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The purpose of this paper is to propose an appropriate symbolic representation, as well as its metaphorical interpretation, to illustrate the special role of information in the knowledge acquisition process.

Besides the literature review, this is a speculative study based on a symbolic and metaphorical point of view.

The proposed symbolic representation was derived from the conceptual designation of information “as a flow” and, accordingly, by the corresponding redrawing of the data-information-knowledge-wisdom (DIKW) pyramid. The knowledge acquisition process is symbolically represented by the growth of a “tree of knowledge” which is planted on a “data earth,” filled with “information sap” and lit by the rays of the “sun of the mind,” a new symbol of the concept of wisdom in the DIKW model. As indicated, a key concept of this metaphorical interpretation is the role of “information sap” which rises from the roots of the “tree of knowledge” to the top of the tree and it is recognized as an invisible link between “world of data” and “world of knowledge.” This concept is also proposed as a new symbolic representation of the DIKW model.

On the basis of specific symbolic-metaphorical representation, this paper provides a relatively new concept of information which may help bridge observed gaps in the understanding of information in various scientific fields, as well as in its understanding as an objective or subjective phenomenon.

The purpose of this paper is to inform on results of the study based on the dissertation project – the study of newcoming university students and their information literacy experience. It describes the three categories of information literacy experience as perceived by these students.

The document is based on a qualitative phenomenographic study of 40 first-year undergraduate students of teacher education programs from five faculties of Comenius University in Bratislava. Data were collected from each participant in two stages by three methods: written statements, drawings and interviews.

The phenomenographic analysis results in three categories of information literacy: (1) the conception of digital technologies, (2) the conception of knowledge and (3) the conception of truth. The outcome space presented by two alternative models points to a strong interrelation of all three categories. The resulting conceptions point to the diversity of the concept of information literacy in relation to other types of literacies, especially digital, reading and media literacy, as well as to intersections with other scientific disciplines such as psychology, cognitive science or philosophy.

The most important limits of this qualitative research are the low numbers of participants and the high degree of subjectivity in data evaluation. For this reason, a verification study was carried out one-year later.

Although phenomenographic studies of information literacy in the educational context are quite common, the third category of this study brings a new contribution to the information literacy theory – the dimension of truth or truthfulness of information.

Knowledge- and communication-intensive domains still long for a better support of creativity that considers legal requirements, compliance rules and administrative tasks as well, because current systems focus either on knowledge representation or business process management. The purpose of this paper is to discuss our model of integrated knowledge and business process representation and its presentation to users.

The authors follow a design science approach in the environment of patent prosecution, which is characterized by a highly standardized, legally prescribed process and individual knowledge study. Thus, the research is based on knowledge study, BPM, graph-based knowledge representation and user interface design. The authors iteratively designed and built a model and a prototype. To evaluate the approach, the authors used analytical proof of concept, real-world test scenarios and case studies in real-world settings, where the authors conducted observations and open interviews.

The authors designed a model and implemented a prototype for evolving and storing static and dynamic aspects of knowledge. The proposed solution leverages the flexibility of a graph-based model to enable open and not only continuously developing user-centered processes but also pre-defined ones. The authors further propose a user interface concept which supports users to benefit from the richness of the model but provides sufficient guidance.

The balanced integration of the data and task perspectives distinguishes the model significantly from other approaches such as BPM or knowledge graphs. The authors further provide a sophisticated user interface design, which allows the users to effectively and efficiently use the graph-based knowledge representation in their daily study.

Civil society is increasingly digitized and virtual in many parts of the globalized world of today. The networked society and the invisible “second economy” (Arthur, 2011) which powers the developed and developing countries generate debates about the degree to which the benefits outweigh the potential hazards. Artificial intelligence (AI) powered by its machine learning underpin much of the digital networked systems, and “free” services such as search engines, paid for by the “tailored advertising” we get when we view webpages. Most now recognize that the helpful “suggestions” on the web are simply adverts personally targeted at individuals who have searched for information on a topic or visited a webpage with sponsored material and cookies.

There have been cases of major political misuse of data such as the voter manipulation by the Cambridge Analytica company. We are not just referring to the hacking and “fake news” used by some governments to influence the affairs of another country. Some organizations have used AI to cynically target consumers’ weaknesses, for example, in financial management (Larsson, 2018).

Perhaps more significantly the network technology is often promoted as having potential for improving civil society through “failsafe” or default forms of regulation using the embedded Apps in domestic equipment and algorithms in much the same manner it is suggested that automatic self-driving vehicles help to improve road safety by cautious driving and sticking to speed limits and so on (Cockburn, Jahdi, & Wilson, 2015, pp. 6–7). However, algorithms and the associated machine technology have also been described as a “black box” technology where even those people running the algorithms cannot always fully understand or explain how decisions are reached in diverse systems used to evaluate many things from medical care to credit rating and finance (Danaher et al., 2017). There are issues of the budding “surveillance society” emerging from the proliferating “intelligent” apps enabling corporate “spying” on our everyday lives as some hackers have done by tapping into baby monitoring systems in homes. In addition to hacking there are large power asymmetries involved as between commercial data users and the lay public who are often the data suppliers as their personal data are harvested each time the web is used.

Therefore, it is hardly surprising that, according to the Pew Research Center report by Aaron Smith, released in November 2018, over half of Americans surveyed found it unacceptable to use algorithms to make decisions with real-world consequences for humans. In the age of connectedness and the emergent internet of things many people are not yet ready to cede more control of their currently offline lives to current online technology. This chapter reviews arguments for and against algorithmic governance.

