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The use of facial recognition method has spread to many different industries, most notably smart attendance systems. But one major obstacle these systems must overcome is spoofing, which is the practice of people trying to trick the system by utilizing counterfeit or modified photos or videos. In the context of a smart attendance framework, this paper presents a smart anti-spoofing mechanism that can distinguish amongst real and fake faces with accuracy. The system that has been developed entails the generation of datasets that contain both actual and fake facial photos and videos, in addition to the application of a feature extraction and classification pipeline that is driven by deep learning. Furthermore, a number of data augmentation techniques, including translation, scaling, and rotation, are used to improve the effectiveness of the suggested system against different spoofing strategies. Empirical results show that these strategies support increased accuracy and durability in thwarting various spoofing attack types. With a 97% recognition rate overall, our anti-spoofing/mocking technology affords a stable and secure foundation for intelligent attendance systems. The study emphasizes how effective it is to create a strong anti-spoofing system specifically for keen attendance applications by integrating deep learning and soft computing models. [ABSTRACT FROM AUTHOR]

Attendance monitoring is an essential task for any organization and traditional methods such as pen and paper or electronic card systems have their limitations with advancements. The proposed model is a real time face presence monitoring system using Raspberry Pi. The system captures image of individuals using camera and processes the images using deep learning algorithms to detect faces. These faces are then matched against a pre-existing database of known individuals. Door interlock controls add an extra layer of security, ensuring that only authorized person have access to restricted areas. The system provides a user-friendly interface to view everyone's attendance records and generate attendance management reports. The proposed system provides a reliable and accurate solutions for attendance tracking and can be used in various organizations. [ABSTRACT FROM AUTHOR]

Fake news is false or misleading information presented as news. False stories tend to spread farther, faster, and more broadly than true stories. Some individuals and organizations intentionally applied misleading information for certain benefits. The internet goes quickly, mobile devices became popular, and social media users grow more significant in number, make no boundaries world and connect every user. As a result, information circulates in immense numbers makes to handle fake news manually is impossible. Fake news also affects human psychology. Hoax related to how to cure COVID-19 and the lack of toilet paper has led to casualties and riots. Fake news potentially differs in writing technique compare to real information. In this paper, we discussed a few studies related to fake news detection. Their algorithm can capture writing style in content viz lexicon, syntax, semantic, and discourse. Also, those studies implemented user-based, content-based, and social context-based. It used machine learning, deep learning, and reinforcement learning. Furthermore, education plays a vital role in making people understand the impact of fake news. Itis necessary to do a campaign for all internet users. The national government necessity collaborates with society implement the particular rule for better internet environment. [ABSTRACT FROM AUTHOR]

There has been a substantial growth of technology and it has evolved a lot, and with the evolving technology credit cards seem to be replacing cash payments. There is no second thought about credit cards getting used on a large scale but this extensive use brings a huge number of fraudulent uses of credit cards. In this paper, discussions on different technologies and methods that are used to detect and prevent credit card fraud have been presented. In the paper discussion on the statistics of the credit card frauds detected using machine learning and deep learning has been presented. In this paper, we have presented the survey on Machine learning techniques and analyzed their accuracy and speed, also we have discussed the methods that are used in detecting credit card fraud using deep learning and artificial intelligence. Furthermore, in this paper, various types of datasets are explained in detail. Also, the paper highlights the challenges and possible solutions to detect fraud. [ABSTRACT FROM AUTHOR]

Failure to account for human values in software (e.g., equality and fairness) can result in user dissatisfaction and negative socioeconomic impact. Engineering these values in software, however, requires technical and methodological support throughout the development life cycle. This paper investigates to what extent top Software Engineering (SE) conferences and journals have included research on human values in SE. We investigate the prevalence of human values in recent (2015 - 2018) publications in these top venues. We classify these publications, based on their relevance to di erent values, against a widely used value structure adopted from the social sciences. Our results show that: (a) only a small proportion of the publications directly consider values, classified as directly relevant publications; (b) for the majority of the values, very few or no directly relevant publications were found; and (c) the prevalence of directly relevant publications was higher in SE conferences compared to SE journals. This paper shares these and other insights that may motivate future research on human values in software engineering. [ABSTRACT FROM AUTHOR]

The prize for recent successes in the field of Artificial Intelligence (AI) has been black-box models with lack of algorithmic transparency. Especially the use of deep learning algorithms for machine learning tasks have produced opaque systems, which escape explanation of how results are provided. Apparently, AI-developers have been more occupied with creating powerful AI systems than with ensuring explainability. On this backdrop, this work in progress paper presents preliminary reflections and argues that interpretability viewed as transparency wrt system functionality does not necessarily bring explanatory clarity, whereas interpretability in the shape of post hoc explainability may work by giving reasons for why the system reacted in a certain way without full reference to its inner workings and black-box models. Hence, interpretability understood as post hoc explanations may satisfy a request for epistemic transparency. Still, future work on the paper is needed to clarify the degree to which the demand for interpretability can be ethically satisfied in the light of epistemic opacity. Moreover, it is generally acknowledged that explainability is pivotal to foster trust in AI. Therefore, the notion of trust is clarified by pointing to a distinction between trust as a fundamental condition in all human interaction and reliance as reflecting rational confidence in others or artifacts. [ABSTRACT FROM AUTHOR]

