This post is part of the continuing mini-series on RQ–RH–D–M across fields. Its purpose is to provide a compact, practical toolkit showing how research questions, research hypotheses or working propositions, data, and methodology can be aligned in one specific discipline.
Information Technology is especially suitable for this exercise because it naturally combines technology adoption, user behavior, system quality, digital trust, platform use organizational implementation, human–computer interaction and information systems performance. It also supports quantitative, qualitative and mixed methods designs.
In this section, several examples explicitly draw on major theoretical frameworks of technology adoption and information systems, such as Technology Acceptance Model (TAM), TAM2/TAM3, Unified Theory of Acceptance and Use of Technology (UTAUT/UTAUT2), Diffusion of Innovations, Theory of Planned Behavior, Task-Technology Fit, the DeLone and McLean IS Success Model and the Technology-Organization-Environment (TOE) framework. These models define constructs such as perceived usefulness, ease of use, social influence, facilitating conditions, trust, system quality, task fit or implementation readiness, which are then operationalized through composite scales, usage metrics, system logs, adoption indicators, performance measures, interview responses or integrated digital-behavior evidence.
Note: The entries in the Methodology are intentionally general and indicative. They are meant to illustrate plausible methodological directions, not to exhaust the full range of possible methods, model variants or analytic choices available to the researcher. Researchers are not expected to apply all of the methodological tools listed in column Methodology in a single study. The entries are intended to indicate suitable methodological options or families of approaches from which the researcher selects those that best fit the research question, hypothesis, data, and design.
Information technology – quantitative research
Descriptive questions
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RQ: What is the average perceived usefulness score of a new university learning management system among undergraduate students?
RH: The average perceived usefulness score of the new learning management system is above the midpoint of the scale.
D: Perceived usefulness score (continuous/scale, TAM); student ID; faculty (categorical); year of study (ordinal).
M: Descriptive statistics, one-sample t-test against scale midpoint, confidence intervals, subgroup summaries.
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RQ: What proportion of employees use a cloud collaboration platform daily after its rollout?
RH: More than half of employees use the cloud collaboration platform daily after rollout.
D: Daily platform use (binary/ordinal after recoding); employee ID; department (categorical); job role (categorical).
M: Frequencies, proportions, binomial test, confidence intervals, weighted estimation if survey-based.
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RQ: What is the average system quality rating of a hospital information system among nurses?
RH: The average system quality rating of the hospital information system exceeds the neutral benchmark.
D: System quality score (continuous/scale, DeLone–McLean); nurse ID; ward (categorical); shift type (categorical).
M: Descriptive statistics, one-sample t-test, confidence intervals, distribution plots.
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RQ: How many cybersecurity awareness training modules are completed on average by new hires in the first three months?
RH: New hires complete more than 3 cybersecurity awareness modules on average in the first three months.
D: Number of completed modules (count); employee ID; month/time marker; business unit (categorical).
M: Descriptive statistics, one-sample tests, Poisson/negative binomial summaries, interval estimation.
Comparative questions
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RQ: Do users receiving a high-usability interface differ from users receiving a standard interface in behavioral intention to use the system?
RH: Users receiving the high-usability interface report higher behavioral intention to use the system than users receiving the standard interface.
D: Behavioral intention score (continuous/scale, TAM); interface condition (categorical: high-usability/standard); user ID.
M: Independent-samples t-test, ANOVA, OLS regression, ANCOVA if controls are added.
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RQ: Do organizations with top-management support differ from organizations without strong top-management support in ERP implementation success?
RH: Organizations with strong top-management support show higher ERP implementation success than organizations without such support.
D: ERP implementation success score (continuous/scale); top-management support category (binary/categorical, TOE); organization ID; firm size (continuous/categorical).
M: t-test, ANOVA, OLS regression, multilevel regression if respondents nested within firms.
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RQ: Are mobile banking users and non-users different in digital trust levels?
RH: Mobile banking users report higher digital trust than non-users.
D: Digital trust score (continuous/scale); user status (categorical: user/non-user); respondent ID; age controls.
