Adoption Curve Research Articles

Ransomware Reloaded: Re-examining Its Trend, Research and Mitigation in the Era of Data Exfiltration

Ransomware has grown to be a dominant cybersecurity threat by exfiltrating, encrypting, or destroying valuable user data and causing numerous disruptions to victims. The severity of the ransomware endemic has generated research interest from both the academia and the industry. However, many studies held stereotypical assumptions about ransomware, used unverified, outdated, and limited self-collected ransomware samples, and did not consider government strategies, industry guidelines, or cyber intelligence. We observed that ransomware no longer exists simply as an executable file or limits to encrypting files (data loss); data exfiltration (data breach) is the new norm, espionage is an emerging theme, and the industry is shifting focus from technical advancements to cyber governance and resilience. We created a ransomware innovation adoption curve, critically evaluated 212 academic studies published during 2020 and 2023, and cross-verified them against various government strategies, industry reports, and cyber intelligence on ransomware. We concluded that many studies were becoming irrelevant to the contemporary ransomware reality and called for the redirection of ransomware research to align with the continuous ransomware evolution in the industry. We proposed to address data exfiltration as priority over data encryption, to consider ransomware in a business-practical manner, and recommended research collaboration with the industry.

Estimating a Network Adoption Curve through Price Trends “Lowest Price Forward: Why Bitcoin’s Price is Never Looking Back”

Estimating a Network Adoption Curve through Price Trends “Lowest Price Forward: Why Bitcoin’s Price is Never Looking Back”

Dinámica en la conformación y adopción de las Organizaciones Cafetaleras en el Perú

One of the forms of work in the agricultural sector at the Latin American level is the formation of organizations such as cooperatives. The Peruvian coffee sector has not been alien to this model, which after 50 years seems to have found an organizational model through the formation and strengthening of Associations or Cooperatives of Multiple Services, increasing their quality. The objective was to determine the adoption curve of the new organizational model of coffee organizations in the Peruvian coffee sector during the last 20 years and the consequences of adopting the new organizational model (OC) in the Peruvian coffee sector. Were used reports of coffee exporting OCs, analysis of information on coffee exports, consequences of capital analysis. Of the exporting POs in 2021, in the group of those formed before 2000, which are only 18, there were 112 in 2021 with the largest increases in the years 2000-2008 and 2012-2019, between consequences found there are better prices of the OCs per quintal exported, increase in certified coffees, and recomposition of the exporters, with a 37% share of exports for the OCs in the year 2021.

User Experience in Cloud Computing Services-Based LMS: a Case Study

This research explores user experiences with Learning Management Systems (LMS) in the context of online education, focusing on the Google Classroom application. Conducted at SDIT Salsabila 4 Jetis, Indonesia, the study navigates challenges faced during the transition to remote learning, particularly amid the COVID-19 pandemic. Evaluating features such as media upload, assignment scheduling, quizzes, and grading, the research identifies key areas of user engagement that require optimization. Effective teacher-student communication emerged as pivotal, underlining the need for tailored training programs to enhance teachers' proficiency with LMS features. Network stability also significantly influenced user experience. The study's insights emphasize the LMS's potential as a powerful educational tool and highlight specific areas for improvement. Recommendations include broader data collection across institutions, facilitating a nuanced understanding of LMS adoption curves. Moreover, targeted training initiatives are crucial, ensuring educators' comprehensive grasp of LMS functionalities. These findings provide a foundational framework for refining online education practices, promoting a more seamless and effective learning environment for educators and students alike.

Impact of innovative marketing strategy behind Balaji wafers brand profitability in Pune city

Balaji Wafers' success attributed to its innovative marketing strategies, which include word-of-mouth advertising, quality control checks, and a dedication to sustainability. The company's regional focus and distribution strategy may be responsible for its leadership in Gujarat and Maharashtra. Balaji has succeeded in growing its market share by offering a greater selection of goods and services at competitive prices. Current trends should be included, social media platforms should be used actively, and sustainable practises should be used. The Innovation Adoption Curve clearly shows Balaji's astute product strategies. Future growth is possible if local as well as Internaltional presence is maximised, undiscovered markets are investigated, and corporate social responsibility is given top priority. The importance of consumer-centric marketing as a crucial element in establishing long-term success in fiercely competitive industries is highlighted by this case study.

