Chapter 1: 1. Nature, Evolution and Scope of Information Technology

1. Introduction

Information Technology (IT) is often associated with computers, software, networks, databases, and digital services. Such associations are correct but incomplete. IT is a socio-technical field concerned with the creation, representation, processing, storage, communication, protection, and use of information. It includes technical artefacts and infrastructures, but it also includes methods, institutions, human practices, organizational arrangements, and theoretical principles.

At doctoral level, IT must be understood not merely as a collection of tools but as a field of inquiry. Its central questions include: What is information? How can information be represented and transformed? What makes computation possible? How do technological systems alter organizations and societies? What are the limits of automation? How should digital systems be governed? These questions connect IT with mathematics, logic, computer science, systems theory, management, cognitive science, economics, law, and ethics.

The nature of IT has changed considerably over time. Early information practices depended on oral traditions, writing, physical records, mechanical calculation, and bureaucratic procedures. Later developments introduced electronic computation, databases, telecommunications, personal computing, the Internet, mobile platforms, cloud computing, artificial intelligence, and pervasive sensing. Each transition changed not only the speed of information processing but also the scale, structure, and social meaning of information.

This chapter establishes a conceptual foundation for studying IT. It distinguishes data, information, knowledge, computation, technology, and information systems; examines abstraction, representation, modeling, and paradigm change; and considers the strategic, ethical, and future implications of IT.

2. Learning Objectives

After completing this chapter, the reader should be able to:

1. Explain the intellectual history, nature, and scope of information technology.

2. Distinguish data, information, knowledge, wisdom, computation, technology, and information systems.

3. Describe the role of abstraction, representation, modeling, and systems thinking in IT.

4. Analyze major technological paradigms and explain how paradigm changes occur.

5. Relate theoretical IT concepts to organizational and practical applications.

6. Evaluate the strategic importance, limitations, risks, and governance requirements of IT.

7. Assess emerging developments such as artificial intelligence, edge computing, quantum computing, and digital twins.

3. Meaning and Concept

Information technology may be defined as the organized use of computational devices, software, communication networks, data resources, and human capabilities to create, process, store, retrieve, transmit, secure, and apply information. This definition emphasizes purpose as well as equipment. A computer disconnected from meaningful work is a technical object; an integrated arrangement that supports decision-making, coordination, service delivery, or knowledge creation is an information technology system.

The distinction between several related concepts is fundamental. Data are recorded symbols, observations, measurements, or signals. A sequence such as “18, 22, 25” is data, but its meaning is unclear without context. Information is data interpreted within a context so that it reduces uncertainty or supports understanding. If the sequence represents daily temperatures in degrees Celsius, it becomes information. Knowledge involves organized understanding of relationships, principles, procedures, and experience. A meteorologist may use temperature trends, atmospheric models, and historical evidence to explain a weather pattern. Wisdom concerns informed judgment about appropriate action, including values, consequences, and priorities.

These categories are not strictly linear. Information does not automatically become knowledge merely because it is stored in a database. Knowledge requires interpretation, learning, validation, and often social interaction. Similarly, the transformation of knowledge into wise action requires judgment and ethical consideration.

Computation is the systematic transformation of representations according to rules. Computation may be performed by humans, mechanical devices, electronic computers, distributed networks, or biological systems. In IT, computation generally involves algorithms, data structures, processing resources, and formal or semi-formal rules. Technology is the practical application of knowledge through tools, techniques, processes, and systems. IT is therefore broader than computing hardware and narrower than technology as a whole.

An information system is an organized combination of people, processes, data, technology, and controls that collects inputs, transforms them, and produces outputs for a purpose. A hospital information system, for example, includes medical records, software, networks, clinicians, administrative procedures, access controls, and organizational policies. The system’s performance depends on the interaction of all these elements, not simply on software quality.

4. Fundamental Principles

4.1 Representation

Information cannot be processed without representation. Text, sound, images, locations, identities, and transactions must be encoded in forms that systems can store and manipulate. Digital systems commonly represent information using binary states, but binary encoding is only one layer of a larger representational structure. A photograph may be represented as pixels, compressed into a file, described by metadata, stored in a database, and interpreted through an application.

Representation is selective. Every representation preserves some properties and omits others. A map represents spatial relationships but does not reproduce every physical feature of a landscape. A customer profile may represent purchasing behavior while omitting personal motives. Consequently, representation involves assumptions and may introduce distortion or bias.

