Chapter 4: Theory Development and Conceptual Frameworks

1. Introduction

Theory development is central to doctoral research in information technology (IT). Advanced research does not merely describe technologies, report correlations, or evaluate whether users like a system. It explains why phenomena occur, under what conditions they occur, through which mechanisms they operate, and with what consequences. A strong theory therefore gives research explanatory power and enables findings to be transferred beyond a single organization, platform, or technological episode.

IT research presents distinctive theoretical challenges. Technologies are simultaneously social, organizational, economic, and technical. They evolve rapidly, are embedded in institutional settings, and often produce effects that depend on human interpretation and organizational routines. Consequently, doctoral researchers must select theories critically rather than apply fashionable models mechanically. They must also be able to integrate theories, define constructs precisely, formulate propositions, identify boundary conditions, and design empirical tests.

This chapter presents a systematic approach to theory development and conceptual framework construction. It distinguishes theories from models and frameworks, examines major components of theoretical explanation, reviews influential theories used in IT research, and illustrates how theory can be developed through a detailed case. The emphasis is on reasoning: how to move from an important phenomenon and a literature gap to a defensible conceptual explanation.

2. Learning Objectives

By the end of this chapter, students should be able to:

1. Distinguish theory, conceptual framework, model, construct, variable, proposition, and hypothesis.

2. Critically evaluate the relevance, assumptions, scope, and limitations of theories used in IT research.

3. Define constructs and explain relationships among them using mechanisms and boundary conditions.

4. Integrate theories without producing redundant or internally inconsistent frameworks.

5. Develop conceptual models and propositions appropriate to qualitative, quantitative, and mixed-methods research.

6. Identify strategies for testing, extending, and building theory.

7. Apply theory-development principles to contemporary digital and organizational problems.

8. Evaluate ethical, governance, and practical implications of theoretical choices.

3. Meaning and Concept

A theory is a logically connected set of concepts, definitions, assumptions, and propositions that explains or predicts a phenomenon. A theory is more than a list of factors associated with an outcome. It specifies relationships and provides a rationale for those relationships. For example, a theory of algorithmic decision acceptance might propose that perceived procedural fairness increases trust, that trust increases reliance, and that the effect is weaker when users perceive the algorithm as opaque.

A construct is an abstract concept created for theoretical purposes. Examples include trust, digital maturity, perceived usefulness, organizational resilience, and algorithmic transparency. Constructs are not directly observable in their entirety. Researchers therefore use indicators, interview evidence, behavioral traces, or archival measures to represent them. A variable is an operationalized representation of a construct that can take different values. The construct “trust in an AI system” may be measured through survey items, observed reliance, or patterns of override behavior.

A conceptual framework is an organized representation of the concepts and relationships relevant to a particular study. It may be expressed through a diagram, narrative, or both. Unlike a general theory, a conceptual framework is usually tailored to a research problem and may combine elements from several theories. A model is a simplified representation of a phenomenon. Models can be descriptive, explanatory, predictive, or process-based. A framework becomes theoretically valuable when it identifies mechanisms, clarifies assumptions, and supports systematic inquiry.

Theory building differs from theory testing. Theory testing examines whether observations are consistent with propositions derived from an existing theory. Theory building develops new concepts, relationships, or explanations from empirical material or theoretical reasoning. Theory extension modifies an existing theory by adding constructs, relationships, levels of analysis, or boundary conditions. Most sophisticated IT studies combine these activities: they use existing theory as a foundation while adapting it to new technologies and contexts.

4. Fundamental Principles

4.1 Explanatory adequacy

A theory should answer not only what happens but why. A claim that digital training improves performance is incomplete unless it specifies mechanisms such as increased self-efficacy, improved task knowledge, or reduced cognitive load. Explanatory adequacy requires a defensible causal or process account.