Generative artificial intelligence and large language models for digital banking: First outlook and perspectives

After several years of steady progress, the Generative artificial intelligence (AI) and large language models (LLMs, their applications to text) fields have accelerated tremendously since the end of 2022 and the public launch of ChatGPT. This is due to record-breaking model sizes and performances in the last couple of months, triggering unprecedented adoption curves from end users across the world. Even though regulators reacted fast, sharing their first recommendations, auditing emerging players, amending their AI regulation drafts or launching dedicated working groups, these efforts will require several months or years to come to fruition. There are multiple reasons for this. LLMs are complex technological objects made of gigantic foundational models trained on enormous quantities of texts, coupled with dedicated interfaces and action agents. They present a huge potential to perform high varieties of tasks with very high quality but also important risks in terms of costs, content accuracy, transparency, data privacy, security and ethics. Finally, the current ecosystem of stakeholders is very dynamic but also immature. In this uncertain context, the digital banking industry has been reacting ambivalently, with major players banning employee access to ChatGPT and publicly communicating on new LLM initiatives at the same time. This can be explained by the huge potential offered by these technologies to transform their business, coupled with many open questions in terms of technological set-up, usage, compliance and profitability. As these technologies seem to be too transformative for the industry incumbents to just wait and see, they should start creating the right conditions to learn how to use them, by identifying relevant use cases, choosing adapted and simple solutions, designing relevant user experiences, building the right teams, environment, data sets and operating model, and actively engaging in regulatory conversations.

Technology diffusion and uneven development

We propose a conceptualisation of the process of technology adoption that takes into account the uneven relative costs of technology implementation, especially country differences in wage levels. The novelties and contributions of our approach are the following. First, we introduce a dynamic macroeconomic model of technology diffusion, which is the first to directly account for the difference in factor cost proportions in an endogenous cross-country setting. Second, we utilise the Cross-country Historical Adoption of Technology (CHAT) and Penn World Table databases to calibrate the model using non-linear approximation across countries and technologies, which explains about 50% of the variability in technology density. Third, the results of the calibrated model offer a new insight into the dynamics and patterns of technology diffusion of differently developed countries, offering both an approximation of the average technology adoption across differently developed countries over time and an approximation of the relative technology density adoption curves, which are country specific generalisations of the logistic curves and depend highly on the general level of development and wage levels.

A Cognitive Model for Technology Adoption

The widespread adoption of advanced technologies, such as Artificial Intelligence (AI), Machine Learning, and Robotics, is rapidly increasing across the globe. This accelerated pace of change is drastically transforming various aspects of our lives and work, resulting in what is now known as Industry 4.0. As businesses integrate these technologies into their daily operations, it significantly impacts their work tasks and required skill sets. However, the approach to technological transformation varies depending on location, industry, and organization. However, there are no published methods that can adequately forecast the adoption of technology and its impact on society. It is essential to prepare for the future impact of Industry 4.0, and this requires policymakers and business leaders to be equipped with scientifically validated models and metrics. Data-driven scenario planning and decision-making can lead to better outcomes in every area of the business, from learning and development to technology investment. However, the current literature falls short in identifying effective and globally applicable strategies to predict the adoption rate of emerging technologies. Therefore, this paper proposes a novel parametric mathematical model for predicting the adoption rate of emerging technologies through a unique data-driven pipeline. This approach utilizes global indicators for countries to predict the technology adoption curves for each country and industry. The model is thoroughly validated, and the paper outlines highly promising evaluation results. The practical implications of this proposed approach are significant because it provides policymakers and business leaders with valuable insights for decision-making and scenario planning.