M: t-test, Mann–Whitney U test, OLS regression, logistic regression if user status modeled as dependent.
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RQ: Do employees trained with interactive cybersecurity simulations differ from those trained with static materials in phishing detection accuracy?
RH: Employees trained with interactive simulations achieve higher phishing detection accuracy than employees trained with static materials.
D: Detection accuracy score (continuous/proportion); training condition (categorical); employee ID; department ID.
M: t-test, ANOVA, ANCOVA, mixed-effects model if repeated tasks are used.
Relational / correlational questions
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RQ: Is perceived usefulness associated with behavioral intention to use an e-government portal?
RH: Higher perceived usefulness is associated with stronger behavioral intention to use the e-government portal.
D: Perceived usefulness score (continuous/scale, TAM); behavioral intention score (continuous/scale, TAM); respondent ID; demographic controls.
M: Pearson/Spearman correlation, OLS regression, SEM/path analysis, PLS-SEM as alternative.
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RQ: Is effort expectancy associated with actual use frequency of a telemedicine platform?
RH: Higher effort expectancy is associated with higher actual use frequency of the telemedicine platform.
D: Effort expectancy score (continuous/scale, UTAUT); use frequency (count/continuous); user ID; profession (categorical).
M: Correlation, OLS/count regression, SEM, multilevel regression if users nested in clinics.
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RQ: Is task-technology fit associated with job performance in an enterprise analytics environment?
RH: Higher task-technology fit is associated with better job performance in enterprise analytics use.
D: Task-technology fit score (continuous/scale, TTF); job performance score (continuous/scale); employee ID; role type.
M: Correlation, OLS regression, SEM, mediation analysis as alternative.
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RQ: Is system quality associated with user satisfaction in a library information system?
RH: Higher system quality is associated with greater user satisfaction in the library information system.
D: System quality score (continuous/scale, DeLone–McLean); user satisfaction score (continuous/scale); user ID; usage experience.
M: Correlation, OLS regression, SEM/path analysis, multivariable regression.
Causal / experimental-style questions
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RQ: What is the effect of personalized onboarding on adoption of a new project-management system compared with standard onboarding?
RH: Users receiving personalized onboarding will show higher system adoption than users receiving standard onboarding.
D: Onboarding condition (binary); adoption score or usage metric (continuous/count); user ID; time point.
M: Experimental/quasi-experimental design, ANCOVA, mixed-effects model, difference-in-differences if rollout-based.
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RQ: Does adding explainable AI feedback increase user trust in an automated decision-support tool?
RH: Users exposed to explainable AI feedback report higher trust than users without such feedback.
D: Explanation condition (binary); trust score (continuous/scale); participant ID; task ID.
M: Experimental design, ANOVA, OLS regression, mixed-effects model if repeated decisions are used.
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RQ: What is the effect of social influence messaging on intention to adopt a mobile health app?
RH: Social influence messaging increases intention to adopt the mobile health app relative to a control message.
D: Messaging condition (categorical); adoption intention score (continuous/scale, UTAUT/TPB); respondent ID.
M: Survey experiment, ANOVA, ANCOVA, OLS regression.
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RQ: Does interface simplification reduce task completion time in an enterprise software workflow?
RH: Interface simplification reduces task completion time relative to the original workflow interface.
D: Interface condition (binary); task completion time (continuous/time); error count (count); participant ID.
M: Experimental design, t-test, ANOVA, repeated-measures ANOVA or LMM if within-subject.
Information technology – qualitative research
Technology adoption and user meaning
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RQ: How do employees describe the meaning of “ease of use” when adopting a new workplace platform?
RH: Employees are likely to describe ease of use through learnability, reduced friction, confidence, and compatibility with daily routines.
D: In-depth interviews, adoption reflections, onboarding notes, platform-use diaries.
M: Thematic analysis, phenomenological analysis, narrative inquiry, qualitative case study.
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RQ: How do students interpret the usefulness of AI-assisted study tools in everyday learning?
RH: Students are likely to interpret usefulness through time saving, clarity, personalization, and performance expectations.
D: Interviews, study reflections, tool-use diaries, course-context notes.
