Evaluating the impact of learning from climate data, in addition to bridge data, on the performance of concrete deck condition rating prediction is critical for identifying the right data needed to enhance bridge maintenance decision making. Few studies have considered such an evaluation and utilized a small size of samples that prevent revealing the knowledge hidden within the big size of data. Although, such evaluation over big data seems quite necessary, class imbalance problem makes it challenging. To alleviate such a problem, five states, including Alabama, Iowa, New York, Pennsylvania, and South Carolina, were selected as the case study. Not only are the states located in three different climatically consistent regions defined by the National Ocean and Atmospheric Administration (NOAA), but also their concrete deck conditions ratings are somewhat balanced. To conduct the evaluation, this research developed the bridge data set pertaining to 56,288 bridges across the afore-mentioned states through employing the GIS technology. The bridge data set contains bridge data derived from National Bridge Inventory (NBI), and climate data derived from Parameter-elevation Relationships on Independent Slopes Model (PRISM) climate maps and NOAA. Then, two machine learning algorithms, including random forest and GBM, were trained - with and without climate …

This workshop is presented by members of a University of North Texas research team. First, the team will overview their experience as members of the research team and share experience in areas such as trust formation, team roles, productivity, work-life balance, faculty-students interaction, peer and faculty mentorship, dissertation preparation, and job seeking. Second, the workshop will discuss and brainstorm how this format can be implemented for organizations both with faculty-student teams and with peer-directed teams. Finally, successes and challenges are openly discussed with audience.

The 17th International Conference on Knowledge Management was held in the historic city of Potsdam, Germany. Since the conference was among the first post-pandemic face to face conferences, the overall theme of the 17th edition of the ICKM conference rightly focused on “Knowledge, Uncertainty and Risks: From individual to global scale” at different levels of analysis and agency. This document highlighted one of the panels that provide an overview of the different methodologies and theories of sense-making from several of the seminal originators of sensemaking.

Global warming is the term used to describe critical environmental issues and concerns. Social media such as Twitter provides a platform for people to share information, exchange ideas, and express their opinions about current and timely issues. This study utilized contextual analysis to analyze data collected from Twitter for the hashtag "global warming" during the period 2010 & 2011. Using sentiment analysis and topic modeling, the study aimed first at assessing people's perception towards global warming issues, and second study the impact of misinformation from the standpoint of social noise on people's perception of global warming during critical environmental events. The outcome of this study helps create a better understanding of the environmental issues discussed on social media. The sentiment analysis from the data analyzed so far shows that most of the tweets were based on Twitter users' personal opinions and not science. The topic modeling results suggest that Twitter users typically tweeted when a major environmental event occurred due to global warming. Topic modeling also aids in the identification of terms that is associated with social noise. The presence of social noise suggests that misinformation does exist and spreads faster.

Building predictive models for companies often relies on inference using historical data of companies in the same industry sector. However, companies are similar across a variety of dimensions that should be leveraged in relevant prediction problems. This is particularly true for large, complex organizations which may not be well defined by a single industry and have no clear peers. To enable prediction using company information across a variety of dimensions, we create an embedding of company stocks, Stock2Vec, which can be easily added to any prediction model that applies to companies with associated stock prices. We describe the process of creating this rich vector representation from stock price fluctuations and characterize what the dimensions represent. We then conduct comprehensive experiments to evaluate this embedding in applied machine learning problems in various business contexts. Our experiment results demonstrate that the four features in the Stock2Vec embedding can readily augment existing cross-company models and enhance cross-company predictions.

Considerable research has been conducted on the topic of information overload using different approaches, from marketing and customer demand to information technologies and sciences, and even among mental health professionals. In business the critical question is how does information overload impact processes, operations, and profitability, and how can data visualization help to solve issues with data management and consumption in organizations. The ability to quickly and effectively process information and make decisions equates to organizational survival in a dynamic, knowledge-based economy where all segments of society are heavily affected by information technologies and systems and data management industries. The growing number of systems apparatuses challenges both individuals and organizations, resulting in reports of fatigue and experiences that compromise successful performance. The objective of this literature review is to discuss how data visualization tools help address information overload and optimize decision making and the business intelligence process in organizations. It concludes that data visualization, indeed, is critical in helping individuals capture, manage, organize, visualize, and present understandable data, but that decision making is affected by cognitive factors that interfere with data processing and interpretation in decision makers.

The 17th International Conference on Knowledge Management was held in the historic city of Potsdam, Germany. The conference was among the first post-pandemic face to face conferences, and the overall theme of the 17th edition of the ICKM conference rightly focused on “Knowledge, Uncertainty and Risks: From individual to global scale” at different levels of analysis and agency.

