Abstract
Scientific research in digital transformation is expanding in scope, quantity, and relevance, bringing forth diverse perspectives on which factors and specific dimensions-such as organizational structure, culture, and technological readiness-affect the success of digital transformation initiatives. Numerous studies have proposed mechanisms to assess an organization's maturity through digital transformation across various models. Some of these models focus on external influences, others on internal factors, or both. Although these assessments provide valuable insights into a company's transformation state, they often lack consistency, and recent research highlights key gaps. Specifically, many models primarily reflect the views of senior management on the general progress of digital transformation rather than on measurable outcomes. Moreover, these models tend to target large enterprises, overlooking small and medium enterprises (SMEs), which are crucial to economic growth yet face unique challenges, such as limited resources and expertise. Our study addresses these gaps by concentrating on SMEs and introducing a novel approach to assessing digital transformation readiness-a metric that reflects how prepared an organization is to optimize transformation outcomes. Following design science research methodology, we develop a model that centers on the perspectives of general employees, offering companies an in-depth view of their readiness across 20 dimensions. Each dimension is evaluated through behaviors indicative of the highest level of digital transformation readiness, helping companies identify areas to maximize potential benefits. Our model focuses not on technological quality but on the degree to which behaviors essential for leveraging technology and innovative business models are integrated within the organization.

Abstract
This chapter examines how Artificial Intelligence (AI) and Learning Analytics (LA) are transformingdistanceeducation, accelerated by the COVID-19 shift toe-learning. By using data from Learning Management Systems (LMS), these technologies can personalize learning, improve student retention, and automate tasks. AI, particularly machine learning, enables dynamic adaptation to student needs, while LA provides valuable insights for informed instructional decisions. However, ethical concerns, including data privacy and algorithmic bias, must be addressed to ensure equitable access and fair learning outcomes. The future of distance learning lies in responsible integration of AI and LA, creating immersive and inclusive educational experiences. © 2025 by IGI Global Scientific Publishing. All rights reserved.

Abstract
The ever-changing landscape of distance learning AI and learning analytics transforms engagement and efficiency in education. AI systems analyze behavior and performance data to provide real-time feedback for improved outcomes. Learning analytics further help educators to identify at-risk students while fostering better teaching strategies. By integrating AI with learning analytics, distance education becomes more inclusive, ensuring learners receive the support necessary to thrive in an increasingly digital and knowledge-driven world. AI and Learning Analytics in Distance Learning explores the development of distance learning. It examines the challenges of using these systems and integrating them with distance learning. The book covers topics such as AI, distance learning technology, and management systems, and is an excellent resource for academicians, educators, researchers, computer engineers, and data scientists. © 2025 by IGI Global Scientific Publishing. All rights reserved.

Abstract
[No abstract available]

Abstract
Business Continuity Management (BCM) is critical for organizations to mitigate disruptions and maintain operations, yet many struggle with fragmented and non-standardized self-assessment tools. Existing frameworks often lack holistic integration, focusing narrowly on isolated components like cyber resilience or risk management, which limits their ability to evaluate BCM maturity comprehensively. This research addresses this gap by proposing a structured Self-Assessment System designed to unify BCM components into an adaptable, standards-aligned methodology. Grounded in Design Science Research, the system integrates a BCM Model comprising eight components and 118 activities, each evaluated through weighted questions to quantify organizational preparedness. The methodology enables organizations to conduct rapid as-is assessments using a 0-100 scoring mechanism with visual indicators (red/yellow/green), benchmark progress over time and against peers, and align with international standards (e.g., ISO 22301, ITIL) while accommodating unique organizational constraints. Demonstrated via focus groups and semi-structured interviews with 10 organizations, the system proved effective in enhancing top management commitment, prioritizing resource allocation, and streamlining BCM implementation-particularly for SMEs with limited resources. Key contributions include a reusable self-assessment tool adaptable to any BCM framework, empirical validation of its utility in identifying weaknesses and guiding continuous improvement, and a pathway from initial assessment to advanced measurement via the Plan-Do-Check-Act cycle. By bridging the gap between theoretical standards and practical application, this research offers a scalable solution for organizations to systematically evaluate and improve BCM resilience.

Abstract
Numerous Robotic Process Automation (RPA) market solutions with wildly disparate capabilities and business models are being put forth. RPA is still in its infancy, and its technology framework is continually evolving. There are very few comparative studies of RPA systems, and they do not make it simple to tailor the solution to the needs of the business choosing it. Thus, the research question is that it feasible to design a procedure that enables the choice of the most appropriate RPA tool while accounting for a particular business domain, reality, and set of requirements? In order to accomplish this, this study builds an artifact that comprises a collection of indicators to enable the long-term selection of the best RPA solution for each organization and/or business process using the methodological approach of Design Science Research. The artifact offers a methodology to categorize the level of adaptability of each solution for automating business processes, performs a comparative analysis of existing RPA solutions using a particular framework, and provides an overview of the features of currently available solutions on the market. The viability of the artifact is demonstrated using a real-world case situation. This test demonstrated the artifact’s capacity to meet the goals. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.