This paper aims to understand how higher education institutions (HEIs) can support small and medium-sized enterprises (SMEs) and start-ups to become more sustainable through aligning more closely with the ISO 56002:2019 standard on Innovation Management Systems. The paper utilises an international study with data gathered through semi-structured interviews with eight (n=8) case companies from five (n=5) countries across numerous industries. Preliminary results show HEIs can greatly support and play a large role in facilitating the green transition and sustainability activities of SMEs and start-ups. Results indicate understanding a company's innovation management system by both the company and the HEI involved improves the effectiveness of the provided support and its resulting impact on increasing the innovative sustainability capacity. [ABSTRACT FROM AUTHOR]

This paper addresses and investigates ways in which knowledge creation in a local textile ecosystem can have an impact on global business. The starting point is the co-creation of a local R&D micro factory. The purpose is to research the application of recycled local textile resources to production aiming to slowing down the use of virgin materials. Theoretically, the paper draws on theories on knowledge creation, including theories on embodied and tacit knowledge. Empirically, the paper uses the local R&D micro factory as a site of research (READY n.d). The participants in the READY research project are key industrial stakeholders and researchers from the textile cluster in Denmark. [ABSTRACT FROM AUTHOR]

The recycling technology for spent lithium-ion batteries is critical for achieving a circular economy since it transforms hazardous wastes into valuable resources. Technology opportunities refer to the potential for technological progress in a technology field. Based on the perspective of technology opportunity analysis, this study focuses on identifying the technology opportunities of lithium-ion battery recycling technology by exploring the knowledge gaps between scientific and technological publications of the technology. This study improves the previous framework by using a deep learning-based clustering approach and introducing the Latent Dirichlet Allocation approach to identify the knowledge topics. The findings indicate that the scientific knowledge of recycling and preparing anode materials and the management and monitoring of the batteries need to be applied further in technological development. The technological knowledge of separating and recycling electrode materials has received less attention in scientific publications. [ABSTRACT FROM AUTHOR]

Cities have been studied as a platform for sustainability transformation and social innovation, mainly focusing on environmental sustainability and emphasizing governance and citizen roles as transition actors. Previous studies have paid limited attention to the social aspects of sustainability and the role of business organizations as producers of socially targeted innovation. Our study develops a framework for the identification of social sustainability impacts of communal neighborhoods. Considering the residents' collaborative efforts for creating the sense of community, this paper explores whether the concept of social innovation is useful in conceptualizing the social sustainability impacts of communal neighborhoods. We use the framework to illustrate initial findings of a case analysis of two Finnish communal neighborhood concepts. The findings of our paper contribute to the discussion on social value, which has been identified as a central theme in the development of social innovation research. [ABSTRACT FROM AUTHOR]

Knowledge management (KM) is more important than ever for organisations of all sizes. The systematic identification, acquisition, development, distribution, utilisation and preservation of knowledge can lead to competitive advantage. In times of parallel transformations, it is crucial for companies to bundle and link different types of knowledge and make it available to employees in a targeted way. This conference paper proposes a fourfold approach in which the organisational culture and the mind-set of the employees play an equally important role in KM as the organisational framework of KM, and the tool-set and skill-set of a company. Based on the data collected in a 1.5 year project, this conference paper links six KM building blocks with cultural dimensions to show that the mind-set of the employees also plays a crucial role in ensuring that an organisation effectively identifies, develops, shares, uses and retains knowledge. [ABSTRACT FROM AUTHOR]

Our EU-funded project, conducted in Jönköping County, Sweden from 2021-2023, aimed to advance AI in the local business sector through collaboration between academia, industry, and business promotion systems. Using a stepwise approach, we transitioned 88 companies from basic AI knowledge to implementing AI functions. This included assessing AI maturity and developing tailored educational materials. Collaborating with Jönköping University, we engaged 19 SMEs, integrating real-world AI challenges with academic courses. Emphasizing sustainable and ethical AI practices, we educated companies on possible alternatives to AI usage. In this paper we report on the results of an evaluative study of the participating partners, revealing key parameters for advancing AI implementation. [ABSTRACT FROM AUTHOR]

This paper aims to explore how organisational ambidexterity development is linked to a systematic approach to managing innovation. Specifically, it focuses on the adoption of Innovation Management Systems, such as the recently developed ISO 56002. Therefore, this paper presents preliminary results of a planned exploratory research focusing on Small and Medium Sized Enterprises (SMEs), which will be carried out as a longitudinal international project. The research will be conducted in several European and Canadian Small SMEs with a series of semi-structured interviews. The preliminary results of our pilot cases based on 6 organizations reveal that SMEs are open to the systematic approach to innovation capability development and display systematic tendency for some capabilities, such as collaboration and leadership. [ABSTRACT FROM AUTHOR]

