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Citation analysis is an important measure for the assessment of quality and impact of academic entities (authors, papers and publication venues) used for ranking of research articles, authors and publication venues. It is a common observation that high-level publication venues, with few exceptions (Nature, Science and PLOS ONE), are usually topic specific. The purpose of this paper is to investigate the claim correlation analysis between topic specificity and citation count of different types of publication venues (journals, conferences and workshops).

The topic specificity was calculated using the information theoretic measure of entropy (which tells us about the disorder of the system). The authors computed the entropy of the titles of the papers published in each venue type to investigate their topic specificity.

It was observed that venues usually with higher citations (high-level publication venues) have low entropy and venues with lesser citations (not-high-level publication venues) have high entropy. Low entropy means less disorder and more specific to topic and vice versa. The input data considered here were DBLP-V7 data set for the last 10 years. Experimental analysis shows that topic specificity and citation count of publication venues are negatively correlated to each other.

This paper is the first attempt to discover correlation between topic sensitivity and citation counts of publication venues. It also used topic specificity as a feature to rank academic entities.

In this introductory chapter, we collaborate on how digital transformation (DT) supports a value-driven educational approach, emphasizing the need for regular assessments of stakeholder needs, enhancing students' abilities to solve complex problems, applying learned knowledge effectively, nurturing creativity, and boosting employment prospects through skill development. Strategic considerations for implementing DT include creating a shared vision through collaborative strategy development, establishing clear objectives, designing a detailed action plan for DT initiatives, encouraging active participation from all educational community members, and maintaining the DT strategy through continuous evaluation and adaptation. By interweaving DT with these strategic educational priorities, higher education institutions can not only improve the learning experience but also equip students to succeed in a rapidly evolving future.

This chapter explores the transformative impact of artificial intelligence (AI) on higher education, particularly in the context of accelerating technological and societal changes. As higher education institutions face the need to offer more flexible, adapted and relevant academic programmes, AI presents significant opportunities and challenges. In the first part of this chapter, the authors elaborated on characteristic the evolution of AI including characterizing the emerging AI landscape. One of our contributions in this concluding chapter is to conceptualize the next areas of deployment of AI in higher education considering the novel, innovative services that will disrupt the entire market in the next few years. The strategic proposition for deployment of AI in higher education highlighted six pillars, namely large language models, research.AI, content creation.AI, personalised learning.AI, skill building assistants.AI and education out of the Box.AI. The authors presented opportunities to harness AI to enhance teaching, learning and research under each pillar, along with a detailed list of potential application areas and services. Universities are exploring innovative ways to use AI-driven solutions to improve research, teaching and learning experiences, and the authors also developed indicative scenarios for the use of AI in higher education based on the six pillars. One of the bold contributions in this chapter is the structured framework for understanding the evolution and use of AI in higher education, utilizing a matrix to map the intersection of market penetration and product development. Finally, the authors discuss future directions and strategies for Higher Education 2030 in light of advances in AI technology.

This chapter delves into the pivotal role of digital transformation (DT) strategies in fostering educational innovation, particularly through the lens of transformative learning (TL). By outlining a five-stage TL model, we explore how DT strategies can not only support but also significantly enhance educational reforms. Actions and multipliers, constituting the core elements of this model, interact dynamically to advance TL within academic institutions. Actions, such as strategic initiatives and the development of learning environments, account for the tangible steps toward transformation. Meanwhile, multipliers amplify these efforts, emphasizing the importance of strategy, commitment and the sustainable impact of educational transformations. We also highlight the emerging influence of artificial intelligence (AI) in reshaping learning contexts, demonstrating its capacity to personalize learning experiences and foster problem-solving skills. Additionally, we envision the future trajectory of higher education (HE) toward 2035, emphasizing the integration of AI and DT in creating a responsive and adaptive educational ecosystem. This chapter argues that DT is not just a tool but also a catalyst for active and transformative learning, proposing a holistic approach to integrating technology in education that addresses current challenges and anticipates future needs.

The evolution of Artificial Intelligence (AI) in higher education marks a paradigm shift, driving significant changes in pedagogical approaches and learning methodologies. With the rise of generative AI and artificial general intelligence (AGI), institutions have witnessed a transformative era where traditional content creation and delivery are being redefined. Start-ups like OpenAI and Anthropic have been at the forefront, offering tools like ChatGPT and Claude-3, which reshape natural language processing and forecast a future where AI integrations are seamless and pervasive. This chapter provides a critical overview of the current AI-driven applications enhancing personalized learning, content generation, and remote learning. Tools such as Mainstay, CourseGenie, and AIDES demonstrate AI's capacity to improve student engagement and success rates, while Degreed and Gnowbe showcase the broadening horizons of AI in skills building and microlearning experiences. Furthermore, platforms like Elicit and Research Rabbit exemplify the transformation in research and academic writing, albeit not without raising ethical concerns. In conclusion, AI's permanence in the educational landscape is unquestionable, calling for strategic frameworks that empower educators and students to harness its benefits effectively. The imminent expansion of the AI tool ecosystem necessitates preparedness for substantial shifts in educational practices, where ethical considerations and value-based strategies become paramount. Higher education institutions must align with this technological momentum, ensuring AI's potential is maximized in an ethical, inclusive, and impactful manner.