4.2 Abstraction

Abstraction is the deliberate omission of detail in order to focus on relevant structure. A programmer uses a function without needing to inspect every machine instruction used to implement it. A manager uses a performance dashboard without examining every transaction record. Abstraction enables complexity to be controlled through layers, interfaces, and models.

Abstraction is powerful but not neutral. The details omitted may become important under unusual conditions. A financial model that abstracts away fraud, informal labor, or economic uncertainty may produce misleading conclusions. Sound IT practice therefore requires knowing both what an abstraction hides and when its assumptions cease to hold.

4.3 Algorithms and Automation

An algorithm is a finite, ordered procedure for solving a problem or performing a computation. Algorithms make repeatability and automation possible. However, an algorithm’s output depends on its inputs, assumptions, implementation, and operating environment. Automation does not eliminate human judgment; it relocates judgment into requirements, data selection, system design, exception handling, and governance.

4.4 Modularity and Layering

Complex IT systems are commonly divided into modules and layers. Modularity allows components to be developed, replaced, and tested with limited effects on the whole system. Layering separates concerns, such as physical transmission, networking, application logic, and user interaction. These principles reduce complexity and encourage interoperability, although excessive modularity can create coordination problems and hidden dependencies.

4.5 Feedback and Emergence

Information systems operate within environments and are affected by feedback. A recommendation system influences user behavior, and user behavior then changes the data used by the recommendation system. This creates a feedback loop. At larger scales, interactions among components may produce emergent outcomes that were not explicitly designed, such as network effects, information cascades, or unexpected market concentration.

4.6 Reliability, Security, and Resilience

Trustworthy IT requires more than functionality. Reliability concerns consistent performance; availability concerns accessibility when needed; security protects confidentiality, integrity, and availability; and resilience concerns the capacity to absorb disruption and recover. These properties must be designed into systems rather than added only after failure occurs.

5. Major Components / Elements

The principal elements of IT can be grouped into six interconnected categories.

Hardware includes processors, memory, storage devices, sensors, input and output devices, servers, mobile devices, and networking equipment. Hardware determines physical capabilities and constraints.

Software includes operating systems, applications, middleware, development tools, algorithms, and configuration rules. Software expresses procedures and enables hardware to perform useful work.

Data include operational records, documents, multimedia, sensor readings, metadata, models, and logs. Data quality, provenance, ownership, and lifecycle management strongly influence system value.

Networks and communications connect devices, users, services, and organizations. Connectivity makes distributed computing, collaboration, electronic commerce, and real-time services possible.

People and knowledge include users, developers, analysts, administrators, managers, domain experts, and affected communities. Human competence and organizational learning are frequently more decisive than technical sophistication.

Processes and governance define how work is performed, authorized, monitored, audited, and improved. Policies, standards, contracts, laws, and professional norms shape the responsible use of IT.

6. Process / Framework / Methodology

A useful framework for analyzing any IT initiative is the information value cycle:

1. Purpose definition: Identify the decision, service, problem, or research question to be supported.

2. Data acquisition: Determine what data are needed, where they originate, and whether collection is lawful and ethical.

3. Representation and storage: Select formats, schemas, repositories, metadata, and retention rules.

4. Processing and analysis: Apply algorithms, models, queries, or human interpretation.

5. Communication: Present results through reports, visualizations, interfaces, alerts, or automated actions.

6. Decision and action: Use outputs to guide organizational or individual behavior.

7. Feedback and evaluation: Assess accuracy, usefulness, consequences, and unintended effects.

8. Governance and improvement: Revise controls, models, processes, and objectives as conditions change.

This cycle should be combined with a systems-development methodology. A typical methodology includes problem framing, stakeholder analysis, requirements engineering, feasibility assessment, architecture and design, implementation, testing, deployment, monitoring, maintenance, and retirement. Contemporary practice often uses iterative and agile approaches, but iteration does not replace disciplined thinking. A rapidly delivered system can still solve the wrong problem, encode unfair assumptions, or create unacceptable risks.

7. Important Theories and Models

7.1 General Systems Theory

General Systems Theory views an organization or technology arrangement as a set of interacting components within an environment. Inputs are transformed into outputs, while feedback supports control and adaptation. The theory encourages analysts to examine relationships, boundaries, interdependence, and unintended consequences rather than isolated technical components.