In this paper we discuss how digital transformation in finance is leading to increased focus on customer-centricity. We describe how this is often understood as data-driven innovation based on quantitative collection and analysis of digital customer information. We explore the potential of using qualitative data to understand customer contexts and pain points. We share research that illustrates how quantitative data can overlook important knowledge about customers' decision-making and customers' use of different financial solutions, while it can underestimate customers' ability to manage new technological solutions. We recommend that financial service providers extend their innovation toolbox to include qualitative research methods and that they build a customer centric research culture. We also recommend including customers and partners in innovation practices. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between entrepreneurial education and action, emphasizing the complexity of transitioning from education to entrepreneurial journey. Highlighting the significance of universities in fostering entrepreneurship, it underscores the necessity of educational programs extending beyond traditional business education. Drawing from various theoretical frameworks, including Shapero and Sokol's Entrepreneurial Event Model and Ajzen's Theory of Planned Behaviour, the paper proposes an Intention Model to elucidate the transition from opportunity identification to intention formation and subsequent entrepreneurial action. Through a combination of literature review and empirical research, the study explores the impact of entrepreneurship education on graduates' entrepreneurial actions, shedding light on factors such as personal desirability, social norms, and perceived efficacy. Findings suggest that entrepreneurship education significantly influences students' social norms and behavioural tendencies, thus affecting their entrepreneurial actions. [ABSTRACT FROM AUTHOR]

This research aims to present a base literature exploration study on the existing relationship between the development of deep learning-based assembly assistance systems to promote quality assurance and quality control in industrial assembly lines. The use of artificial intelligence in industry has become more common with the advance of Industry 4.0. Understanding that quality assurance is a difficult term to describe and evaluate in industrial contexts due to the difficulty of measurement methods and their limitations, an artificial intelligence approach may lead to total quality control within an assembly process by checking every step and every product assembled within a process. This study aims to understand how to develop an augmented intelligence-based assembly assistance system to guarantee the quality of a product during the assembly process. [ABSTRACT FROM AUTHOR]

Text Summarization is one of the most experimental tasks in deep learning that decreases the size of a text while it's important to keep its core and key information. Previous work in this domain is going to give remarkable results by using highly dependent data. Natural language processing-related works are trending due to complications in the implementation. Abstractive text summarization is a critical idea in natural language processing and also it's a trending, hardest problem that varies the solution or outcome based on the dataset. The objective of the paper is to rewrite into well-simplified and core information in text format that is retrieved from files or documents or datasets. Abstractive text summarization using RNN is the methodology going to be implemented in our paper. Attention mechanism and LSTM are going to be add-on advantages to the model. Both the methodologies improve the performance of the model and are also used to solve the complications problem. Keyword extracting is the one of features of this paper. State-of-art is going to be used to verify the model's efficiency. BLEU and ROUGE are going to be used to identify the accuracy and validate the model. The greatest thing about the dataset is the relationship between columns is independent of each other. Finally, the aim of the paper is to generate shortened text from whole huge data (text format). These kinds of problems are often used to solve in sequence-to-sequence passion [ABSTRACT FROM AUTHOR]

Deep learning has been increasingly popular in the area of information engineering over recent years (SE). However, there are so many unfinished concerns to be studied. How can researchers incorporate deep learning into SE issues? What SE phases are supported by in-depth learning? Are the professionals benefiting from deep learning? Responses allow clinicians and researchers to build functional deep learning models for SE activities. To address these questions, we perform a bibliography review of 98 SE academic articles using deep learning techniques. We notice that 41 SE activities have been enabled by deep learning optimised solutions in all SE phases. In which 84.7 percent of papers only use traditional deep learning techniques and their variants to overcome Software Engineering problems. Practicability is becoming a problem with the use of deep learning methods. More Software Engineering researchers may be attracted in the future to Enhance the effectiveness, efficiency, comprehensibility and testability with deep learning- based methods. [ABSTRACT FROM AUTHOR]

Edge computing has emerged as a promising tool to resolve the issues like high bandwidth and privacy requirements for Internet of Things (IOT) devices that were not resolved by traditional cloud computing. The data generated at edge nodes are often private and sensitive. Edge computing emphasizes doing computation nearer to the data source, which also helps transfer lesser data over the cloud to minimize the traffic. It is very important that proper security measures are taken to protect the edge nodes and the data from any security breach. This paper first discusses various aspects of cloud computing, its threats and limitations, followed by a discussion of edge computing, its architecture, threats, and security challenges. The paper further reviews the literature on anomaly detection, i.e., one of the security challenges in edge computing for the year 2021. The extensive review results in developing a taxonomy of methods used to do anomaly detection in edge computing. The review later discussed how researchers have tried to implement their model that does anomaly detection, keeping in mind the limitation of edge nodes such as computation ability, storage, and high bandwidth requirement. The review also summarizes a few usecases in which researchers have used anomaly detection. The review also points out that deep learning methods have been favored by the majority of the researchers and suggest future research direction. The taxonomy presented in this paper can be served as a summarized framework to anchor research work in the area of anomaly detection. Practitioners can also use the taxonomy as a ready-to-go reference tool for the researchers could utilize this working in the area of anomaly detection. [ABSTRACT FROM AUTHOR]

