This glossary defines selected research, cyberpsychology, cybersecurity, technology-adoption, and quantitative-methods terms used across CyberPsy.us.
Disclaimer. This glossary is provided for informational and educational purposes only. It is not an exhaustive or authoritative list of all research, cybersecurity, cyberpsychology, or statistical terms, and it does not replace formal methods texts, professional guidance, institutional policies, or legal, technical, or academic consultation.
Research and Dissertation Terms
Academic Review Board
An internal university committee that reviews and approves academic projects before Institutional Review Board submission.
Adoption
The process of accepting and implementing a new system, practice, or technology. In cybersecurity contexts, adoption may refer to integrating a cyber incident response plan, tool, policy, or practice into organizational operations.
Assumptions
Conditions accepted as true for the purposes of a study. Assumptions provide a foundation for the research, such as the assumption that participants will answer survey questions honestly.
Behavioral Intention
A person’s perceived likelihood or subjective probability that they will engage in a given behavior. In technology-adoption research, behavioral intention often refers to whether a person expects or intends to use, adopt, or support a particular system, practice, or technology.
Conceptual Framework
A visual or written representation of how concepts in a study are related, based on the researcher’s understanding of the problem, literature, and theory.
Construct
An abstract concept or variable that is defined and measured within a study, such as performance expectancy, effort expectancy, or social influence.
Construct Validity
The extent to which a measurement tool accurately measures the theoretical construct it is intended to measure.
Convergent Validity
A measure of how closely related two measures of the same construct are. In structural equation modeling, convergent validity is often assessed using average variance extracted.
Correlational Research
A type of non-experimental research that examines statistical relationships between two or more variables without manipulating them.
Cross-Sectional Study
A research design that analyzes data collected from a population or sample at a specific point in time.
Delimitations
Boundaries intentionally set by the researcher to narrow the scope of the study, such as limiting the population, setting, industry, or geographic area.
Dependent Variable
The outcome variable measured in a study. The dependent variable is expected to change based on one or more independent or predictor variables.
Explanatory Research
Research conducted to explain observed patterns or relationships among variables. Explanatory research often examines why or how certain relationships occur.
Independent Variable
A variable used to explain, predict, or influence changes in a dependent variable.
Institutional Review Board
A governing body that reviews research involving human participants to ensure ethical standards and participant protections are met.
Latent Variable
A variable that is not directly observable but is inferred from multiple observed indicators. Latent variables are commonly used to represent abstract concepts such as attitudes, perceptions, beliefs, or intentions.
Limitations
Potential weaknesses or factors beyond the researcher’s control that may affect the interpretation, reliability, or generalizability of study findings.
Mediating Variable
A variable that explains the mechanism through which an independent variable affects a dependent variable.
Moderating Variable
A variable that affects the strength or direction of the relationship between an independent variable and a dependent variable.
Non-Experimental Research
Research in which variables are observed as they naturally occur, without intervention or manipulation by the researcher.
Positivist Paradigm
A philosophical approach that assumes reality can be objectively measured using observation, numerical data, and hypothesis testing.
Predictor Variable
A variable used to forecast or explain changes in another variable. In quantitative research, predictor variable is often used similarly to independent variable.
Quantitative Research
A systematic investigation that uses numerical data and statistical analysis to understand phenomena, test hypotheses, examine relationships, or make predictions.
Reliability
The consistency of a measure. A reliable instrument produces stable and consistent results under similar conditions.
Theoretical Framework
A structure of concepts and theories that guides a study by explaining why the research problem exists and how key variables are expected to relate to one another.
Cybersecurity and Cyber Incident Response Terms
Confidentiality
The principle of ensuring that information is accessible only to those authorized to access it.
Cyber Incident Response Plan
A documented and structured plan that defines how an organization prepares for, responds to, manages, communicates about, and recovers from a cyber incident.
Cybersecurity Incident
An event that potentially jeopardizes the confidentiality, integrity, or availability of an information system. Examples include unauthorized access, malicious code, denial of service, misuse of authorized access, or other activity that may compromise systems or data.