Machine learning systems may be efficient to a high degree without being unbiased in impact across different segments of society. AI may also be fully effective in its operation without even being fully understood because the decision-making is so arcane. Importantly, though, even for those systems that have some human mediation or supervision, societal regulation is aimed at ensuring ends and means are aligned with human social, political and economic justice and thus socially effective as well as being technically efficient. Consequently, these systems have to require socio-emotional as well as cognitive safeguards. Although levels of implicit trust may vary demographically as between say millennials and baby boomers, high levels of trust, accountability and a culture of moral integrity must still form the bedrock for societal benefits.

Technology is revolutionizing the management logic of service systems. The increasing use of artificial intelligence (AI), in particular, is challenging interaction between humans and machines changing the service systems’ value co-creation configurations and logic. To envision possible future scenarios, this paper aims to reflect upon how the humans’ use of AI technology can impact value co-creation.

The study is developed, at a conceptual level, using selected elements from managerial and marketing theoretical frameworks interested in value co-creation – Service-Dominant Logic, Service Science and Viable Systems Approach (VSA) – used as interpretative tools to reframe value co-creation in the digital age.

The interpretative approach adopted and, in particular, the new VSA notion of Intelligence Augmentation (IA), in the perspective of the information variety model, shed new light on value co-creation in the digital age framing a possible “IA effect” that can empower value co-creation in complex decision-making contexts.

The study provides insights useful in the design and management of service systems suggesting a rethinking of the view of AI as a means for mainly increasing the smartness of service systems and a new focus on the enhancement of the human resources contribution to make the service systems wiser.

The paper provides a refocused interpretative view of the interaction between humans and AI that looks at a possible positive impact of the use of AI on humans in terms of augmented decision-making capabilities in conditions of complexity.

The global pandemic and the resulting rapid and large-scale digitization changed the way firms recognized and understood knowledge curation and management. The changing nature of work and work systems necessitated changes in knowledge management (KM), some of which are likely to have a long-term impact. Using the lens of technology in practice, the purpose of this study is to examine the impact of technology agency on KM structures and practices that evolved across five knowledge-intensive global organizations. This study then argues that sustainable knowledge management (SKM) systems evolve in specific contexts.

This study adopts a qualitative case study design to examine five multinational knowledge-intensive global organizations’ KM systems and practices across diverse industry sectors.

Based on the findings, the authors develop SKM systems and practices model relevant to a post-pandemic organizational context. The authors argue that KM digitization and adoption support socialization in knowledge sharing. Further formalization through organizational enabling systems aids the externalization of knowledge sharing. Deliberate practices promoted with leadership support are likely to sustain in the post-COVID era. Further, organizations that evolved ad-hoc or idiosyncratic approaches to managing hybrid working are more likely to revert to legacy KM systems. The authors eventually theorize about the socialization of human-to-human and technology-mediated human interactions and develop the three emerging SKM structures.

This study contributed to practitioners and researchers by developing the various tenets of SKM.

In this paper, the editors argue that management accounting research should seek to expand to examine the broader ecosystem of information sources that influence organizational performance. The editors introduce the concept of the management accounting ecosystem as a means of linking discrete management accounting research topics to the broader environment in which organizations operate. By doing this, a stronger connection can be established between management accounting research and management accounting practice. The goal is to encourage more cross-disciplinary research that provides a better understanding of the ecosystem in which management accounting practitioners operate. The editors encourage researchers to submit studies to “Advances in Management Accounting” that evaluate the effectiveness of new management accounting information sources and the techniques used to analyze them in the broader ecosystem to enhance the effectiveness of management accounting practices. By exploring the wider information sources within the management accounting ecosystem, future management accounting research can become more innovative and better address the decision-making needs of organizational members.

Traditional classification algorithms always have an incorrect prediction. As the misclassification rate increases, the usefulness of the learning model decreases. This paper presents the development of a wisdom framework that reduces the error rate to less than 3% without human intervention.

The proposed WisdomModel consists of four stages: build a classifier, isolate the misclassified instances, construct an automated knowledge base for the misclassified instances and rectify incorrect prediction. This approach will identify misclassified instances by comparing them against the knowledge base. If an instance is close to a rule in the knowledge base by a certain threshold, then this instance is considered misclassified.

The authors have evaluated the WisdomModel using different measures such as accuracy, recall, precision, f-measure, receiver operating characteristics (ROC) curve, area under the curve (AUC) and error rate with various data sets to prove its ability to generalize without human involvement. The results of the proposed model minimize the number of misclassified instances by at least 70% and increase the accuracy of the model minimally by 7%.

This research focuses on defining wisdom in practical applications. Despite of the development in information system, there is still no framework or algorithm that can be used to extract wisdom from data. This research will build a general wisdom framework that can be used in any domain to reach wisdom.

Nowadays, there is a rapid growth in different sciences that has led to thousands of publications in the form of scientific research papers. The readers of these papers are generally the people that are involved in science (i.e. researchers, students, teachers and professors). On the other hand, practitioners rarely use these articles as a resource to learn and apply new methods. They prefer an easy to understand, step-by-step guide (i.e. cookbook) helping them skip over the difficult scientific terms and structures. Therefore, because of a shortage of tools in this space, it takes practitioners many years to use newly developed methods.

The purpose of this study is to review the literature on verified repositories and presents the necessity of method repositories.

This paper aims to introduce method repositories as new tools to bridge the gap between science and practice.

Method repositories presented in this paper act as an easy to understand guide for newly developed methods in specific fields.