[ { "id": "10.3390/su18189359", "type": "article-journal", "title": "Regulatory Volatility in Digital Supply Chains: An Information Systems Analytics Study of Decision-Maker Risk Perceptions and Sustainability-Related Outcomes", "author": [ { "family": "Strang", "given": "Kenneth David" }, { "family": "Vajjhala", "given": "Narasimha Rao" } ], "container-title": "Sustainability", "issued": { "date-parts": [ [ 2026, 9, 11 ] ] }, "volume": "18", "issue": "18", "number": "9359", "publisher": "MDPI", "ISSN": "2071-1050", "DOI": "10.3390/su18189359", "URL": "https://www.narasimharao.net/research/regulatory-volatility-digital-supply-chains-esg-risk/", "abstract": "Geopolitical conflict, tariff shocks, and rapidly evolving environmental, social, and governance (ESG) regulation have made global supply chains markedly less predictable. This study examined how manufacturing supply chain decision-makers perceive six regulatory-volatility risks, including labor, environmental, customs, ownership, military-logistics, and distribution, and whether those perceptions predict recorded engagement outcomes. Adopting an information systems perspective, we retrospectively analyzed 1988 fully anonymized decision-maker records from the digital lessons-learned repository of a multinational supply chain logistics firm, applying descriptive statistics, exploratory factor analysis with parallel analysis, and binary logistic regression with classification diagnostics. The perceived risks did not converge on a unified regulatory-volatility construct: sampling adequacy was weak (KMO = 0.470), and parallel analysis retained three single-indicator factors (distribution, environmental, and ownership risk) that were close to orthogonal apart from one moderate environment-distribution association. Critically, the sustainability-motivated risks were rated lowest, with a median labor severity of 0 and an environmental severity of 1 on 0–5 scales, during a period of record forced-labor enforcement; this perception gap invites sustainability leakage, whereby surprise enforcement provokes supplier exit and sourcing flight rather than remediation within scrutinized regions. The logistic regression separated success from failure perfectly in-sample (accuracy = AUC = 1.000), driven jointly by the near-collinear distribution and supply items—a complete-separation pattern warning of label leakage when digitized organizational records are mined for artificial intelligence-based decision support. The two items are near-duplicate measures (r = 0.915), so no individual predictor effect is identified. The findings favor multidimensional rather than composite digital risk dashboards, provenance-aware data governance for supply chain analytics pipelines, and digitally enabled ESG compliance under volatile regulation, contributing an empirically grounded information systems lens to sustainable supply chain management.", "keyword": "supply chain management, regulatory volatility, ESG compliance, sustainable supply chains, risk perception, forced labor enforcement, information systems analytics, logistic regression, label leakage, AI decision support, data governance, manufacturing", "language": "en" }, { "id": "10.3390/systems14080990", "type": "article-journal", "title": "Machine Learning and Data Science for ESG Compliance Measurement in Financial Software Engineering Projects: A Single-Case Socio-Technical Systems Analysis", "author": [ { "family": "Strang", "given": "Kenneth David" }, { "family": "Vajjhala", "given": "Narasimha Rao" } ], "container-title": "Systems", "issued": { "date-parts": [ [ 2026, 8, 14 ] ] }, "volume": "14", "issue": "8", "number": "990", "publisher": "MDPI", "ISSN": "2079-8954", "DOI": "10.3390/systems14080990", "URL": "https://www.narasimharao.net/research/machine-learning-esg-compliance-financial-software-projects/", "abstract": "The integration of artificial intelligence (AI) and data science into organizational evaluation practices creates socio-technical systems in which human rating behavior, organizational templates, regulatory pressure, and analytical algorithms jointly determine what can be measured and learned. This single-case study examines the measurement of Environmental, Social, and Governance (ESG) compliance in financial software engineering projects, treating one firm’s project evaluation practice as a socio-technical system and applying an open-source data science and machine learning workflow to 207 anonymized archival project records. Correlation analysis revealed near-unity associations between the stakeholder-rated social and governance factors and the overall project score (r = +0.995 and +0.966, p < 0.001), while the environmental factor was unrelated to the score; post-hoc diagnostics (a seven-component principal-component structure, Harman’s screen, selective near-zero same-source correlations, and marker-variable partial correlations) bind, but cannot eliminate, method-based explanations, so the coefficients are interpreted as a descriptive property of the firm’s evaluation system rather than as estimates of relationships between validated, distinct constructs. Exploratory machine learning classifiers performed weakly—kNN at chance (AUC = 0.497) and SVM only modestly above the no-information baseline (accuracy 61.8%)—a result consistent with the constraints that the social subsystem imposes on the learnability of the records it generates, although technical factors, including the dichotomization of the target variable, the modest sample size, and model configuration, cannot be ruled out as contributing explanations; descriptive statistics are reported for all variables, and a diagnostic analysis reconciles the apparent divergence between the near-unity correlations and the weak classification performance by showing that the two rest on different feature sets, the near-redundant social and governance ratings having been withheld from the classifiers. The findings offer a proof of concept and a structured agenda for AI-enabled, project-level ESG measurement in socio-technical systems.", "keyword": "ESG compliance measurement, machine learning, data science, socio-technical systems, software engineering projects, financial services, project evaluation, k-nearest neighbors, support vector machine, common method bias, AI in project management", "language": "en" }, { "id": "10.3390/info17080751", "type": "article-journal", "title": "A Comparative Evaluation of Deep Learning Architectures for Weed Classification, with an Exploratory Out-of-Distribution Analysis of Albanian Field Images", "author": [ { "family": "Kumar", "given": "Rajesh" }, { "family": "Vajjhala", "given": "Narasimha Rao" }, { "family": "Ramollari", "given": "Ervin" }, { "family": "Joca", "given": "Earta" } ], "container-title": "Information", "issued": { "date-parts": [ [ 2026, 8, 2 ] ] }, "volume": "17", "issue": "8", "number": "751", "publisher": "MDPI", "ISSN": "2078-2489", "DOI": "10.3390/info17080751", "URL": "https://www.narasimharao.net/research/deep-learning-weed-classification-deepweeds-albania/", "abstract": "Automated weed classification supports site-specific weed management by enabling targeted interventions and reducing environmental and operational costs. This study presents a multi-seed evaluation of six deep learning configurations—YOLO26n-cls, Vision Transformer (ViT-B/16), DINOv2 (ViT-S/14) with linear probing and full fine-tuning, ResNet-50, and EfficientNet-B0—for nine-class weed classification using a stratified subset of the DeepWeeds benchmark. The models were evaluated using a leakage-checked train/validation/test split and repeated training across multiple random seeds to assess performance stability and statistical reliability. Among the evaluated approaches, DINOv2-FT and EfficientNet-B0 demonstrated the strongest overall performance, while statistical analysis showed that differences among several high-performing models were not consistently significant across seeds. The study also identifies the importance of appropriate transfer-learning strategies and hyperparameter selection, demonstrating that model performance can be strongly affected by optimisation choices. A reproducibility issue affecting ViT-B/16 was identified and transparently