4.2 Conceptual clarity

Constructs must be defined in a way that distinguishes them from related concepts. “Technology readiness,” “digital competence,” and “innovation capability” should not be used interchangeably. Clear definitions identify the essential attributes of a construct, its level of analysis, and its relationship to neighboring constructs.

4.3 Parsimony and completeness

A useful framework includes enough concepts to explain the phenomenon but not so many that relationships become unfalsifiable or trivial. Parsimony does not mean simplicity at any cost. A complex digital ecosystem may require multilevel or process explanations, but every element should serve a clear theoretical purpose.

4.4 Alignment

Research questions, theory, constructs, methods, measures, and claims must be aligned. A study asking how employees interpret an AI system should not rely solely on standardized survey measures. Similarly, a framework proposing organizational-level capabilities cannot be tested adequately using only individual perceptions unless aggregation is justified.

4.5 Falsifiability and empirical assessability

Propositions should imply observations that could support or challenge them. A statement such as “technology affects organizations in complex ways” may be true but is not sufficiently specific for rigorous testing. A stronger proposition identifies a direction, mechanism, context, or condition.

4.6 Context sensitivity

IT theories travel imperfectly across contexts. A platform effect observed among consumers may not apply to public-sector employees. Cultural norms, regulatory conditions, task characteristics, resource availability, and technological maturity can alter relationships. Strong theory identifies its boundary conditions rather than claiming universal validity.

4.7 Multilevel reasoning

IT phenomena frequently operate across levels. Individual trust may influence system use, while organizational governance influences trust formation and institutional regulation shapes governance. Researchers must distinguish individual, team, organizational, interorganizational, ecosystem, and societal levels and avoid invalidly inferring one level from another.

4.8 Temporal and process awareness

Digital phenomena change over time. Initial adoption may be driven by usefulness, whereas continued use may depend on habit, institutionalization, or switching costs. Theory should specify whether relationships are cross-sectional, longitudinal, recursive, or stage-dependent.

5. Major Components / Elements

Constructs and dimensions

Constructs may be unidimensional or multidimensional. A construct such as digital transformation capability may include strategic alignment, data capability, process redesign, and organizational learning. Researchers must determine whether dimensions are reflective, where indicators reflect an underlying construct, or formative, where indicators combine to create the construct. Confusing these forms can produce invalid measurement and interpretation.

Relationships

Relationships may be direct, mediated, moderated, reciprocal, or curvilinear. A mediator explains how an antecedent produces an outcome. A moderator changes the strength or direction of a relationship. For example, perceived transparency may increase trust through perceived accountability, while technical expertise may moderate the transparency–trust relationship.

Mechanisms

A mechanism is the process through which a relationship operates. Mechanisms may be cognitive, behavioral, social, organizational, institutional, or technical. “AI adoption improves productivity” is theoretically weak without explaining whether productivity rises through automation, decision augmentation, coordination, or learning.

Assumptions

Assumptions state conditions accepted for the theory to operate. A model of voluntary system use may assume that users have discretion. A model of organizational resilience may assume access to alternative suppliers. Making assumptions explicit helps identify appropriate contexts and prevents overgeneralization.

Boundary conditions

Boundary conditions specify when, where, for whom, or under what circumstances a proposition applies. They may involve industry regulation, task complexity, culture, technology maturity, resource constraints, or user expertise. Boundary conditions are not weaknesses; they are indicators of theoretical precision.

Levels and units of analysis

The unit of analysis is the entity about which conclusions are drawn. It may be a user, team, organization, platform, transaction, project, or policy. The level of analysis refers to the conceptual level at which a phenomenon is located. A study of organizational AI capability must not treat a single employee’s opinion as a complete organizational measure without justification.

Propositions and hypotheses

A proposition is a theoretically reasoned statement about a relationship, often used in conceptual or qualitative research. A hypothesis is a testable statement, usually expressed in measurable terms. For example: “Organizations with stronger data governance are more likely to scale AI applications because governance increases data reliability and managerial confidence.” A quantitative hypothesis might predict a positive association between data-governance maturity and AI-scaling success.