7.2 Shannon’s Information Theory

Information theory treats information quantitatively in relation to uncertainty and communication. Entropy measures the uncertainty associated with a set of possible messages, while channel capacity concerns the maximum rate at which information can be transmitted reliably under specified conditions. Shannon’s work does not define information in terms of meaning, truth, or social value. Nevertheless, it provides essential foundations for data compression, coding, communication systems, and error correction.

7.3 Turing’s Model of Computation

The Turing machine provides an abstract model of computation based on a finite control mechanism, a tape, and rules for reading and writing symbols. Its importance lies not in physical implementation but in clarifying what can be computed through formal procedures. Turing’s work also supports the study of computability and the recognition that some problems cannot be solved algorithmically in general.

7.4 DIKW Model

The data–information–knowledge–wisdom model is a useful educational framework for discussing increasing levels of interpretation and judgment. Its limitation is that it may imply a simple upward hierarchy. In practice, knowledge can shape what data are collected, and values can influence the definition of information. The model should therefore be used as a heuristic rather than a universal law.

7.5 Technology Acceptance and Sociotechnical Perspectives

Technology acceptance models explain adoption through factors such as perceived usefulness and perceived ease of use. Sociotechnical theory adds that technology and social arrangements must be designed together. A technically efficient system may fail if it conflicts with professional identities, incentives, organizational routines, or cultural expectations.

7.6 Paradigm Change

A paradigm is a dominant way of defining problems, selecting methods, and judging solutions. IT has experienced several paradigm changes: centralized mainframes to personal computing; isolated applications to networked systems; on-premises infrastructure to cloud services; rule-based automation to data-intensive machine learning; and transactional systems to pervasive, intelligent, and context-aware platforms. Paradigm change is not merely the adoption of a new device. It often changes assumptions about control, ownership, expertise, architecture, and value creation.

8. Practical Applications

IT supports nearly every sector. In health care, electronic records, diagnostic systems, telemedicine, and clinical analytics support care and administration. In manufacturing, sensors, enterprise systems, robotics, and digital twins coordinate production. In finance, IT enables payment networks, risk analysis, fraud detection, and algorithmic trading. In education, learning platforms, digital libraries, and analytics support teaching and research. In government, digital identity, online services, geographic information systems, and open-data platforms improve access and administrative coordination.

The application of IT should always be linked to a defined purpose. Digitizing a slow paper process without redesigning it may preserve inefficiency in electronic form. Conversely, a modest technology supported by sound process design and user participation may generate substantial value.

9. Examples

A university admissions system illustrates the distinction between data and information. Individual test scores, application dates, and program choices are data. A report showing acceptance rates by program and demographic group is information. An admissions committee’s understanding of recruitment patterns is knowledge. Deciding how to widen access while maintaining academic standards requires judgment and values.

A supermarket recommendation engine illustrates feedback. Historical purchases are used to generate recommendations. Recommendations influence future purchases, which then become new training data. If the system repeatedly promotes already popular products, it may reduce visibility for smaller suppliers and reinforce existing patterns.

A city traffic-management platform illustrates an information system rather than a standalone application. It combines road sensors, cameras, communication networks, databases, predictive models, traffic operators, emergency procedures, and public policy. Failure in any one component can reduce the effectiveness of the whole arrangement.

10. Technology and Digital Applications

Relevant contemporary applications include cloud computing, mobile platforms, Internet of Things (IoT) devices, artificial intelligence, machine learning, blockchain-based ledgers, big-data analytics, edge computing, digital twins, and extended reality.

Cloud computing provides scalable access to processing, storage, and software through shared infrastructure. It can reduce capital expenditure and support rapid expansion, but it introduces dependency on providers, concerns about data location, and complex cost management.

IoT connects sensors and actuators to networks and analytical services. Its value depends on reliable sensing, interoperability, security, and the ability to convert streams of observations into useful actions. Edge computing moves some processing closer to the data source, reducing latency and bandwidth requirements.

Artificial intelligence and machine learning identify patterns, generate predictions, or produce content from data and models. Their effectiveness depends on data quality, task definition, evaluation design, and human oversight. Blockchain can support tamper-evident distributed records in suitable contexts, but it is not a universal replacement for databases or governance institutions.