reported, leading to its exclusion from multi-seed statistical comparisons. An exploratory out-of-distribution probe using unlabelled Albanian field images further highlights the challenges of geographic domain shift and the need for locally collected, labelled datasets before reliable regional deployment. Overall, this work provides a systematic comparison of modern deep learning architectures for weed classification and emphasises reproducibility, statistical validation, and careful interpretation of model rankings.", "keyword": "weed classification, deep learning, computer vision, precision agriculture, DeepWeeds dataset, DINOv2, EfficientNet, Vision Transformer, YOLO, ResNet-50, transfer learning, reproducibility, out-of-distribution generalization, domain shift, Albania", "language": "en" }, { "id": "10.3390/info17040311", "type": "article-journal", "title": "Verifying SDG ESG Compliance in Manufacturing Industry Projects by Surveying Sponsors", "author": [ { "family": "Strang", "given": "Kenneth David" }, { "family": "Vajjhala", "given": "Narasimha Rao" } ], "container-title": "Information", "issued": { "date-parts": [ [ 2026, 3, 24 ] ] }, "volume": "17", "issue": "4", "number": "311", "publisher": "MDPI", "ISSN": "2078-2489", "DOI": "10.3390/info17040311", "URL": "https://www.narasimharao.net/research/esg-compliance-manufacturing-projects-survey-instrument/", "abstract": "This study addresses a critical gap in the operationalization of sustainability frameworks at the project level by developing and validating an empirically grounded measurement instrument for assessing Environmental, Social, and Governance (ESG) compliance in manufacturing industry projects. While the United Nations Sustainable Development Goals (SDGs) articulate sustainability aspirations at the national and global level, and ESG frameworks capture organizational-level sustainability performance, no validated instrument exists for measuring ESG integration at the project level where sustainability commitments are ultimately operationalized. Drawing on the theoretical foundations of sustainable project management, stakeholder theory, and the ESG governance literature, the authors developed a 30-item survey instrument capturing six conceptual dimensions of ESG-aligned project performance. Data were collected from 2231 project sponsors and decision-makers in North American goods manufacturing firms classified under NAICS codes 31–33, which collectively encompass the entire manufacturing sector in North America. Through a sequential analytical approach employing principal component analysis (PCA) for initial item reduction, exploratory factor analysis (EFA) for dimensionality assessment, and structural equation modelling (SEM) for confirmatory validation, a parsimonious two-factor model emerged with excellent fit indices (CFI = 0.99, TLI = 0.98, RMSEA = 0.052, SRMR < 0.035). The first factor captures ESG planning activities undertaken during project initiation and planning phases, while the second factor represents ESG monitoring and controlling functions during project execution. The reduction from six theoretical dimensions to two empirical factors reflects lifecycle governance theory, where planning-phase governance and execution-phase control emerge as functionally distinct but correlated constructs. The validated instrument offers practical utility for project managers, organizational sustainability officers, and policy-makers seeking standardized benchmarks for ESG compliance at the operational project level. The validated instrument and complete survey are shared for replication and testing across different industries and countries.", "keyword": "ESG compliance, Sustainable Development Goals (SDGs), sustainable project management, manufacturing projects, project sponsors, survey instrument, scale validation, structural equation modelling, exploratory factor analysis, stakeholder theory, NAICS 31–33, North America", "language": "en" }, { "id": "10.3390/info16110955", "type": "article-journal", "title": "Can Project Team Members’ Willingness to Disclose Past Performance During Procurement Improve Organizational Business Process Success?", "author": [ { "family": "Strang", "given": "Kenneth David" }, { "family": "Vajjhala", "given": "Narasimha Rao" } ], "container-title": "Information", "issued": { "date-parts": [ [ 2025, 11, 4 ] ] }, "volume": "16", "issue": "11", "number": "955", "publisher": "MDPI", "ISSN": "2078-2489", "DOI": "10.3390/info16110955", "URL": "https://www.narasimharao.net/research/project-team-past-performance-disclosure-fintech-project-success/", "abstract": "Projects continue to fail approximately half the time, both before and after the COVID-19 pandemic. While prior studies highlight the influence of project leadership and individual competencies, little is known about whether team members’ willingness to disclose past performance can improve team allocation decisions and enhance business process success. However, we do not know if team members’ willingness to disclose their past performance may improve teamwork allocation in projects, thereby increasing business process success while reducing the likelihood of the project failing. We applied a rigorous post-positivist research design using correlation, conditioned correlation, t-tests, and ordinary least squares (OLS) linear regression to test the hypotheses. Controlling established predictors including budget, end user community size, and certification, we found that team members’ willingness to share their past performance evaluations significantly improved project success, increasing explained variance from 9.6% to 18.8%. The results indicate that transparency factors—specifically, willingness to share past performance—outweigh traditional resource allocation variables in predicting Fintech project outcomes, explaining an additional 19% of the variance in project success.", "keyword": "project success, project failure, project team selection, procurement, past performance disclosure, transparency, Fintech projects, business process success, OLS regression, project management", "language": "en" }, { "id": "10.3390/su162310240", "type": "article-journal", "title": "Evaluating the Anti-Corruption Factor in Environmental, Social, and Governance Indices by Sampling Large Financial Asset Management Firms", "author": [ { "family": "Strang", "given": "Kenneth David" }, { "family": "Vajjhala", "given": "Narasimha Rao" } ], "container-title": "Sustainability", "issued": { "date-parts": [ [ 2024, 11, 22 ] ] }, "volume": "16", "issue": "23", "number": "10240", "publisher": "MDPI", "ISSN": "2071-1050", "DOI": "10.3390/su162310240", "URL": "https://www.narasimharao.net/research/esg-ratings-anti-corruption-asset-management-firms/", "abstract": "Current ESG indices suffer from incomplete and inconsistent data, with some factors irrelevant to specific industries. Regulators emphasize CO2 emissions reporting for finance/insurance firms, yet rented offices and governance issues like money laundering prove more material. This study examined USD 1+ trillion asset management firms using AI to collect undisclosed legal decisions measuring anti-corruption governance (GRI 206-1). Bayesian correlation with bootstrapping revealed ESG ratings failed reflecting legal cases (BF+0 odds ratio: 3005, 99% CI: 0.617–0.965). Misconduct fines correlated significantly with arbitration cases (Vovk-Selke p-ratio: 4411), yet most ESG scores diverged for identical firms. Three corroborating studies identified specific sample firms as unethical. The authors recommend deeper investigation of implications regarding public interest and stakeholder theory.", "keyword": "ESG ratings, ESG rating divergence, anti-corruption, corporate governance, GRI 206-1, asset management firms, financial services, money laundering, Bayesian correlation, AI data collection, stakeholder theory", "language": "en" }, { "id": "10.13106/jafeb.2018.vol5.no2.73", "type": "article-journal", "title": "A Study on Application of Web 3.0 Technologies in Small and Medium Enterprises of India", "author": [ { "family": "Potluri", "given": "Rajasekhara Mouly" }, { "family": "Vajjhala", "given": "Narasimha Rao" } ], "container-title": "The Journal of Asian