Incident Response
A structured and documented approach to responding to an incident, limiting or preventing damage, remediating the cause, and reducing the likelihood or impact of future incidents.
Incident Response Plan
A formal strategic document that outlines responsibilities, procedures, communication protocols, and recovery steps for managing cybersecurity incidents.
Integrity
The principle of safeguarding the accuracy and completeness of information and processing methods.
Post-Adoption Usage Patterns
The ways a cyber incident response plan, system, or technology is used and sustained after initial adoption. This may include frequency of use, consistency of application, compliance with procedures, ongoing improvement, and integration into routine organizational practice.
Ransomware
A type of malicious software that encrypts or restricts access to data or systems, often with the attacker demanding payment for restoration or decryption.
Security Breach
A cybersecurity incident that results in the confirmed disclosure of sensitive data to an unauthorized party.
Threat Actor
An individual, group, or organization responsible for conducting or supporting malicious cyber activity.
Technology Adoption Theories and Models
Combined Technology Acceptance Model and Theory of Planned Behavior
The Combined Technology Acceptance Model and Theory of Planned Behavior integrates elements of the Technology Acceptance Model and the Theory of Planned Behavior. The C-TAM-TPB combines perceived usefulness, perceived ease of use, attitudes, subjective norms, and perceived behavioral control to help explain technology adoption.
Diffusion of Innovations Theory
The Diffusion of Innovations Theory explains how new ideas, technologies, and practices spread through populations over time. The DOI emphasizes adopter categories and innovation characteristics such as relative advantage, compatibility, complexity, trialability, and observability.
Model of PC Utilization
The Model of PC Utilization explains personal computer usage behavior through factors such as job fit, complexity, long-term consequences, affect toward use, social factors, and facilitating conditions. The MPCU was one of the models synthesized into the UTAUT.
Motivational Model
The Motivational Model emphasizes the role of intrinsic and extrinsic motivation in technology adoption and use. The MM includes concepts such as enjoyment, perceived usefulness, and reward-related motivation.
Social Cognitive Theory
Social Cognitive Theory explains behavior as a dynamic interaction among personal factors, behavior, and environmental influences. The SCT emphasizes concepts such as self-efficacy, outcome expectations, and observational learning.
Technology Acceptance Model
The Technology Acceptance Model explains technology acceptance through perceived usefulness and perceived ease of use. The TAM is one of the most widely used models in technology-adoption research.
Theory of Planned Behavior
The Theory of Planned Behavior explains behavioral intention as a function of attitudes toward the behavior, subjective norms, and perceived behavioral control. The TPB extends the Theory of Reasoned Action by accounting for perceived control over behavior.
Theory of Reasoned Action
The Theory of Reasoned Action explains behavior as driven by behavioral intention, which is influenced by attitudes toward the behavior and subjective norms. The TRA is a foundational model in behavioral-intention research.
Unified Theory of Acceptance and Use of Technology
The Unified Theory of Acceptance and Use of Technology integrates multiple prior technology-adoption models to explain technology usage through four core constructs: performance expectancy, effort expectancy, social influence, and facilitating conditions. The UTAUT is an important foundation for later technology-adoption research.
Unified Theory of Acceptance and Use of Technology 2
The Unified Theory of Acceptance and Use of Technology 2 extends the UTAUT by adding hedonic motivation, price value, and habit as predictors of behavioral intention and use. The UTAUT2 is often used to study individual-level behavioral intentions related to technology adoption.
UTAUT2 Constructs
Effort Expectancy
The degree of ease associated with using a technology or system. In cyber incident response contexts, effort expectancy may refer to whether cybersecurity professionals believe a cyber incident response plan is easy or difficult to use.
Facilitating Conditions
The degree to which individuals believe organizational and technical infrastructures exist to support the use of a technology. In cyber incident response contexts, facilitating conditions may include resources, training, leadership support, technical infrastructure, and clear procedures.
Habit
The extent to which people tend to perform behaviors automatically because of prior learning, repetition, or routine practice. In cyber incident response contexts, habit may refer to whether professionals routinely use or follow cyber incident response plans.