M: Thematic analysis, qualitative content analysis, narrative inquiry, case study.
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RQ: How do users describe the role of social influence in deciding whether to adopt a new app?
RH: Users are likely to describe social influence through peer recommendation, visibility, trust transfer, and fear of being left behind.
D: Interviews, app adoption narratives, peer-influence reflections, screenshot prompts.
M: Thematic analysis, discourse analysis, narrative inquiry, digital ethnography.
Trust, risk, and digital uncertainty
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RQ: How do users describe trust in AI-based recommendation systems?
RH: Users are likely to describe trust through perceived transparency, consistency, relevance, and controllability.
D: Interviews, recommendation-use reflections, trust narratives, interaction logs used qualitatively.
M: Thematic analysis, phenomenological analysis, case study, discourse analysis.
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RQ: How do consumers interpret privacy risk when using mobile payment services?
RH: Consumers are likely to interpret privacy risk through data visibility, institutional trust, convenience trade-offs, and prior experience.
D: Interviews, payment-use narratives, privacy reflections, service screenshots.
M: Thematic analysis, narrative inquiry, qualitative content analysis, digital case study.
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RQ: How do employees describe cybersecurity fatigue in highly digitized workplaces?
RH: Employees are likely to describe cybersecurity fatigue through overload, repetition, alert saturation, and conflicting productivity demands.
D: Interviews, workplace reflections, security-policy notes, diary prompts.
M: Thematic analysis, phenomenological analysis, practitioner-oriented case study, framework analysis.
System use, work practice, and fit
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RQ: How do professionals describe the fit between analytics tools and their daily tasks?
RH: Professionals are likely to describe fit through relevance, workflow integration, output clarity, and time efficiency.
D: Interviews, workflow narratives, usage reflections, task notes.
M: Thematic analysis, narrative inquiry, task-practice case study, framework analysis.
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RQ: How do remote workers describe the impact of collaboration platforms on coordination?
RH: Remote workers are likely to describe impact as simultaneously enabling connection and increasing fragmentation or interruption.
D: Interviews, collaboration diaries, meeting reflections, communication notes.
M: Thematic analysis, phenomenological analysis, digital work ethnography, case study.
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RQ: How do IT support staff interpret recurring user resistance to enterprise software changes?
RH: IT support staff are likely to interpret resistance through habit, low fit, poor communication, and change fatigue.
D: Interviews, support logs used qualitatively, implementation notes, reflective accounts.
M: Thematic analysis, practitioner inquiry, discourse analysis, qualitative case study.
Organizational implementation and change
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RQ: How do managers describe the role of top-management support during digital transformation?
RH: Managers are likely to describe top-management support as meaningful when it provides legitimacy, resources, and continuity.
D: Interviews, implementation memos, management reflections, project documents.
M: Thematic analysis, organizational case study, framework analysis, narrative inquiry.
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RQ: How do implementation teams describe barriers to ERP rollout in medium-sized firms?
RH: Implementation teams are likely to describe barriers through resource limits, process misfit, training gaps, and cross-unit resistance.
D: Team interviews, project notes, rollout documentation, meeting records.
M: Thematic analysis, team-based case study, qualitative process tracing, framework analysis.
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RQ: How do employees interpret communication quality during major IT system migration?
RH: Employees are likely to interpret communication quality through timing, clarity, honesty, and practical relevance.
D: Interviews, migration reflections, internal communication materials, field notes.
M: Thematic analysis, discourse analysis, case study, qualitative content analysis.
Information systems quality and experience
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RQ: How do users describe satisfaction with an academic information portal?
RH: Users are likely to describe satisfaction through reliability, speed, usability, and successful task completion.
D: Interviews, user reflections, portal experience diaries, support comments.
M: Thematic analysis, phenomenological analysis, user-experience case study, framework analysis.
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RQ: How do software developers describe technical debt in relation to system maintainability?
RH: Developers are likely to describe technical debt as a trade-off between delivery speed, complexity, and long-term sustainability.
D: Interviews, development reflections, project notes, technical narratives.
M: Thematic analysis, practitioner inquiry, narrative analysis, case study.