Finance, Economics and Business", "issued": { "date-parts": [ [ 2018, 5, 31 ] ] }, "volume": "5", "issue": "2", "page": "73-79", "publisher": "Korea Distribution Science Association (KODISA)", "ISSN": "2288-4645", "DOI": "10.13106/jafeb.2018.vol5.no2.73", "URL": "https://www.narasimharao.net/research/web-3-0-technologies-indian-smes/", "abstract": "The purpose of this study is to explore how small and medium enterprises in India has identified the opportunities and challenges in adopting Web 3.0 technologies to improve their productivity and efficiency. After an in-depth literature review, researchers framed a semistructured questionnaire with open-ended questions for collecting responses from managers working in 40 Indian SME’s representing five key economic sectors. The collected data was analyzed, and themes were encoded using the NVivo 11 computer-assisted qualitative data analysis software. Content analysis was used to analyze the semi-structured interviews. This study identified 12 subthemes illustrating the advantages and challenges as perceived by the managerial leadership of SMEs. The key themes include integration of services, creation of new functionalities, privacy and security, financial and technological challenges, and organizational challenges. The results will benefit SMEs planning and developing their short-term and long-term information systems strategies which will enable SME leaders to make optimal use of their technology assets, improving the productivity and competitiveness of firms.", "keyword": "Web 3.0, semantic web, small and medium enterprises (SMEs), India, technology adoption, information systems strategy, qualitative research, NVivo, content analysis, productivity, privacy and security", "language": "en" }, { "id": "10.1504/IJPOM.2025.146727", "type": "article-journal", "title": "Exploring project manager commitment using machine learning on fuzzy big data", "author": [ { "family": "Strang", "given": "Kenneth David" }, { "family": "Vajjhala", "given": "Narasimha Rao" } ], "container-title": "International Journal of Project Organisation and Management", "issued": { "date-parts": [ [ 2025 ] ] }, "volume": "17", "issue": "2", "page": "135-152", "publisher": "Inderscience", "ISSN": "1740-2891", "DOI": "10.1504/IJPOM.2025.146727", "URL": "https://www.narasimharao.net/research/project-manager-commitment-machine-learning-big-data/", "abstract": "This study addresses two critical organisational challenges: retaining human talent and reducing high project failure rates. Our approach diverges from traditional methods by employing machine learning (ML) to analyse retrospective big data. This study’s innovation lies in utilising secondary, unstructured data to derive predictive factors of a project manager’s (PM) commitment, moving away from the speculative nature and limited impact of survey-based perceptions. We developed a new conceptual framework that focuses on actual behaviour rather than espoused theories to identify fuzzy predictors of organisational commitment. Based on three distinct ML models, our findings reveal that one model showed a notable 25% effect size, highlighting various features connected to a PM’s tenure and organisational commitment. These insights have broad implications, offering valuable global knowledge for stakeholders in projects and programs. This study underscores the significance of non-traditional data sources in understanding and predicting critical human resource metrics, opening new avenues for organisational research and decision-making.", "keyword": "project management, big data analysis, talent retention, project failure rates, predictive modelling, unstructured data, behavioural analysis, human resources metrics, machine learning, ML, organisational commitment, organizational commitment, project manager tenure, project failure, big data, IT projects, US military projects, linear regression, random forest, support vector machine, multi-dimensional scaling, predictive analytics", "language": "en" }, { "id": "10.1504/IJSS.2024.140078", "type": "article-journal", "title": "Profitability, effectiveness, operational efficiency, and market growth of SMEs in Albania after piloting data analytics", "author": [ { "family": "Vajjhala", "given": "Narasimha Rao" }, { "family": "Strang", "given": "Kenneth David" } ], "container-title": "International Journal of Services and Standards", "issued": { "date-parts": [ [ 2024 ] ] }, "volume": "14", "issue": "1", "page": "51-64", "publisher": "Inderscience Enterprises Ltd.", "DOI": "10.1504/IJSS.2024.140078", "URL": "https://www.narasimharao.net/research/data-analytics-sme-performance-albania/", "abstract": "The study examines the impact of data analytics on the performance of small- and medium-sized enterprises (SMEs) in Albania. This post-positivist study adopts a quantitative-based approach drawing from both parametric statistics and machine learning models to analyse data collected from 41 SMEs in Albania. The research study was guided by two main hypotheses: the first hypothesis states that data analytics is related to operational efficiency, optimised operations, profitability, and market growth in SMEs; the second hypothesis states that the use of data analytics by the SME was perceived by the participants to significantly increase production. The results of the analysis showed that 80% of SMEs have accepted data analytics, which was positively associated with all key performance indicators. The outcomes reveal a strong association between data analytics and improved operating efficiency, efficient operations, profitability, and market expansion.", "keyword": "data analytics, SME performance, operational efficiency, market growth, profitability, process optimisation, quantitative analysis, machine learning models, post-positivist approach, female entrepreneurs, statistical correlation, business strategy, innovation in SMEs, data-driven decision making, competitive advantage, SMEs, Albania, emerging markets, business performance, machine learning, random forest, support vector machine, MANOVA, Pearson correlation", "language": "en" }, { "id": "10.31181/oresta/0601131", "type": "article-journal", "title": "Comparative Analysis of Bit-Parallel String Pattern Matching Algorithms for Biological Sequences", "author": [ { "family": "Muhammad", "given": "Muhammad Yusuf" }, { "family": "Fonkam", "given": "Mathias" }, { "family": "Thandekatu", "given": "Salu George" }, { "family": "Rakshit", "given": "Sandip" }, { "family": "Vajjhala", "given": "Rao Narasimha" } ], "container-title": "Operational Research in Engineering Sciences: Theory and Applications", "issued": { "date-parts": [ [ 2023, 4, 15 ] ] }, "volume": "6", "issue": "1", "page": "322-331", "publisher": "Regional Association for Security and Crisis Management (RABEK)", "ISSN": "2620-1747", "DOI": "10.31181/oresta/0601131", "URL": "https://www.narasimharao.net/research/bit-parallel-string-matching-algorithms-biological-sequences/", "abstract": "The inherent parallelism in a bit operation like AND/OR inside a computer word is known as bit parallelism. It plays a greater role in string pattern matching and has good application in the analysis of biological data. The use of recently developed bit parallel string matching algorithms approaches help in improving the efficiency of the other string pattern matching algorithms. This paper discusses the working of some of these bit parallel string matching algorithms and their application on biological sequences. It also shows how bit-parallelism can be efficiently used to address various matching problems in Bioinformatics to analyze biological sequences such as Deoxyribonucleic acid (DNA), Ribonucleic acid (RNA) and Protein with examples. It can also serve as greater tool for the researchers when looking for the appropriate method to us on Biological sequences.", "keyword": "Bit-parallelism, Automaton, Pattern matching, Ribonucleic acid, Parameterized matching, bioinformatics, bit-parallelism, string pattern matching, exact and approximate string matching, DNA sequence analysis, RNA secondary structure, protein sequences, Shift-Or and Shift-And algorithms, BNDM (Backward Non-deterministic DAWG Matching), parameterized matching, prev-encoding, algorithm