Glossary

Key terms in
the research

Definitions of the concepts, methods and constructs used across the publications on this site, as each publication uses them. Every definition links to its source publication, which gives the full citation and DOI. For what the research found, see the research focus areas.

A

Agricultural extension worker
An experienced, well-educated farming mentor who works in the field and liaises with local government and value chain stakeholders to support rural farmers. — as used in Strang et al. (2022)
An experienced, well-educated farm practitioner hired and funded by the government to mentor farmers. — as used in Che et al. (2020)
Agricultural information system (AIS)
A system in which agricultural information is generated, transformed, transferred, consolidated, received and fed back so that these processes function synergistically to underpin knowledge utilisation by agricultural producers (Röling, 1988); in this study it includes farm management information systems. — as used in Strang et al. (2019)
Andragogy
An approach to teaching focused on helping adults learn to learn, contrasted in the chapter with pedagogy (lecturing and delivering materials). — as used in Strang et al. (2020)

B

Bayes factor (BF)
The ratio of evidence for one hypothesis over another; values above 100 are conventionally treated as extreme evidence. — as used in Strang & Vajjhala (2024)
Bayesian network
A directed probabilistic graphical model representing the joint probability distribution over a set of random variables as the product of each variable's probability given its parents. — as used in Biba & Vajjhala (2022)
Behavioral intention (BI)
A person’s self-reported intention to use a technology, usually treated as the antecedent of actual use. — as used in Strang & Vajjhala (2017)
Big data
Data too big for conventional systems such as relational database management systems, characterized by volume, velocity, variety, veracity and value. — as used in Vajjhala & Strang (2017)
Binary logistic regression
A regression technique that uses maximum likelihood to estimate the odds of a two-valued outcome (here, will purchase versus unlikely to purchase) from a set of predictors. — as used in Strang & Vajjhala (2020)
Bit parallelism
The inherent parallelism in a bit operation like AND/OR inside a computer word, used to simulate a non-deterministic automaton during string matching. — as used in Muhammad et al. (2023)
Blockchain
A distributed and decentralized database of all transactions executed among participating nodes, in which cryptographically linked blocks form an immutable, auditable ledger (Shrestha et al.). — as used in Strang et al. (2020)
BRICS
The group of emerging economies Brazil, Russia, India, China and South Africa, which the paper notes account for 40% of the world’s population and 26% of its land area. — as used in Strang & Vajjhala (2020)

C

Coefficient of determination (R²)
A measure of how much variance in the target is explained by the model, where values close to 1 indicate a near-perfect fit. — as used in Gigov et al. (2025)
The proportion of variance in the target variable explained by a model’s input features, used here as the effect size for comparing machine learning models. — as used in Strang & Vajjhala (2025)
The proportion of variance in the target variable explained by a model, used here as the effect size of each ML regression model. — as used in Vajjhala & Strang (2024)
Collaborative filtering
The chapter’s definition: collaborative filtering allows recommendations to users taking into account how other users have rated items, using a database of user ratings over items. — as used in Biba et al. (2017)
Commitment tenure
In this study, the number of years a project manager worked for the same employer, used as a proxy for organisational commitment. — as used in Strang & Vajjhala (2025)
Common method bias
Inflated correlations that arise because several variables were measured by the same source or instrument rather than because the constructs are truly related. — as used in Strang & Vajjhala (2026)
Communities of practice (CoPs)
Informal groups of individuals sharing knowledge and experience within or outside an organization (the chapter's own definition). — as used in Vajjhala (2016)
Consensual qualitative research (CQR)
A qualitative method used at the group level of analysis in which researchers interpret and reach consensus on data collected from participants (Hill et al., 2005). — as used in Strang et al. (2020)
A formal qualitative method in which a group discusses semi-structured open-ended questions and reaches consensus on domains and core themes, with an external auditor cross-checking the data. — as used in Che et al. (2020)
Continuance intention
A user's intention to continue using an existing system rather than discontinue it. — as used in Strang et al. (2019)
Correspondence analysis
A multivariate exploratory technique that estimates the interdependence (inertia) between categorical variables in a contingency table and plots them as distances in a low-dimensional map. — as used in Vajjhala et al. (2015)
Cyber extortion (ransomware)
A cyberattack that demands money or assets, often by encrypting critical systems with ransomware or threatening to publish stolen data until a ransom, typically in bitcoin, is paid. — as used in Strang & Vajjhala (2023)
Cybersecurity awareness
The degree of users’ understanding of the importance of information security to protect organizational data and networks (Rahim et al.). — as used in Strang et al. (2020)

