Digital Transformation

Nowadays, business process management (BPM) contributes to the success of companies by ensuring that their processes are both effective and efficient. A comprehensive description of a business process can serve as a foundation for designing IT systems, ensuring data quality, establishing performance metrics, and implementing processes using Business Process Management Systems (BPMS), among other applications. Currently, many Iranian companies are also interested in evaluating their Business Process Management (BPM) practices. In recent decades, significant advancements in the digital realm have become increasingly vital for companies, making it essential to utilize these developments effectively to impact business processes. Consequently, the current research has ranked BPM measurement methods within the context of digital transformation, employing the COCOSO hierarchical analysis technique in Semnan Industrial Town. In this context, BPM measurement methods and measurement criteria with the digital transformation approach and data quality, are derived from a review of the research literature. Subsequently, an appropriate BPM evaluation method is identified using a multi-criteria decision-making approach. The results of this ranking indicate that BPM measurement models grounded in comprehensive quality management are the most appropriate. Additionally, a sensitivity analysis has been conducted to validate these findings. 

The current paper reviews the present literature in the most known scientific databases in the management and business fields about artificial intelligence (AI) and digital transformation within organizations. The main objective is to extract related research axes and uncover gaps in this emergent topic. The methodology used is a systematic literature review with RStudio software based on 36 selected papers from the Scopus and Web of Science (WOS) databases in the period of 2019-2024. The main axes identified are AI potential for organizations’ performance, innovation and AI potentials, and AI adoption determinants. Regarding the discussion and analysis of the results, future directions are projected to cover all sides of digital transformation through AI tools. The main contribution of this paper is to provide researchers and practitioners with current advancements and changes in Al tools utilized to facilitate digital transformation within evolving economic and social landscapes for companies.

Purpose: The purpose of this research is to design a model for evaluating digital innovation and transformation in startup business models.Method: The methodology of this reseatch is descriptive-exploratory and employs a qualitative approach. The thematic analysis was employed to study digital innovation and transformation in startup business models. The participants in this research were academic and industrial experts with a background in research and executive work in lean startups. Initially, a purposive sampling method was used to select the samples, and this was later extended using the snowball method. Ultimately, the researcher conducted 14 expert interviews to collect data. The data obtained from the interviews were reviewed and analyzed using coding based on theme analysis. Initially, codes were extracted from the text of the interviews. These codes were aggregated into more general codes, which were further studied and integrated into components. From these components, relevant dimensions were proposed, leading to the presentation of a model based on these dimensions and extracted components.Findings: The results indicate that the digital Innovation and transformation evaluation model for startup business models includes six main dimensions and 19 sub-themes. The proposed model consists of the following dimensions: "monitoring and analysis of market needs" with three components, "evaluation of product development costs" with three components, "digital innovation and transformation in the business model" with four components, "coordination and integration inside and outside the organization" with two components, "evaluation of learning ability and absorption of organizational knowledge" with three components, and "organizational resources and capacities" with four components.Conclusion: Based on the research findings, it is suggested that managers in the technology field understand the importance of lean startups. The indicators in the proposed model may be considered to help prepare and empower startups to improve their products and services. Each proposed dimension can be seen as a management skill necessary for the success of lean startups. Managers may create the appropriate conditions to integrate all of company capabilities.

The purpose of this study is to investigate the impact of digital transformation on audit efficiency in companies listed on the Iranian capital market. Audit delay is used as a key measure to evaluate audit efficiency. To assess digital transformation, an index based on the frequency of keywords related to digital technologies, such as "Internet of Things," "Artificial Intelligence," "Cloud Computing," and "Big Data," in annual reports of companies is calculated. Control variables include company size, financial leverage, board independence, ownership concentration, Return on Assets (ROA), company losses, and CEO duality.This research is applied and descriptive survey in nature, and data were collected from financial reports of companies listed on the Tehran Stock Exchange between 2018 and 2022. Data analysis was performed using linear regression and the Eviews software, with the study adopting a panel data methodology.The results indicate that digital transformation negatively affects audit efficiency, leading to increased audit report delays. Additionally, companies with higher levels of digital transformation experienced more significant audit delays. These effects were particularly evident in firms with higher financial leverage and lower ownership concentration.This study highlights that digital transformation presents new challenges for the auditing profession, emphasizing the need for enhanced skills and the adoption of relevant technologies in the audit process