The paper reviews and assesses the state of blockchain research and conceptualises blockchain uses as socio-technical phenomena. The first blockchain use case emerged with the release of Bitcoin in 2008. Since then, blockchain use has proliferated in various other areas and encompassed an array of applications, such as tokens (cryptocurrencies and non-fungible tokens), decentralised autonomous organisations and smart contracts. The paper presents a structured literature review of 113 research papers in information systems and related fields (e.g., organization studies, management and humancomputer interaction) that commonly study socio-technical phenomena and organisational uses of technology. Conceptualising blockchain uses as socio-technical phenomena highlights the necessity to account for business models, social implications and stakeholders' values regarding blockchains beyond the blockchain technology 'as such'. The review reveals that the existing literature has articulated substantial knowledge of some aspects of blockchain uses but lacks knowledge in other aspects. Therefore, we propose a research agenda. [ABSTRACT FROM AUTHOR]

This paper investigates the effectiveness of sentiment analysis (SA) methods, ranging from rule-based approaches to deep learning architectures, in analysing textual data. The study focuses on three Python libraries: TextBlob, VADER, and Flair, evaluating their accuracy on a public dataset of Twitter posts. Additionally, custom neural network architectures are developed to optimize sentiment classification. Results indicate that while rule-based libraries offer simplicity, deep learning-based libraries show promise for higher accuracy. The customized LSTM models, particularly LSTM2 with architectural adjustments and regularization techniques, demonstrate improved performance over baseline models with classification accuracy as high as 76.3%. [ABSTRACT FROM AUTHOR]

Digital technologies and data are necessary enablers when it comes to measuring, monitoring and verifying the climate impacts of agricultural processes. As the availability of data has significantly increased due to the rapid development of sensor technologies, the enablers of data-assisted decisionmaking in agriculture have improved. Timely and informed decision-making at farm level not only increases productivity and profitability, it also facilitates climate-smart production. The goal of this paper is to improve understanding of farmers' expectations and needs regarding data-driven decision-making and investigate what kind of data transfer interface would be useful for farmers to assist their decision-making. This paper also presents an application prototype, called FarmerApp, that could assist farmers in making informed decisions based on their farming activities as part of climate-smart agriculture. The development of the prototype followed the principles of user-centered design and a design thinking process to ensure the usability and usefulness of the application. [ABSTRACT FROM AUTHOR]

Digital transformation has emerged as a critical strategy in today's dynamic business environment. However, the high failure rate of digital transformation initiatives underscores the need for a deeper understanding of success factors. This study conducts a comprehensive examination of digital transformation success factors through a systematic literature review. Assessing 34 peer-reviewed journal articles published between 2017 and 2023, the study evaluates research areas, temporal trends, geographical distributions, journals, significant keywords and main authors based on co-authorship, using bibliometric analysis. It unveils the current digital transformation research landscape, synthesises 119 enabling factors into 13 primary success factors and presents a new holistic digital transformation model. This model offers valuable guidance for organisations navigating transformation challenges, contributing significantly to both academic discourse and practitioners in this field. [ABSTRACT FROM AUTHOR]

The origins of radical innovation are not well understood. Described as the "fuzzy front end", successful innovation is typically recognised only in hindsight, obscuring the process through which it emerged. Sarasvathy's (2001) and later work on effectuation shows clear patterns in how successful entrepreneurs think, but offers few details on how they learned to defy conventional wisdom in management theory. This paper draws on a coproduced auto ethnographical study of how 8 student teams in Higher Education delivered radically innovative products (products unlike anything currently on offer). It draws on education theory and neuroscience, specifically situated cognition and the learning paradox arguing that radical innovation emerges in co-creation through interactions between cognitively diverse people in a complex adaptive system. Its key contributions are to add more detail to the effectual process and fill some of the gap between theory and practice. [ABSTRACT FROM AUTHOR]

With a present sense of urgency in taking actions towards sustainable development and the 17 goals, it is necessary to create functional partnerships in society. This study will discuss the university-industry collaboration (UIC), which we suggest needs new prerequisites. The innovation management problem we address is: How can UIC be organized to support transformative innovation? This paper will focus on understanding the expectations of different organizations of collaboration, new forms of working together, and incentives to support this transition. Data comes from two parallel surveys, based on the UIC framework from Ankrah & Tabbaa (2015). Tentative results show that expectations are of importance but vary, with industry waiting for academia to both address and progress with the SDGs while academia suggests industry to be leading the development. Respondents propose ideas concerning mutual learning rather than knowledge transfer. However, the incentives for creating such UIC conditions appear to be lacking. [ABSTRACT FROM AUTHOR]

This paper draws attention to commercialization projects carried out at the university and the interaction of academic and business experts in project meetings. It demonstrates how structural and socially agreed power relations are involved in the process in which scientists and industry and innovation experts make sense of commercialization and actively prepare commercialization activities for research-driven innovations. Adopting the heuristics of critical sensemaking, the research claims that university-business cooperation has little impact on translating science into business if the cooperation remains at the level of meetings and occasional interaction. This is often the case in specific research to business projects. The main contribution of this paper is the illustration of the effect of power relations on how a specific sense of commercialization became privileged over the others. [ABSTRACT FROM AUTHOR]