D

Data analytics
The use of data and analytical tools to inform business decisions, for example to predict sales, control inventory, understand customers and streamline operations. — as used in Vajjhala & Strang (2024)
Data envelopment analysis (DEA)
A non-parametric method used for evaluating the efficiency of decision-making units. — as used in Vajjhala & Eappen (2024)
Data triangulation
Checking the reliability and validity of findings by comparing results across multiple methods or data sources. — as used in Strang & Vajjhala (2024)
Decision-making unit (DMU)
An entity, such as a hospital or clinic, whose conversion of inputs into outputs is assessed for relative efficiency in DEA. — as used in Vajjhala & Eappen (2024)
Discriminant analysis
A statistical technique that uses several independent factors to predict membership in the categories of a single categorical outcome, here adopting versus not adopting FMIS. — as used in Vajjhala et al. (2021)
A classification technique that assigns cases to predefined groups using linear discriminant functions of the predictor variables. — as used in Strang & Vajjhala (2020)
A statistical technique that builds a function from predictor variables to classify cases into groups — here, consumers likely versus unlikely to purchase a smartphone online. — as used in Vajjhala & Strang (2019)
A statistical technique that uses predictor variables to classify cases into predefined groups, such as likely versus unlikely purchasers. — as used in Vajjhala & Strang (2018)

E

E-adoption
The acceptance and use of electronic means for accomplishing one or more tasks (the chapter’s definition). — as used in Vajjhala et al. (2021)
ESG compliance
The degree to which an organization or project meets Environmental, Social, and Governance criteria set by regulators, investors, or internal policy. — as used in Strang & Vajjhala (2026)
ESG rating divergence
When different rating agencies give the same company substantially different ESG scores. — as used in Strang & Vajjhala (2024)
Expectation confirmation theory (ECT)
A theory explaining how continued use of information technology is sustained, linking confirmation of expectations, perceived usefulness and satisfaction to continuance intention. — as used in Strang et al. (2019)
Expected Calibration Error (ECE)
The weighted average absolute difference between a model’s predicted confidence and its observed accuracy across probability bins. — as used in Haveri & Vajjhala (2026)
Explained variance (R²)
The share of the variation in an outcome — here, project success — that a regression model accounts for. — as used in Strang & Vajjhala (2025)
Extension worker
An experienced farmer, selected and hired by the government to mentor and train local farmers, using their credibility as a farmer to approach their clients (the chapter's own definition). — as used in Strang et al. (2021)

F

Farm management information systems (FMIS)
A planned system of collecting, processing, storing, and disseminating data in a form required to carry out farm-related operational functions (the chapter’s definition, after Sørensen et al., 2010). — as used in Vajjhala et al. (2021)
Food insecurity
A situation in which people lack reliable access to sufficient, safe and nutritious food; the chapter describes Nigeria as in a food security crisis for the last 10 years. — as used in Strang et al. (2021)
Food security
The condition in which people have reliable access to sufficient, safe and nutritious food; the paper notes food insecurity affects about 11% of the world’s population and up to 64% in some Nigerian states. — as used in Strang et al. (2020)
Functional programming
A programming paradigm that builds programs from pure functions, emphasizing immutability, composability and declarative semantics rather than step-by-step state changes. — as used in Fonkam & Vajjhala (2026)

