Posted on Saturday, February 3, 2024

Digitization and Remote Agency: A New Era

Insights from “Blockchain Smart Contracts and the Law”

Digitization and remote agency have continued to accelerate as software eats the world. With the advent of advanced technologies, businesses are now able to operate remotely, transcending geographical boundaries and time zones.

The digitization of business processes has revolutionized the way organizations operate. It has enabled them to streamline operations, improve efficiency, and deliver better customer experiences. From cloud computing to artificial intelligence, digital technologies are reshaping the business landscape. One of the most significant developments in this digital revolution is the emergence of blockchain technology. Blockchain, with its decentralized and transparent nature, offers a secure platform for conducting business transactions. It eliminates the need for intermediaries, thereby reducing costs and increasing efficiency.

A key application of blockchain technology is smart contracts. These are self-executing contracts with the terms of the agreement directly written into code. They automatically execute transactions when predefined conditions are met, eliminating the need for manual intervention. However, as with any new technology, blockchain and smart contracts present legal challenges. Understanding these challenges and how to navigate them is crucial for businesses looking to leverage these technologies.

This is where the book “Blockchain Smart Contracts and the Law” comes in. This comprehensive guide provides an in-depth understanding of the legal aspects of blockchain and smart contracts. It offers valuable insights into how businesses can mitigate risks and ensure compliance while reaping the benefits of these technologies. As we move further into the digital age, the role of remote agency will continue to evolve. Businesses that adapt to these changes and leverage new technologies will be the ones that thrive.

In conclusion, the digitization of business processes and the advent of technologies like blockchain and smart contracts are transforming the concept of remote agency. To navigate this new landscape, it is essential to understand the legal implications of these technologies.Equip yourself with the knowledge to navigate this new era. Get your copy of “Blockchain Smart Contracts and the Law” today and stay ahead of the curve.

Posted on Friday, December 1, 2023

ML Fairness

Ethics in action

Fairness is a moral or ethical concept that involves giving each person what he or she deserves or what is appropriate for the situation. Fairness can be applied to individual actions, interpersonal relations, social institutions, and public policies. Fairness can also be understood as a virtue that guides one’s conduct and character. Law is a legal concept that involves the rules and principles that govern the behavior of individuals and groups within a society. Law can also be understood as a system of authority and enforcement that regulates social order and justice.

Fairness and law are related but distinct concepts. Fairness can be seen as a moral foundation or justification for law, as well as a criterion or standard for evaluating law. Law can be seen as a formal expression or implementation of fairness, as well as a means or instrument for achieving fairness. However, fairness and law can also diverge or conflict in some cases. Fairness can be subjective or relative, depending on one’s perspective, values, or interests. Law can be objective or absolute, depending on its source, validity, or universality. Fairness can be flexible or adaptable, depending on the context, circumstances, or consequences. Law can be rigid or fixed, depending on its form, content, or application.

ML Fairness

Fairness as an ethical concept and fairness as a legal concept are not identical or interchangeable. They can complement or support each other, but they can also differ or oppose each other. A fair law is one that is consistent with the ethical principles and values of fairness. A fair action is one that is in accordance with the legal rules and norms of fairness. But a law may not be fair if it violates the ethical rights or interests of some people. And an action may not be fair if it disregards the legal duties or obligations of others.

Machine Learning (ML) technology is a branch of artificial intelligence that enables computers to learn from data and make predictions or decisions. However, ML can also produce unethical results, unfair or biased outcomes that discriminate against certain groups or individuals based on their characteristics, such as race, gender, age, disability, or sexual orientation. Here are some of the issues with fairness resulting from the adoption of ML technology:

