UPRAVLENIE / MANAGEMENT (Russia)

Sharma D.P., Habibi Lashkari A., Parizadeh M.

Turulja L., Celjo A., Pejić Bach M., Bajgoric N.

This chapter presents literature review with the aim of developing conceptual models that clarify the role of ERP in the organization, and the relationship between ERP, KM and business performance. Using a systematic literature review (SLR), this chapter presents the current body of knowledge on the impact of ERP on organizational business performance, and then the relationship and logic of the connection between ERP and KM. The chapter describes the fundamental role that ERP systems play in simplifying and combining business processes in companies, thereby providing a framework for knowledge management techniques. These approaches then improve operational efficiency and decision-making capacity, thereby strengthening organizational performance. Emphasizing the need for strategic integration of both to achieve exceptional performance and competitive advantage, the chapter shows how synergy between ERP and KM supports and improves organizational performance.

Turulja L., Celjo A., Pejić Bach M., Bajgoric N.

This chapter explores the development and integration of enterprise resource planning (ERP) within the information systems (IS) field, thus offering background knowledge based on a short literature review from selected books/textbooks. Beginning with a historical overview, this chapter follows the beginnings and developmental milestones of ERP systems, emphasizing their central importance in integrating several organizational functions and thereby changing organizational operations. The chapter also highlights the impact of ERP on strategic organizational change and business process reengineering, thus setting the stage for detailed investigations of ERP adoption and its interaction with knowledge management in subsequent chapters. This chapter highlights the theoretical foundations of ERP systems and shows their implications in contemporary corporate environments, bridging the gap between theoretical models and actual implementations.

Turulja L., Celjo A., Pejić Bach M., Bajgoric N.

This chapter introduces the Technology Acceptance Model (TAM) and provides a short history of TAM and TAM-related studies in ERP adoption. The model is widely used for the acceptance of technology, including Enterprise Resource Planning (ERP) systems within organizations. It analyses major adoption success factors suggested in the literature and presents the phases of ERP adoption and related sub-steps and methods. This chapter concludes by providing the all stages and corresponding documentation for successful practical ERP adoption.

Turulja L., Celjo A., Pejić Bach M., Bajgoric N.

This chapter aims at analyzing the adoption factors from the Knowledge Management (KM) perspective for successful Enterprise Resource Planning (ERP) adoption. It provides an overview of how knowledge is created and classified. Further, it explains knowledge creation and transfer through ERP. Implementing ERP systems and KM may bring several advantages in modern business. Through advanced modules and technology, ERP systems integrate business processes to optimize operations. KM and ERP systems work together as a bridge in linking and integration information systems with cognitive assets of the company. Chapter concludes with overview of usual KM activities and knowledge conversion dimension across ERP adoption stages.

Turulja L., Celjo A., Pejić Bach M., Bajgoric N.

This chapter, first, proposes and then empirically tests two separate models related to ERP systems and knowledge management (KM) and their impact on organizational performance. Incorporating the culture of knowledge sharing and based on the Technology Acceptance Model (TAM), Model I, the ERP Acceptance Model, explores how these elements influence ERP acceptance. Model II, called the strategic ERP & KM synergy model, focuses on the combined impact of ERP and KM on organizational performance, looking at how ERP systems and KM practices interact. Using thorough empirical research, this chapter offers a comprehensive study of the interactions between several dimensions of knowledge management with ERP functions to improve operational performance and strategic decision-making.

Wasilewski A.

To provide a customized user interface in e-commerce, it is essential to obtain knowledge about customers, including their behaviors, preferences, and decision-making processes. To achieve this, it is imperative to collect data on all users’ actions within the e-shop. This data collection should be conducted with utmost respect for customer privacy and adherence to legal requirements. One viable method for gathering this data involves using cookies with the prior consent of clients, in conjunction with tag management tools and web analytics. The information thus acquired can then be examined to identify patterns and dependencies, allowing for the classification of customers based on their behavior. Various grouping techniques can be used for this purpose, with the choice of the appropriate algorithm depending on the dataset—a crucial factor in advancing interface variant implementation. Additionally, the UX specialist should utilize the gathered data on user behavior to suggest modifications that will mold the interface variants offered to specific consumer segments. This chapter outlines methods for collecting customer behavioral data in online retail environments, delves into clustering as a technique for grouping users based on their behavior, and highlights current and potential applications of this approach in e-commerce. The chapter also explores ways to analyze the resulting clusters in terms of their utility for designing specialized layout variants.

Wasilewski A.

From the perspective of an e-shop customer, the most prominent aspect of a multi-interface solution is the availability of interface variants offered to specific user groups. The design of these variants should not be arbitrary but based on an analysis of the collective traits of customer clusters and the typical behavior exhibited by members of these groups. Consequently, it is imperative to establish both a flexible and an efficient mechanism for crafting modifications to UI variants and a means of accessing information concerning characteristics that may influence the deployment of specific changes. Given that the impact of UI changes on customer behavior cannot be definitively assessed ex ante, it is necessary to devise a method for evaluating the quality of proposed UI variants. This evaluation should take into account the choices made and the behavior of users who have been exposed to the personalization. It plays a pivotal role in deciding whether to retain or withdraw the modifications. By relying on a list of potential changes and the values of selected indicators obtained through a tool that assesses their impact on purchasing decisions, one can iteratively seek the optimal user interface variants for each customer cluster. In practice, this iterative process can lead to the development of a self-adaptive mechanism that utilizes a multivariant user interface to automatically fine-tune the implemented UI modifications to suit the unique characteristics of individual e-commerce customer clusters. This chapter delves into a practical methodology for crafting custom e-commerce user interface variants. It outlines the process of translating the attributes of the identified customer clusters and their behaviors into tailored layout modifications. Furthermore, it explores methods for quantifying the efficacy of implemented UI changes and proposes algorithms that facilitate the self-adaptation of e-commerce UI variants.