The dawn of Artificial Intelligence (AI) in higher education (HE) is not just on the horizon; it's here, promising a transformative leap forward. This shift is not simply about adopting new technologies; it's about redefining educational paradigms to meet specific challenges – from enhancing support and critical thinking to improving outcomes and fostering teamwork. This chapter outlines a comprehensive strategy to integrate AI into HE, spotlighting personalized learning, content generation, and remote learning, among others, as key domains ripe for AI's influence. An effective AI strategy will foster excellence and enable HE institutions to unlock the potential of technology for students and faculty alike. At its core, the proposed AI development strategy targets five critical areas: training, career growth, skill enhancement, learning, and team building. These areas ensure that all HE community members are well-equipped to navigate the AI-enhanced landscape of future jobs and challenges. However, realizing the full benefits of AI transcends the deployment of tools and systems; it requires strategic planning, investment in people, and policy changes. HE must cultivate champions to spearhead this transformation, emphasizing that success is not just measured in output but in the cultivation of socially responsible citizens. To harness AI's full capacity, we must transcend outdated stereotypes and metrics, fostering an educational environment that prepares students for the future. The ultimate goal is not just to integrate AI into HE but to use it as a catalyst for growth, innovation, and a better future for all.

Science and technology parks (STP) are established to facilitate innovation and the rapid development of cutting-edge technologies. The innovation performance of tenants is the primary feature of all successful STPs globally. The purpose of this study is to investigate firms’ innovation and economic performance at the National Science and Technology Park (NSTP), Islamabad, Pakistan.

The CDM (the acronym of the three authors’ names, Crépon, Duguet and Mairesse) model following a two-stage approach was used to analyze the survey data of 105 tenants. The innovation performance of tenants was estimated through probit regression at Stage 1, and the economic performance of tenants given their innovation performance was examined at Stage II using Tobit regression and the Heckman model.

The findings suggest that compatibility of innovation with the existing competitive advantage of a firm increases the innovation performance of firms, whereas collaboration of firms with NUST for research and development has only a marginal effect on innovation performance. However, the tenant’s business and social networking were weak, possibly due to the short time spent on NSTP.

These STPs are expected to be hubs of technology development and transfer by fostering open innovation through internal and external collaborations. To the best of the authors’ knowledge, this is the first study to estimate the innovation performance of tenants at NSTP, the first fully integrated STP in Pakistan. Despite shortcomings, the innovation and economic performance of NSTP tenants warrant further public policy support to inculcate open innovation culture.

This study attempts to use a new source of data collection from open government data sets to identify potential academic social networks (ASNs) and defines their collaboration patterns. The purpose of this paper is to propose a direction that may advance our current understanding on how or why ASNs are formed or motivated and influence their research collaboration.

This study first reviews the open data sets in Taiwan, which is ranked as the first state in Global Open Data Index published by Open Knowledge Foundation to select the data sets that expose the government’s R&D activities. Then, based on the theory review of research collaboration, potential ASNs in those data sets are identified and are further generalized as various collaboration patterns. A research collaboration framework is used to present these patterns.

Project-based social networks, learning-based social networks and institution-based social networks are identified and linked to various collaboration patterns. Their collaboration mechanisms, e.g., team composition, motivation, relationship, measurement, and benefit-cost, are also discussed and compared.

In traditional, ASNs have usually been known as co-authorship networks or co-inventorship networks due to the limitation of data collection. This study first identifies some ASNs that may be formed before co-authorship networks or co-inventorship networks are formally built-up, and may influence the outcomes of research collaborations. These information allow researchers to deeply dive into the structure of ASNs and resolve collaboration mechanisms.

This study reviews the methods found in the literature for the ranking of authors, identifies the pros and cons of these methods, discusses and compares these methods. The purpose of this paper is to study is to find the challenges and future directions of ranking of academic objects, especially authors, for future researchers.

This study reviews the methods found in the literature for the ranking of authors, classifies them into subcategories by studying and analyzing their way of achieving the objectives, discusses and compares them. The data sets used in the literature and the evaluation measures applicable in the domain are also presented.

The survey identifies the challenges involved in the field of ranking of authors and future directions.

To the best of the knowledge, this is the first survey that studies the author ranking problem in detail and classifies them according to their key functionalities, features and way of achieving the objective according to the requirement of the problem.

This paper aims to demonstrate how to make emergency decision when decision makers face a complex and turbulent environment that needs quite different decision-making processes from conventional ones. Traditional decision techniques cannot meet the demands of today’s social stability and security.

The main work is to develop an instance-driven classifier for the emergency categories based upon three fuzzy measures: features for an instance, solution for the instance and effect evaluation of the outcome. First, the information collected from the past emergency events is encodes into a prototype model. Second, a three-dimensional space that describes the locations and mutual distance relationships of the emergency events in different emergency prototypes is formulated. Third, for any new emergency event to be classified, the nearest emergency prototype is identified in the three-dimensional space and is classified into that category.

An instance-driven classifier based on prototype theory helps decision makers to describe emergency concept more clearly. The maximizing deviation model is constructed to determine the optimal relative weights of features according to the characteristics of the new instance, such that every customized feature space maximizes the influence of features shared by members of the category. Comparisons and discusses of the proposed method with other existing methods are given.

To reduce the affection to economic development, more and more countries have recognized the importance of emergency response solutions as an indispensable activity. In a new emergency instance, it is very challengeable for a decision maker to form a rational and feasible humanitarian aids scheme under the time pressure. After selecting a most suitable prototype, decision makers can learn most relevant experience and lessons in the emergency profile database and generate plan for the new instance. The proposed approach is to effectively make full use of inhomogeneous information in different types of resources and optimize resource allocation.

The combination of instances can reflect different aspects of a prototype. This feature solves the problem of insufficient learning data, which is a significant characteristic of emergency decision-making. It can be seen as a customized classification mechanism, while the previous classifiers always assume key features of a category.