Data bias: ML models depend on the quality and quantity of the data they are trained on. If the data is skewed, incomplete, or inaccurate, it can introduce bias into the model and affect its performance and fairness. For example, if a facial recognition system is trained on mostly white faces, it may not recognize faces of other races accurately.
Algorithmic bias: ML models are designed and implemented by humans, who may have their own conscious or unconscious biases that influence the choice of features, algorithms, parameters, and evaluation metrics. For example, if a credit scoring system uses gender as a feature, it may discriminate against women who have lower incomes or less credit history than men.
Predictive bias: ML models make predictions or decisions based on the patterns they learn from the data. However, these patterns may not reflect the true causal relationships or the underlying distribution of the data, leading to errors or inaccuracies that affect some groups more than others. For example, if a recidivism prediction system uses past arrests as a proxy for criminal behavior, it may overestimate the risk of reoffending for minorities who are more likely to be arrested due to systemic racism.
Feedback loop: ML models can influence the behavior of users or systems that interact with them, creating a feedback loop that reinforces or amplifies the existing biases in the data or the model. For example, if a hiring system recommends candidates based on their similarity to previous hires, it may exclude qualified candidates from underrepresented groups and reduce their chances of applying or being hired in the future.
Lack of transparency: ML models are often complex and opaque, making it difficult to understand how they work and why they produce certain outcomes. This can limit the ability of users, regulators, or auditors to monitor, explain, or challenge the model’s decisions and hold it accountable for its fairness and accuracy. For example, if a loan application is rejected by a ML system, the applicant may not know the reason or have an opportunity to appeal.
Lack of interpretability: ML models are often based on statistical correlations rather than causal explanations, making it hard to interpret their results and implications. This can lead to confusion, misunderstanding, or misuse of the model’s outputs and affect the trust and acceptance of users and stakeholders. For example, if a health diagnosis system predicts a high risk of a disease for a patient, the doctor may not know how to communicate or justify this result to the patient or prescribe an appropriate treatment.
Lack of representation: ML models are often developed and deployed by a small group of experts who may not represent the diversity and needs of the users and beneficiaries of the technology. This can result in a mismatch between the model’s assumptions and objectives and the real-world contexts and scenarios where it is applied. For example, if a speech recognition system is developed by English speakers only, it may not recognize accents or dialects of other languages or cultures.
Lack of participation: ML models are often imposed on users or communities without their consent or involvement in the design and evaluation process. This can undermine their autonomy, dignity, and agency and expose them to potential harms or risks. For example, if a social welfare system uses ML to assess eligibility for benefits, it may violate the privacy and rights of applicants who have no say in how their data is collected or used.
Lack of regulation: ML models are often subject to little or no legal or ethical oversight or governance that can ensure their compliance with standards and principles of fairness and justice. This can create loopholes or gaps that allow for abuse or exploitation of the technology by malicious actors or unintended consequences. For example, if a political campaign uses ML to target voters with personalized messages, it may manipulate their opinions or preferences without their awareness or consent.
Lack of education: ML models are often used by people who have limited knowledge or skills in ML or its ethical implications. This can impair their ability to critically evaluate and responsibly use the technology and its outcomes. For example, if a teacher uses ML to grade students’ assignments, they may not be able to detect errors or biases in the model’s feedback or provide adequate guidance and support to students.

Are you a technical, business or legal professional who works with technology adoption? Do you want to learn how to apply ethical frameworks and principles to your technology work and decision-making, understand the legal implications and challenges of new technologies and old laws, and navigate the complex and dynamic environment of technology innovation and regulation? If so, you need to check out this new book: Ethics, Law and Technology: Navigating Technology Adoption Challenges. This book is a practical guide for professionals who want to learn from the experts and stay updated in this fast-changing and exciting field.

Posted on Saturday, November 11, 2023

Organizational Readiness/Maturity Considerations for Adoption of Blockchain & DAOs

Blockchain - technology vs organization (DAOs)

Achieving a digitalized economy assumes a process of digital transformation with digital technologies being adopted and new management techniques to effectively manage the identification of suitable technologies; match technologies with organizational opportunities; and then administer the organization in the digitalized economy. Digital transformation involves new concepts, radical innovation, and radical organizational change across multiple organizational dimensions. Blockchains can be considered a form of digital transformation for organizations. An aspect of the radical nature of blockchains flows from the capabilities it can provide for trustworthy transactions between organizations. Blockchains are associated with a decentralized implementation architecture which often contradicts centralization assumptions inherent in both IT infrastructure (e.g., Client-Server) and in organizational processes and management structures. Blockchains also enable Decentralized Autonomous Organizations (DAOs) which may be better considered as a software implementation of organizational governance rather than a typical technology for process automation.