Smart and Automated Infrastructure Management: A Deep Learning Approach for Crack Detection in Bridge Images

Artificial Intelligence (AI) and allied disruptive technologies have revolutionized the scientific world. However, civil engineering, in general, and infrastructure management, in particular, are lagging behind the technology adoption curves. Crack identification and assessment are important indicators to assess and evaluate the structural health of critical city infrastructures such as bridges. Historically, such critical infrastructure has been monitored through manual visual inspection. This process is costly, time-consuming, and prone to errors as it relies on the inspector’s knowledge and the gadgets’ precision. To save time and cost, automatic crack and damage detection in bridges and similar infrastructure is required to ensure its efficacy and reliability. However, an automated and reliable system does not exist, particularly in developing countries, presenting a gap targeted in this study. Accordingly, we proposed a two-phased deep learning-based framework for smart infrastructure management to assess the conditions of bridges in developing countries. In the first part of the study, we detected cracks in bridges using the dataset from Pakistan and the online-accessible SDNET2018 dataset. You only look once version 5 (YOLOv5) has been used to locate and classify cracks in the dataset images. To determine the main indicators (precision, recall, and mAP (0.5)), we applied each of the YOLOv5 s, m, and l models to the dataset using a ratio of 7:2:1 for training, validation, and testing, respectively. The mAP (Mean average precision) values of all the models were compared to evaluate their performance. The results show mAP values for the test set of the YOLOv5 s, m, and l as 97.8%, 99.3%, and 99.1%, respectively, indicating the superior performance of the YOLOv5 m model compared to the two counterparts. In the second portion of the study, segmentation of the crack is carried out using the U-Net model to acquire their exact pixels. Using the segmentation mask allocated to the attribute extractor, the pixel’s width, height, and area are measured and visualized on scatter plots and Boxplots to segregate different cracks. Furthermore, the segmentation part validated the output of the proposed YOLOv5 models. This study not only located and classified the cracks based on their severity level, but also segmented the crack pixels and measured their width, height, and area per pixel under different lighting conditions. It is one of the few studies targeting low-cost health assessment and damage detection in bridges of developing countries that otherwise struggle with regular maintenance and rehabilitation of such critical infrastructure. The proposed model can be used by local infrastructure monitoring and rehabilitation authorities for regular condition and health assessment of the bridges and similar infrastructure to move towards a smarter and automated damage assessment system.

Single-port robotic partial nephrectomy: impact on perioperative outcomes and hospital stay.

Single-port (SP) robotic surgery is a novel technology and is at the beginning of its adoption curve in urology. The goal of this narrative review is to provide an overview of SP-robotic partial nephrectomy (PN) 4 years after the introduction of the da Vinci SP dedicated platform, focusing on perioperative outcomes, length of stay, and surgical technique. A nonsystematic review of the literature was conducted. The research included the most updated articles that referred to SP robotic PN. Since its commercial release in 2018, several institutions have reproduced robotic PN by using the SP platform, both via a transperitoneal and a retroperitoneal approach. The published SP-robotic PN series are generally based on preliminary experiences by surgeons who had previous experience with conventional multi-arms robotic platforms. The reported outcomes are encouraging. Overall, three studies reported that SP-robotic PN cases had nonsignificantly different operative time, estimated blood loss, overall complications rate, and length of stay compared to the conventional 'multi-arms' robotic PN. However, in all these series, renal masses treated by SP had overall lower complexity. Moreover, two studies underlined decreased postoperative pain as a major pro of adopting the SP system. This should reduce/avoid the need for opioids after surgery. No study compared SP-robotic versus multi-arms robotic PN in cost-effectiveness. Published experience with SP-robotic PN has reported the feasibility and safety of the approach. Preliminary results are encouraging and at least noninferior with respect to those from the multi-arms series. Prospective comparative studies with long-term oncologic and functional results are awaited to draw more definitive conclusions and better establish the more appropriate indications of SP robotics in the field of PN.

Big data curation framework: Curation actions and challenges

Big data curation represents an emerging topic of inquiry but still in an early phase along its adoption curve. The term big data itself is a nebulous concept, and the differences between small data curation and big data curation are nuanced. The goal of this research is to provide a theoretical framework that identifies big data curation actions and associated curation challenges. This study is based on the practices of big data research and data curation by systematically examining literature. The outcome of the study includes the big data curation framework that provides overview of curation activities and concerns that are essential to perform such activities. The study also provides practical implications for libraries, archives, data repositories and other information organisations that concerns the issue of big data curation as big data presents a multidimensional array of exigencies in relation to the mission of those organisations.

