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If the leadership relationship resides, at least in part, in the expectations of leaders and followers, then is its imperative to understand multiple perspectives of similarity in expectations for how leaders and followers should behave. Unfortunately, little is known about how similarity in expectations for leader and follower roles influences other important work and non-work outcomes. This symposium expands the measurement of congruence from both variable and pattern-oriented approaches as well as outcomes of congruence to provide recommendations for studying leadership dyads and exemplars of novel insights gained through this approach.

Beyond Exchange: Revisiting Leadership as a Relationship emerged in response to scholarly calls to steer away from the 'exchange' rhetoric and ‘give-and-take’ conceptualizations in the study of leader-follower interactions and instead shift the focus to the fundamental element of 'relationship'. The book delves into the very essence of leader-follower relationship dynamics, revisits fundamental assumptions of popular relationship-based leadership theories such as Leader-Member Exchange, takes stock of progress made in this research strand, and proposes exciting avenues for future research.This book frames leader-follower interactions as a complex and dynamic relational phenomenon that evolves over time and encompasses a multiplicity of relationship types. Relationships are not static, and one size does not fit all. The book cross-pollinates ideas from other research domains, such as relationship science, and highlights the role of the follower and the importance of affective processes, ethics, and fairness on different levels of analysis. It further extends the notion of leader-follower relationship beyond the dyad to more complex network interactions. We hope readers will find ideas that resonate, and the book helps rekindle scholarly passion for relational phenomena in organizational settings.

In this document the definition of Knowledge and Knowledge management is provided in an attempt to depict the ambiguity of the term and the different authors’ views on the subjects. Most scholars tend to agree that there are two type of Knowledge, Tacit and Explicit. The “Knowledge Cycle,” “Knowledge Conversion” and “Knowledge Refinery” methodologies have been addressed along with strategies and different schools of Knowledge Management. Once the theoretical framework was addressed, the document is detailing the Knowledge concepts focusing on Banking and thereafter in IT. In this way, an understanding of the Banking IT knowledge culture is demonstrated. Influences and interdependencies on multinational Organizational structures and cultures are presented under the prism of sociocultural perspective. The importance of Knowledge being a corporate initiative is discussed and respective opportunities and challenges are identified and described. The Knowledge Management framework was projected on current IT operations of the Bank in an attempt to identify a road map. Suggestions in relation to enhancements and future developments have been outlined thus providing with a stepwise approach of implementation.

The automation of new knowledge from the manipulation and analysis of existing knowledge is one of the primary objectives of any cognitive system. Most of the effort on Big Data research has been focused upon Volume and Velocity, while Variety, “the ugly duckling” of Big Data, is often neglected and difficult to solve. A principal challenge with variety is being able to understand and comprehend the data. This paper proposes and evaluates a semi-automated approach for metadata identification and enrichment in describing Big Data. The paper focusses upon the use of self-learning systems that will enable automatic compliance of data against regulatory requirements along with the capability of generating valuable and readily usable metadata towards data classification. Two experiments towards data confidentiality and data identification were conducted in evaluating the feasibility of the approach. The focus of the experiments was to confirm that repetitive manual tasks can be automated reducing the focus of a Data Scientist on data identification and thereby providing more focus towards the extraction and analysis of the data itself. The origin of the data sets used were Private/Business and Public/Governmental and exhibited diverse characteristics in relation to number of files and size of the files. The experimental work confirmed that: a) the use of algorithmic techniques attributed to the substantial decrease in false positives regarding the identification of confidential information; b) evidence that the use of a fraction of a data set along with statistical analysis and supervised learning is sufficient in identifying the structure of information within it. With this approach, the issues of understanding the nature of data can be mitigated, enabling a greater focus on meaningful interpretation of the heterogeneous data.