time complexity, literature review", "language": "en" }, { "id": "10.4018/IJITPM.317221", "type": "article-journal", "title": "Mining Project Failure Indicators From Big Data Using Machine Learning Mixed Methods", "author": [ { "family": "Strang", "given": "Kenneth David" }, { "family": "Vajjhala", "given": "Narasimha Rao" } ], "container-title": "International Journal of Information Technology Project Management", "issued": { "date-parts": [ [ 2023, 2, 3 ] ] }, "volume": "14", "issue": "1", "page": "1-24", "publisher": "IGI Global", "DOI": "10.4018/IJITPM.317221", "URL": "https://www.narasimharao.net/research/machine-learning-it-project-failure-indicators-big-data/", "abstract": "The literature revealed approximately 50% of IT-related projects around the world fail, which must frustrate a sponsor or decision maker since their ability to forecast success is statistically about the same as guessing with a random coin toss. Nonetheless, some project success/failure factors have been identified, but often the effect sizes were statistically negligible. A pragmatic mixed methods recursive approach was applied, using structured programming, machine learning (ML), and statistical software to mine a large data source for probable project success/failure indicators. Seven feature indicators were detected from ML, producing an accuracy of 79.9%, a recall rate of 81%, an F1 score of 0.798, and a ROCa of 0.849. A post-hoc regression model confirmed three indicators were significant with a 27% effect size. The contributions made to the body of knowledge included: A conceptual model comparing ML methods by artificial intelligence capability and research decision making goal, a mixed methods recursive pragmatic research design, application of the random forest ML technique with post hoc statistical methods, and a preliminary list of IT project failure indicators analyzed from big data.", "keyword": "Big Data, Information Technology, Machine Learning, Model, Prediction, Project Failure, Project Management, Random Forest, machine learning, random forest, project failure, IT project management, big data, mixed methods, logistic regression, project success factors, project manager experience, U.S. government projects, defense projects, data mining, predictive analytics", "language": "en" }, { "id": "10.19255/JMPM02904", "type": "article-journal", "title": "Testing Risk Management Decision Making Competency of Project Managers in a Crisis", "author": [ { "family": "Strang", "given": "Kenneth David" }, { "family": "Vajjhala", "given": "Narasimha Rao" } ], "container-title": "The Journal of Modern Project Management", "issued": { "date-parts": [ [ 2022 ] ] }, "volume": "10", "issue": "1", "page": "52-71", "DOI": "10.19255/JMPM02904", "URL": "https://www.narasimharao.net/research/project-manager-risk-decision-bias-crisis-experiment/", "abstract": "The objective of the current study was to use a rigorous controlled experiment simulating a project failure to measure how cognitive bias and competency impact a PM’s risk management decision making in a crisis while controlling for other project and firm level variables including lying or faking responses. The MANOVA, repeated measures ANOVA controlled experiment and post-hoc analysis techniques were rigorous because the study took place in approximately the same point in time and each participant received all treatments. The 24 respondents in these repeated measures experiment outperforms most psychology factorial research design, which would require a 4 x 24 = 96 sample size to accommodate 3 treatments and a control group. We found bias significantly impacted PM risk management decision making in a crisis, but certification and competency resulted in the best decisions. Generalizations are cautioned due to the exploratory nature of this study. However, the literature review and methods were articulated well enough to encourage replications and extensions by other researchers.", "keyword": "Project management, risk management, project manager, decision making, crisis, project failure, repeated measures ANOVA, experiment, MANOVA, project management, cognitive bias, project manager competency, PM certification, controlled experiment, prospect theory, COVID-19, PERT estimation, United States", "language": "en" }, { "id": "10.4018/IJITPM.304059", "type": "article-journal", "title": "How Bias Impacted the Project Manager Decision to Not Terminate a Failing Project", "author": [ { "family": "Strang", "given": "Kenneth David" }, { "family": "Vajjhala", "given": "Narasimha Rao" } ], "container-title": "International Journal of Information Technology Project Management", "issued": { "date-parts": [ [ 2022 ] ] }, "volume": "13", "issue": "1", "page": "1-17", "publisher": "IGI Global", "DOI": "10.4018/IJITPM.304059", "URL": "https://www.narasimharao.net/research/project-manager-bias-failing-project-termination-experiment/", "abstract": "Almost half of projects have failed globally during the last 50 years, yet most studies in the literature review were inclusive. The research design was a robust repeated measure controlled experiment where the 16 participants received all treatments, which may be contrasted to a similar 4x4 factorial experiment with a control group (common in psychology or healthcare) resulting in a group size of only four. All but the individual project manager (PM) factors were controlled, while primary demographic and behavior data were collected. PMs were tested for competence using a risk management scenario and given two manipulated conditions (a basic and a biased treatment). Since the organizational and project level factors were controlled, some individual level factors impacted the decision. PMs with higher competence made better decisions, with a 22% effect size, when all other factors in the model were accounted for. Competent non-certified PMs made better decisions as compared to certified incompetent PMs.", "keyword": "Crisis, Decision Making Bias, Experiment, Project Failure, Project Management, Project Manager, Risk Management, project failure, project management, cognitive bias, decision making, risk management, project termination, prospect theory, project manager competency, PM certification, repeated measures experiment, ANOVA, IT projects, COVID-19, USA", "language": "en" }, { "id": "10.1007/s40003-021-00588-2", "type": "article-journal", "title": "Thematic Analysis of Agricultural Government Policy and Operational Problems", "author": [ { "family": "Strang", "given": "Kenneth David" }, { "family": "Che", "given": "Ferdinand" }, { "family": "Vajjhala", "given": "Narasimha Rao" } ], "container-title": "Agricultural Research", "issued": { "date-parts": [ [ 2021 ] ] }, "publisher": "Springer (on behalf of NAAS, National Academy of Agricultural Sciences)", "DOI": "10.1007/s40003-021-00588-2", "URL": "https://www.narasimharao.net/research/agricultural-policy-rural-farmers-nigeria-thematic-analysis/", "abstract": "The purpose of this study was to uncover ground truth policy insights underlying the agriculture crisis from the perspectives of rural farm operations in Nigeria. The needs of rural farmers were otherwise not adequately reflected in national or regional economic development policies. The grounded theory formal method was applied using NVivo software. A focus group was conducted with 16 agricultural extension workers, who are an advanced category of educated experts acting as mentors to rural Nigerian farmers. Audio data were coded into thematic concept maps using discourse analysis. The findings of this study were generally consistent with the extant literature, but several different issues emerged. Rural farmers in Nigeria have significant agricultural challenges with government support in six core areas: farming input quality and dissemination, fair input subsidization, training, market facilitation, social responsibility to reduce corruption, and poverty.", "keyword": "Nigeria, Agriculture governance, Poverty, Agricultural extension worker, Grounded theory, Focus group, agricultural policy, agriculture governance, Northeast Nigeria, rural farmers, smallholder farmers, agricultural extension workers, food security, corruption, Boko Haram insurgency, grounded