6. Process / Framework / Methodology

Theory development begins with a significant phenomenon rather than with a preferred theory. The researcher identifies an important puzzle: an unexpected outcome, an underexplored process, conflicting findings, or a new technological condition. The literature is then reviewed comparatively, asking what existing theories explain, what they overlook, and whether their assumptions fit the phenomenon.

The next step is to define the phenomenon and its boundaries. “Digital transformation” is too broad unless the researcher specifies whether the focus is strategic change, process redesign, workforce impact, platform formation, or institutional change. A precise phenomenon supports precise construct development.

Researchers then select a theoretical perspective. Selection should be based on ontological assumptions, research purpose, level of analysis, temporal orientation, and explanatory fit. A theory should not be selected merely because it is frequently cited. The researcher should explain why its concepts and assumptions illuminate the problem and where they require modification.

Construct development follows. Researchers examine prior definitions, identify common attributes, distinguish dimensions, and specify relationships with related constructs. Qualitative researchers may use interviews, observations, documents, and cases to discover categories and mechanisms. Quantitative researchers may refine constructs through scale development, expert assessment, pilot studies, and factor analysis. In both approaches, construct definitions precede measurement decisions.

The researcher then maps the proposed relationships. A useful conceptual diagram shows direction, levels, mediators, moderators, feedback loops, and outcomes. The diagram should be accompanied by a narrative explaining every relationship. Arrows alone do not constitute theory.

Propositions or hypotheses are formulated next. Each should identify the constructs involved, the expected relationship, the rationale, and relevant conditions. Researchers should avoid producing a long list of unsupported links. A small number of well-developed propositions is stronger than a crowded model.

The framework is evaluated through logical, empirical, and practical tests. Logical evaluation asks whether concepts overlap and whether relationships are consistent. Empirical evaluation examines measurement quality, alternative explanations, robustness, and evidence across settings. Practical evaluation asks whether the framework informs decisions or interventions. Revision is expected: theory develops through comparison between conceptual expectations and evidence.

A final step is to articulate contribution. A study may contribute a new construct, identify a mechanism, reveal a boundary condition, integrate previously separate theories, explain an anomaly, or transfer a theory to a new level or context. Claims should be proportional to the evidence.

7. Important Theories and Models

The Technology Acceptance Model explains individual acceptance through perceived usefulness, perceived ease of use, attitude, behavioral intention, and use. Its strength is parsimony and its limitation is that it can underrepresent social influence, institutional pressures, material constraints, and mandatory use.

The Unified Theory of Acceptance and Use of Technology integrates performance expectancy, effort expectancy, social influence, and facilitating conditions, with moderators such as age, experience, and voluntariness. It is useful for adoption studies but may require adaptation for collective, organizational, or algorithmic contexts.

Diffusion of Innovations explains how innovations spread through perceived attributes such as relative advantage, compatibility, complexity, trialability, and observability. It is valuable for studying organizational and interorganizational diffusion, although diffusion is not always linear and adoption may not imply effective use.

The Technology–Organization–Environment framework explains technological adoption through technological characteristics, organizational readiness, and environmental pressures. It is particularly useful for enterprise technologies, small-business adoption, cloud computing, and cybersecurity practices.

The DeLone and McLean Information Systems Success Model links system quality, information quality, service quality, use, user satisfaction, and net benefits. It helps evaluate outcomes, but researchers must define “net benefits” carefully and account for reciprocal relationships.

The Resource-Based View explains sustained advantage through valuable, rare, inimitable, and non-substitutable resources and capabilities. In IT research it supports analysis of data capabilities, digital skills, platforms, and architectural knowledge. Its critics note that rapidly changing technologies can make resource value temporary and that social and institutional factors may be underdeveloped.