Hedonic Motivation
The enjoyment, satisfaction, or positive experience derived from using a technology. In cybersecurity contexts, hedonic motivation may be less obvious than in consumer technology but can still relate to satisfaction, confidence, or professional engagement with effective tools and procedures.
Performance Expectancy
The belief that using a system, technology, or process will help achieve job-related gains or desired outcomes. In cyber incident response contexts, performance expectancy may refer to whether professionals believe a cyber incident response plan improves organizational readiness, response effectiveness, or protection of confidentiality, integrity, and availability.
Price Value
A user’s assessment of the tradeoff between the benefits of using a technology and the associated financial, time, or effort-related costs. In organizational cybersecurity contexts, price value may involve whether the perceived benefits of a cyber incident response plan outweigh the costs of implementation and maintenance.
Social Influence
The degree to which individuals perceive that important others believe they should use a particular system, technology, or practice. In cyber incident response contexts, social influence may include expectations from supervisors, peers, executives, regulators, clients, or professional communities.
PLS-SEM and Statistical Terms
Alternative Hypothesis
A statement proposing that a relationship, difference, or effect exists between variables.
ANOVA
Analysis of variance is a statistical procedure used to test whether mean differences exist across groups.
Average Variance Extracted
Average variance extracted evaluates convergent validity by estimating how much variance in a construct’s indicators is explained by the latent variable. In many research contexts, an AVE value of .50 or higher is considered acceptable.
Bootstrapping
A resampling method used in PLS-SEM to estimate the statistical significance of path coefficients, weights, and loadings. Bootstrapping produces values such as t-values, p-values, and confidence intervals.
Boxplot
A visual representation of a distribution showing the median, quartiles, and potential outliers.
Chi-Square
A statistic used to test relationships between categorical variables or to assess model fit in covariance-based structural equation modeling. It is not typically used as a primary fit index in PLS-SEM.
Collinearity
Collinearity occurs when two predictor variables are highly correlated. In PLS-SEM, high collinearity can inflate standard errors and distort path coefficients.
Composite Reliability
A measure of internal consistency reliability used in PLS-SEM. Composite reliability is often preferred over Cronbach’s alpha for reflective measurement models because it uses item loadings rather than assuming equal item weights.
Confidence Interval
A range of values within which the true population parameter is expected to fall with a specified level of confidence, often 95%.
Correlation
A standardized measure of the relationship between two variables, ranging from -1 to +1. Correlation indicates strength and direction but does not establish causation.
Covariance
A measure of how two variables change together. Positive covariance means variables tend to move in the same direction, while negative covariance means they tend to move in opposite directions.
Cronbach’s Alpha
A classic measure of internal consistency reliability for a set of scale or test items. Higher values indicate greater reliability, with .70 commonly used as a general benchmark.
Degrees of Freedom
The number of independent pieces of information available to estimate a parameter or test statistic.
Descriptive Statistics
Statistics that summarize data, such as mean, standard deviation, minimum, maximum, frequency, and percentage.
Discriminant Validity
The extent to which constructs are distinct from one another. Common approaches include the Fornell–Larcker criterion, cross-loadings, and the heterotrait–monotrait ratio.
Effect Size
A measure of the magnitude of an observed effect, independent of sample size. Common effect sizes include Cohen’s d, f², and R².
Effect Size f²
In PLS-SEM, f² measures the impact of a predictor on an endogenous construct by evaluating the change in R² when the predictor is removed.
F-Statistic
A ratio of explained variance to unexplained variance used in ANOVA and regression models.
Heteroscedasticity
Unequal variance in residuals across levels of predicted values. Heteroscedasticity can affect the accuracy of coefficients and significance testing.
Heterotrait–Monotrait Ratio
A discriminant validity metric used to assess whether constructs are empirically distinct. HTMT values below commonly accepted thresholds suggest adequate discriminant validity.
Homoscedasticity
The assumption that residuals have equal variance across levels of predicted values.
Indicator
An observed variable or survey item used to measure a latent variable.