theory, focus group, thematic analysis, NVivo", "language": "en" }, { "id": "10.1108/IJDI-08-2019-0136", "type": "article-journal", "title": "Voice of farmers in the agriculture crisis in North-East Nigeria: Focus group insights from extension workers", "author": [ { "family": "Che", "given": "Ferdinand Ndifor" }, { "family": "Strang", "given": "Kenneth David" }, { "family": "Vajjhala", "given": "Narasimha Rao" } ], "container-title": "International Journal of Development Issues", "issued": { "date-parts": [ [ 2020 ] ] }, "volume": "19", "issue": "1", "page": "43-61", "publisher": "Emerald Publishing Limited", "ISSN": "1446-8956", "DOI": "10.1108/IJDI-08-2019-0136", "URL": "https://www.narasimharao.net/research/voice-of-farmers-agriculture-crisis-north-east-nigeria/", "abstract": "Purpose – The purpose of this study is to uncover ground truth insights underlying the agriculture crisis from the perspectives of rural farmers in North-East Nigeria. The needs of individual farmers are otherwise not adequately reflected in national or regional economic development strategies. Design/methodology/approach – A unique sequential mixed-methods research design was adopted for this study. A grounded theory approach was used for the literature review followed by a consensual qualitative research (CQR) technique. Data were collected through a semi-structured sense-making focus group (FG) held at a field site with agricultural extension workers. The CQR technique included brainstorming, the nominal group technique, open discussions, sense-making and consensual agreement on the most important ideas. The FG sense-making was recorded, and discourse analysis was conducted to develop thematic concept maps using NVivo software. Findings – Agriculture crisis ground truth insight themes were consistent with the extant literature but several different issues were also found. Rural farmers in North-East Nigeria have significant challenges with government support in six core areas, namely, farm input quality and dissemination, fair input subsidization, training, market facilitation, corruption and insecurity. Research limitations/implications – The target population of this study was rural farmers in Adamawa State, North-East Nigeria. A relatively small sample of 16 agricultural extension workers – very experienced farmers who also act as mentors and are paid incentives by the government for doing so – was used. Practical implications – In tackling the agriculture crisis in Nigeria, policymakers will do well to recognize the realities that the rural farmers face and their needs, the government must address the areas highlighted in this study where support for farmers lacks and urgently review the current process of farm inputs dissemination. Originality/value – Agriculture crisis problems were explored from the perspectives of rural North-East Nigerian farmers, who have not been previously sampled due to cultural, language, literacy and schedule constraints. The extension workers were better able to communicate agriculture crisis insights in modern economic planning terminology because they are well-educated farmers, knowledgeable about the problems due to their field experience and because they have more flexible work schedules. A unique sequential mixed-methods constructivist research design was used with an embedded CQR technique, which would be of interest to scholars and research institutions.", "keyword": "North-East Nigeria, Agriculture crisis, Agricultural extension worker, Consensual qualitative research (CQR), Focus group, Qualitative data analysis software (QDAS), Discourse analysis, agriculture crisis, food security, Nigeria, Adamawa State, rural farmers, agricultural extension workers, smallholder farmers, consensual qualitative research, focus group, grounded theory, NVivo, discourse analysis, corruption and insecurity, agricultural input subsidies", "language": "en" }, { "id": "10.4018/IJESMA.2020010103", "type": "article-journal", "title": "Predictors of e-service Consumption in a Highly Productive Brazil-Russia-India-China-South Africa Region Sample", "author": [ { "family": "Strang", "given": "Kenneth David" }, { "family": "Vajjhala", "given": "Narasimha Rao" } ], "container-title": "International Journal of E-Services and Mobile Applications", "issued": { "date-parts": [ [ 2020 ] ] }, "volume": "12", "issue": "1", "page": "39-56", "publisher": "IGI Global", "DOI": "10.4018/IJESMA.2020010103", "URL": "https://www.narasimharao.net/research/e-service-consumption-predictors-brics-india-consumers/", "abstract": "The authors investigated consumer e-commerce behavior in a Brazil-Russia-India-China-South-Africa (BRICS) region from a socio-cultural perspective. BRICS countries are important to study because they have a large population representative of other global e-services markets, they account for 40% of the world’s population, 26% of the world’s land and approximately a third of the world’s gross domestic economic e-commerce production, plus residents are habitual consumers of mobile technology like smartphones. A binary logistic regression model revealed that young educated consumer satisfaction with e-services, e-service happiness, positive feelings and e-service pleasant feelings, but not e-service excitement, could predict purchase behavior. The model correctly classified 87.3% of the e-commerce consumers using two factors and a second model with one factor correctly categorized 90.5% of them. These results are important for managers and academics to consider.", "keyword": "Brazil-Russia-India-China-South-Africa, Empirical Predictive Model Study, Mobile E-Services, Online Purchase Behavior, Socio-Cultural Factors, e-services, e-commerce, online consumer behavior, purchase intention, BRICS, India, consumer satisfaction, socio-cultural factors, binary logistic regression, discriminant analysis, mobile e-services, young consumers, emerging markets", "language": "en" }, { "id": "10.1504/IJSAMI.2019.10019819", "type": "article-journal", "title": "Factors impacting farm management decision making software adoption", "author": [ { "family": "Strang", "given": "Kenneth David" }, { "family": "Bitrus", "given": "Sarah Nankyer" }, { "family": "Vajjhala", "given": "Narasimha Rao" } ], "container-title": "International Journal of Sustainable Agricultural Management and Informatics", "issued": { "date-parts": [ [ 2019 ] ] }, "volume": "5", "issue": "1", "page": "1-14", "publisher": "Inderscience", "DOI": "10.1504/IJSAMI.2019.10019819", "URL": "https://www.narasimharao.net/research/nigerian-farmers-agricultural-information-systems-continuance/", "abstract": "In this study, we use an unconventional socio-cultural ideology to examine if Western African farmers think agricultural information systems (AISs) improve economic production, at the individual level of analysis. Food production is a necessity for our survival but it has been negatively impacted by unstable financial markets, climate change, political upheavals and health pandemics, especially in Western African-based developing countries. We developed a four factor model based on the information systems expectancy confirmation theory which could determine why Nigerian farmers adopt agricultural information systems. We used a survey to collect data from farmers, we validated the questions using a pilot study, and we developed a structural equation model to quantitatively explain why farmers make the decision to adopt or discontinue the use of AIS electronic software. We found that farmer’s satisfaction was positively influenced by high confirmation experience with AIS. To a lesser extent we found that farmer’s satisfaction was positively impacted by AIS perceived usefulness (PU). Interestingly, we found no evidence that any factor was related to farmer’s behavioural intent for continued use of AIS. The results raise controversial issues concerning AIS effectiveness and government technology funding in Western African countries.", "keyword": "agricultural information system, AIS, farm management, crop planning, decision making software, adoption, perceived use, continuance intention, satisfaction, confirmation experience, expectancy confirmation theory, Western Africa, Nigeria, agricultural information