Digital twins are computational representations of physical objects, processes, or environments that are updated with operational data. They can support simulation, maintenance, and planning, but their usefulness depends on model fidelity and data quality. Extended reality combines digital content with physical or simulated environments and may support training, design, and remote collaboration.

11. Strategic / Managerial Importance

IT is strategically important because it influences operational efficiency, innovation, customer experience, organizational agility, and competitive positioning. It can lower transaction costs, support evidence-based decisions, enable new business models, and connect organizations to global ecosystems.

Managers should not evaluate IT solely by acquisition cost or technical performance. They should assess total value, including process redesign, training, cybersecurity, integration, compliance, maintenance, vendor dependence, and social consequences. Strategic alignment requires that IT investments support organizational objectives and that business leaders understand technological possibilities and constraints.

IT governance establishes decision rights, accountability, performance measures, risk controls, and investment priorities. Effective governance distinguishes portfolio-level choices from operational management while ensuring that architecture, data, security, and ethics are considered across projects.

12. Detailed Case Study

Background

The following is a fictional illustrative case. Meridian Regional Hospital serves a large population through emergency, outpatient, and specialist services. It operated separate systems for registration, laboratory results, pharmacy orders, and billing.

Problem

Clinicians frequently waited for information, duplicate tests were ordered, and patients received inconsistent instructions. Management proposed purchasing a sophisticated artificial-intelligence platform to predict readmissions. However, the hospital’s data were incomplete, inconsistently coded, and difficult to exchange between systems.

Analysis

A systems analysis showed that the principal problem was not the absence of advanced analytics. It was fragmented information governance and poorly integrated workflows. Different departments used conflicting patient identifiers and varied definitions of “readmission.” Staff also lacked confidence that automated recommendations would be explainable or clinically appropriate.

Strategy / Solution

The hospital first established a cross-functional governance board involving clinicians, administrators, data specialists, patients, and privacy officers. It adopted a master patient index, common data definitions, interoperability standards, role-based access controls, and data-quality monitoring. Workflows were redesigned with clinician participation. A limited predictive model was then piloted for one medical unit, with explanations, human review, bias testing, and an appeal process. Outcomes were evaluated against clinical, operational, equity, and safety measures.

Outcome

The pilot reduced duplicate data entry and improved the timeliness of discharge information. Predictive performance was useful but less important than anticipated; the greatest gains came from data standardization and workflow redesign. The model was not deployed in units where data quality remained inadequate.

Learning Points

The case demonstrates that IT value depends on alignment among technology, data, people, processes, and governance. Advanced tools cannot compensate for weak foundations. It also shows that responsible innovation requires staged deployment, evaluation, transparency, and the ability to discontinue a system when risks exceed benefits.

13. Advantages / Benefits

IT can increase speed, accuracy, scalability, accessibility, coordination, transparency, and analytical capability. It supports remote work, personalized services, scientific discovery, operational automation, and preservation of institutional memory. Digital systems can also improve inclusion when designed for accessibility and multilingual use. These benefits are conditional: they emerge only when systems are reliable, usable, secure, maintained, and connected to meaningful objectives.

14. Limitations / Challenges

Major challenges include cybersecurity threats, privacy loss, unreliable data, algorithmic bias, system complexity, technical debt, vendor lock-in, unequal access, skills shortages, and environmental costs. Automation may eliminate some tasks while creating new forms of surveillance, dependency, or deskilling. Digital systems can also fail at scale: a single configuration error may affect millions of users.

The digital divide includes differences in connectivity, affordability, devices, literacy, accessibility, language, and trust. Consequently, a digital-only service may exclude those it is intended to assist. Organizations must retain alternative channels and assess impacts across different communities.

15. Ethical and Governance Considerations

Ethical IT requires attention to privacy, autonomy, fairness, accountability, safety, transparency, accessibility, and sustainability. Privacy protection includes purpose limitation, data minimization, informed consent where appropriate, secure storage, controlled sharing, and deletion or retention policies.

Fairness requires examining whether data, models, interfaces, or institutional procedures disadvantage particular groups. Transparency should be proportionate to the context: users may need understandable reasons for an automated decision, while technical auditors may require detailed documentation and reproducible testing. Accountability means assigning responsibility for decisions and ensuring mechanisms for correction and redress.