Evaluating organisational performance in relation to the Circular Economy (CE) has become increasingly important, yet the literature highlights certain limitations of micro-level CE indicators, such as the lack of a widely accepted method for measuring CE in both academia and industry. Among various Performance Management Systems (PMSs), the Balanced Scorecard (BSC) stands out as one of the most widely used by organisations, offering a more comprehensive perspective beyond purely financial metrics. Therefore, this paper aims to explore how CE measurement can be combined with organisational strategy assessment using the BSC. To achieve this, a theoretical framework that defines Circular Balanced Scorecard (CBSC) archetypes and integrates them with CE indicators is proposed. This framework serves as a tool for seamlessly integrating CE measurement into the PMS of organisations with different levels of CE maturity. [ABSTRACT FROM AUTHOR]

The present paper examines the phenomenon of "Sciencefluencing", meaning the use of Social Media for science communication in Germany. It investigates the role of Social Media, with a focus on Instagram, in communicating science and research to a broader public, which is not well investigated yet. Previous studies focused mainly on Twitter or other forms of online media, e.g. blogs. This study aims at filling the gap. In order to achieve that, a study is conducted including qualitative interviews with scientists and, based on the results of those, a quantitative survey that aims at understanding the use of Social Media for science communication from scientists in Germany. The theoretical framework is made up of theories of influencer marketing, opinion leadership and parasocial relations. The focus on Instagram stems from its broad relevance in the general population. [ABSTRACT FROM AUTHOR]

Including environmental aspects in the digital Supply Chain (SC) transformation of large, medium, and small size enterprises is a complex procedure involving interactions between internal and external organizations, requiring future proof and cost-efficient decision making based on the available knowledge. The purpose of this paper is to review the occurrence and importance of the environmental aspects in available research literature, critically review their significance in assessing the enterprise maturity, and find gaps preventing implementation of the environmental aspects. As reference the occurrence of environmental themes as part of the Technology-Organization-Environment (TOE) framework and as one of the three pillars of sustainability are used. It can be proven environmental themes cover ~20% of the TOE area publications. The result also shows declining interest in publications for Supply Chain 4.0 maturity but increasing interest in sustainability. [ABSTRACT FROM AUTHOR]

Generating and maintaining competitive advantage in today's hypercompetitive and globalized market environment becomes increasingly challenging. Innovative approaches are essential to differentiate from competitors and to preserve company performance. Context-oriented innovation approaches provide companies with a choice of suitable process models and methodology to conduct innovation projects while taking the context into account. The current paper aims to operationalize the contextoriented innovation approach in form of a decision-making framework called 'Smart-Innovation-Cube'. The framework allows to select and adapt processes and methods according to the three influential context factors 'External Change', 'Internal Change' and 'Path Dependency'. By applying the framework companies can chose appropriate processes and methods to increase the innovation efficiency. Moreover, the innovation process becomes customizable to fit individual innovation projects and thus becomes more agile. [ABSTRACT FROM AUTHOR]

How can workplace innovation be measured in a self-producing innovation system with autonomous and limited employee behavior? Seeking answer to this question this study begins with an inquiry on workplace innovation as high involvement in innovation of all employees (HII). HII's framework, is further refined through the intersection of autopoiesis, emphasizes self-creation among employees while acknowledge the necessity of controlling employee behavior. The intersection lays the foundation for the developed conceptual model. Utilizing a mixed method with multiple case study of 32 regional firms, the paper demonstrates the concepts in practice. The primary contribution is measurement of a firm's workplace innovation capacity, grounded in its capabilities and daily routines for employee behavior. The findings suggest a construct that aids to identifying management style intentions, balancing top-down and bottom-up approaches to explain employees' involvement in innovation as autonomous or limited, with the added dimension of self-creating or autopoietic employee behavior. [ABSTRACT FROM AUTHOR]

The study thoroughly and systematically reviewed the extent literature on knowledge management research to examine 1) how knowledge management is defined by researchers, 2) trends in knowledge management research, 3) what theories and methodologies have been applied in literature on knowledge management research, 4) what constructs and variables has been assessed in the knowledge management literature, 5) what is the future of knowledge management research in relation to industry 4.0. Peer-reviewed articles mainly from AIS library (journal and conference papers) and other sources published between 2012 and 2018 are content analyzed. The results of the study provide valuable information to knowledge management researchers by identifying gaps and disparities in the knowledge management literature. The study provides several important suggestions and directions for future research. [ABSTRACT FROM AUTHOR]