systems, farm management information systems, technology adoption, expectation confirmation theory, perceived usefulness, user satisfaction, structural equation modeling, confirmatory factor analysis, survey, smallholder farmers, Jos Plateau, West Africa, food security", "language": "en" }, { "id": "10.1177/0030727019873012", "type": "article-journal", "title": "Urgently strategic insights to resolve the Nigerian food security crisis", "author": [ { "family": "Strang", "given": "Kenneth David" }, { "family": "Che", "given": "Ferdinand" }, { "family": "Vajjhala", "given": "Narasimha Rao" } ], "container-title": "Outlook on Agriculture", "issued": { "date-parts": [ [ 2019, 8 ] ] }, "page": "1-9", "publisher": "SAGE", "DOI": "10.1177/0030727019873012", "URL": "https://www.narasimharao.net/research/nigeria-food-security-crisis-agriculture-extension-strategies/", "abstract": "The food security crisis is a serious worldwide predicament in developing countries but it is a relatively larger problem in Nigeria. We argued there was no solution for the Nigerian food security crisis because researchers had not customized theoretical models with data-driven priorities grounded on local agriculture subject matter expert knowledge. We collected data from local agriculture extension workers who had specialized knowledge of the problems. We applied the consensual qualitative research method with embedded nominal brainstorming and multiple correspondence statistical techniques at the group level of analysis to develop a proposed solution. Our final model highlighted strategically urgent ideas to increase agriculture productivity and appease the most severe constraints in rural Nigeria. The results extended what was already published in the literature and should generalize to rural farmers in Nigeria as well as to government policymakers in developing countries around the world.", "keyword": "Food insecurity, agriculture extension research, consensual qualitative research, focus group, correspondence analysis, agricultural policy, agricultural economics, food security, Nigeria, rural farmers, agriculture extension workers, agricultural productivity, nominal brainstorming, multiple correspondence analysis, Adamawa State, developing countries, farm financing and credit, agricultural inputs", "language": "en" }, { "id": "10.4018/JOEUC.2017070103", "type": "article-journal", "title": "Student Resistance to a Mandatory Learning Management System in Online Supply Chain Courses", "author": [ { "family": "Strang", "given": "Kenneth David" }, { "family": "Vajjhala", "given": "Narasimha Rao" } ], "container-title": "Journal of Organizational and End User Computing", "issued": { "date-parts": [ [ 2017, 7 ] ] }, "volume": "29", "issue": "3", "page": "49-67", "publisher": "IGI Global", "DOI": "10.4018/JOEUC.2017070103", "URL": "https://www.narasimharao.net/research/student-resistance-mandatory-lms-tam3-supply-chain/", "abstract": "The authors explored how a technology model captured the factors that motivated and demotivated students to accept a new learning management system in online supply chain courses at an accredited public American university. Technology resistance is a well-known social science problem that results in reduced performance in business although it has rarely been examined in higher education. A new LMS is mandatory technology that is necessary to use in order to perform well in online global supply chain management courses. The authors drew a sample of graduating supply chain management students to explore this problem because these participants represent the next generation of employees who are likely to work with mandatory technology. The authors argue that it is important to study management students because their technology acceptance motivations will generalize to the future supply chain workforce. If decision makers could understand why management students resist new software, then they could develop strategies to address the critical success factors in hopes that organizational performance might be increased. The design was statistically powerful according to social research standards because the authors examined actual behavior by measuring grade as the dependent variable instead of relying on behavioral intent (BI) which is a subjective perceptional factor because participants are generally asked to self-report this through a survey. In keeping with published empirical literature the authors determined that perceived usefulness predicted BI using multiple regression. In contrary to the existing literature, they did not find that perceived resources (PR) was causally related to actual performance, although they did observe through regression that BI could be forecasted from PR. Their regression model indicated that perceived ease of use (PEOU) was not related to BI but in regression they found PEOU significantly impacted actual performance. These were two contradictory findings that PU impacted BI but not actual performance yet PR and PEOU predicted actual performance but not BI. Another unique departure from the empirical literature was that Computer Self Efficacy (CSE), Perceptions of External Control (PEC), and Computer Anxiety (CANX) were not related to either BI or actual performance. An interesting finding was that perceived enjoyment was strongly related to both BI and actual performance, although the perceptions were opposite between intention versus actual behavior. Multiple regression revealed that lower perceptions of enjoyment was significantly linked to BI while strong perceptions of enjoyment predicted actual performance. Although the authors were certain that peer influence would impact BI and actual performance, in their sample they did not find any support for subjective norm pressure on BI or actual performance. In a similar breakthrough they determined that job relevance and output quality were not causally linked to BI or actual performance. Another statistically significant finding was that positive perceptions of voluntariness were causally related to BI through multiple regression tests and also positively related to actual performance based on hierarchical regression. The authors determined that results demonstrability was causally related to BI from their multiple regression tests but interestingly it was not related to actual performance. The authors extensively discuss the above findings in their conclusions and provide many recommendations for future research. Finally, another valuable contribution the authors made to the scholarly community of practice through this study was to develop large effect-size parsimonious models with very few required factors that captured 56% of the variance on BI and 49% of the variance on actual performance.", "keyword": "Actual Behavior, Behavioral Intent, Higher Education, Learning Management System, Online Supply Chain Course, Technology Resistance, technology resistance, technology acceptance, TAM3, learning management system, Moodle, online learning, higher education, supply chain management education, behavioral intention, actual behavior, perceived enjoyment, multiple regression, hierarchical regression, United States", "language": "en" }, { "id": "10.1142/S179300571740004X", "type": "article-journal", "title": "Measuring Organizational-Fit Through Socio-Cultural Big Data", "author": [ { "family": "Vajjhala", "given": "Narasimha Rao" }, { "family": "Strang", "given": "Kenneth David" } ], "container-title": "New Mathematics and Natural Computation", "issued": { "date-parts": [ [ 2017, 7, 3 ] ] }, "volume": "13", "issue": "2", "page": "145-158", "publisher": "World Scientific Publishing Company", "ISSN": "1793-0057", "DOI": "10.1142/S179300571740004X", "URL": "https://www.narasimharao.net/research/organizational-fit-socio-cultural-big-data/", "abstract": "We propose that businesses, government, and not-for-profit entities could benefit from a better understanding of organizational behavior through the lens of a contemporary global culture model. Human resourcing and partnering decisions could be improved by using global culture to ensure a better organizational-fit as well as to reduce the risk of destructive relationship dependencies. For an extreme-limits