Governance should address the complete lifecycle of systems, from procurement and design to deployment, monitoring, incident response, and retirement. Important controls include impact assessments, audit trails, access management, independent review, security testing, model documentation, and clear ownership of data and decisions.

16. Current and Emerging Trends

Current trends include generative artificial intelligence, privacy-enhancing computation, zero-trust security, autonomous systems, low-code development, sustainable computing, quantum information processing, and increasingly integrated cyber-physical systems. Generative AI expands the capacity to produce text, images, code, and other outputs, but raises concerns about hallucination, copyright, labor displacement, provenance, and misuse.

Privacy-enhancing technologies seek to analyze information while reducing exposure of identifiable data. Zero-trust security assumes that no user or device should be trusted automatically, even when operating inside an organizational network. Quantum computing may eventually transform selected optimization, simulation, and cryptographic tasks, although practical large-scale applications remain limited.

A significant trend is the movement from isolated digital tools toward intelligent ecosystems. In such ecosystems, platforms, APIs, sensors, human users, algorithms, and institutions interact continuously. This increases the importance of interoperability, standards, resilience, and governance.

17. Best Practices

Sound IT practice begins with a clearly defined problem and measurable outcomes. Organizations should involve stakeholders early, map processes before automating them, and distinguish essential requirements from attractive features. Data should be governed through defined ownership, quality standards, provenance, retention, and access rules.

Systems should be designed for security, privacy, accessibility, maintainability, and interoperability from the beginning. Testing should include functional, performance, usability, security, bias, and failure-mode assessment. Deployment should be incremental where risk is significant, with monitoring and rollback procedures.

Documentation is essential. Architecture decisions, data definitions, model assumptions, changes, incidents, and responsibilities should be recorded. Finally, organizations should evaluate not only whether a system works, but for whom it works, under what conditions, and with what unintended consequences.

18. Future Scope

The future scope of IT extends toward more autonomous, distributed, context-aware, and human-machine collaborative systems. Computing will increasingly be embedded in buildings, vehicles, medical devices, industrial equipment, and public infrastructure. The boundary between physical and digital environments will become less visible.

At the same time, foundational questions will become more important. Researchers must examine the limits of automated reasoning, the social construction of data, the governance of powerful platforms, the environmental sustainability of computation, and the distribution of technological benefits. Doctoral research in IT can contribute by developing new theories, improving system architectures, studying human and institutional behavior, and designing accountable forms of innovation.

19. Chapter Summary

Information technology is a socio-technical field concerned with the representation, processing, communication, protection, and use of information. Data become information through context, while knowledge and wisdom require interpretation and judgment. Computation transforms representations according to rules, and information systems combine people, processes, data, technology, and controls.

Abstraction, modularity, layering, feedback, representation, and modeling make complex systems manageable, but each introduces assumptions and possible blind spots. General Systems Theory, information theory, models of computation, DIKW, technology acceptance, and sociotechnical theory provide complementary ways to understand IT. IT creates strategic value across sectors, but its benefits depend on governance, data quality, human capability, security, ethics, and inclusion. Future IT will be increasingly intelligent, pervasive, and interconnected, making theoretical understanding and responsible governance indispensable.

20. Key Terms

1. Abstraction: Selective simplification that emphasizes relevant features while omitting detail.

2. Algorithm: A finite, ordered procedure for solving a problem or performing a computation.

3. Artificial intelligence: Computational systems designed to perform tasks associated with human-like perception, reasoning, learning, or generation.