G

Grad-CAM (Gradient-weighted Class Activation Mapping)
A technique that produces heatmaps highlighting the image regions most influential in a model’s classification decision. — as used in Haveri & Vajjhala (2026)
GRI 206-1
The Global Reporting Initiative disclosure on legal actions for anti-competitive behavior, anti-trust, and monopoly practices — used here as an anti-corruption governance indicator. — as used in Strang & Vajjhala (2024)
Grounded theory
A qualitative research method in which codes, themes and theory are developed inductively from the data rather than tested from prior hypotheses. — as used in Strang et al. (2022)

H

Haskell
A purely functional, statically typed programming language with type inference, algebraic data types, type classes and monads. — as used in Fonkam & Vajjhala (2026)
Hyperlocal weather prediction
Forecasting weather conditions at a fine spatial scale such as a city or community, rather than the coarse grids (e.g. 15 km × 15 km) used by global forecasting systems. — as used in Gigov et al. (2025)

I

Independent and identically distributed (i.i.d.)
A property of random variables that share the same probability distribution and are mutually independent — an assumption most relational data, including genomic data, violates. — as used in Biba & Vajjhala (2022)
Industry 4.0
The fourth industrial revolution: the digitalization of industry through technologies such as the Internet of Things, cyber-physical systems, big data analytics, cloud computing, artificial intelligence and autonomous machines. — as used in Strang & Vajjhala (2023)
Inertia
In correspondence analysis, the measure of association between row and column factors, whose proportion accounted for by each dimension indicates how much of the relationship a plot captures. — as used in Vajjhala et al. (2015)

K

Knowledge sharing
The paper defines it as a process of capturing, organising and distributing knowledge gained from others or through years of work and personal experience. — as used in Vajjhala & Baghurst (2014)

L

Label leakage
When a predictor variable effectively encodes the outcome being predicted, producing unrealistically perfect model accuracy that will not hold in real use. — as used in Strang & Vajjhala (2026)
Learning management system (LMS)
Software such as Moodle used to deliver and manage online course content and assessments. — as used in Strang & Vajjhala (2017)
Linear probing vs. fine-tuning
Linear probing trains only a new output layer on top of a frozen pretrained model; full fine-tuning updates all of the model’s weights. — as used in Kumar et al. (2026)

M

Machine learning (ML)
A subset of artificial intelligence that lets computer systems perform tasks through algorithms and statistical models without explicit instructions, relying on pattern recognition and inference. — as used in Vajjhala & Strang (2024)
Medium-sized enterprise
A firm in the medium size band of SMEs, identified in this study using the European Commission’s criteria. — as used in Vajjhala & Baghurst (2014)
Member checking
A validation step in which participants review transcripts, codes and themes to confirm they reflect their meaning. — as used in Strang et al. (2022)
Model fit indices
CFI and TLI near 1 and RMSEA and SRMR near 0 indicate that a measurement model reproduces the observed survey data well. — as used in Strang & Vajjhala (2026)
Monad
An abstraction from category theory used in functional languages to structure and isolate state and side effects in otherwise pure code. — as used in Fonkam & Vajjhala (2026)
Multi-seed evaluation
Training and testing each model several times with different random initializations to check whether performance differences are stable rather than due to chance. — as used in Kumar et al. (2026)
Multiple correspondence analysis
A non-parametric multivariate exploratory technique that estimates and plots the relationships among more than two categorical factors so that related ideas appear close together. — as used in Strang et al. (2020)

N

National culture
The distinctive shared values, beliefs and assumptions guiding the behavior of the inhabitants of a nation (Stonehouse et al., 2004). — as used in Vajjhala & Strang (2014)
Node purity
In random forest, the increase in node purity measures a feature's importance, which the authors liken to the change in r2 effect size when a factor is added to a regression model. — as used in Strang & Vajjhala (2023)
Nominal group technique
A structured technique in which participants first independently generate ideas before they are pooled and discussed by the group. — as used in Che et al. (2020)