Inferential Statistics
Statistical methods used to make conclusions or predictions about a population based on sample data.
Linearity
The assumption that the relationship between variables is linear. Linearity is often assessed using scatterplots or residual plots.
Listwise Deletion
A missing-data procedure that excludes a participant from an analysis if they have missing data for any variable used in that analysis.
Measurement Model
The part of a structural equation model that specifies how latent variables are measured through indicators. Measurement models may be reflective or formative.
Multicollinearity
High correlations among three or more predictor variables. Severe multicollinearity can make it difficult to isolate the effect of individual predictors.
Non-Normal Data
Data that do not follow a normal distribution. Non-normality may result from skewness, kurtosis, outliers, or other distributional patterns. PLS-SEM is often used because it does not require multivariate normality.
Nonparametric Test
A statistical test that does not assume a specific population distribution. Examples include the Mann–Whitney U test and Kruskal–Wallis test.
Normality
The assumption that data follow a normal distribution. Many classical inferential tests assume normality, although PLS-SEM does not require multivariate normality.
Null Hypothesis
A statement asserting that there is no relationship, difference, or effect between variables. Hypothesis testing often begins by assuming the null hypothesis is true.
Outer Loading
The relationship between an indicator and its latent construct in a reflective measurement model. Higher outer loadings indicate stronger relationships between indicators and the construct.
Outlier
A data point that is substantially distant from other values in a distribution. Outliers may influence correlations, regression coefficients, and other statistical results.
Pairwise Deletion
A missing-data procedure that uses all available data for each calculation and excludes cases only when they are missing data relevant to that specific calculation.
Parametric Test
A statistical test that assumes certain conditions about the population distribution, such as normality. Examples include t-tests and ANOVA.
Partial Least Squares Structural Equation Modeling
Partial least squares structural equation modeling is a statistical modeling technique used to analyze complex relationships between observed and latent variables. PLS-SEM is often used in predictive, exploratory, and technology-adoption research.
Path Coefficient
A standardized coefficient representing the strength and direction of the relationship between constructs in a structural model.
p-value
The probability of observing a result as extreme as, or more extreme than, the result obtained if the null hypothesis were true. A p-value below the selected significance level is typically considered statistically significant.
Predictive Relevance
A measure of whether a model has predictive capability for a construct. In PLS-SEM, Q² values greater than zero generally suggest predictive relevance.
R²
The coefficient of determination. R² indicates how much variance in an endogenous construct is explained by its predictors.
Regression Coefficient
A value representing the strength and direction of the relationship between a predictor and an outcome variable.
Residual
The difference between an observed value and a predicted value.
Scatterplot
A visual tool used to inspect relationships between variables, linearity, and potential outliers.
Shapiro–Wilk Test
A statistical test for normality. A nonsignificant result suggests the data are not significantly different from a normal distribution.
Significance Level
The threshold for rejecting the null hypothesis, commonly set at .05.
Standard Deviation
A measure of how much individual scores vary around the mean.
Standard Error
The standard deviation of a sampling distribution. Standard error is used in confidence intervals and significance testing.
Structural Equation Modeling
A multivariate statistical technique used to analyze relationships among observed variables and latent constructs.
Structural Model
The part of a structural equation model that specifies relationships among latent variables.
t-Test
A statistical test used to compare means or determine whether an observed effect differs significantly from zero. In PLS-SEM, t-tests are often produced through bootstrapping.
t-value
A statistic used to determine whether a path coefficient, loading, or other estimate is statistically significant.
Type I Error
Incorrectly rejecting a true null hypothesis. This is also known as a false positive.
Type II Error
Failing to reject a false null hypothesis. This is also known as a false negative.
Variance
The average squared deviation from the mean. Variance is the foundation for standard deviation and many statistical procedures.
Variance Inflation Factor
A statistic used to assess collinearity. Higher VIF values indicate greater collinearity among predictors.
Greek Symbols and Notation
α — Alpha
Alpha is commonly used to represent Cronbach’s alpha, a measure of internal consistency reliability. Alpha is also used to represent the significance level in hypothesis testing.