DeepSolar++: Understanding residential solar adoption trajectories with computer vision and technology diffusion models

•We use computer vision and technology diffusion models to study PV adoption over time •Lower-income communities are delayed in PV adoption onset and saturate more quickly •Performance-based incentives are associated with high PV saturation for low income •Property tax incentives are associated with high PV saturation but not for low income Although the United States has generally experienced a rapid adoption of residential photovoltaics (PV), many communities are lagging behind. To investigate why, we developed a computer vision model that addresses the challenge of low image resolution to identify the installation year of PVs from historical aerial and satellite images. We used the model to construct a granular spatiotemporal dataset of PV deployment across 46 US states and analyzed these data from a technology adoption life cycle perspective. Our analysis of adoption curves and phases showed that low-income communities are not only delayed in their adoption onset but also saturate more quickly at lower levels. We further demonstrated the value of our data via an illustrative analysis of financial incentives and found that performance-based incentives are positively associated with saturated adoption levels—particularly for lower-income communities. Our study highlights the importance of analyzing PV adoption trajectories from dynamic perspectives to inform policy design. Although the United States has generally experienced a rapid adoption of residential photovoltaics (PV), many communities are lagging behind. To investigate why, we developed a computer vision model that addresses the challenge of low image resolution to identify the installation year of PVs from historical aerial and satellite images. We used the model to construct a granular spatiotemporal dataset of PV deployment across 46 US states and analyzed these data from a technology adoption life cycle perspective. Our analysis of adoption curves and phases showed that low-income communities are not only delayed in their adoption onset but also saturate more quickly at lower levels. We further demonstrated the value of our data via an illustrative analysis of financial incentives and found that performance-based incentives are positively associated with saturated adoption levels—particularly for lower-income communities. Our study highlights the importance of analyzing PV adoption trajectories from dynamic perspectives to inform policy design.

A Propensity-Matched Comparison of the Perioperative Outcomes Between Single-Port and Multi-Port Robotic Assisted Partial Nephrectomy: A Report from the Single Port Advanced Research Consortium (SPARC).

Purpose: Single-port (SP) robotic surgery is a new technology and early in its adoption curve. The goal of this study is to compare the perioperative outcomes of SP to multi-port (MP) robotic technology for partial nephrectomy. Materials and Methods: This is a prospective cohort study of patients who have undergone robot-assisted partial nephrectomy using SP and MP technology. Baseline demographic, clinical, and tumor-specific characteristics and perioperative outcomes were compared using χ2, t-test, and Mann-Whitney U test in the overall cohort and in a 1:1 propensity score-matched cohort, adjusting for baseline characteristics. Results: After propensity matching, 146 SP patients were matched with 146 MP patients. SP and MP groups had similar mean age (58 ± 12 years vs 59 ± 12 years; p = 0.606) and proportion of men (54.11% vs 52.05%; p = 0.725). The SP had a longer mean ischemia (18.29 ± 10.49 minutes vs 13.79 ± 6.29 minutes; p < 0.001). Estimated blood loss (EBL) and length of hospital stay (LOS), operative time, positive margin rate, and any complication rate were similar between the two groups. Conclusions: SP partial nephrectomy had a longer ischemia time, and a comparable LOS, EBL, operative time, positive margin rates, and complication rates to MP. These early data are encouraging. However, the role of SP requires further study and should evaluate safety and long-term data when compared with the standard MP technique.