4. Computation: Rule-governed transformation of representations.

5. Data: Recorded symbols, observations, measurements, or signals.

6. Digital divide: Unequal access to digital technologies, skills, infrastructure, or benefits.

7. Information: Data interpreted within a context to reduce uncertainty or support understanding.

8. Information system: An organized combination of people, processes, data, technology, and controls serving a purpose.

9. Information theory: The mathematical study of uncertainty, encoding, and communication.

10. Interoperability: The ability of systems or organizations to exchange and use information effectively.

11. Knowledge: Organized understanding based on information, relationships, experience, and interpretation.

12. Model: A purposeful representation of a real or conceptual system.

13. Paradigm change: A major shift in the assumptions and methods used to define and solve problems.

14. Representation: The encoding of an object, event, or concept in a form suitable for interpretation or processing.

15. Resilience: The capacity of a system to withstand disruption and recover.

16. Sociotechnical system: A system in which social and technical elements interact and must be considered together.

17. Technology: The practical application of knowledge through tools, techniques, processes, and systems.

18. Wisdom: Judicious application of knowledge in light of values, consequences, and purpose.

21. Review Questions

Short Answer Questions

1. Distinguish data, information, knowledge, and wisdom using an organizational example.

2. Why is abstraction necessary in information technology?

3. What are the principal elements of an information system?

4. Explain the difference between computation and technology.

5. Why is interoperability important in contemporary IT?

Long Answer Questions

1. Explain the nature and scope of information technology as a socio-technical field.

2. Discuss representation, abstraction, modeling, and modularity as foundations of IT design.

3. Compare General Systems Theory, Shannon’s Information Theory, and Turing’s model of computation.

4. Analyze how paradigm changes have transformed organizational use of IT.

5. Discuss the strategic value and governance requirements of information technology.

Analytical / Application Questions

1. A public agency plans to automate eligibility decisions using historical records. Analyze the data, modeling, fairness, accountability, and governance issues that should be addressed before deployment.

2. A university’s learning platform produces detailed student analytics, but many students lack reliable connectivity. Propose a systems approach that improves learning support without deepening inequality.

3. An organization wants to adopt a cloud-based artificial-intelligence service. Develop a framework for evaluating strategic value, privacy, security, vendor dependence, data quality, and long-term sustainability.

22. Multiple Choice Questions with Answers

Question 1

Which statement best distinguishes information from raw data?

A. Information is always stored electronically.

B. Information is data interpreted within a meaningful context.

C. Information consists only of numerical measurements.

D. Information cannot be used for decision-making.

Correct Answer: B. Information is data interpreted within a meaningful context.

Question 2

A hospital combines patient records, clinicians, procedures, software, networks, and access controls. This arrangement is best described as:

A. A hardware platform

B. A database schema

C. An information system

D. An algorithm

Correct Answer: C. An information system

Question 3

What is the principal purpose of abstraction in IT?

A. To eliminate all system complexity

B. To hide relevant requirements from users

C. To focus attention on important structure while omitting unnecessary detail

D. To prevent systems from being modified

Correct Answer: C. To focus attention on important structure while omitting unnecessary detail

Question 4

A recommendation engine changes user behavior, and that behavior changes the data used by the engine. This illustrates:

A. A feedback loop

B. Data minimization

C. Channel capacity

D. Hardware virtualization

Correct Answer: A. A feedback loop

Question 5

Which example best demonstrates a paradigm change in IT?

A. Replacing one keyboard with another

B. Moving from isolated applications to networked and cloud-based services

C. Increasing the brightness of a monitor

D. Changing the color of a software interface

Correct Answer: B. Moving from isolated applications to networked and cloud-based services

Question 6

According to the chapter, why might an advanced predictive model fail to improve a hospital?

A. Predictive models never work in health care.

B. Electronic systems cannot support clinical decisions.

C. Weak data quality and fragmented workflows may undermine its usefulness.

D. Governance is unnecessary when a model is accurate.

Correct Answer: C. Weak data quality and fragmented workflows may undermine its usefulness.

Question 7

Which property primarily concerns protection of confidentiality, integrity, and availability?

A. Security

B. Modularity

C. Scalability

D. Usability

Correct Answer: A. Security

Question 8

What is a major limitation of the DIKW model?

A. It denies that data can be stored.

B. It may imply an overly simple hierarchy from data to wisdom.

C. It applies only to mechanical computation.

D. It excludes human judgment entirely.

Correct Answer: B. It may imply an overly simple hierarchy from data to wisdom.

Question 9

Which practice most directly supports responsible deployment of a high-risk automated system?

A. Removing all human oversight

B. Deploying it everywhere immediately

C. Using impact assessment, monitoring, documentation, and mechanisms for correction

D. Avoiding evaluation to protect innovation

Correct Answer: C. Using impact assessment, monitoring, documentation, and mechanisms for correction

Question 10

Why should managers evaluate more than the purchase price of an IT system?

A. Technical systems have no measurable costs.

B. Total value and risk also depend on training, integration, security, maintenance, and governance.

C. Purchase price is always irrelevant.

D. Managers should evaluate only employee satisfaction.

Correct Answer: B. Total value and risk also depend on training, integration, security, maintenance, and governance.