Urban areas globally face escalating challenges from Urban Heat Islands (UHI), characterized by elevated temperatures in urban regions compared to surrounding areas. This paper addresses the urgent need for comprehensive UHI analysis and mitigation strategies by harnessing advanced Artificial Intelligence (AI) and Computer Vision techniques. The primary objectives encompass leveraging high-resolution satellite imagery and semantic segmentation algorithms for accurate UHI detection through detailed land cover classification. Additionally, the study integrates weather information application programming interfaces (APIs) to correlate real-time and historical weather data with UHI intensity, providing a holistic understanding of UHI effects. Our proposed methodology involves a twofold architectural design, comprising the preprocessing of satellite images using patchify-ing and semantic segmentation for land cover classification. Experimental results demonstrate the model's ability to differentiate between urban and rural areas, showcasing its potential for automated UHI detection. The research emphasizes the importance of acquiring ample satellite data to enhance accuracy and resolution, acknowledging current limitations in the available dataset. In conclusion, this paper underscores the necessity of a holistic approach to UHI, combining AI and Computer Vision for precise detection and comprehensive analysis. While presenting a case study for a single city, the outlined pipeline provides essential steps for detecting UHI effects in any area equipped with necessary satellite imagery and temperature data, contributing to the broader understanding and mitigation of UHI in urban environments. [ABSTRACT FROM AUTHOR]

This section presents several conference paper abstracts on management education and development. The paper 'Film in Teaching: Copyright Issues' presents guidelines and observations for legal use in different situations, based on the Copyright Law of the United States. The paper 'Testing the Applicability of Learning Theories to Web-based MBA Courses' attempts to help address this literature gap by reporting on a study of objectivist, collaborative, and cognitive constructivist learning theories in a sample of courses conducted from Summer 2000 through Summer 2002. The results of this study indicate that while characteristics associated with objectivist and cognitive constructivist learning theory explain significant variance in perceived learning and delivery medium satisfaction, students reported significantly higher scores for those variables in courses where collaborative constructivist learning techniques were the dominant instruction mode. The paper 'Creating Critical Mass in Russian Management: Managers' Perceptions of What Remains to be Done' reports on the self-identified training and education needs of a select cadre of Russian managers. The paper also examines the challenges facing their firms, the skills needed to overcome them, and whether the methods identified by the managers for improving their skills will actually be able to deliver them.

This study investigates the current potential for artificial intelligence (AI) to support personalized learning (PL). Personalized learning can provide a customized learning environment to support student learning processes based on individual needs, competencies, and interests. One way to conduct personalized learning is by using a recommender system that employs deep learning, an AI technique. To date, a limited number of researchers have discussed the application of deep learning methods to develop advanced recommenders in personalized learning environments. This study examines the literature that describes deep learning as a recommender system to support personalized learning environments. This initial phase of the project seeks to synthesize the issues and opportunities associated with personalized learning experiences and the potential of using deep learning to support the process. Because the topic intersects the education and information technology (IT) fields, we selected three databases for this literature review project: Scopus, ERIC, and Engineering Village. We used the phrase "deep learning recommender system for personalized learning environments" as our search string. We focused only on papers that experts had evaluated in the field to ensure accuracy. Therefore, terms such as "peer review," "literature review," and "systematic review" were added to the original search string. The initial search results included 409 documents. After applying inclusion/exclusion criteria, 20 papers emerged as the focus of this study. Thematic analysis was used to look for various themes to identify how deep learning methods are used in education and their potential to inform personalized learning environments. The analysis process utilized Mendeley and NVivo to quickly capture themes by focusing on six features to peruse within the articles. The features involved research questions, goals of studies, research methodology, research design, primary outcomes, and limitations. We then generated three themes from the six features in the analysis phase of the 20 papers. The first theme categorized the type of study into primary and secondary studies. These categories identify the types of studies that employed deep learning methods in the development of a recommender system and their integration with personalized learning. The second theme, recommender system (RS) techniques, highlighted the AI methodologies most frequently utilized in previous research. And the third theme was a list of e-learning platforms that applied RS for personalized learning. The main findings revealed that the deep learning method was effective in big data analysis due to its ability to forecast students' achievements, behaviors, and future paths. Therefore, we considered that deep learning could be widely applied as a technique to develop recommender systems to support personalized learning environments. Furthermore, because we found that only a few studies have investigated the implementation of this AI technology, researchers will have a great opportunity to explore deep learning to develop more innovative solutions in educational fields. [ABSTRACT FROM AUTHOR]

We carried out an experiment to extract ecological‐restoration‐technology‐related entities (i.e., entities related to the fragile ecological remediation technology of desertification, rocky desertification, water and soil erosion control areas, place entities, and time entities related to the technology performed,) from full‐text documents published in the CNKI from 1978 to 2017. Based on the extraction of the time entities, place entities, and fragile ecological remediation technology entities, we analyzed the technologies with the greatest potential from among China's fragile ecological restoration technologies using the category clustering of fragile ecological restoration technologies with LDA and the place distribution and evolution of fragile ecological treatment technologies. This research shows that the proposed Bi‐LSTM+CRF combined with the feature‐based named entity knowledgebase can be used to perform geographical knowledge discovery from text, and it has has good prospects for applications in intelligence analysis based on text mining. [ABSTRACT FROM AUTHOR]

Entrepreneurial ecosystems have developed into an interesting field of research that is attracting a significant amount of academic attention. Nevertheless, practical experience shows that not all ecosystems are equally successful. This paper analyses the success factors of six different ecosystems. Using a methodology that combines in-depth content analysis and expert interviews in a multiple case study, we identify and compare the specific factors that determine the success of these ecosystems. By identifying which factors are transferable across different contexts and which are unique to specific ecosystems, this study contributes to a deeper understanding of how entrepreneurial ecosystems can be effectively designed and cultivated. The results are relevant for both practitioners and researchers seeking to foster a stable entrepreneurial environment. [ABSTRACT FROM AUTHOR]