Dynamic capabilities theory focuses on an organization’s ability to sense opportunities and threats, seize opportunities, and transform or reconfigure resources. It is appropriate for digital transformation and innovation, especially when competitive environments change quickly. Researchers must distinguish capabilities from ordinary routines and outcomes.

Institutional theory explains how organizations respond to coercive, normative, and mimetic pressures. It is useful for studying compliance, cybersecurity standards, responsible AI, and public-sector digitization. Institutional conformity may explain adoption even when direct efficiency benefits are uncertain.

Socio-technical systems theory treats technical and social elements as interdependent. It warns against evaluating systems independently of work practices, power relations, skills, incentives, and institutional arrangements. This perspective is especially important for automation, enterprise systems, and workplace analytics.

Actor-network theory examines how human and nonhuman actors become connected in networks and how meanings and capabilities are constructed through translation. It can illuminate platform ecosystems and technology implementation, though its vocabulary and analytical procedures require careful specification.

Critical theories examine power, inequality, ideology, surveillance, and exclusion. They challenge the assumption that efficiency and innovation are neutral outcomes. Critical approaches are essential when studying algorithmic discrimination, digital labor, data colonialism, and unequal access.

8. Practical Applications

Theory guides system design by identifying factors that influence adoption, trust, learning, and resistance. It guides implementation by revealing how workflows, authority, incentives, and identity shape outcomes. It guides evaluation by defining meaningful outcomes rather than relying on usage counts alone. It also supports policy by clarifying how regulation, standards, and institutional expectations influence organizational behavior.

For example, a hospital introducing clinical decision support should not evaluate success solely through installation or login data. A framework may connect system explainability, clinician trust, workflow fit, professional autonomy, and patient-safety outcomes. The framework can then guide requirements, training, governance, and evaluation.

9. Examples

A researcher studying remote-work platforms might integrate media richness theory with job demands–resources theory. Rich communication features may improve coordination, but notification overload may become a demand that reduces well-being. Team psychological safety may moderate the effects because employees who feel safe are more willing to disclose problems through digital channels.

A study of cybersecurity behavior might combine protection motivation theory with institutional theory. Individual threat appraisal and coping appraisal may influence secure behavior, while organizational policies, peer norms, and regulatory pressure shape whether those appraisals result in action.

A study of generative AI in universities might examine perceived usefulness, academic integrity concerns, professional identity, institutional governance, and task uncertainty. Rather than assuming adoption is uniformly beneficial, the framework could distinguish assistance, substitution, augmentation, and inappropriate dependence.

10. Technology and Digital Applications

Contemporary technologies create new theoretical questions. Artificial intelligence raises questions about explainability, human oversight, delegation, trust calibration, and responsibility. Platform ecosystems require theories of network effects, governance, complementors, and data control. Digital twins require analysis of representation accuracy, organizational learning, and decision authority. Blockchain applications invite examination of institutional trust, decentralization, and accountability rather than assuming that technical immutability creates social trust.

Big-data analytics requires attention to data quality, interpretive capability, privacy, and organizational decision processes. Internet-of-Things research must connect sensing infrastructure with work practices, security, and physical consequences. Extended-reality systems raise questions about presence, embodiment, accessibility, and changes in collaboration. In each case, the technology should be treated as part of a sociotechnical arrangement rather than as an isolated causal object.

11. Strategic / Managerial Importance

Managers use theory to diagnose why a digital initiative succeeds in one setting and fails in another. A theory-informed diagnosis distinguishes poor system design from inadequate skills, weak sponsorship, regulatory constraints, or misaligned incentives. It also helps leaders prioritize interventions. If resistance is caused by threats to professional identity, additional technical training alone will not solve the problem.

Conceptual frameworks support strategic alignment by linking digital investments to capabilities and outcomes. They assist governance by clarifying decision rights, accountability, and escalation mechanisms. They improve communication among executives, technical staff, users, regulators, and researchers because abstract concerns are translated into explicit constructs and relationships.