Knowledge is the central component of knowledge management (KM); it comes into existence, is managed, and has effects through the activities performed upon it and the results of those activities. Much of the foundational KM literature identifies and classifies these activities (Baskerville, Dulipovici, 2006; Becerra-Fernandez, 2005; Evans, Ali, 2013; Evans, Dalkir, Bidian, 2014; Girard, Girard, 2015; Pee, Kankanhalli, 2009; Mohajan, 2016; Sajeva, 2010). We propose an extension of these activity classifications.

It is understood that Social Innovation (SI) is at the service of society, building a positive social value. Collaborative governance favors the government, with characteristics for a better form of applicability of its theories. Therefore, this article aims to identify how collaborative governance and social innovation collaborate for better governance in the public sector.

Emerged in the early 1990’s, governance is a construct which enables to broaden the management focus to a more comprehensive view, assessing efficiency capacity from different perspectives, such as social and political. Among its applications, Corporate Governance is the most known. In the private sector, governance has become a mandatory practice for companies aiming at increasing their market value or maintaining it high, due to the credibility earned by companies with good governance practices. In the public sector governance is a major challenge, once it concerns itself with generating value for society, in addition to the need for transparency and credibility in the management of public affairs. In this context, the objective of this article is to identify the differences and similarities between Corporate Governance in Private Companies and Public Administration.

The pandemic of the COVID-19 (coronavirus) has presented us a new world-wide reality. In a short time, huge changes had been necessary. In the sanitation it has not been different. For handle with essential services, in the majority through public concessions, the rendering of services has been strongly influenced for the chaotic economic situation, the necessity of cares with the health of the employees and for governmental decisions that restrict some activities. Thus, risk management becomes an increasingly urgent need, as the impacts of wrong decisions can lead to huge losses. Starting from the hypothesis that the better risk management within organizations, the better the decision-making process will be, this article aims to present the importance of risk management for mitigation and contingency in the face of the impacts caused by the pandemic of COVID-19.

Knowledge Management Best Practices are established through leveraged Data Science Technique based on results found on published articles of reputed literature and both advertisements and news items of newspapers. Further, the recognition showed threats to the values of Higher Education such as the quality of courses. As such, a Postgraduate study is conducted to reveal a solution to those kinds of issues in Higher Education. Consequently, based on the proposal of literature to such issues in other contexts, existences of Knowledge Management Practices are revealed in the qualitative paradigm. Meanwhile, the present study questioned whether the confirmed Practices of the Postgraduate study are Best Practices of Higher Educational Institutes. Besides, the Oxford dictionary specifies that practice becomes Best Practice if “commercial or professional procedures that are accepted or prescribed as being correct or most effective”. Accordingly, confirmed practices of Higher Educational Institutes became Best Practices due to the practices that are accepted by the industry of the Postgraduate research through a qualitative paradigm and by the other industries of researches that those reputed publications concerned into the Postgraduate study. Besides, Knowledge Management is an emerging/ emerged era. As such, present research proposes resulting practices of the Postgraduate study as Based …

Twitter is one of the major social media platforms highlighting public opinion. With over 330 million users across the globe, Twitter provides insights into global sentiments on many topics. One can estimate global sentiments towards certain events relating to COVID-19 by analyzing the most common phrases and their related sentiment scores from Twitter API data. This project has compiled the most used trigrams in tweets relating to COVID-19 to calculate sentiment scores for the period from March 22 to August 7, 2020. Another goal of the project is to optimize data collection from Twitter API. Twitter limits access to tweet contents to 900 requests per 15 minutes for unpaid API users. For student data scientists, paying for increased API usage is financially infeasible. So, to deal with the rate limit, the project has written functions using the Tweepy python library to collect Twitter API data. The Pandas library has also been used to sample 139000 tweets from over 300 million. The IEEE Dataset provided sentiment scores for the full population. So, to check the integrity of my sample, I performed a Pearson correlation test between the full dataset and sample data, and got 0.84, showing the sample is representative of …

Stakeholder-centered knowledge elicitation and acquisition for study counseling and informed decision making on the selection of study programs and study locations.

This paper aims to compile organizational innovation management indicators of papers that included some empirical step in the research and were published in the last 5 years (2015-2019). Based on PRISMA protocol, a systematic review was conducted and 356 indicators were extracted. They were categorized in 11 dimensions, following Dziallas & Blind (2019) previous literature research. The results make it possible to update the studies of innovation indicators at the organizational-level, filling an existing time gap in the research field. As for future research, we recommend the unification of similar indicators, the establishment of objective criteria for indicators categorization, and the development of an innovation indicator framework containing relevant indicators based on scholars’ and practitioners’ opinions, their description and measurement/evaluation methods.