β — Beta
Beta is commonly used to represent a standardized path coefficient or regression coefficient. In PLS-SEM, beta indicates the strength and direction of a relationship between constructs.
δ — Delta
Delta may represent measurement error in reflective indicators. In simplified notation, measurement error can be expressed as one minus the squared loading.
ε — Epsilon
Epsilon commonly represents an error term or residual, including unexplained variance in a dependent or endogenous variable.
λ — Lambda
Lambda represents an outer loading or indicator loading in PLS-SEM. It reflects the strength of the relationship between an indicator and its latent construct.
ρA — Rho_A
Rho_A is a reliability measure used in PLS-SEM. It is often considered a useful reliability coefficient because it accounts for actual outer loadings.
ρC — Composite Reliability
Rho_C represents composite reliability, a measure of internal consistency reliability in PLS-SEM.
φ — Phi
Phi may represent the correlation between latent variables, particularly in measurement or model diagnostics.
SPSS Terms
Data View
The spreadsheet-like interface in SPSS where each row represents a case and each column represents a variable.
Explore Function
An SPSS tool used to generate descriptive statistics, boxplots, and normality tests.
Frequencies
Tables showing the count and percentage of values in categorical or ordinal variables.
Histogram
A bar graph used to visually assess distribution shape, skewness, and normality.
Item-Total Statistics
An SPSS reliability-analysis table showing how each item correlates with the total scale and how Cronbach’s alpha changes if an item is removed.
Levene’s Test
A test for homogeneity of variance, often used with t-tests and ANOVA.
Measurement Levels
SPSS measurement levels include nominal, ordinal, scale, and string. Scale variables are commonly used for correlations, regression, and many inferential tests.
Missing Values
Values that are absent from a dataset. SPSS distinguishes among system-missing values, user-defined missing values, and values excluded during analysis.
Normal Q-Q Plot
A diagnostic plot comparing observed data to a theoretical normal distribution. Points close to the diagonal line suggest approximate normality.
Pearson Correlation Table
A matrix showing correlations, significance levels, and sample sizes for relationships among variables.
Regression Output
SPSS regression output typically includes coefficients, t-values, p-values, confidence intervals, and collinearity statistics.
Reliability Analysis
An SPSS procedure used to compute Cronbach’s alpha, item-total correlations, and related scale-reliability statistics.
Sig. 2-tailed
The p-value associated with a two-tailed test. Values below the selected significance level are typically considered statistically significant.
Standardized Residuals
Residuals converted into standard deviation units. They are useful for identifying outliers and evaluating model assumptions.
Variable View
The SPSS interface where variables are defined, including names, labels, types, values, missing-data codes, and measurement levels.
Additional Section
Academic Review Board (ARB) — An internal Capitol Technology University committee that reviews and approves academic projects prior to Institutional Review Board (IRB) submission.
Adoption — The process of accepting and implementing a new system, practice, or technology (Dissanayake et al., 2022). In this study, adoption specifically indicates the act of integrating a cybersecurity incident response plan into operations.
Assumptions — Conditions accepted as true without proof for the purposes of the study. They form the foundation on which the research is conducted (e.g., assuming participants will answer survey questions honestly).
Behavioral Intention — A person’s perceived likelihood or subjective probability that they will engage in a given behavior (Venkatesh et al., 2012). In this study, behavioral intention refers to whether participants believe they will integrate a cybersecurity incident response plan into organizational operations.
Combined TAM and TPB (C-TAM-TPB) — Developed by Taylor & Todd (1995) as an integration of the Technology Acceptance Model (Davis, 1989) and the Theory of Planned Behavior (Ajzen, 1991), combining perceived usefulness, perceived ease of use, attitudes, subjective norms, and perceived behavioral control to predict technology adoption.
Conceptual Framework — A visual or written representation of how concepts in a study are related, based on the researcher’s understanding of the problem, literature, and theory.
Confidentiality — The principle of ensuring that information is accessible only to those authorized to have access (NIST, 2024).