12. Detailed Case Study

Background

The following is a fictional illustrative case. Meridian, a regional insurance company, introduced an AI system to prioritize claims for human adjusters. The system used historical claims data, customer information, and fraud indicators. Executives expected faster processing and lower fraud losses.

Problem

After six months, system use varied sharply across branches. Some adjusters relied heavily on recommendations, while others ignored them. Complaints also increased among customers whose claims received low priority. Management initially attributed the variation to employee resistance and proposed mandatory training.

Analysis

A theory-informed investigation identified several interacting constructs. Perceived usefulness differed because the system performed well for standardized claims but poorly for unusual cases. Trust depended not only on accuracy but also on explainability and perceived procedural fairness. Professional identity influenced whether adjusters viewed the system as a decision aid or a threat to expertise. Branch-level leadership shaped experimentation and discussion. Regulatory uncertainty increased reluctance to rely on recommendations that could not be explained to customers.

The proposed framework linked system quality and explainability to trust; trust and workflow fit to reliance; professional identity and perceived accountability to the relationship between trust and reliance; and reliance to processing speed and customer fairness. The framework also proposed that regulatory clarity would strengthen the relationship between trust and reliance.

Strategy / Solution

Meridian redesigned the interface to display contributing factors and confidence levels. Adjusters received scenario-based training focused on appropriate reliance rather than unconditional compliance. Branch managers created review forums where employees could report errors and unusual cases. A human-override process was documented, and an audit committee monitored differences in outcomes across customer groups. The company also established a policy that final accountability remained with authorized adjusters.

Outcome

Use increased in standardized cases but remained cautious in ambiguous cases, which management accepted as appropriate. Processing time improved without eliminating human review. Error analysis identified a data-quality problem affecting a particular geographic region, and the model was recalibrated. Customer communication improved because adjusters could explain the basis and limits of recommendations.

Learning Points

The case shows why adoption cannot be reduced to ease of use. Trust is multidimensional, and accuracy alone does not guarantee reliance. Professional identity, accountability, governance, and context shaped outcomes. The framework also illustrates how boundary conditions prevent a misleading conclusion that more reliance is always better. Finally, theory informed both research design and managerial intervention.

13. Advantages / Benefits

Theory provides disciplined explanation, cumulative knowledge, and a basis for comparison across studies. It improves construct validity, guides data collection, supports prediction, and helps identify mechanisms that can be influenced by managers. Conceptual frameworks also expose hidden assumptions and make interdisciplinary integration more transparent. In practice, they reduce the risk of treating symptoms as causes or transferring interventions from one context to another without analysis.

14. Limitations / Challenges

Theories can be overly abstract, culturally narrow, or poorly adapted to fast-changing technologies. Researchers may force data into established categories, confuse correlation with mechanism, or combine theories whose assumptions conflict. Measurement may fail to represent the intended construct, especially for emerging phenomena. Complex models can become difficult to test and may encourage post hoc storytelling. There is also a danger of technological determinism: attributing outcomes to technology while ignoring power, institutions, work practices, and history.

15. Ethical and Governance Considerations

Theory development must consider who benefits, who bears risk, and whose experiences are represented. Constructs such as trust, fairness, and inclusion should not be defined solely from the perspective of system owners. Researchers must protect participants, especially when studying employee behavior, surveillance, or vulnerable communities.

Governance requires attention to transparency, accountability, privacy, security, explainability, contestability, and human oversight. Data used to test a theory may contain sensitive or biased information. Researchers should minimize data collection, document provenance, assess disparate impacts, and avoid presenting predictive performance as evidence of social legitimacy. Ethical theory development also requires acknowledging uncertainty and reporting negative or contradictory findings.