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RQ: How do end users interpret “system quality” beyond formal performance metrics?
RH: End users are likely to interpret system quality through responsiveness, predictability, ease, and emotional comfort in use.
D: Interviews, usage narratives, user reflections, support-case notes.
M: Thematic analysis, phenomenological analysis, qualitative content analysis, case study.
Information technology – mixed methods
Adoption, intention, and actual use
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RQ: How is perceived usefulness associated with behavioral intention to use a new enterprise platform, and how do users describe what makes the platform genuinely useful in practice?
RH: Higher perceived usefulness will be associated with stronger behavioral intention; users are likely to describe usefulness through task support, time saving, and practical relevance; integration is expected to explain why similar usefulness scores may still produce different adoption outcomes.
D: Quantitative: perceived usefulness, behavioral intention, actual use, TAM variables; Qualitative: interviews, adoption narratives, workflow reflections.
M: Explanatory sequential design, regression/SEM plus thematic analysis, joint display integration.
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RQ: What differences exist in adoption across groups with high and low effort expectancy, and how do users explain those differences in lived experience?
RH: Users with higher effort expectancy scores will show stronger adoption outcomes; users are likely to explain differences through usability, confidence, and prior digital competence; integration is expected to refine interpretation of adoption differences.
D: Quantitative: effort expectancy, usage frequency, user characteristics, UTAUT variables; Qualitative: interviews, usage diaries, onboarding reflections.
M: Convergent mixed methods design, group comparison/regression plus thematic analysis, integrated interpretation using joint displays.
Trust, risk, and intelligent systems
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RQ: How is trust in AI decision support associated with continued use intention, and how do users describe the factors that make AI feel trustworthy or untrustworthy?
RH: Higher trust in AI decision support will be associated with stronger continued use intention; users are likely to describe trust through transparency, consistency, explainability, and perceived fairness; integration is expected to explain variation in continued use beyond trust scores alone.
D: Quantitative: trust score, continued use intention, explainability perceptions; Qualitative: interviews, AI-use narratives, decision reflections.
M: Explanatory sequential design, regression/SEM plus thematic analysis, matrix-based integration.
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RQ: What differences exist in privacy concern across mobile payment user groups, and how do users interpret those concerns in everyday use?
RH: Users with higher privacy concern will show lower adoption or weaker continued use; users are likely to interpret concern through data control, institutional trust, and convenience trade-offs; integration is expected to clarify the practical meaning behind privacy scores.
D: Quantitative: privacy concern score, adoption/continued use, demographic variables; Qualitative: interviews, payment-use reflections, privacy narratives.
M: Convergent mixed methods design, group comparison/logistic or OLS regression plus thematic analysis, joint display integration.
Organizational implementation and IS success
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RQ: How is system quality associated with user satisfaction after ERP implementation, and how do employees describe the qualities of the system that matter most to them?
RH: Higher system quality will be associated with greater user satisfaction; employees are likely to describe important qualities through reliability, speed, fit, and support for work continuity; integration is expected to explain which dimensions of quality are most meaningful in practice.
D: Quantitative: system quality, user satisfaction, net benefits, DeLone–McLean variables; Qualitative: interviews, implementation reflections, support experiences.
M: Explanatory sequential design, regression/SEM plus thematic analysis, integrated interpretation.
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RQ: What differences exist in implementation success across organizations with different TOE conditions, and how do managers explain those differences?
RH: Organizations with stronger technological, organizational, and environmental readiness will show higher implementation success; managers are likely to explain those differences through capability, leadership, vendor relations, and external pressure; integration is expected to clarify mechanisms behind TOE-based differences.
D: Quantitative: TOE indicators, implementation success metrics, organizational profile variables; Qualitative: manager interviews, project documents, rollout narratives.
M: Convergent mixed methods design, multivariable regression plus thematic analysis, joint display and subgroup integration.
Director of Wellington based My Statistical Consultant Ltd company. Retired Associate Professor in Statistics.
Has a PhD in Statistics and over 45 years experience as a university professor, consultant, international researcher and government advisor.