Construct — An abstract concept or variable that is specifically defined and measured within the study (e.g., performance expectancy, social influence in UTAUT2).
Construct Validity — The extent to which a measurement tool accurately measures the theoretical construct it is intended to measure.
Convergent Validity — A measure of how closely related two measures of the same construct are, often assessed in structural equation modeling using average variance extracted (AVE).
Correlational Research — A type of non-experimental research that examines the statistical relationships between two or more variables without manipulating them.
Cronbach’s Alpha — A measure of internal consistency or reliability for a set of scale or test items, with higher values indicating greater reliability.
Cross-Sectional Study — A research design that analyzes data collected from a population, or a representative subset, at a specific point in time.
Cyber Incident Response Plan (CIRP) — A documented, structured plan that defines how an organization responds to and recovers from a cyber incident.
Cybersecurity Incident — An event that potentially jeopardizes the confidentiality, integrity, or availability of an information system. Incidents include unauthorized access, malicious code, denial of service, or misuse of authorized access (Zamfiroiu & Sharma, 2022).
Delimitations — Boundaries or constraints intentionally set by the researcher to narrow the scope of the study (e.g., focusing only on medium and large U.S. businesses).
Dependent Variable (DV) — The outcome variable the researcher measures, which is expected to change based on the independent variable(s).
Diffusion of Innovations Theory (DOI) — Introduced by Rogers (1962, updated 2003), explains how new ideas and technologies spread through populations over time, highlighting adopter categories (innovators, early adopters, early majority, late majority, laggards) and factors affecting adoption.
Effort Expectancy — The degree of ease associated with the use of a technology (Venkatesh et al., 2003). In this study, effort expectancy refers to whether participants believe cybersecurity incident response plans will be easy or difficult to use.
Explanatory Research — Research conducted to identify cause-and-effect relationships and provide explanations for observed patterns. “I would think that your research should be explanatory because you are
attempting to explain the phenomenon by illustrating cause and effect
relationships between the variables.”
Facilitating Conditions — The degree to which individuals believe that organizational and technical infrastructures exist to support the use of technology (Venkatesh et al., 2003). In this study, facilitating conditions refer to whether participants believe their organization provides the resources, support, and infrastructure necessary to implement and use cybersecurity incident response plans.
Habit — The extent to which people tend to perform behaviors automatically due to learning or repetition (Venkatesh et al., 2012). In this study, habit refers to whether participants routinely use cybersecurity incident response plans.
Hedonic Motivation — The enjoyment or satisfaction derived from using a technology (Venkatesh et al., 2012). In this study, hedonic motivation refers to whether participants believe they will derive satisfaction from implementing a cybersecurity incident response plan.
Incident Response — A structured, well-documented, and formalized strategic approach to responding to an incident, limiting or preventing damage to an organization, and remediating the cause to reduce the risk of future incidents (Brown et al., 2021).
Incident Response Plan — A formal, strategic document that outlines responsibilities, procedures, and communication protocols for managing cybersecurity incidents (Khan, 2024).
Independent Variable (IV) — The variable(s) that are manipulated or categorized to observe their effect on the dependent variable(s).
Institutional Review Board (IRB) — A governing body that reviews research involving human subjects to ensure compliance with ethical standards and participant protections.
Integrity — The principle of safeguarding the accuracy and completeness of information and processing methods (NIST, 2024).
Latent Variable — A variable that is not directly observable or measurable, but is inferred from patterns among multiple observed variables, also called indicators or manifest variables. Latent variables are used to represent abstract concepts such as attitudes, perceptions, or intentions, and are commonly estimated using statistical techniques such as factor analysis or structural equation modeling (SEM) (Byrne, 2016).
Limitations — Potential weaknesses or factors beyond the researcher’s control that may affect the results or generalizability of the study (e.g., sample size, self-reported data).
Mediating Variable — A variable that explains the mechanism through which the independent variable affects the dependent variable.
Model of PC Utilization (MPCU) — Proposed by Thompson, Higgins, & Howell (1991), focuses on factors influencing personal computer usage behavior, including job fit, complexity, long-term consequences, affect toward use, social factors, and facilitating conditions.