This paper explores collectively shared and sustained craftsmanship, worker agency instantiated by physical fixtures and collective creativity in work processes and contrasts this practice with a pilot test of extended digitisation of tools, i.e. the application of augmented reality. The ability of the collective to establish, maintain and recover meaningful activities, procedures and processes is important in most workplaces and in collaboration in social groups. Collective know-how, i.e. procedural memory. The unit of study is an industrial team creative practice developing fixtures to support the assembly procedures of lever tumbler locks. Collective practices establish the skills needed to carry out a particular set of procedures. In summary, craft skills exist relationally - in the relationships between materials, objects, technologies, memory, feeling, different human actors and the chains of associations between them. [ABSTRACT FROM AUTHOR]

This research paper aims to explore the impact of resources, dynamic capabilities, and strategic orientation on innovation as well as growth within small and medium-sized enterprises (SMEs). Specifically, we have developed eight theoretical constructs as research models, integrating a range of organizational factors that influence the innovation capabilities of SMEs and, consequently, their growth potential. The theoretical foundation of our perspective incorporates the Resource-Based View, Dynamic Capabilities Theory, and Strategic Management Theory. Methodologically, the investigation employs rigorous data collection, encompassing both quantitative and qualitative measures, to construct and validate theoretical models that illuminate the multifaceted relationships between these determinants and SME growth outcomes. The goal is to provide actionable insights into the factors shaping SME evolution and the efficacy of innovation-driven interventions. [ABSTRACT FROM AUTHOR]

Users are regularly highlighted as a potential source of innovation (von Hippel, 1988, 2016, Bogers et al, 2010) but although they have considerable potential for enhancing both the front end of innovation and its downstream diffusion there are still questions about this approach. Whilst 'lead users; may well be willing and able to contribute their valuable insights there is a large gap between their involvement and the larger population of users who have the potential to contribute. We still need to learn more about the extent to which users can be involved and how their contribution can be articulated and deployed. The focus of this paper is on finding ways to give 'voice' to such potential user innovators in the healthcare sector. [ABSTRACT FROM AUTHOR]

The climate crisis has spurred legislative changes, prompting the restructuring of energy networks to combat environmental issues and reduce carbon dioxide emissions. Nevertheless, existing business structures may not fully facilitate and support energy transition process. Green Innovation Ecosystem (GIE) emerges as a promising framework for fostering the adoption of green technologies and new environmental solutions. This paper aims to delve into the concept by examining a high-temperature heat pump energy integration case. Our goal is to contribute to the conceptualization of GIE, enhancing the theoretical framework, and offering practical insights for GIE actors. Through this exploration, we seek to advance understanding, promote effective interorganizational interaction, and green innovation adoption in the face of environmental challenges. [ABSTRACT FROM AUTHOR]

This submission focuses on the values influencing innovativeness of Baltic States - Estonia, Latvia, Lithuania - and Poland. The main focus is put on comparative analysis in the field of influential values, specific for each analyzed state, that have an impact on its innovativeness. Research is based mainly on the data from statistical summaries (EIS, GII, GSCI) that include several dimensions of innovation and sustainable competitiveness. The research emphasizes high differentiation in values influencing the innovativeness in individual countries and their importance depending on the country. Also, it confirms that there is no single recipe for innovation since the pace of innovation development in subsequently analyzed countries depends on different factors. Finally, he paper highlights the impact of the holistic, systemic approach on innovative development of selected states. [ABSTRACT FROM AUTHOR]

This paper presents a research idea that aims to explore the research question: How can Responsible Innovation (RI) principles be implemented incrementally and transform common rules and strategies into responsible policies? The idea is based on the understanding that it is not only innovation policies that hinder or enable innovation and transition, but that all different kinds of policies and regulations have the potential to hinder or support innovation and the (sustainable) transformation of our societies. The main practical and theoretical knowledge bases, in addition to the RI-domain, are proposed to be the Three Horizons-framework's methods and theories, as well as methods and theories for responsible research and innovation (RRI). Through this research idea, the ambition is to contribute to the RI-field by complementing the current understanding of RI-practice by providing knowledge on how to gradually develop policy implementation as a means to enable transformative innovation. [ABSTRACT FROM AUTHOR]

An increasing number of incumbent manufacturing firms is nowadays incorporating sustainability principles into their business models. This shift is often referred to as sustainable business model innovation. This transformation process is at the same time beneficial and challenging and there is a need for more research clarity on leveraging ambidexterity to develop sustainable business models in response to environmental and societal goals in manufacturing industry. This paper addresses this gap by conducting a single case study on incumbent multinational manufacturing company from oil and gas industry. The findings suggest that balanced integration of contextual ambidexterity at the individual level and structural separation of exploitation and exploration activities at the company level can enhance sustainable business innovation. The findings provide considerable assistance and guidance to managers in manufacturing industry searching for ways to navigate challenges when aligning their business models with sustainability goals. [ABSTRACT FROM AUTHOR]