Blockchain Technology (including DAOs)

This creates opportunities for new business models by disintermediation of some parties to traditional transaction flows in the same industry or supply chain. Multiple parties have to agree to adopt the new style of transactions. Decentralization is an architectural approach to restructuring the power and influence of elements within an economic system. Early approaches to decentralized distributed computing (such as Autonomous Decentralized Systems (ADSs) focused on building operational resilience for large-scale infrastructure, more recent DAO innovations have focussed on the organizational aspects. Both intra-organizational and inter-organizational technology adoption tend to be analyzed with similar frameworks such as the Technology, Organization, and Environment (TOE) framework. While most technology adoption frameworks focus on a single organization, blockchain exhibits network effects when deployed across multiple organizations.

Blockchain ( & DAOs) in or between organizations

The digital transformation of an organization for the digitalized economy goes beyond mere technology adoption within existing organizations and includes new forms of digital native organizations such as DAOs. Scorecards and metrics have been applied in many areas within organizations from accounting to ethics; but multiparty technology adoption has an additional scope that metrics within a single organization do not. Metrics and scorecards help organizations evaluate their readiness for blockchain implementations. Organizational readiness and maturity metrics for effectively utilizing blockchains have to address the broad range of business considerations that management should consider when evaluating opportunities for digital transformation via blockchain. A digitalized economy, and blockchains, need readiness metrics that apply across organizations.

For additional information refer to Wright, S. A. (2022). Organizational Readiness/Maturity Considerations for Blockchain Adoption. In Handbook of Research on Digital Transformation Management and Tools (pp. 344-365). IGI Global.

Posted on Wednesday, November 1, 2023

Ethical Responsibilities in ML

Ethics in Action

Machine learning (ML) is a branch of artificial intelligence that enables computers to learn from data and make predictions or decisions. However, ML can also raise ethical issues and challenges that affect individuals and society. Ethical responsibilities lie with the human stakeholders associated with implementing and adopting ML.

Ethical Responsibilities in ML

Here are some of the types of entities that bear ethical responsibilities associated with the adoption of ML technologies:

– ML developers: ML developers are the people who design, implement, and test ML models and systems. They have an ethical responsibility to ensure that their models are accurate, reliable, transparent, and fair, and that they do not cause harm or discrimination to others. They also have a responsibility to document and communicate their methods, assumptions, limitations, and outcomes of their models to users and stakeholders.

– ML users: ML users are the people who interact with or benefit from ML models and systems. They have an ethical responsibility to use ML in a responsible and informed manner, and to respect the rights and interests of others who may be affected by their actions. They also have a responsibility to provide feedback and report any errors or biases they encounter in ML systems. Some users of ML may have additional professional ethical constraints impacting their use of ML.

– ML organizations: ML organizations are the entities that develop, deploy, or provide ML models and systems. They have an ethical responsibility to ensure that their ML products and services are aligned with their mission, vision, and values, and that they do not harm or exploit their customers, employees, partners, or society at large. They also have a responsibility to monitor, audit, and evaluate their ML systems for performance, quality, and fairness, and to address any issues or risks that arise.

– ML regulators: ML regulators are the entities that oversee or govern the use of ML models and systems. They have an ethical responsibility to ensure that ML complies with legal and ethical standards and principles, and that it protects the rights and interests of individuals and society. They also have a responsibility to establish clear and consistent rules and guidelines for ML development and deployment, and to enforce them effectively.

– ML researchers: ML researchers are the people who conduct scientific or academic studies on ML models and systems. They have an ethical responsibility to ensure that their research is rigorous, valid, reliable, and transparent, and that it contributes to the advancement of knowledge and human well-being. They also have a responsibility to respect the privacy and dignity of their research subjects or participants, and to disclose any conflicts of interest or potential harms or benefits of their research.