O

Online purchase intention
A consumer’s stated likelihood of buying a product online; here, a yes/no response to buying a $100 smartphone. — as used in Vajjhala & Strang (2019)
Open big data
Very large datasets made publicly available, here a meta dataset summarizing terrorist activity as keyword frequencies from public news articles. — as used in Vajjhala et al. (2015)
Organisational commitment
An employee’s psychological attachment and loyalty to the organisation (Meyer and Allen, 1991), comprising affective, continuance and normative commitment. — as used in Strang & Vajjhala (2025)
Organizational commitment
An employee's psychological attachment and loyalty to their organization; in this study, PM tenure was the critical measure of commitment. — as used in Vajjhala & Strang (2024)
Organizational fit
The degree to which an individual or partner matches an organization, which the authors propose could be measured through online personality and socio-cultural behavior. — as used in Vajjhala & Strang (2017)
Out-of-distribution (OOD) data
Data that differ systematically from a model’s training data — here, field images from a different country, soil, and lighting than the benchmark. — as used in Kumar et al. (2026)

P

Parameterized matching
Matching in which two strings are equivalent if one can be obtained from the other by renaming parameter symbols through a bijective mapping while constant symbols match exactly. — as used in Muhammad et al. (2023)
Pareto analysis
A prioritization technique that identifies the small share of items (here the top 20% of phrases) that account for most of the occurrences (at least 80%). — as used in Strang et al. (2021)
Parkerian hexad
Six elements of information security — confidentiality, control, integrity, authenticity, availability and utility — that cybersecurity investment aims to protect. — as used in Strang & Vajjhala (2023)
Past performance disclosure
A team member voluntarily sharing evaluations of their performance on earlier projects when being considered for a new project team. — as used in Strang & Vajjhala (2025)
PERT estimate
A three-point estimate combining optimistic, most likely and pessimistic durations, used here to test project manager risk-estimation competency (correct answer: 32 days). — as used in Strang & Vajjhala (2022)
Power distance index (PDI)
The extent to which less powerful individuals in a society accept inequality in power. — as used in Vajjhala & Strang (2014)
Prev-encoding
An encoding scheme introduced by Baker that keeps each constant symbol and replaces each parameter symbol with the distance from its previous occurrence. — as used in Muhammad et al. (2023)
Project sponsor
The senior person or group who authorizes a project, provides its funding, and is accountable for its business outcome. — as used in Strang & Vajjhala (2026)
Project-level ESG compliance
The extent to which Environmental, Social, and Governance requirements are planned for and then monitored and controlled within a specific project, as opposed to being reported only at company level. — as used in Strang & Vajjhala (2026)
Prospect theory
A model of risk decision making in which people choose between value prospects based on loss aversion, reference points and decision weights, tending to be risk-averse for losses and risk-seeking for gains (Kahneman and Tversky, 1979). — as used in Strang & Vajjhala (2022)
A model of decision making under risk in which choices are made between value prospects or gambles, shaped by loss aversion, reference points and decision weights. — as used in Strang & Vajjhala (2022)
Psycho-demographic factors
In this study, a combination of demographic characteristics (age, gender, ethnicity, income) and psychological factors such as trust in online information sources and feelings of online system control. — as used in Vajjhala & Strang (2019)
Purchase intention
A consumer’s stated likelihood of buying a product or service in the near future; here measured as a yes/no ‘probable’ versus ‘improbable’ response. — as used in Vajjhala & Strang (2018)

R

Random forest
A machine learning method that combines many decision trees to predict an outcome; here it best explained SME performance (R² = 0.661). — as used in Vajjhala & Strang (2024)
An ensemble machine learning technique that builds many decision trees and combines their votes to classify cases, here IT projects as failed (breached) or successful. — as used in Strang & Vajjhala (2023)
Random forest regression
An ensemble of decision trees trained on bootstrapped samples and random feature subsets whose predictions are averaged to give the final prediction. — as used in Gigov et al. (2025)
Recommender systems
The chapter’s definition: software tools and techniques for suggesting items to users by considering their preferences in an automated fashion. — as used in Biba et al. (2017)
Regulatory volatility
Rapid, hard-to-predict change in the rules that govern trade and operations — tariffs, customs regimes, labor and environmental (ESG) regulation — that makes supply chains less predictable. — as used in Strang & Vajjhala (2026)
Repeated measures design
An experimental design in which every participant receives all treatments, so individual differences remain the only varying factors. — as used in Strang & Vajjhala (2022)
Risk management decision making
The emotional or thinking processes a project manager uses to solve complex problems in a project beyond business-as-usual activities, such as deciding whether to escalate or cancel a failing project after a severe risk event. — as used in Strang & Vajjhala (2022)
ROC area (ROCa)
The area under the Receiver Operating Characteristic curve, estimating how well the model's classifications separate the two outcome classes; also called AUC. — as used in Strang & Vajjhala (2023)