This article examines digital transformation in the construction industry, which begins with adopting digital technologies and culminates in comprehensive organizational change. The diverse and often conflicting conceptualizations in this field have created ambiguity in the theoretical framework of digi-tal transformation in construction. Using topic modeling and pattern analysis, this study identifies key themes and trends in the domain. The article analyzes 1,308 articles published between 1990 and 2023 to review research areas related to digital transformation in the construction industry. It identifies six main topics: Security & Safety, Organization & Project Management, Digital Simulation & Interaction, Sustainability, Innovative Building Materials, and Dynamic Monitoring Methods. The analysis reveals that Organization & Project Management is the most researched topic, while Sustainability receives the least attention. The article offers recommendations for advancing research in this field and serves as a valuable reference for researchers and practitioners interested in digital transformation in construction. By addressing existing criticisms, it provides a clear map of the field’s structure and trends, comple-menting previous qualitative studies with a broader, more structured, and objective analysis.

The banking industry is undergoing a significant transformation driven by digital technologies, evolving customer behaviors, and increasing regulatory pressures. To remain competitive, banks must adopt governance frameworks that integrate digital innovations to enhance operational efficiency and improve environmental, social, and governance (ESG) performance. This study identifies governance framework archetypes essential to digital transformation in banks through a comprehensive research methodology, including a literature review of digital governance frameworks, a comparative analysis of 11 leading digital banks worldwide, and latent class analysis to uncover key archetypes. Our findings reveal nine distinct governance archetypes, categorized into three dimensions: structural, including Centralized, Semi-centralized, and Open Innovation-oriented banks; dedicated processes, comprising Continuous Improvement, Vanguard, and Fast Follower banks; and relational mechanisms, featuring Self-empowering, Explorer, and Relationship-oriented banks. This classification advances the understanding of governance approaches that effectively support banks in their digital transformation journeys. The implications of these archetypes are substantial, offering a framework for banks to align their strategies with digital transformation initiatives. By adopting these governance structures, banks can better navigate the complexities of the digital landscape, foster innovation, and ultimately enhance their service offerings while addressing the evolving demands of customers. This research contributes to the growing body of knowledge on digital governance in banking and provides guidance for financial institutions striving to succeed in an increasingly digital world.

Purpose : The purpose of this research is to design a model for evaluating digital innovation and transformation in startup business models. Method : The methodology of this reseatch is descriptive-exploratory and employs a qualitative approach. The thematic analysis was employed to study digital innovation and transformation in startup business models. The participants in this research were academic and industrial experts with a background in research and executive work in lean startups. Initially, a purposive sampling method was used to select the samples, and this was later extended using the snowball method. Ultimately, the researcher conducted 14 expert interviews to collect data. The data obtained from the interviews were reviewed and analyzed using coding based on theme analysis. Initially, codes were extracted from the text of the interviews. These codes were aggregated into more general codes, which were further studied and integrated into components. From these components, relevant dimensions were proposed, leading to the presentation of a model based on these dimensions and extracted components. Findings : The results indicate that the digital Innovation and transformation evaluation model for startup business models includes six main dimensions and 19 sub-themes. The proposed model consists of the following dimensions: "monitoring and analysis of market needs" with three components, "evaluation of product development costs" with three components, "digital innovation and transformation in the business model" with four components, "coordination and integration inside and outside the organization" with two components, "evaluation of learning ability and absorption of organizational knowledge" with three components, and "organizational resources and capacities" with four components. Conclusion : Based on the research findings, it is suggested that managers in the technology field understand the importance of lean startups. The indicators in the proposed model may be considered to help prepare and empower startups to improve their products and services. Each proposed dimension can be seen as a management skill necessary for the success of lean startups. Managers may create the appropriate conditions to integrate all of company capabilities.

Background: The cloud-native architectures have reinvented the original strategies of the companies’ IT infrastructure approach and became popular due to the concepts of modularity, scalability, and resilience. These architectures respond to the shortcomings of the monolithic architectures to meet the new business challenges and workloads, including embracing innovation technologies like Artificial Intelligence and big data processing solutions. Objective: This study was designed with the objective of assessing the performance and business viability of cloud-native systems, based on critical indicators such as availability, resilience to failure, resource use, and compatibility with innovative technologies. The objective was to define the barriers and possibilities for improving cloud native architectures in various enterprises. Methods: A cross-sectional research, consideration, experiment test and case study and performance analysis. Response time, CPU and memory consumption and recovery time were compared across the range of throughput from 1000 to 12000 requests per second. To enhance the interpretational framework, key usage scenarios in the three sectors of healthcare, retail and finance were collected and compared with the results. Results: Cloud-native systems proved to provide high availability rates (> 99.9%), resource scalability, and component resource efficiency. With the use of AI in combination with big data analytics, improvement in performance was realized. But some of the problems that were seen include vendor lock, integration issues, and fluctuating peak load issues. Conclusion: All identified improvements signify the potential of cloud-native architectures for improving enterprise IT functioning. It is thus possible to continue perfecting the identified challenges to enhance their effectiveness, optimal for the current dynamic digital environment.