– ML educators: ML educators are the people who teach or train others on ML models and systems. They have an ethical responsibility to ensure that their education is accurate, comprehensive, and accessible, and that it fosters critical thinking and ethical awareness among their students or trainees. They also have a responsibility to promote diversity and inclusion in ML education, and to encourage responsible and informed use of ML among their students or trainees.

– ML communities: ML communities are the groups of people who share a common interest or goal related to ML models and systems. They have an ethical responsibility to foster a culture of collaboration, innovation, and excellence in ML development and use. They also have a responsibility to engage with other stakeholders and communities on ML issues and challenges, and to advocate for ethical values and principles in ML.

– ML beneficiaries: ML beneficiaries are the people who receive positive outcomes or impacts from ML models and systems. They have an ethical responsibility to acknowledge the sources and contributions of ML to their well-being or success. They also have a responsibility to share the benefits of ML with others who may not have access or opportunity to use it.

– ML victims: ML victims are the people who suffer negative outcomes or impacts from ML models and systems. They have an ethical responsibility to seek justice or redress for the harms or injustices they experience due to ML. They also have a responsibility to raise awareness and voice their concerns about the issues or challenges they face due to ML.

– ML critics: ML critics are the people who question or challenge the assumptions, methods, outcomes, or implications of ML models and systems. They have an ethical responsibility to provide constructive criticism and alternative perspectives on ML development and use. They also have a responsibility to support evidence-based arguments and respectful dialogue on ML issues and challenges.

Posted on Monday, October 16, 2023

S-Curve Adoption Models

Technology commercialization

S-Curve adoption models are frequently referenced to describe the adoption of new technologies. The S-curve is a graphical representation of how a new technology diffuses through a population over time. This is a contrast to the market perspectives which are typically only valid at a given point in time. Both can be affected by the specific market strategies of technology proponents. The curve has an S-shape because it starts slowly, then accelerates, and then slows down again as it reaches saturation. The S-curve can be divided into four phases:

The introduction phase is when the technology is first invented or introduced to the market, and only a few innovators adopt it.
The growth phase is when the technology gains popularity and acceptance among early adopters and early majority, and its adoption rate increases rapidly.
The maturity phase is when the technology reaches its peak adoption among late majority, and its adoption rate slows down as it approaches saturation.
The decline phase is when the technology becomes obsolete or replaced by a newer technology, and its adoption rate decreases as only laggards remain.

S-Curve

Several mathematical formulae for S-Curve Adoption Models have been developed in modeling various physical phenomena and can also be applied for technology adoption. The main models are:

Logistic Curve: This S-Curve Adoption Model is based on a differential equation that accounts for the limited potential market size and the diminishing returns of adoption. The logistic curve can also be divided into four phases similar to the S-curve: introduction, growth, maturity, and decline.The logistic curve can be expressed by the formula:y=L/(1+e^(-k(x-x_0)) ) where y is the cumulative adoption level, L is the maximum potential market size, k is the growth rate, x is the time variable, and x_0 is the inflection point where the adoption rate reaches its maximum.
Bass Diffusion Model: This S-Curve Adoption Model assumes that there are two types of adopters: innovators and imitators. Innovators are those who adopt the technology independently of others, while imitators are those who adopt the technology based on social influence or word-of-mouth. The model can also generate an S-shaped curve similar to the S-curve and the logistic curve. The Bass Diffusion model can be expressed by the formula: f(t)=(p+qF(t))/(1+qF(t)) where f(t) is the probability of adoption at time t, p is the coefficient of innovation, q is the coefficient of imitation, and F(t) is the cumulative fraction of adopters at time t.

While S-Curve Adoption Models provide some insight into the deployment scale of a particular technology over time, they do not provide insight into any individual or aggregate decision where market participants would grapple with the ethical considerations of technology adoption.