Recent years have seen the development of quite a few measures of wisdom (e.g. Ardelt (2003); Glück (2017); Staudinger and Pasupathi (2003); Sternberg (1998); Webster (2003)). With a focus on information professionals working in the GLAM (Galleries, Libraries, Archives, and Museums) sector, this study is unique in the way it translated the quantitative measures used in previous study into a qualitative instrument that can allow wisdom aspects to be explored through interviews. Thus the purpose of this research was to investigate the key characteristics of wisdom for professionals working in the GLAM sector.

The dynamics proposed by the information and communication technologies enter economic sectors and require new behaviors. Digital Agriculture is a way to adapt to the new scenario. However, the modernization movement requires that views on agricultural industries be compensated.

This study considers Communities of Practice as a group of library and information science professionals who share their common interests and problems on a topic in order to exchange their expertise and knowledge on that topic. The study sheds lights on the ways of knowledge sharing, and assess the reasons of knowledge sharing of LIS professionals through Communities of Practice in the context of university libraries in Bangladesh.

The ever-increasing market turbulence has turned today’s corporate landscape more competitive and complex. Particularly during the last two decades, the increased utilization of ICT systems and technologies globally transformed the services sector in terms of ease of business processes and improved client service delivery. However, in the current knowledge-based era, ICT-enabled systems and tools would only be meaningful if these are appropriately utilized by the knowledgeable and skilled workforce. However, leveraging these necessitates a knowledge-enabled work culture and recognizing that people are crucial to building a robust Intellectual Capital (IC) that is central to achieving long-term market competitiveness. IC comprising of intangible assets and knowledge resources is central to value creation for the firm as evident from the growth of the knowledge-based industries. Nevertheless, the true potential of IC for deriving value advantage for diverse organizational stakeholders has not been fully utilized. Hence, by conducting 12 face2face interviews with the senior executives within Australian Professional Service Firms (PSFs), this study offers renewed approach to IC valuation by introducing ‘Triple Value Bottom-line’ perspective in PSFs. The results highlight that the IC offers enormous potential towards deriving broader value outcomes for multiple organizational stakeholders.

While explicit knowledge can be to some extent separated from human brain and stored in organizational memory, tacit knowledge cannot be detached from the individuals who possess it, therefore its management cannot rely primarily on technologies. This calls for knowledge-worker centered approach. Individuals with different cognitive styles process information differently and use a variety of reasoning patterns for decision making and building their personal knowledge bases. The paper overviews the potential applications of the construct of cognitive style to managerial practice in knowledge intensive organizations. It also presents a model that can help visualize the relationships between different cognitive styles and knowledge management processes. The model also demonstrates how such cognitive dimensions manifest themselves at different stages of KM cycle.

This study aims to understand how individuals communicated and acknowledged to COVID-19 pandemic on Twitter. It mainly focused on identifying and demonstrating the differences in the perspective of United States and Canadian residents.

The 17th International Conference on Knowledge Management was held in the historic city of Potsdam, Germany. Since the conference was among the first post-pandemic face to face conferences, the overall theme of the 17th edition of the ICKM conference rightly focused on “Knowledge, Uncertainty and Risks: From individual to global scale” at different levels of analysis and agency. This document highlighted one of the local keynote speeches by Prof. Ortwin Renn, scientific director at the International Institute for Advanced Sustainability Studies (IASS) in Potsdam (Germany) and professor for environmental sociology and technology assessment at the University of Stuttgart.

The 17th International Conference on Knowledge Management was held in the historic city of Potsdam, Germany. Since the conference was among the first post-pandemic face to face conferences, the overall theme of the 17th edition of the ICKM conference rightly focused on “Knowledge, Uncertainty and Risks: From individual to global scale” at different levels of analysis and agency. This presentation provides an overview of the host institution, The Information Sciences Department, University of Applied Sciences Potsdam, Germany.

The 17th International Conference on Knowledge Management was held in the historic city of Potsdam, Germany. Since the conference was among the first post-pandemic face to face conferences, the overall theme of the 17th edition of the ICKM conference rightly focused on “Knowledge, Uncertainty and Risks: From individual to global scale” at different levels of analysis and agency. This document highlighted one of the keynote speeches by Dave Snowden is the founder of one of the five schools of sense-making, and takes an approach which draws heavily on natural science, in particular complex adaptive systems theory, cognitive neuroscience and the biological end of anthropology. He is the creator of the Cynefin framework and lead author of the recently published EU Field Guide to managing complexity.