Organizations have been investing in analytics relying on internal and external data to gain a competitive advantage. However, the legal and regulatory acts imposed nationally and internationally have become a challenge, especially for highly regulated sectors such as health or finance/banking. Data handlers such as Facebook and Amazon have already sustained considerable fines or are under investigation due to violations of data governance. The era of big data has further intensified the challenges of minimizing the risk of data loss by introducing the dimensions of Volume, Velocity, and Variety into confidentiality. Although Volume and Velocity have been extensively researched, Variety, “the ugly duckling” of big data, is often neglected and difficult to solve, thus increasing the risk of data exposure and data loss. In mitigating the risk of data exposure and data loss in this article, a framework is proposed to utilize algorithmic classification and workflow capabilities to provide a consistent approach toward data evaluations across the organizations. A rule-based system, implementing the corporate data classification policy, will minimize the risk of exposure by facilitating users to identify the approved guidelines and enforce them quickly. The framework includes an exception handling process with appropriate approval for extenuating circumstances. The system was implemented in a proof of concept working prototype to showcase the capabilities and provide a hands-on experience. The information system was evaluated and accredited by a diverse audience of academics and senior business executives in the fields of security and data management. The audience had an average experience of ∼25 years and amasses a total experience of almost three centuries (294 years). The results confirmed that the 3Vs are of concern and that Variety, with a dmajority of 90% of the commentators, is the most troubling.

Big Data was identified as the biggest innovation of computing technology in 2010's (Bryant, Katz and Lazowska, 2008). Institutions in several industries have embarked on a journey to utilize and gain from the use of Big Data. This paper will provide with an overview on the founding principles of Big Data, namely Volume, Velocity and Variety. In addition to the terms definition and associated technologies, related publications will be presented in providing with an evolutionary review of Big Data. Publications on each V are analysed with regards to quantity and quality, in identifying the degree of research performed. Variety which is ―the ugly duckling of Big Data (Lennard, 2014) is further correlated to Data Heterogeneity in an attempt to learn from past experience, since Data Heterogeneity is being tacked for decades now (Luo et al., 2008). Having established an understanding of Big Data, analysed the V‗s in terms of definition and challenges and correlated the most challenging V (Variety) with another interrelated and actively researched for several decades topic (Data Heterogeneity), this paper serves as the basis to further research towards a solution to these challenges.

In this document the definition of Knowledge and Knowledge management is provided in an attempt to depict the ambiguity of the term and the different authors’ views on the subjects. Most scholars tend to agree that there are two type of Knowledge, Tacit and Explicit. The “Knowledge Cycle,” “Knowledge Conversion” and “Knowledge Refinery” methodologies have been addressed along with strategies and different schools of Knowledge Management. Once the theoretical framework was addressed, the document is detailing the Knowledge concepts focusing on Banking and thereafter in IT. In this way, an understanding of the Banking IT knowledge culture is demonstrated. Influences and interdependencies on multinational Organizational structures and cultures are presented under the prism of sociocultural perspective. The importance of Knowledge being a corporate initiative is discussed and respective opportunities and challenges are identified and described. The Knowledge Management framework was projected on current IT operations of the Bank in an attempt to identify a road map. Suggestions in relation to enhancements and future developments have been outlined thus providing with a stepwise approach of implementation.

Corporate Knowledge is an asset but its management is no trivial task. In this publication the voyage towards a Corporate Knowledge Management system for a large organization (more than 15000 employees and 1000 location) will be presented. Managerial aspects along with technical issues will be detailed. The project management techniques used, technical decisions, corporate strategy along with Business and Technical challenges accompanied by their resolution and courses of action will provide the guidelines for the successful implementation of a Knowledge Management System.

Developing a cash demand forecasting model for ATMs' network is a challenging task since there are substantial fluctuations over time and depends on the location of the ATM. Variety is one of the principal V's of Big Data, having context being one of the most important aspects. In this paper the ATM cash withdrawals datasets have been "placed into context" by incorporating socioeconomic datasets. This contextual enhancement improves the effectiveness of the ANN model devised.

This study presents the findings of a preliminary investigation into the effects of integrating basic generative AI prompts into a flipped classroom methodology for an introductory information technology course in higher education. The flipped classroom approach, rooted in constructivist learning theories, involves students engaging with digital resources before class to learn foundational concepts, freeing up class time for active learning activities [1], [2]. Generative AI tools like ChatGPT offer new possibilities for personalizing and enriching these pre-class and in-class components through customized content generation and interactive simulations [3]–[6]. An experimental study was conducted with undergraduate students to examine whether implementing generative AI prompts as supplementary resources in a flipped classroom environment could enhance student engagement, learning outcomes, and overall educational experience compared to a traditional flipped classroom approach without AI integration. Both quantitative and qualitative data were collected and analyzed. The results indicate that thoughtful incorporation of generative AI capabilities into flipped instructional design holds promise for amplifying its pedagogical benefits, though considerations around AI literacy and responsible technology use arose. This exploratory research contributes insights into the synergistic potential of combining generative AI with the flipped classroom model in information technology education.