Background: Telecommunications has been rapidly moving from legacy systems to highly flexible modern architectures to accommodate the expanding demand on its services. This evolution is critical in providing the capacity needed for new technologies like 5G, IoT, and applications powered by AI. Objective: The study aims at establishing a literature review on the evolution from the more or less obsolete telecommunication structures to new generation digital structures, opportunity factors, technologies that facilitate this change as well as the value addition by this evolution. Methods: The literature review was followed by an examination of industry case studies of 50 telecommunications firms across the globe. The study looked at best practices including network resource utilization, operational price, and service delivery effectiveness, pre and post implementation of technologies like software-defined networking (SDN), network function virtualization (NFV), and cloud-native architectural strategies. Results: The analyses brought out the fact that with the new architectures, network scale up capabilities were enhanced by 70%, operation costs were brought down by up to 30% and service delivery rates were boosted by 40%. Nonetheless, 85% of the firms that implemented the software upgrade faced issues with system integration, which took fifteen months on average before the new system was fully incorporated, and the firms incurred an additional 20% in implementation costs in accommodating integration issues. Conclusion: Extension of telecommunication architectures towards digital landscape improves performance, capacity, and affordability thereby allowing the providers to address next generation applications. However, while making this transition, there are a number of risks that organizations have to face and it is very important to manage them in order to have maximum benefits from using new digital technologies.

Background : As digital transformation accelerates globally, effective IT governance has become critical for organizational success. With global spending on IT governance and risk management projected to reach $16 billion by 2025, emerging technologies such as artificial intelligence (AI), blockchain, and cloud computing are introducing new governance complexities that demand adaptive strategies. Objective : The article explores the key factors and anticipated trends in IT governance that are expected to shape organizational management by 2025. The aim is to understand how evolving technological landscapes influence governance models and risk management practices. Method : A qualitative methodology was adopted, involving a systematic review of 100 scholarly and industry articles focused on recent trends and future directions in IT governance. The analysis highlights issues related to risk management, regulatory compliance, cybersecurity, and technology integration. Results : The review revealed that 83% of organizations reported significant governance challenges due to technological disruption, while 68% indicated a transition toward decentralized governance models, particularly within blockchain-based systems. Additionally, AI-powered decision-making tools are projected to be adopted by over 70% of large enterprises for IT governance functions by 2025. Conclusion : The findings underscore the growing need for flexible and adaptive IT governance frameworks that align with both agile and traditional business objectives. By anticipating and addressing future risks and compliance demands, organizations can enhance their current governance strategies to remain resilient and competitive in the digital era.

The convergence of digital transformation and artificial intelligence (AI) is reshaping sports globally, yet para-sport organizations face uniquely complex challenges, from precision in classification to equitable access to technology. Despite growing interest, research remains fragmented, lacking an integrated framework linking technological capability, ethical governance, and inclusivity imperatives. This narrative review critically synthesizes contemporary evidence (2005–2025) to illuminate how AI and digital systems can reconfigure para-sport governance, performance optimization, and athlete empowerment. Literature was sourced from major scholarly databases, analyzed thematically, and integrated into a foresight-oriented conceptual model. Findings reveal five strategic domains: organizational digital readiness; AI applications for adaptive training and decision-making; governance frameworks for ethical and transparent implementation; mitigation of algorithmic bias; and future scenario planning for resilient, inclusive systems. Opportunities include enhancing classification accuracy, personalizing performance strategies, and democratizing digital resources. However, risks such as entrenched bias, data governance failures, and regulatory fragmentation remain critical threats. This review advocates for urgent, coordinated action to integrate ethics, accessibility, and co-creation into AI development. Policy recommendations include stakeholder-driven algorithm design, routine bias audits, capacity-building initiatives, and harmonized global regulatory standards. Ultimately, para-sport stands at a pivotal inflection point: without intentional, equity-focused strategies, technological advances risk reinforcing structural disparities.