial for enhancing intelligence gathering in the dynamic OSINT landscape. The results also demonstrated the potential of AI integration in OSINT for audio data processing in military intelligence.

Higher Education Students’ Task Motivation in the Generative Artificial Intelligence Context: The Case of ChatGPT

Artificial intelligence has been attracting the attention of educational researchers recently, especially ChatGPT as a generative artificial intelligence tool. The context of generative artificial intelligence could impact different aspects of students’ learning, such as the motivational aspect. The present research intended to investigate the characteristics of students’ task motivation in the artificial intelligence context, specifically in the ChatGPT context. The researchers interviewed 15 students about their experiences with ChatGPT to collect data. The researchers used inductive and deductive content analysis to investigate students’ motivation when learning with ChatGPT. To arrive at the categories and sub-categories of students’ motivation, the researchers used the MAXQDA 2022. Five main categories emerged: task enjoyment, reported effort, result assessment, perceived relevance, and interaction. Each category comprised at least two sub-categories, and each sub-category was further organized into codes. The results indicated more positive characteristics of motivation than negative ones. The previous results could be due to the conversational or social aspect of the chatbot, enabling relationships with humans and enabling the maintenance of good quality conversations with them. We conclude that a generative AI could be utilized in educational settings to promote students’ motivation to learn and thus raise their learning achievement.

Smart Learning Environments: A Comprehensive Review on Artificial Intelligence and Learning Theories

The term “smart learning” is now commonly used to describe online education. It shows how learners can use knowledge and skills more easily, successfully, and simply because of modern technologies. The goal is to create a smart learning environment that is relevant, timely, and efficient, allowing students to learn more effectively and successfully. By setting realistic goals, focusing on the big picture, and ensuring a clear timeline for task completion, a smart learning environment can provide a more engaging and efficient educational experience for students. The term “smart technology” describes remote-controllable and internet-connected devices that are used in smart learning environments (SLEs) to personalize and improve the educational experience for each student. This research paper explores the use of technology, specifically artificial intelligence, in creating a smart learning environment. A comprehensive review has been conducted in two dimensions, the dimension of AI in smart learning and the learning theories for smart learning environment. The study reveals that the impact of artificial intelligence in enhancing student performance and instructional design has received wide attention from the academic research. At the same time, the learning theories like activity theory, self- determination theory, and connectivism learning theory, collaborative learning theory, and situated learning theory are still explored by academic community within the context of artificial intelligence and smart learning environment.

ESG-стратегия управления устойчивой цифровой трансформацией бизнеса в экономике искусственного интеллекта

Introduction: the subject area under the study is managing digital business transformation, with the focus on adapting Russian business to the modern context of the artificial intelligence (AI) economy, namely, to a new model of organizing the Russian economic system, the specificity of which is the mass dissemination of AI technologies. Objectives: to develop an ESG strategy for managing sustainable digital business transformation in the artificial intelligence economy. Methods: based on international experience and official statistics for 2022, using the method of regression analysis, the authors compiled an econometric model that mathematically described the influence of the global AI index components on the trade sustainability index. Results: a scientific argumentation has been developed for criticizing the existing strategy for managing digital transformation of business in terms of its sustainability. The market position of a business and its financial well-being are found to be improving to the detriment of society and the environment. As an alternative, ESG management is proposed, ensuring the balanced development of nature, society, economy, and technology. The prospects for sustainable digital business transformation in the Russian AI economy through Pareto-optimization of ESG management has been disclosed. An improved ESG strategy has been developed, the specific feature of which being reliance on integration mechanisms and the focus on infrastructure projects in the AI economy. The proposed strategy scope of application is digital transformation management in the Russian businesses practice. The advantage of the ESG strategy is that it ensures the business system stability in the process of its digital transformation through the unified implementation of the SDGs, the sanction stability and sustainability of positions in the global markets. Conclusions: it is concluded that development of infrastructure support for the artificial intelligence economy is the basis for sustainable digital transformation of business. Maintaining sustainability of the business in this process needs using ESG management to help introduce AI technologies.

Triplet extraction leveraging sentence transformers and dependency parsing

Knowledge Graphs are a tool to structure (entity, relation, entity) triples. One possible way to construct these knowledge graphs is by extracting triples from unstructured text. The aim when doing this is to maximise the number of useful triples while minimising the triples containing no or useless information. Most previous work in this field uses supervised learning techniques that can be expensive both computationally and in that they require labelled data. While the existing unsupervised methods often produce an excessive amount of triples with low value, base themselves on empirical rules when extracting triples or struggle with the order of the entities relative to the relation. To address these issues this paper suggests a new model: Unsupervised Dependency parsing Aided Semantic Triple Extraction (UDASTE) that leverages sentence structure and allows defining restrictive triple relation types to generate high-quality triples while removing the need for mapping extracted triples to relation schemas. This is done by leveraging pre-trained language models. UDASTE is compared with two baseline models on three datasets. UDASTE outperforms the baselines on all three datasets. Its limitations and possible further work are discussed in addition to the implementation of the model in a computational intelligence context.

Hey Google, I trust you! The consequences of brand anthropomorphism in voice-based artificial intelligence contexts

Users’ increasing adoption of voice assistant services is fostering the growth of a novel strand of marketing research on the branding implications of brand anthropomorphism (BA). However, the branding outcomes of brand anthropomorphization in this research area remain underinvestigated. Accordingly, in the name-brand voice assistant (NBVA) interaction field, this study tests a model of the consequences of brand anthropomorphism, outlining the relationships among brand anthropomorphism, brand trust, and multidimensional consumer–brand engagement (CBE), i.e., the relevant cognitive, affective, and behavioral dimensions, as well as the moderating role of perceived privacy risk. A survey of young adults shows that brand anthropomorphism positively affects brand trust as well as the affective and behavioral dimensions of CBE. Furthermore, perceived privacy risk positively moderates the relationship between brand anthropomorphism and brand trust. Specifically, the influence of brand anthropomorphism on brand trust is strengthened at higher levels of perceived privacy risk. This article thus enriches the understanding of brand anthropomorphism and user VA response by exploring underresearched branding outcomes of BA in the context of NBVA interaction.