16. Current and Emerging Trends

Current research increasingly examines human–AI collaboration, algorithmic management, responsible innovation, digital platform governance, and data-driven public services. Scholars are developing theories of trust calibration, appropriate reliance, algorithm aversion, and human oversight. Digital transformation is being reconceptualized as an ongoing capability rather than a one-time project.

Other trends include multilevel theory, longitudinal process models, computationally assisted theorizing, and stronger integration of critical and design-oriented perspectives. Researchers increasingly combine digital trace data with interviews and institutional analysis. There is also growing attention to sustainability, energy consumption, electronic waste, accessibility, and the social consequences of large-scale infrastructure.

17. Best Practices

Begin with a meaningful phenomenon and a clearly stated puzzle. Read theories comparatively rather than searching for a single fashionable label. Define every central construct, including what it is not. State the level and unit of analysis. Explain mechanisms instead of merely drawing arrows. Identify boundary conditions and alternative explanations. Match theory to method and measurement. Use diagrams only when the accompanying narrative explains the logic. Seek disconfirming evidence, revise concepts when data require it, and make contribution claims proportional to the study design. Finally, distinguish theoretical importance from managerial popularity: an important technology is not automatically a theoretically important phenomenon.

18. Future Scope

Future IT theory will need to explain systems that learn, adapt, and interact with institutions at multiple speeds. Research should examine how responsibility is distributed across developers, organizations, users, platforms, and regulators. More work is needed on theory transfer across cultures, sectors, and levels of analysis; on unintended consequences of automation; and on the relationship between digital innovation and sustainability.

Researchers can also develop theories that combine technical performance with dignity, autonomy, equity, and ecological impact. Longitudinal and comparative designs will be particularly valuable because digital systems change after deployment. Design science, ethnography, experiments, archival analysis, and computational methods can be combined to build explanations that are both rigorous and practically useful.

19. Chapter Summary

Theory development gives IT research explanatory depth. A theory connects constructs through reasoned propositions, mechanisms, assumptions, and boundary conditions. A conceptual framework adapts these elements to a specific research problem. Strong theory requires conceptual clarity, alignment, parsimony, multilevel awareness, contextual sensitivity, and empirical assessability. Researchers may test, build, extend, or integrate theories, but they must preserve logical consistency and explain their contribution. Major IT theories address acceptance, diffusion, information-systems success, capabilities, institutions, and sociotechnical relationships. Their value depends on critical application rather than mechanical citation. Ethical and governance concerns must be incorporated because digital technologies distribute benefits and risks. Ultimately, theory connects evidence to explanation and explanation to responsible action.