Mapping the development of various solar thermal technologies with hype cycle analysis

This paper focuses on the technological development of solar thermal technologies through the analysis of patent records and search traffic. First, various solar thermal technologies are reviewed and discussed. Then, the related published patents are analysed. To improve the accuracy of patents retrieval, a new framework is proposed and employed based on the combination of keywords and keycodes. In this new framework, the proposed methodology can enhance the retrieved patent pool resolution by eliminating the patent with the relevant title but irrelevant technology scopes. Furthermore, new technological features such as technology attractiveness and acceleration are introduced and applied to solar thermal technologies. These two introduced features help to recognize interested areas by industrial players in the technological field. The pace of technology being developed and becoming matured could be identified by how fast the industry invests in technology by protecting its intellectual properties. The logistic S-curve is employed for assessing the current state of solar technologies on the technology life cycle. The market diffusion of each solar thermal technology is assessed by developing adoption curves using search traffic. Finally, the hype cycle of solar thermal technologies is demonstrated by combining the two technological assessment graphs. Through the proposed methodology, 8740 related patents are retrieved and screened. The analysis shows that Photovoltaic-thermal has the highest share in published patents and the parabolic trough and evacuated tube collectors are in their maturity phase. The parabolic dish collector is identified as one of the highest accelerated technologies and also located near the maturity phase. Therefore, it can be predicted that the next technology reaching the maturity phase would be the parabolic dish collector. Overall, all solar-heat driver cycles are in their growth stage, but the Rankine cycle is more developed. Text analysis determines the main challenge with cycles is the operation and maintenance that should be overcome to meet the maturity phase.

Analysis of a Teleworking Technology Adoption Case: An Agent-Based Model

An agent-based model for teleworking technology adoption is presented, including the risk of office closure in the event of a lockdown. It analyzes an adoption case using simulations and can be adapted to other cases and teleworking promotion strategies to contribute to sustainability. Simulations produce smooth sigmoidal curves that reasonably fit to real adoption curves. The simulation results suggest that the main reason for the observed increase in the adoption rate is the increase in the risk of office closures, the consequent increase in the usefulness of teleworking technology, and the increase in external influence that motivates them.

¿ES POSIBLE MEJORAR LA FERTILIDAD CON PROTOCOLOS DE PROESTRO PROLONGADO? BASES FISIOLÓGICAS, RESULTADOS Y NUEVAS PERSPECTIVAS

The objective of this work was to carry out a bibliographic search of all the programs with prolonged proestrus in the fertility of bovine cattle dedicated to meat production. The use of artificial insemination at fixed time (IATF) in cattle has not stopped growing in the countries of the region during the last 20 years, demonstrating that it is a technology that is here to stay. Its adoption curve is in a stage of maturity, having first been adopted by the most innovative and today it is already used by more pragmatic or conservative producers. Therefore, in this review we will emphasize the protocols that we have worked the most in recent years and with which high pregnancy rates have been achieved.

A Tale of Three Technologies: A Survival Analysis of Municipal Adoption of Websites, Twitter, and YouTube

This study addresses and compares the adoption rate of three technologies by Dutch municipalities: the adoption of websites between 1994 and 2000, the adoption of Twitter between 2008 and 2018, and the adoption of YouTube between 2006 and 2018. It analyses the municipal adoption curves of websites, Twitter, and YouTube, relating to DOI theory. Survival analysis is conducted of Twitter and YouTube adoption by municipalities, assessing the relationship between technology adoption and organizational and environmental factors. Additional survival analysis is conducted of the relationship between municipal adoption, media coverage of the technology, and user engagement with the technology. Media coverage appears to be a strong predictor for municipal adoption of websites, Twitter, and YouTube, as well as the level of user engagement. Twitter was adopted the fastest by municipalities, followed at a greater distance by website and YouTube adoption. Other findings include that municipalities already using YouTube were quicker to adopt Twitter, but not the other way around. Recently amalgamated municipalities were slower to adopt Twitter, as well as municipalities with a relatively larger presence of IT professionals.

Exploring the journey of BIM in the Indian AECO industry (2008–2022) an excelize perspective