Moderating Variable — A variable that affects the strength or direction of the relationship between the independent and dependent variables.
Motivational Model (MM) — Advanced by Davis, Bagozzi, & Warshaw (1992), emphasizes the role of intrinsic (enjoyment) and extrinsic (perceived usefulness, rewards) motivation in influencing technology adoption and usage.
Non-Experimental Research — Research in which variables are observed as they naturally occur, without intervention or manipulation by the researcher.
Non-Normal Data — Data that does not follow a normal (Gaussian) distribution, meaning it deviates from the bell-shaped curve where most values cluster around the mean. Non-normality can result from skewness, kurtosis, outliers, or underlying patterns in the data. Many statistical tests assume normality, so non-normal data may require transformation or the use of nonparametric methods.
Examples in this study’s context:
- A cluster of survey respondents giving extremely high or extremely low scores on hedonic motivation compared to the majority.
- Skewed price value ratings where most participants strongly disagree with a statement but a few strongly agree, pulling the distribution to one side.
- Outlier habit scores from a small group of cyber incident responders who always adopt new tools immediately, unlike the majority who adopt slowly.
Methodological note:
Partial least squares structural equation modeling (PLS-SEM), the method used in this study, does not require data to be normally distributed, making it well-suited for handling non-normal datasets without transformation.
Partial Least Squares Structural Equation Modeling (PLS-SEM) — A statistical modeling technique used to analyze complex relationships between observed and latent variables, particularly in exploratory research.
Performance Expectancy — The belief that using a system or technology will help achieve gains in job performance or desired outcomes (Venkatesh et al., 2003). In this study, performance expectancy refers to whether participants believe cybersecurity incident response plans will support their ability to protect data confidentiality, integrity, or availability.
Positivist Paradigm — A philosophical approach that assumes reality is objective and can be measured using quantitative methods; emphasizes observation, measurement, and hypothesis testing.
Predictor Variable — A variable used to forecast or explain changes in another variable; in quantitative analysis, often synonymous with independent variable.
Post-Adoption Usage Patterns — In the context of medium and large U.S. businesses, post-adoption usage patterns refer to the ways a cyber incident response plan (CIRP) is utilized and sustained after its initial adoption.
Price Value — A user’s assessment of the tradeoff between the benefits of using a technology and the associated financial cost (Venkatesh et al., 2012). In this study, price value refers to whether participants believe the benefits of using cybersecurity incident response plans outweigh the effort, time, or cost required.
Quantitative Research — A systematic investigation that primarily uses numerical data and statistical analysis to understand phenomena, test hypotheses, or make predictions.
Ransomware — A type of malicious software that encrypts a victim’s data, with the attacker demanding payment for the decryption key.
Reliability — The consistency of a measure; a reliable instrument yields the same results under consistent conditions.
Security Breach — A cybersecurity incident that results in the confirmed disclosure of sensitive data to an unauthorized party (He et al., 2022). Breaches typically require notification under regulatory frameworks (Greenwood, 2021).
Social Cognitive Theory (SCT) — Originated by Bandura (1986), proposes that behavior is shaped by the interaction of personal factors, environmental influences, and behavior itself (reciprocal determinism); includes concepts like self-efficacy and outcome expectations.
Social Influence — The degree to which individuals perceive that important others (e.g., peers, supervisors, colleagues) believe they should use a particular system or technology (Venkatesh et al., 2003). In this study, social influence refers to whether participants believe important organizational stakeholders will recommend implementing cybersecurity incident response plans.
Structural Equation Modeling (SEM) — A multivariate statistical analysis technique used to analyze structural relationships, often between latent constructs measured by multiple indicators.
Technology Acceptance Model (TAM) — Introduced by Davis (1989), suggests that perceived usefulness and perceived ease of use are the primary factors determining user acceptance of technology.
Theory of Planned Behavior (TPB) — Developed by Ajzen (1991), states that behavioral intention is shaped by attitudes toward the behavior, subjective norms, and perceived behavioral control.