This study investigates the perspectives of college students on flipped classroom methodology and case study learning compared to instructional teaching. A sample of 215 undergraduate students from Deree College in Greece, were surveyed on their experiences with each teaching approach. The results showed that the majority of students preferred flipped classroom methodology and case study learning to traditional instructional teaching. They reported that these approaches allowed for more interactive and engaging learning experiences, as well as greater opportunities for critical thinking and problem-solving. These findings suggest that flipped classroom methodology and case study learning may be effective alternatives to instructional teaching in higher education settings, especially concerning the student experience delivered by these methods in contrast to instructional learning. This paper explores and evaluates the perspectives of students exposed to all three methods in a variety of business courses.

Information Quality is a term that has been largely misunderstood, misinterpreted, and misused, in all aspects, of everyday life, research and the business domain. Information quality has been a synonym of data quality. However, the two of them are completely different. While data quality can only be assessed in an objective manner information quality is quite subjective in its assessment. This paper intends to clarify the difference between the two terms and provide a thorough understanding of how information quality can be assessed in the business context. We have conducted a literature review to identify documents that not only clarify this difference between data and information quality but also provide a way of assessing information quality in different business contexts. Finally, a summary of the different assessment methods is provided enabling the researcher to distinguish the differences between those methods. In this summary, we compare and contrast the different methods in a tabular format while at the same time we provide a narrative of this comparison. This comparison is based on criteria that are relevant to the assessment of information quality irrespective of the business context. The outcome of this research is important, as it will enable the researchers and practitioners to gain an understanding of how to assess information quality. Throughout the literature review, the number of papers focusing on assessment methods of information quality seems to decrease as time progresses. This is a phenomenon that needs to be further investigated since, while data seems to explode, information is getting more difficult to obtain.

Since the 1990’s higher education institutions (HEIs) have been making attempts to incorporate Enterprise Resource Planning (ERP) systems in their business undergraduate curricula. Additionally, major global ERP vendors have invested in establishing academic alliances to assist higher education institutions in their efforts to provide students with necessary industry skills. However, simply using an ERP system in a taught component is not enough to meet the predefined course learning outcomes, unless the latter are coupled with hands-on experience and both integrated into a teaching framework. This paper draws from the extant literature and follows a documented methodology for identifying relevant sources. The sources of the works consulted and cited, were benchmarked with respect to impact factor or peer review, and include academic journals, books, and conference proceedings in higher education. As a next step, sources were analyzed by matching their methods and results against the principle of constructive alignment in higher education. The analysis results identified key areas where the existing literature is incomplete and needs further enrichment. Moreover, results showed lack of a comprehensive method for “teaching ERP systems”, including teaching methodologies, assessment practices and learning theories relevant to the Information Systems discipline. This paper is introducing a framework for guiding faculty into effectively teaching an ERP Systems course to undergraduate business students. The guidelines for synthesizing the framework rely on three axes, namely, learning theories, the results from the analysis of current literature and the experience drawn from teaching an ERP Systems course in a business school for the past 4 years. This paper concludes with recommendations for further research, mostly relevant to quantitative studies on the performance of teaching and learning strategies relevant to ERP systems in higher education.

Learning Management Systems (LMS) have been largely adopted by higher education institutions. Being mostly used as knowledge repositories, they offer a notable advantage of making learning content globally available, free of time and location limitations as long as there is access to the Web. Existing LMS platforms currently provide with the technical infrastructure required to apply constructive learning methods in support to traditional ones, given the availability of eLearning design expertise. Despite the LMS performance, technical innovation and access to unlimited eLearning material, it seems that LMS implementations have failed producing and maintaining a critical mass of engaged learners. Consequently, LMSs seem to be used primarily as learning-related content repositories instead of learning enablers. At the same time, Social Networks' (SN) services and technologies have achieved impressive engagement by both students and instructors. Additionally, through their mobile applications, they have succeeded to become an inseparable ubiquitous daily habit of vast numbers of users, with Facebook being on the front line with more than two million active users. This paper presents a case study deployed at a higher education institution where a flipped classroom learning methodology was implemented through the means of combining LMS with Social Network services in an introductory course in Information Systems. The goal is to recognize potential benefits for student learning and educational practices by taking advantage of the given student engagement to social media (SM). For this purpose, a flipped classroom exercise was simultaneously deployed to a controlled group of students through both Blackboard (LMS) and Facebook (SN), aiming firstly at collecting access and usability evidence and secondly at determining engagement variations in respect to the level of engagement and the behavior of students in using each one of these platforms.

