Objective Since its inception in 2009, Bitcoin has experienced tremendous growth in terms of public acceptance as the oldest cryptocurrency. However, transitioning from an experimental digital currency to a mainstream payment network introduced new challenges related to scalability and capacity. This study investigates how bitcoin transaction fees respond to financial and technical factors within the network over different periods of its lifespan. The investigation provides novel insights into the dynamics of a decentralized currency and the maturity of a payment network.   Methods Bitcoin blockchain data from 2009 to 2023 was categorized into three distinct periods for analysis: the Initial Period (2009-2014), Speculation Period (2014-2018), and Scalability Challenge Period (2018-2023). Autoregressive Distributed Lag (ARDL) modeling was used to analyze short-term and long-term relationships. The dependent variable was the transaction fee, and the explanatory variables included the bitcoin price, average transaction value, average block size, network difficulty, and transaction volume.   Results The results showed that during the Initial Period, in the short term, the price and the previous day’s fee had a significant effect on the transaction fee, and in the long term, the price also affected the transaction fee. During the Speculation Period, the transaction volume, block size, and the previous day’s fee in the short term, as well as network difficulty in the long term, had a significant effect on the transaction fee. During the Scalability Challenge Period, in the short term, the previous day’s transaction fee, Bitcoin price, the average value of transactions in Bitcoin, block size, network difficulty, and transaction volume had significant effects on the transaction fee, while in the long term, network difficulty and block size remained significant. Moreover, during the Scalability Challenge Period, the previous day’s fee had a strong effect on the current day’s fee, creating a kind of stickiness that persisted until the end of the period. Overall, fees were stabilized over time as users and miners learned the factors influencing them, optimizing their behavior around block limits and mining reward incentives. During the Initial Period, transaction fees were highly volatile due to Bitcoin’s nascency and limited usage. As cryptocurrencies increasingly became utilized as a payment mechanism in the Speculation Period, the technical parameters affecting block sizes and processing capacity caused fees to be more reflective of demand on the network. The emergence of scalability constraints facing the blockchain in the Scalability Challenge Period has led to linking the dynamics of fees to more metrics that act as proxies for the level of activity and network usage.   Conclusion As bitcoin has become a payment network in the new concept, the fees have been aligned with the demand and motivations of network participants during the scaling challenge period, instead of randomly fluctuating during the Initial Period. The separation of bitcoin network data into three time periods provides a new perspective on how decentralized networks work and the factors affecting the fee market in different periods of network maturity.

Nowadays, business environment is so unpredictable that the only definitive point is change. Major consultancies have recognized that organizations embracing digital transformation have gained, and continue to gain, the knowledge, insight and understanding critical for deriving a competitive advantage over untransformed competitors. Many articles have highlighted digital transformation as vital to the survival of the organization. However, transformation poses significant challenges. Enterprises are concerned with uncertainty and unpredictability within their environment, which pressures their organizational ability to capture and react to external changes. Such organizational adaptability is primarily associated with the concept of agility. Agile transformation requires a holistic approach throughout the organization which requires a meaningful change in the entire mindset of an organization and its culture. Digital transformation requires business agility, and applying the lessons of Agile across the entire organization is the only approach that has shown consistent success. Numerous articles and reports have introduced Agile as an appropriate framework for digital transformation, yet there is a lack of academic papers in this field. This research, using a systematic literature review, aims to design a life cycle of digital transformation to improve the process of digital transformation in organizations through agile thinking.

Today’s, more than 10000 types of different medical devices are currently employed in medium and large hospitals. These devices have an important role in the diagnosis, treatment, and monitoring processes in health centers. An efficient preventative maintenance program of these devices is essential to guarantee the correct functioning and ensure the safe and reliable operation of them. As the maintenance activities increase as increasing the variety of medical devices, the need for a better maintenance management system become more essential than the past. In this paper a new multi-criteria decision-making approach of system engineering to lifecycle management and preventive maintenance priority of medical devices based on a kind of quality function deployment is presented. The model was implemented and tested in three public hospitals in one of the western provinces of Iran. None of the three hospitals had established a proper assessment management program processes to identify their equipment needs and required maintenance priority based on their annual budgets. Our suggested model solved one of the big decision problems in their management system, the results helped them to have a priority list of the medical devices that should be replaced and the preventative maintenance priority of critical devices based on the wards, type and number of them according to the available budget. Implementation of our proposed model in medical centers can maximize the reliability of equipment and systems among the limitation of organizational resources and budgets