Posted on Sunday, September 10, 2023

IoT Blockchains for Digital Twins

Digital twins (DTs) have emerged as a critical concept in cyberspace infrastructure. DTs are fit-for-purpose digital representations of an observable manufacturing element with a means to enable convergence between the element and its digital representation at an appropriate rate of synchronization. Human DTs (HDTs) are also emerging for healthcare and social interaction. Blockchain Digital Twins (BDTs) are a subset of the DTs that incorporate blockchains to provide additional trust-based features, typically relying on underlying capabilities of IoT Blockchains. The ITU-T recognized DTs as a use case driving additional requirements for 6G features.

Blockchain Digital Twins

The value provided by DTs relies on their fidelity in representation. A dynamic DT maintains a digital representation of the current state of the physical object. Blockchains provide trust assurance mechanisms, particularly where multiple parties are involved. For users of DTs to benefit from this digital representation, they must trust that it provides an adequate representation for their purposes. The expected life cycle operations of the IoT, blockchain, and DT need to be considered to develop economically useful blockchain digital twin (BDT) models. Blockchains can be used for assurance of authenticity of actions by DT. BDTs do not exist in isolation, but rather within a DT environment (DTE). A metaverse as a collection of virtual worlds may include virtual worlds that are DTEs ie capable of supporting the operation of DTs within them. A DTE may include multiple DTs of different objects to enable interactions between these objects to be evaluated in both virtual reality and mixed reality cases.

To populate DTEs with multiple DTs requires industrialized tooling to support the rapid creation of DTs.The industrialization of DT creation requires frameworks, architectures, and standards to enable interoperability between DTs and DTEs. While blockchains developed from fintech applications, BDT applications will have different requirements for blockchain features and performance – e.g. in notions of privacy.

For further information refer to Wright, S. A. (2023). IoT Blockchains for Digital Twins. In Role of 6G Wireless Networks in AI and Blockchain-Based Applications (pp. 57-79). IGI Global.

Posted on Thursday, August 10, 2023

Perspective Dimensions

The Power of Perspective can be distinguished in multiple dimensions

Perspectives are the lenses through which we see the world whether you are a school student, business professional, social entrepreneur or concerned with your own creativity. Perspectives shape how we interpret information, solve problems, and make decisions. Perspectives can be distinguished by three factors: (i) the data they observe, (ii) the methods of processing that data, and (iii) the values attributed to the outcomes of that data processing. The Power of Perspective is that taking diverse perspectives exposes assumptions and inconsistencies, enabling better problem-solving. Consider how these dimensions of a perspective can vary between different perspectives and shed new insight into the problems you confront.

Perspective Dimenions

Explicit Data Observations exposes the Power of perspective

A specific perspective focuses on the data that it deems relevant. The selected data becomes the basis for the analysis and evaluation associated with that perspective, framing the limits of what that perspective can deliver. Selecting some data for analysis means rejecting other data, and being explicit about the data selection exposes potential blind spots. Consider how these different perspectives are constrained by the data they select.

From a scientific perspective, data is typically primary observations from carefully designed experiments.
From a technological perspective, data might include design objectives, environmental measurements, secondary data on component characteristics etc.
From a market perspective, market data may be primary observations or secondary studies concerning the need for or intended use of a new product or service.
From a regulatory perspective, secondary data on industry performance is typically collected through regulatorily required reporting.

Explicit Processing Methods demonstrate the skills associated with specific perspectives

A specific perspective may have particular skills associated with it that provide methods for processing or analyzing the data selected by that perspective. Some perspectives utilize data analysis methods that are very quantitative with some degree of implied precision, while others are more qualitative recognizing e.g. different categories of data.