Contemporary healthcare is marked by continuous technological advancements, a shift toward patient-centric care and preventive medicine, efforts to enhance accessibility, and challenges such as rising costs, data privacy concerns, and workforce deficits. Within this dynamic landscape, pivotal areas in healthcare include precision medicine and genomics, telemedicine, robotic-assisted surgery, medical imaging, and immersive training. Realizing the benefits of digital technologies demands a systematic approach that addresses both the opportunities and obstacles inherent in digital transformation. Maturity models play a vital role in assisting organizations to evaluate their readiness, establish strategic priorities, and track progress over time. By assessing essential dimensions, these models empower healthcare providers to align digital initiatives with overarching business goals and patient needs. Such initiatives not only accelerate innovation but also foster a culture of continuous improvement and excellence in healthcare delivery. This paper examines the significance of maturity models in guiding digital transformation initiatives within the healthcare domain. Utilizing a real-life scenario and the application of one of the described models, it delves into the major attributes of a holistic digital maturity model. [ABSTRACT FROM AUTHOR]

Efforts to renovate the building stock have become a key issue to accelerate climate action and stimulate energy efficient innovation. The means available to the industry to achieve decarbonisation targets include the deployment of technologies such as heat pumps and the development of more circular business models. This article uses the socio-technical transition framework to examine the socio-technical regime of 'heating' in buildings. The aim of the paper is to examine the extent to which transition intermediaries (here a professional organisation) in the heating sector are impartial facilitators. We observe the role played by the French Heat Pump Association (AFPAC), which positions the economy of functionality as one of the ways to accelerate the renovation of individual dwellings by involving all actors in the sector. We examine its capacity to re-stabilise a socio-technical regime under pressure to evolve. We highlight the dilemma faced by facilitators of the transition. [ABSTRACT FROM AUTHOR]

The decision-making process within public administration is a multifaceted procedure encompassing various stages such as initiation, preparation, actual decision-making, and execution. This paper investigates the structuring of data within municipal decision-making boards, highlighting its importance in fostering efficient data-driven decision-making practices. Specifically, the study focuses on the extraction of data from municipal systems for analysis using Artificial Intelligence techniques. Two primary analyses are conducted: categorization of decision-making cases and extraction of emotions from textual data. Initial findings from these analyses are presented, along with a discussion on the implications for improving public administration decisionmaking processes. Furthermore, this research addresses a significant gap in the literature pertaining to data-driven decision-making in public administration, with a particular emphasis on enhancing citizen participation in decisionmaking processes. [ABSTRACT FROM AUTHOR]

This study aims to create a framework that encourages the adoption of digital and AI-enabled technologies to transform Human Resource Management (HRM) processes in organizations. A systematic literature review utilizing the PRISMA framework was conducted, analyzing 57 selected papers from 238 articles sourced from the Scopus database. The findings reveal that Artificial Intelligence in HRM is utilized across various domains including recruitment, talent management, learning and development, HR operations, reporting, analytics, and self-service assistance. The practical implication of this research is that HRM practitioners and leaders should consider integrating digital technologies throughout the HR value chain to improve organizational performance through digitalization. This research contributes uniquely by developing a framework that facilitates the adoption of transformative digital and AI technologies in HRM. [ABSTRACT FROM AUTHOR]

The paper summarizes the insights of a detailed literature review that links design thinking and digital transformation in the context of strategy development. It addresses the research question: What are the common grounds between design thinking and digital transformation that make them suitable allies in the context of firms engaged in a digital transformation journey? The summary of insights provides a broad answer to the initial research question and a basis for future studies. One of the key outcomes of our literature review was the identification of the key building blocks of a future design thinking approach to the articulation and implementation of an actionable digital transformation framework. The insights will be valuable to both scholars and practitioners who are interested in exploring the adoption of design thinking practices in a digital transformation context. [ABSTRACT FROM AUTHOR]

Our work explores the imperative for innovation leaders to go beyond conventional motivation strategies and delve into empowering their talent for optimal performance. The objective is not only to foster a supportive environment but also to equip innovative talents with the necessary resources, opportunities and tools for enhanced innovation capabilities. However, there is another angle to the relationship between human capital and innovation: the quality, motivation and loyalty of employees at a company can be affected by the innovation activities it performs, thus human capital can also be regarded as an outcome. The presentation outlines operational options to conduct further research on this two-way relationship. Our exploration of this angle also includes an inquiry into incorporating an outsider perspective in managing innovation. The key question addressed is how leaders can leverage external connections not only to stimulate innovation but also to provide learning and development opportunities for talents within the organisation. This would result in an increase in the human capital of the company. Leveraging data from sources such as the EU Innovation Scoreboard and WIPO Global Innovation Index, further research could seek to uncover patterns and relationships that can guide innovation leaders in optimising their strategies based on tangible performance metrics. In conclusion, this paper provides an insight into talent performance in the realm of innovation leadership. By amalgamating motivational culture with practical tools, external connections and collaborative opportunities, organisations can create an approach to let innovation and employee engagement strengthen one another. [ABSTRACT FROM AUTHOR]