Wasilewski A.

Multivariate user interfaces offer a dynamic way to personalize users’ experiences by adapting to their preferences and behavior. Nonetheless, implementing them demands robust justifications for such an investment. Given that web shops infrequently provide various interface versions and the business implications of this approach remain insufficiently explored, testing the proposed solution in practice is essential. The verification process must cover all aspects of the mechanism, with special emphasis on choosing the most suitable clustering method and assessing the effects of UI variants on the performance indicators employed in e-commerce. On the one hand, the research should confirm the viability of the proposed concept and methods of preparing and serving customized UI variations. On the other hand, it should serve as a foundation for future research and development aimed at personalizing the user interface in e-commerce. This chapter presents the background of the research and describes the study work carried out. The first set of experimental studies concerned the selection of the optimal clustering method. The results obtained allowed the selection of two algorithms, which were applied during the second stage of the research. This included a practical verification of the impact of dedicated user interface variants on different customer groups and confirmed that the use of layout personalization in e-commerce can yield positive business results.

Wasilewski A.

E-commerce is becoming an increasingly preferred method of conducting commercial transactions worldwide, and its importance continues to grow. The ability to access potential customers around the world serves as the impetus for discovering novel channels to reach out to buyers and entice them to make a purchase. Personalization, which involves modifying presented content to fulfill the specific needs, requirements, behaviors, or preferences of the user, is one such approach. Personalization can have various technical implementations but requires knowledge of the target recipient. However, obtaining customer information cannot compromise their right to privacy. Stringent legal regulations and market trends currently safeguard this privacy, but the existing standards provide opportunities for a compromise that allows for personalization in e-commerce while respecting the privacy of the customer. This chapter begins with a general introduction to the topic of e-commerce. It then identifies the development trends that are increasing the practical importance of personalization. In the following chapters, the technical aspects of website personalization, as well as the challenges for personalization arising from the growing importance of customer privacy and data protection, are discussed.

Wasilewski A.

The main objective of e-commerce activities is to sell products or services and possibly provide after-sales service. Depending on how this is achieved, various methods of personalizing communications are employed. Customized content or design is sometimes practiced in online shops, B2B sales platforms, auction portals, or marketplaces. Some popular approaches to personalization in e-commerce include product recommendations, dedicated ads, or emails. Occasionally, other solutions are also implemented based on business owner decisions. Most current solutions primarily focus on personalizing content, and the ability to provide a holistic dedicated user interface is less commonly utilized. However, the potential of personalizing layouts by offering multiple UI variants in e-commerce seems underestimated. Undoubtedly, such an approach requires a system architecture design capable of meeting potential users’ requirements. The proposed solution should be modular and scalable, minimizing barriers to entry. A possible starting point for developing a framework to serve dedicated user interfaces could be the recommendation systems used in today’s e-commerce. This chapter discusses the applications of recommender systems and introduces various approaches to using multivariant user interfaces in e-commerce. An analysis of typical modules of recommender systems is provided, with a particular focus on components critical for delivering dedicated layouts. A solution architecture is proposed that enables the practical implementation of the concept of serving user interface variants tailored to the decisions and behaviors of specific groups of e-commerce customers.

Sharma D.P., Habibi Lashkari A., Parizadeh M.

As healthcare professionals, IT professionals, and decision-makers in healthcare organizations, your role in exploring and addressing the potential security and privacy concerns in healthcare systems is crucial and highly valued (Jawad, 2024). Figure 10.1 shows the key potential cybersecurity concerns in digital healthcare systems. These common potential security concerns and the best practices that are used to ensure security and privacy in the healthcare industry are discussed as follows:

Sharma D.P., Habibi Lashkari A., Parizadeh M.

Cybersecurity risk analysis identifies, assesses, and prioritizes potential cyber threats, vulnerabilities, risks, and their impact on information confidentiality, integrity, and availability. The main goal of cybersecurity risk analysis is to develop strategies for managing and mitigating those risks effectively. A healthcare organization can better protect patient data, ensure the integrity of medical services, and contribute to overall patient safety by adopting a proactive and comprehensive approach to cybersecurity risk analysis, assessment, and mitigation strategies. This chapter presents an organizational approach for analyzing cybersecurity risks, assessment, and mitigation strategies in the healthcare industry.

Sharma D.P., Habibi Lashkari A., Parizadeh M.

Data governance is the specification of decision rights and an accountability framework to ensure the appropriate behavior in the valuation, creation, consumption, and control of data and analytics (Gartner, 2024). It refers to the exercise of authority and control over the management of data. Data governance aims to increase data value and minimize data-related costs and risks. In the healthcare context, it is a collection of procedures and plans that ensure the availability, integrity, security, and usability of the structured and unstructured data available to the healthcare organization. The best data governance practice outlines the healthcare data governance framework, guiding principles, organization-wide applications, and best practices. It is not a new topic, but it is still challenging for many healthcare organizations to implement and achieve (Oachs, 2020). Healthcare data governance includes the people, processes, and systems used to manage data throughout the lifecycle. Figure 9.1 shows the data governance lifecycle of a healthcare organization (AHIMA, 2022). The data lifecycle includes creation, process, use, storage/archival, or disposal. It also involves defining policies and procedures for data retention, archival, and disposal following regulatory requirements.

Sharma D.P., Habibi Lashkari A., Parizadeh M.

Detection and prevention of cyberattacks in healthcare are crucial to protect sensitive patient data and ensure the integrity and availability of healthcare systems and services. The healthcare industry is a prime target for cyberattacks. There are some reasons why the healthcare industry is the prime target (Bhosale, 2021).