From a scientific perspective, scientific methods develop models of the world which enable predictions that can be tested for validity, falsifiability etc.
From a technological perspective, design methods include industry best practices, use of scientific models, calculations of expected performance in various conditions
From a market perspective, key requirements and product concepts can be articulated and tested prior to implementation. Such testing can also be used in the estimation of expected market size, value etc.
From a regulatory perspective, economic studies, judicial outcomes and other policy considerations can be used to guide the development of policies affecting specific industries or technologies

The power of perspective is often seen best in divergent valuation approaches

Perspectives use values to gauge the results of their analysis. Values can be idiosyncratic or informed by some explicit rule to associate some meaning of “goodness” to an analytic output. For example, a two perspectives may both look at real estate sales data and conclude that there is a trend of rising prices. One perspective may interpret this as a good result because the value of that perspective holder’s real estate wealth is increasing. A different perspective may conclude this trend is a social disaster as young folks forming new households would not be able to afford to purchase a house. Consider the valuation mechanisms that these perspectives use:

From a scientific perspective, scientific progress is achieved through the dissemination and adoption by others of new models of model extensions.
From a technological perspective, successful implementations are typically evaluated in terms of the delivery of design objectives and various performance metrics such as cost or efficiency
From a market perspective, market success is usually measured in terms of market adoption and value received.
From a regulatory perspective, regulatory outcomes are typically valued in terms of alignment with policy objectives and more general social and legal concepts such as fairness

Conclusions

Perspective-taking is not only a social skill but also a cognitive skill. It enables you to see things differently, think creatively, and solve problems more effectively. Perspective-taking can also help you build rapport, trust, and loyalty with your team and stakeholders. If you are interested in taking this Power of Perspective course, please visit our website for more information and registration details. Don’t miss this opportunity to unlock the Power of Perspective for yourself and your organization.

Posted on Tuesday, July 25, 2023

Technology ethics is important

Technology ethics is important because it helps us address the ethical questions and principles related to the adoption, use and even the development of new technologies and associated products and services.

Technology ethics can help us prevent or mitigate the potential negative impacts of technological products and services, created through technology vulnerabilities, or design flaws, such as loss of control, privacy, and security, that may create chaos or dystopia. Collectivist technology ethics can also help us ensure that technology is fair, healthy, and respectful of the rights and dignity of users, employees, customers, and society at large. Virtue ethics can also help us humanize technology and make it more aligned with our values and goals. Technologies such as artificial intelligence enable us to leverage our capabilities and act at scale. This creates new possibilities, but also new challenges and responsibilities where ethical frameworks can help. Technology ethics can help us earn and maintain trust in technology and its applications. To learn how to apply ethical frameworks and principles to your technology work and decision-making, check out this new book: Ethics, Law and Technology: Navigating Technology Adoption Challenges.

Posted on Tuesday, July 18, 2023

What are Technology Ethics?

Technology ethics is the application of ethical thinking to the practical concerns of technology, especially the adoption of new technology. As new technologies give you more power to act, you have to make choices you didn’t have to make before and are confronted by new situations you have not encountered before. Technology ethics can address issues such as how technology is used, how it affects human beings and society, and what moral values should guide its design and development. Some examples of technology ethics issues are:

How should we protect the privacy and security of personal data in the digital age?
How should we regulate the use of artificial intelligence, biotechnology, and other emerging technologies that may have profound impacts on human life and society?
How should we ensure that technology is accessible,and fair for all people, especially those who are marginalized or disadvantaged?
How should we balance the benefits and risks of technology, especially when it comes to environmental, social, and existential challenges?
How should we foster a culture of responsibility, accountability, and transparency among technology developers, users, and policymakers?

Technology ethics is not only a matter of applying existing ethical principles to new situations, but also accommodating the complexity and diversity of technological innovation. Interdisciplinary collaboration, public engagement, and critical reflection are keystone elements of technology ethics. Technology ethics also challenges us to rethink our own values, assumptions, and perspectives in light of the changing world.

Ethics and the Law

Technologies themselves are inanimate things. The ethical dimension arises from human interactions. Adopting new technologies may have circumstances where the consequences may be difficult to anticipate.

Actionable steps

Are you a technical, business, or legal professional who works with technology adoption? Do you want to learn how to apply ethical frameworks and principles to your technology work and decision-making? Understand the legal implications and challenges of new technologies and old laws? Navigate the complex and dynamic environment of technology innovation and regulation? If so, you need to check out this new book: Ethics, Law and Technology: Navigating Technology Adoption Challenges. This book is a practical guide for professionals who want to learn from an expert and stay updated in this fast-changing and exciting field.