S

SME
A small or medium-sized enterprise; in this study, participating firms ranged from fewer than 50 to 250–499 employees. — as used in Vajjhala & Strang (2024)
Small and medium enterprise — a firm below national thresholds for employees, turnover, or investment. — as used in Potluri & Vajjhala (2018)
Socio-technical system
A system in which people, organizational rules, and technology jointly determine outcomes, so that none can be understood in isolation. — as used in Strang & Vajjhala (2026)
Statistical relational learning (SRL)
An area of machine learning that combines statistical and probabilistic modeling with languages supporting structured data representations, learning from relational data where observations may be missing, partially observed and noisy. — as used in Biba & Vajjhala (2022)
Sustainability leakage
As used in this study: when surprise ESG enforcement leads firms to exit suppliers or move sourcing elsewhere instead of remediating conditions in the scrutinized region. — as used in Strang & Vajjhala (2026)

T

t-SNE (t-distributed stochastic neighbor embedding)
A machine learning technique that reduces high-dimensional data to a lower-dimensional map by minimizing the Kullback-Leibler divergence between pairwise similarity distributions, keeping similar points close and dissimilar points apart. — as used in Strang & Vajjhala (2024)
Technology Acceptance Model 3 (TAM3)
An extension of the Technology Acceptance Model that explains behavioral intention to use a technology through factors such as perceived usefulness, perceived ease of use, enjoyment, subjective norm and computer self-efficacy. — as used in Strang & Vajjhala (2017)
Transfer learning
Adapting a model pretrained on a large dataset (here ImageNet) to a specific task such as chest X-ray classification, useful when annotated clinical data are limited. — as used in Haveri & Vajjhala (2026)
Transition economy
An economy moving from central planning toward a market-based system, which the chapter notes has social and economic conditions different from those of developing and developed countries. — as used in Vajjhala (2016)
A country moving from a centrally planned (often former communist) system to a free market economy through liberalization, stabilization, restructuring, privatization and institutional reform. — as used in Vajjhala & Strang (2014)
A country gradually moving from a centralised command economy toward a free market economy, such as those in Central and Eastern Europe, the former Soviet Union and China. — as used in Vajjhala & Baghurst (2014)

U

Uncertainty avoidance
A Hofstede national-culture dimension describing how far a society tolerates ambiguity and risk; India scores a medium-low 40. — as used in Vajjhala & Strang (2018)
Unsupervised machine learning
Machine learning that finds structure or patterns in data without a labeled outcome variable. — as used in Strang & Vajjhala (2024)

V

Value prospect bias
In this study, the influence of a personal economic and emotional gain (a 400% bonus) offered to a project manager for not cancelling a failing project. — as used in Strang & Vajjhala (2022)
Viability (socio-cultural dimension)
The authors' proposed big data dimension referring to the socio-cultural relevance of big data, based on where it was collected, and whether its meaning will generalize to the intended population. — as used in Vajjhala & Strang (2017)
Visual data mining
The chapter’s definition: visual data mining combines data mining methods and computer-aided, interactive visual techniques to discover novel and interpretable patterns with the help of human perception abilities. — as used in Biba et al. (2017)

W

Web 3.0
The generation of web technologies centred on machine-readable (semantic) data, intelligent services, and integration across applications. — as used in Potluri & Vajjhala (2018)