20. Key Terms

1. Theory — A connected set of concepts, assumptions, and propositions that explains or predicts a phenomenon.

2. Conceptual framework — A study-specific representation of relevant concepts and their relationships.

3. Construct — An abstract concept developed for theoretical analysis.

4. Variable — An operationalized construct that can take different values.

5. Model — A simplified representation of a phenomenon or process.

6. Proposition — A theoretically reasoned statement about a relationship.

7. Hypothesis — An empirically testable statement derived from theory.

8. Mechanism — The process through which one condition produces an outcome.

9. Mediator — A construct that explains how an antecedent affects an outcome.

10. Moderator — A condition that changes the strength or direction of a relationship.

11. Boundary condition — A circumstance defining when a theoretical relationship applies.

12. Unit of analysis — The entity about which conclusions are drawn.

13. Level of analysis — The conceptual level at which a phenomenon is located.

14. Reflective construct — A construct presumed to cause or generate its indicators.

15. Formative construct — A construct formed by its defining indicators or dimensions.

16. Theory building — Developing concepts and explanations from reasoning or empirical evidence.

17. Theory testing — Assessing whether evidence is consistent with theoretical predictions.

18. Theory extension — Modifying an existing theory by adding concepts, relationships, or conditions.

19. Sociotechnical system — An interdependent arrangement of social and technical elements.

20. Falsifiability — The possibility that evidence could challenge a theoretical claim.

21. Review Questions

Short Answer Questions

1. Distinguish a theory from a conceptual framework.

2. What is the difference between a mediator and a moderator?

3. Why are boundary conditions important in IT theory?

4. Explain the difference between reflective and formative constructs.

5. What is meant by a mechanism?

Long Answer Questions

1. Explain the process of developing a conceptual framework from a research problem.

2. Critically compare the Technology Acceptance Model and the Technology–Organization–Environment framework.

3. Discuss how multilevel reasoning improves theory development in digital transformation research.

4. Explain the role of assumptions, mechanisms, and boundary conditions in theory building.

5. Evaluate the contribution and limitations of socio-technical systems theory for contemporary AI research.

Analytical / Application Questions

1. Design a conceptual framework for studying employee reliance on a generative-AI tool. Identify constructs, mechanisms, moderators, and outcomes.

2. An organization adopts cybersecurity controls but employees do not follow them. Use two theories to explain the outcome and propose an empirical study.

3. A digital platform succeeds in one country but fails in another. Develop a theory-based explanation that includes institutional, cultural, and technological boundary conditions.

22. Multiple Choice Questions with Answers

Question 1

Which element explains how an antecedent produces an outcome?

A. Boundary condition

B. Mechanism

C. Unit of analysis

D. Indicator

Correct Answer: B. Mechanism

Question 2

A researcher proposes that transparency increases trust more strongly among users with high technical expertise. Technical expertise is what type of construct?

A. Mediator

B. Outcome

C. Moderator

D. Reflective indicator

Correct Answer: C. Moderator

Question 3

Which statement best distinguishes a conceptual framework from a general theory?

A. A framework cannot contain propositions

B. A framework is usually tailored to a specific study

C. A theory is always quantitative

D. A framework cannot integrate theories

Correct Answer: B. A framework is usually tailored to a specific study

Question 4

Which principle requires a theory to identify observations that could challenge it?

A. Parsimony

B. Falsifiability

C. Institutional conformity

D. Diffusion

Correct Answer: B. Falsifiability

Question 5

A study concludes that an organizational AI capability exists because employees report high confidence in the system. What major issue may this conclusion involve?

A. Invalid movement from individual perceptions to an organizational-level construct

B. Excessive use of formative measurement

C. Failure to identify a technology

D. Overuse of longitudinal evidence

Correct Answer: A. Invalid movement from individual perceptions to an organizational-level construct

Question 6

Which theory is especially concerned with coercive, normative, and mimetic pressures?

A. Institutional theory

B. Technology Acceptance Model

C. Resource-Based View

D. DeLone and McLean model

Correct Answer: A. Institutional theory

Question 7

In the fictional Meridian case, why was mandatory training alone insufficient?

A. The system had no digital interface

B. Adoption depended on trust, workflow fit, identity, and accountability as well as skills

C. Regulation prohibited all training

D. Users had no access to claims data

Correct Answer: B. Adoption depended on trust, workflow fit, identity, and accountability as well as skills

Question 8

Which statement is the strongest hypothesis?

A. Digital systems are important in modern organizations

B. Technology affects people in complex ways

C. Perceived explainability positively affects employee trust in AI recommendations

D. AI will transform every industry

Correct Answer: C. Perceived explainability positively affects employee trust in AI recommendations

Question 9

What is a key danger of combining theories without critical analysis?

A. The framework may contain conflicting assumptions and redundant constructs

B. The study will automatically become qualitative

C. The research cannot include technology

D. All propositions become experimentally proven

Correct Answer: A. The framework may contain conflicting assumptions and redundant constructs

Question 10

Why should IT researchers avoid technological determinism?

A. Technology has no effect on organizations

B. Technological outcomes are shaped by social, organizational, institutional, and technical conditions

C. Only economic theories can explain technology

D. Technology cannot be studied empirically

Correct Answer: B. Technological outcomes are shaped by social, organizational, institutional, and technical conditions