Academic Integrity (AI) has become the focus of multiple studies, especially in the Higher Education (HE) context. Multiple stakeholders are concerned with the increased incidents of breach of Academic Integrity, that have been reported across the globe. Breach of Academic Integrity, is being considered as a crisis in Higher Education, and therefore, different actions have been considered in order to effectively contain this phenomenon. A lot of attention has been directed towards different ways of “catching” plagiarism, mostly through online text matching tools. However, all these tools do have their limitations, and should be used not just for identifying cases of plagiarism, but as tools to guide students in order to properly use and cite sources. There is a general concern of “how much” can technology help, when students turn to paid services which guarantee that their paper will not be “tracked” as plagiarized, automatic translators, online paraphrasing tools, etc. The response to such practices should be a more holistic approach rather than the typical “policing” one, that we have been using up to now. Therefore, there is a need to devise a methodology which will be capable of responding to these challenges. The aim of this methodology is to divert the focus of the faculty from spotting instances of breach of Academic Integrity, towards aligning our academic integrity practices with our overarching goal, which is to teach. The main contribution of our research is the Academic Integrity Integration Methodology (AIIM), which will enable students to develop a thorough understanding on how to go about writing a research paper considering Academic Integrity, thus eliminating or reducing the possibility of instances of breach of academic integrity. In the first part of our research paper, we elaborate on the importance of Academic Integrity in Higher Education and we present findings from surveys conducted at international and national levels. [...]

Web-based Learning Management Systems (LMS) have their way into the methods that students, lecturers and generally the education community communicates, stores, shares and collaborates. Although technically they seem to provide an ideal environment for deploying constructive eLearning activities, yet, research indicates that they seem to fail producing and maintaining a critical mass of engaged learners to such implementations. Eventually, they end up being used as learning related content repositories instead of learning enablers. This paper presents a novel mobile application prototype design that has been positively evaluated by students, instructors and eLearning experts to contribute to learning by drastically increasing the engagement of learners to institutional web-based eLearning platforms. The novelty of the prototype lies in the integration of current popular on-line services (including but not limited to social media, messenger services, SMS, email, etc.) where learners, as research proves, are already engaged. Taking advantage of the unique features of mobile devices and their impressive market penetration, the prototype, achieves in providing the designs for a ubiquitous application that blends a variety of on-line and mobile technologies towards the aim to increase up datedness of learners with respect to eLearning activities. In addition to that, the mobile application, makes eLearning implementations optimally available to directly and easily interact with, without time and geographic restrictions but also without device limitations and extensive device interoperability by transferring content from the mobile to PC, TV, projector etc., directly from within the preferred application used by the learner. Cutting edge technologies in semantics, speech recognition and a user friendly UI are embedded in the prototype design aiming to positively leverage interaction.

The purpose of this study is to assess the level of Information Quality (IQ) and its dimensions with respect to summative assessments as they are perceived by the students in Higher Education. At a first step we are reviewing the literature in order to identify whether and how Information Quality (IQ) has been assessed in the context of Higher Education in terms of summative assessments. We are interested in assessing the role of IQ as one of the components of assessment’s quality. Through the use of an online survey tool, we are examining across all educational levels (QAA Levels 4, 5 and 6) how students perceive the IQ of the description of assessments, as well as of the feedback they receive from faculty. Furthermore, we are trying to identify according to student’s perceptions which of the IQ dimensions seem to be more influential in their preferences with respect to assessment description and feedback. By correlating the level of maturity with respect to their academic progress (Levels 4, 5 and 6) and their age, we aim in providing valuable feedback to the educational community on the aspects of IQ for summative assessments and enable faculty to revisit the way we provide assessment descriptions and feedback to students for the benefit of facilitating their learning experience.