Abstract
Knowledge sharing and team dynamics are essential elements of entrepreneurial success, especially in teams that operate in innovative environments. This chapter explores how participation in an incubation program influences the formation and development of entrepreneurial team identity. It aims to understand the dynamics involved in creating entrepreneurial teams, the practices of knowledge sharing, and the role digital technologies play in supporting and sustaining these processes. The study focuses on teams that completed the CEU iLab Incubation Program, with data gathered through in-depth semistructured interviews from twenty-five entrepreneurs across various startups. Five cases, involving entire entrepreneurial teams, were central to this research. The findings offer valuable insights for enhancing incubation programs, promoting entrepreneurial identity formation, and improving the success of new ventures. These insights are beneficial for both scholars and practitioners in the entrepreneurship field. © 2025 by IGI Global Scientific Publishing. All rights reserved.

Abstract
Critical performance regions of software applications are often accelerated by offloading them onto an FPGA. An efficient end result requires the judicious application of two processes: hardware/software (hw/sw) partitioning, which identifies the regions for offloading, and the optimization of those regions for efficient High-level Synthesis (HLS). Both processes are commonly applied separately, not relying on any potential interplay between them, and not revealing how the decisions made in one process could positively influence the other. This paper describes our primary efforts and contributions made so far, and our work-in-progress, in an approach that combines both hw/sw partitioning and optimization into a unified, holistic process, automated using source-to-source compilation. By using an Extended Task Graph (ETG) representation of a C/C++ application, and expanding the synthesizable code regions, our approach aims at creating clusters of tasks for offloading by a) maximizing the potential optimizations applied to the cluster, b) minimizing the global communication cost, and c) grouping tasks that share data in the same cluster. © 2025 IEEE.

Abstract
This study aims to develop a methodology to assist Small and Medium Enterprises (SMEs) in effectively adopting Business Process Automation (BPA). Despite its growing importance in streamlining routine tasks and enabling employees to focus on more creative activities, numerous organizations face challenges in implementing BPA due to unclear procedures, insufficient knowledge of eligible processes, and uncertainty regarding the necessary technology. In response to these challenges, we introduce the Methodology for Business Process Automation (M4BPA), an artifact designed to guide SMEs through a structured BPA implementation process. The research follows the Design Science Research Methodology (DSRM). The requirements for the artifact came from the results of a previous Systematic Literature Review (SLR). M4BPA was demonstrated within real SME environments, providing solid evidence of its efficacy. The findings suggest that M4BPA significantly enhances SMEs' ability to implement BPA efficiently, offering a practical toolkit that facilitates the process. The novelty of this work lies in the development of a BPA methodology specifically tailored for SMEs, addressing existing gaps in current frameworks and providing a best-practice model for similar organizations. This research contributes to the intermediate results of a doctoral project, offering valuable insights for both practitioners and researchers in the field of BPA. © 2025 by the authors.

Abstract
Interest in artificial intelligence (AI)-driven crowd work has increased during the last few years as a line of inquiry that expands upon prior research on microtasking to represent a means of scaling up complex tasks through AI mediation. Despite the increasing attention to the macrotask phenomenon in crowdsourcing, there is a need to understand the processes, elements, and constraints underlying the infrastructural and behavioral aspects in such form of crowd work when involving collaboration. To this end, this paper provides a first attempt to characterize some of the research conducted in this direction to identify important paths for an agenda comprising key drivers, challenges, and prospects for integrating human-centered AI in collaborative crowdsourcing environments. © 2025 IEEE.

Abstract

Abstract
Distributed energy resources (DERs) offer untapped potential to meet the flexibility needs of power systems with a high share of non-dispatchable renewable generation, and local flexibility markets (LFMs) can be effective mechanisms for procuring it. In LFMs, energy communities (ECs) can aggregate and offer flexibility from their members' DERs to other parties. However, since flexibility prices are only known after markets clear, flexibility bidding curves can be used to deal with this price uncertainty. Building on previous work by the authors, this paper employs a two-stage methodology to calculate flexibility bids for an EC participating in an LFM, including not only batteries and photovoltaic panels, but also cross-sector (CS) flexible assets like thermal loads and electric vehicles (EVs) to assess their impact. In Stage 1, the EC manager minimizes the energy bill without flexibility to define its baseline. In Stage 2, it computes the optimal flexibility to be offered for each flexibility price to build the flexibility bidding curve. Case examples allow to assess the impact of CS flexible assets on the final flexibility offered. © 2025 IEEE.