Theory of Reasoned Action (TRA) — Proposed by Fishbein & Ajzen (1975), posits that a person’s behavioral intention is determined by their attitude toward the behavior and subjective norms.
Theoretical Framework — A structure of concepts and theories that guides the study by explaining why the research problem exists and predicting outcomes. In this dissertation, the theoretical framework is UTAUT2.
Threat Actor — An individual or group responsible for conducting malicious cyber activities.
Unified Theory of Acceptance and Use of Technology (UTAUT) — Developed by Venkatesh, Morris, Davis, & Davis (2003) to consolidate constructs from eight prior models, identifying performance expectancy, effort expectancy, social influence, and facilitating conditions as key predictors of behavioral intention and use behavior.
Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) — Extended version of UTAUT by Venkatesh, Thong, & Xu (2012) adding hedonic motivation, price value, and habit, with adjustments for consumer and individual contexts.
Post-Adoption Usage Patterns
Definition (Dissertation Context): In the context of medium and large U.S. businesses, post-adoption usage patterns refer to the ways a cyber incident response plan (CIRP) is utilized and sustained after its initial adoption.
Key Aspects:
- Frequency and quality of use – How often and how effectively the CIRP is engaged during real incidents and drills.
- Consistency of application – Whether teams follow the plan’s prescribed steps, adapt them, or bypass them.
- Sustained compliance – Ongoing adherence to NIST SP 800-61r3 guidance, organizational policies, and applicable regulations.
- Ongoing improvements – Integration of feedback, lessons learned, and best practices into the CIRP over time.
- Cultural integration – The extent to which the CIRP becomes a routine part of organizational operations rather than a document only consulted during crises.
Relevance to UTAUT2: Post-adoption usage patterns provide evidence of whether behavioral intention translates into consistent and effective action, making them essential for understanding long-term adoption and impact.
ARB — Academic Review Board — Internal Capitol Technology University committee that reviews and approves academic projects before IRB submission.
CIRP — Cyber Incident Response Plan — A documented, structured plan detailing how an organization responds to and recovers from a cyber incident.
C-TAM-TPB — Combined Technology Acceptance Model and Theory of Planned Behavior — Taylor & Todd’s (1995) integration of TAM (Davis, 1989) and TPB (Ajzen, 1991).
DOI — Diffusion of Innovations Theory — Rogers’ (1962; 2003) model explaining how innovations spread through populations.
DV — Dependent Variable — The outcome variable measured in research to assess the effect of the independent variable(s).
IRB — Institutional Review Board — Governing body ensuring human-subject research meets ethical and regulatory standards.
IV — Independent Variable — Variable manipulated or categorized to observe its effect on the dependent variable(s).
MM — Motivational Model — Davis, Bagozzi, & Warshaw’s (1992) framework emphasizing intrinsic and extrinsic motivation in technology adoption.
MPCU — Model of PC Utilization — Thompson, Higgins, & Howell’s (1991) model of factors influencing PC usage behavior.
PLS-SEM — Partial Least Squares Structural Equation Modeling — A statistical method for analyzing complex relationships between observed and latent variables.
SCT — Social Cognitive Theory — Bandura’s (1986) model of behavior shaped by personal, environmental, and behavioral factors.
SEM — Structural Equation Modeling — A statistical technique analyzing structural relationships between latent constructs.
TAM — Technology Acceptance Model — Davis’ (1989) theory that perceived usefulness and ease of use predict technology adoption.
TPB — Theory of Planned Behavior — Ajzen’s (1991) model explaining intention as a function of attitudes, norms, and perceived control.
TRA — Theory of Reasoned Action — Fishbein & Ajzen’s (1975) model stating intention is driven by attitudes and subjective norms.
UTAUT — Unified Theory of Acceptance and Use of Technology — Venkatesh, Morris, Davis, & Davis’ (2003) synthesis of eight prior models.
UTAUT2 — Unified Theory of Acceptance and Use of Technology 2 — Venkatesh, Thong, & Xu’s (2012) extended model adding hedonic motivation, price value, and habit.