The journey of Building Information Modeling (BIM) in India has been from skepticism to acceptance to leveraging the higher potentials of BIM. Excelize Software, serving the global Architecture, Engineering, Construction and Operations industry from their headquarters in Aurangabad, has been a witness to and participant in this transformational journey for nearly last two decades. This paper is aimed at exploring their journey of offering BIM services to a wide variety of projects that were undertaken in the construction industry of India during this period. The construction sector has historically grappled with the challenges of managing productivity with respect to completion of projects on time, management of labor and materials and maintenance of built assets. BIM made its foray into the Indian construction industry towards the end of the first decade in this millennium. Typical of any technology adoption curve, BIM was met with a fair share of skepticism across stakeholders. Yet, awareness about its potential benefits, scope of application in design and management of various aspects of the project and subsequent adoption has grown slowly yet steadily in the past decade. Data published by Invest India (Source: https://www.investindia.gov.in/sector/construction) states that the construction Industry in India is expected to reach $1.4 Tn by 2025. Urban population is said to contribute 75% of GDP (63% present), and 68 cities will have a population of more than 1 million; up from 42 today. The construction industry market in India works across 250 sub-sectors with linkages across sectors. Given these projections, India is likely to go through massive urbanization and economic growth. All construction activities that result from this growth will need proper planning, scheduling, design and information management. Meeting these needs will require a strong framework for implementation, a sophisticated level of design communication, project co-ordination and documentation as well as robust technology tools. BIM is well-designed to be the first building block of such a framework that can radically support and enhance the massive needs of the construction sector in India. Through the various projects executed by Excelize and a set of parameters that reveal the BIM adoption metrics for the said period, this paper will navigate the journey of BIM from ‘We are not interested’ to ‘Nice to have tool’ to ‘Must have technology aid’ to ‘Exploring BIM for greater possibilities.’

Diffusion of Solar PV Energy in the UK: A Comparison of Sectoral Patterns

The paper applies innovation diffusion models to study the adoption process of solar PV energy in the UK from 2010 to 2021 by comparing the trajectories between three main categories, residential, commercial, and utility, in terms of both the number of installations and installed capacity data. The effect of the UK incentives on adoptions by those categories is studied by analyzing the timing, intensity, and persistence of the perturbations on adoption curves. The analysis confirms previous findings on PV adoption, namely the fragile role of the media support to solar PV, the ability of the proposed model to capture both the general trend of adoptions and the effects induced by ad hoc incentives, and the dramatic dependence of solar PV from public incentives. Thanks to the granularity of the data, the results reveal several interesting aspects, related both to differences in adoption patterns depending on the category considered, and to some regularities across categories. A comparison between the models for number of installations and for installed capacity data suggests that the latter (usually more easily available than the former) may be highly informative and, in some cases, may provide a reliable description of true adoption data.

Digital Strategic Governance in the Terrain of Ongoing New Normal: Prospects and Challenges for the Possible Next Normals

Abstract: For both businesses and consumers, the COVID-19 conundrum provides an early view into a future where all contacts will be conducted via digital technology, and the adoption curve will be accelerated almost immediately. At this moment in time, businesses have the potential to learn and advance at an unprecedented rate; therefore, playing it safe is often the wrong strategy. Many businesses are ill-equipped to keep up with the rapid rate of change in the marketplace. Digital technology adoption is strongly linked to value generation, according to a study by McKinsey &amp; Co. Amid a crisis, it is crucial to push your digital agenda aggressively. During a crisis, mergers and acquisitions (M&amp;A) can provide access to new goods, services, solutions, and new market and client segments while also helping organizations acquire people and increase capabilities. However, Richard Branson, CEO of McKinsey &amp; Co., maintains that behaving boldly does not mean acting rashly. Exploration has become both required and expected in the wake of the COVID-19 issue. Making the most of this real-world experience will require mastering the art of productivity maintenance. Consider approaches to standardize your learning to promote the growth of digital solutions in the core business activities. It is challenging to keep up with the pace of change in the digital age, so look for ways to move projects along with more quickly. These hugely scaled digital projects are new digital projects for businesses in crisis mode. Many problems have arisen due to the rapid expansion of digital processes and interfaces to full size. It also provides the opportunity to learn where value is created in real-time. Businesses must find ways to streamline operations and avoid becoming overburdened with work. During this phase of forced virtualization, businesses have the opportunity to collect and evaluate client data in real-time for the first time. In studying the interaction effects of various factors, proliferation presents an ideal opportunity to proliferate. Until the crisis subsides, you can increase the possibility of keeping a more significant number of customers by combining unproductive operations and goods while aggressively fixing growing flaws with your services. Consider how you can best accommodate the learning styles of your talented staff