Posted on Sunday, July 16, 2023

Market Research on Technology Adoption

Technology Commercialization

The adoption of new technologies impacts existing markets and may create new market effecting a form of social transformation. Market research firms have developed a number of diverse perspectives focused on the perceived commercial importance associated with the plethora of new technologies vying for attention in the marketplace. These Market Research on Technology Adoption perspectives position the relative commercial relevance/ maturity of multiple technologies to the market of interest. Examples of market research perspectives on technology adoption include:

Gartner Hype Cycle: The curve has an S-shape similar to the S-curve and the logistic curve, but it focuses on the expectations and perceptions of the technology rather than the actual adoption level or market size. The curve can be divided into five phases: innovation trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment, and plateau of productivity.
The innovation trigger is when a potential technology breakthrough or innovation sparks media interest and public curiosity. Often no usable products exist and commercial viability is unproven.
The peak of inflated expectations is when early publicity produces a number of success stories and failures. Some companies take action while others do not. The expectations of the technology are often unrealistic and exaggerated.
The trough of disillusionment is when interest wanes as experiments and implementations fail to deliver. Producers of the technology shake out or fail. Investments continue only if the surviving providers improve their products to the satisfaction of early adopters.
The slope of enlightenment is when more instances of how the technology can benefit the enterprise start to crystallize and become more widely understood. Second- and third-generation products appear from technology providers. More enterprises fund pilots while conservative companies remain cautious.
The plateau of productivity is when mainstream adoption starts to take off. Criteria for assessing provider viability are more clearly defined. The technology’s broad market applicability and relevance are clearly paying off.
Forrester Wave: The Wave plots the providers on two axes: current offering and strategy. Current offering measures how well each provider delivers value to customers today, based on a set of criteria such as functionality, usability, performance, etc. Strategy measures how well each provider positions itself for future success, based on a set of criteria such as vision, roadmap, innovation, etc. The Wave also divides the providers into four categories: leaders, strong performers, contenders, and challengers.
- Leaders are those who offer a comprehensive and consistent current offering and have a clear vision of market direction.
- Strong performers are those who offer a high-quality current offering but may lack strategic clarity or direction.
- Contenders are those who have a viable strategy but may lack product depth or breadth.
- Challengers are those who have a strong current offering but may not be aggressive or innovative enough in their strategy.
IDC Marketscape: This plots the technology providers on two axes: capabilities and strategies. Capabilities measure how well each provider delivers value to customers today, based on a set of criteria such as functionality, usability, performance, etc. Strategies measure how well each provider positions itself for future success, based on a set of criteria such as vision, roadmap, innovation, etc. The MarketScape also divides the providers into four categories:
- Leaders are those who perform exceedingly well in both capabilities and strategies.
- Major players are those who perform very well in one dimension but still above average in the other dimension.
- Contenders are those who perform above average in one dimension but below average in the other dimension.
- Participants are those who perform below average in both dimensions.

Thoughtworks Technology Radar: The Radar plots various technologies and trends on four concentric circles: adopt, trial, assess, and hold.
- Adopt means that the technology or trend is proven and mature enough to be used with confidence in most situations.
- Trial means that the technology or trend is worth pursuing and experimenting with in projects that can handle some risk.
- Assess means that the technology or trend is promising but not yet ready for widespread use. It requires further exploration and understanding before adoption.
- Hold means that the technology or trend is not recommended for use at this time. It may be too immature, too risky, or too obsolete for most situations.

These Market Research on Technology Adoption perspectives provide macroscopic views of the market and as such show aggregate trends. They can be helpful in identifying new technologies for further study. They do not provide a microscopic view on individual processes associated with the adoption of new technology. This view can help identify the scale of adoption of new technology, but as the focus is on market penetration, it does not provide insight into individual or aggregate ethical considerations associated with the use of the new technology.