example, a company could inadvertently hire a terrorist or a social loafer seeking to steal competitive intelligence. A big data approach supported by a socio-cultural framework could help in hypothesis testing which is essential for advancing the body of knowledge in organizational behavior. This paper will make a scholarly contribution by identifying literature relevant to collecting and analyzing organizational big data that could explain beneficial socio-cultural behavior. This paper will explore how sources of qualitative big data could be collected and then analyzed to measure organizational-fit factors relevant for decision-making.", "keyword": "Big data, qualitative, organizational behavior, culture, organizational-fit, decision-making, big data, organizational fit, organizational culture, national culture, Hofstede cultural dimensions, GLOBE model, Competing Values Framework, social media data, cultural sociology, big data V model, viability dimension, hypothesis testing", "language": "en" }, { "id": "cultural-factors-knowledge-sharing-smes-albania", "type": "article-journal", "title": "Influence of cultural factors on knowledge sharing in medium-sized enterprises within transition economies", "author": [ { "family": "Vajjhala", "given": "Narasimha Rao" }, { "family": "Baghurst", "given": "Timothy" } ], "container-title": "International Journal of Knowledge Management Studies", "issued": { "date-parts": [ [ 2014 ] ] }, "volume": "5", "issue": "3/4", "page": "304-321", "publisher": "Inderscience", "URL": "https://www.narasimharao.net/research/cultural-factors-knowledge-sharing-smes-albania/", "abstract": "Knowledge sharing is subject to organisational and national cultural influences, but medium-sized firms often implement general knowledge-sharing models without considering cultural factors. The purpose of this study was to investigate the influence of cultural factors on knowledge-sharing activities in medium-sized enterprises in transition economies. The study was driven by the central research question: How do cultural factors influence employees’ perceptions of knowledge-sharing initiatives in medium-sized enterprises in transition economies? Qualitative in-depth interviews were conducted with 20 managers working in ten medium-sized enterprises in Albania, which is a transition economy. Six themes revealed the influence of cultural factors on employees’ perceptions of knowledge-sharing initiatives in medium-sized enterprises in transition economies. Specifically, national culture and organisational culture influence employee behaviour and participation in knowledge-sharing activities. Thus, it is critical organisational leaders of medium-sized firms in Albania consider the influence of these factors before implementing knowledge-sharing activities in firms.", "keyword": "knowledge, knowledge sharing, knowledge management, transition economies, national culture, organisational culture, small- and medium-sized enterprises, SMEs, organizational culture, Albania, medium-sized enterprises, post-communist economies, trust, qualitative case study, Hofstede cultural dimensions, top management support, NVivo", "language": "en" }, { "id": "10.1108/CCM-02-2013-0023", "type": "article-journal", "title": "Collaboration strategies for a transition economy: measuring culture in Albania", "author": [ { "family": "Vajjhala", "given": "Narasimha Rao" }, { "family": "Strang", "given": "Kenneth David" } ], "container-title": "Cross Cultural Management", "issued": { "date-parts": [ [ 2014 ] ] }, "volume": "21", "issue": "1", "page": "78-103", "publisher": "Emerald Group Publishing Limited", "ISSN": "1352-7606", "DOI": "10.1108/CCM-02-2013-0023", "URL": "https://www.narasimharao.net/research/albania-national-culture-hofstede-transition-economy/", "abstract": "Purpose – The researchers in this study reviewed the literature to locate the most relevant multicultural theories, factors, and instruments in order to measure Albania’s national culture. The paper aims to discuss these issues. Design/methodology/approach – An innovative combination of exploratory and confirmatory factor analysis was used to fit the multicultural construct to the sample data and then estimate the national culture (n = 73). The multicultural indexes were calculated for five generally accepted national culture factors to compare with the benchmarks published in the literature. Findings – The multicultural indexes were calculated for five generally accepted national culture factors to compare with the benchmarks published in the literature. An asymmetric plot was created for critical comparison of Albania with five other theoretically selected countries, using the indexes for PDi, ICi, MFi, UAi, and LTi. Albania was found to be most similar to its Balkan and Turkish neighbors, as compared with Asian or Western cultures such as that of the USA. Research limitations/implications – The researchers discussed the implications of knowing Albania’s national culture profile with reference to how other countries might collaborate and transact with this emerging transition economy. Practical implications – From a business standpoint, the multicultural indexes for Albania provide general indicators of the national beliefs, norms and values, which foreign organizations may compare to their own cultural profile when interacting with professionals in this country. The best use for such indexes is for benchmarking and comparison. Foreign government, private corporations, or nonprofit organizations may compare their own culture profile with that of Albania to be aware of the similarities and differences. Originality/value – Albanian national culture was estimated for the first time in the literature, using a five-factor model adapted from the work of Hofstede.", "keyword": "Confirmatory factor analysis, Albania, Collaboration strategies, Instrument validation, National global culture model dimensions, Transition economy, national culture, cross-cultural management, Hofstede cultural dimensions, transition economy, power distance, individualism-collectivism, uncertainty avoidance, masculinity-femininity, long-term orientation, exploratory factor analysis, confirmatory factor analysis, survey instrument validation, Balkans, international business collaboration", "language": "en" }, { "id": "10.1109/ICPCSN68523.2026.11543623", "type": "paper-conference", "title": "Deep Learning for Medical Image Analysis: CNN-based Pneumonia Detection on Chest X-Rays", "author": [ { "family": "Haveri", "given": "Katia" }, { "family": "Vajjhala", "given": "Narasimha Rao" } ], "container-title": "2026 6th International Conference on Pervasive Computing and Social Networking (ICPCSN)", "issued": { "date-parts": [ [ 2026 ] ] }, "page": "156-161", "publisher": "IEEE", "ISBN": "979-8-3315-7236-5", "DOI": "10.1109/ICPCSN68523.2026.11543623", "URL": "https://www.narasimharao.net/research/cnn-pneumonia-detection-chest-xray-efficientnetv2/", "abstract": "Pneumonia remains one of the leading causes of mortality worldwide, particularly among paediatric populations, and timely radiographic diagnosis is essential for effective clinical intervention. The proliferation of pervasive computing and communication technologies has enabled the deployment of deep learning models within distributed healthcare systems, facilitating rapid and automated medical image analysis at scale. This study presents a convolutional neural network framework based on EfficientNetV2B0 with transfer learning for binary pneumonia classification from paediatric chest X-ray images. The model employs a two-phase training strategy comprising frozen-backbone feature extraction followed by full fine-tuning, augmented by deterministic data preprocessing, class-weighted loss computation, and Gradient-weighted Class Activation Mapping for interpretability. The input to the model consists of 224×224 pixel RGB chest radiographs, and the output is a sigmoid-activated probability score indicating the likelihood of pneumonia. Evaluated on the benchmark Kermany dataset of 5,863 radiographs (1,583 normal and 4,280 pneumonia cases across training, validation, and test splits), the proposed framework achieves an area under the receiver operating characteristic curve (AUC) of 0.967, confirmed as statistically significant via DeLong testing (p < 0.001), and an overall accuracy of 0.894, with a pneumonia recall of 0.98. The model further yields an Expected Calibration Error (ECE) of 0.083 and a Brier Score of 0.091. Bootstrapped confidence intervals and calibration analysis confirm the robustness and reliability of the predictions. The results demonstrate that lightweight EfficientNetV2 architectures, when combined with rigorous reproducibility protocols compliant with CLAIM and TRIPOD-AI standards, can deliver clinically meaningful diagnostic performance suitable for integration into pervasive healthcare information and communication technology platforms.", "keyword": "deep learning, convolutional neural networks, pneumonia detection, chest X-ray classification, EfficientNetV2, transfer learning, medical image analysis, pervasive computing, chest X-ray, EfficientNetV2B0, paediatric radiology, Grad-CAM, explainable AI, model calibration, reproducibility, healthcare informatics, edge computing", "language": "en" }, { "id": "10.1007/978-3-032-22202-2_5", "type": "paper-conference", "title": "A Spotlight on the Role of Functional Programming & Haskell in Computing & Software Development", "author": [ { "family": "Fonkam", "given": "Mathias" }, { "family": "Vajjhala", "given": "Narasimha Rao" } ], "editor": [ { "literal": "H. R. Arabnia" } ], "container-title": "Computational Science and Computational Intelligence (CSCE 2025)", "collection-title": "Communications in Computer and Information Science (CCIS 2939)", "issued": { "date-parts": [ [ 2026 ] ] }, "volume": "2939", "page": "56-63", "publisher": "Springer Nature Switzerland AG", "DOI": "10.1007/978-3-032-22202-2_5", "URL": "https://www.narasimharao.net/research/functional-programming-haskell-software-education-review/", "abstract": "This paper casts a spotlight on the role of functional programming (FP), using Haskell, in shaping software development education, with particular attention to its foundational mathematical abstractions derived from type theory. Through a systematic review of twenty-eight empirical studies spanning 2015–2025, we examine how Haskell’s emphasis on purity, composability, immutability, and declarative semantics serves as an ideal medium for illustrating core software design principles in mathematically rigorous yet pedagogically meaningful ways. Our analysis reveals three critical themes: educational effectiveness demonstrates mixed but promising outcomes when functional programming concepts are integrated rather than taught in isolation; mathematical reasoning and abstract thinking development show consistent benefits across multiple longitudinal studies; and transfer learning to mainstream object-oriented languages exhibits selective effectiveness requiring targeted pedagogical support. We explore how Haskell’s abstractions—including polymorphic types, algebraic data types, type classes, functors, and monads—relate to abstractions in mainstream languages such as generics, interfaces, inheritance, polymorphism and monads, offering practical strategies for easing the teaching and learning curve in multi-paradigm programming environments. In an era where AI tools increasingly generate code, our findings suggest that FP education becomes more rather than less critical, as it develops the mathematical reasoning and abstract thinking capabilities essential for effective human-AI collaboration in software development. The evidence demonstrates that Haskell serves not only as a powerful FP tool but also as a conceptual bridge enabling students to master mainstream programming languages while developing principled approaches to software construction, requirements analysis, and system design that remain fundamental human competencies.", "keyword": "Haskell, Functional Programming, Abstraction, Type Theory, functional programming, programming education, computer science education, software development, type theory, abstraction, mathematical reasoning, systematic review, object-oriented programming, multi-paradigm languages, AI code generation, large language models, curriculum design", "language": "en" }, { "id": "10.1109/AISummit66170.2025.11411099", "type": "paper-conference", "title": "Hyperlocal Temperature and Humidity Prediction Using Supervised Machine Learning", "author": [ { "family": "Gigov", "given": "Lyuboslav" }, { "family": "Vajjhala", "given": "Narasimha Rao" }, { "family": "Stoilov", "given": "Anton" } ], "container-title": "2025 2nd Global AI Summit – International Conference on Artificial Intelligence and Emerging Technology (AI Summit)", "issued": { "date-parts": [ [ 2025 ] ] }, "page": "940-945", "publisher": "IEEE", "ISBN": "979-8-3315-5379-1", "DOI": "10.1109/AISummit66170.2025.11411099", "URL": "https://www.narasimharao.net/research/hyperlocal-weather-temperature-humidity-prediction-machine-learning/", "abstract": "Machine learning offers computationally efficient alternatives to traditional numerical weather prediction (NWP) models and global forecasting systems (GFS), which struggle with high computational costs and coarse spatial resolution. This study focuses on hyperlocal weather prediction, comparing linear regression (LR), polynomial regression (PR), decision tree regression (DTR), and random forest regression (RFR) for forecasting average temperature and relative humidity. Using ten years of historical data (2015-2025) from Sofia, Berlin, and Tokyo, we achieved substantial predictive accuracy across climatically diverse regions. For temperature prediction, linear regression and random forest demonstrated statistically equivalent performance (R² > 0.994), with linear regression achieving faster execution times (0.001s vs 0.022-0.040s). For humidity prediction, linear regression demonstrated clear superiority (R² > 0.940) over random forest (R² 0.913-0.933), while decision trees showed the poorest performance (R² 0.741-0.814). Decision trees performed competitively for temperature (R² 0.992-0.993) but struggled significantly with humidity's non-linear dynamics. These results demonstrate that model selection should prioritize the relationship structure in data rather than algorithmic complexity, with linear regression offering a balance of accuracy and computational efficiency. The findings of this study challenge the assumption that complex ensemble methods are necessary for accurate weather prediction, offering practical applications in precision agriculture, smart city infrastructure, event planning, and resource-constrained IoT deployments where real-time predictions are essential.", "keyword": "artificial intelligence, machine learning, weather prediction, regression analysis, hyperlocal weather forecasting, temperature prediction, humidity prediction, supervised learning, weather forecasting, hyperlocal weather prediction, relative humidity prediction, linear regression, polynomial regression, decision tree regression, random forest regression, Open-Meteo historical data, Sofia, Berlin and Tokyo, precision agriculture, IoT", "language": "en" }, { "id": "cybersecurity-risks-higher-education-machine-learning", "type": "paper-conference", "title": "Exploring Cybersecurity Risks in Higher Education Environments with Machine Learning", "author": [ { "family": "Strang", "given": "Kenneth David" }, { "family": "Vajjhala", "given": "Narasimha Rao" } ], "container-title": "2024 4th International Conference on Pervasive Computing and Social Networking (ICPCSN)", "issued": { "date-parts": [ [ 2024, 5 ] ] }, "publisher": "IEEE", "URL": "https://www.narasimharao.net/research/cybersecurity-risks-higher-education-machine-learning/", "abstract": "This paper uses unsupervised machine learning techniques (ML) to explore the risk of cybersecurity breaches or attacks in the higher education sector, including schools, universities, and support organizations. A large sample of higher education institutions (N=848) was analyzed by extracting hypertext data from the websites of educational institutions to identify links that may signal a future cybersecurity breach or attack. ML T-distributed Stochastic Neighbor Embedding (t-SNE) was used to train a model for identifying likely indicators of cybersecurity breach risks. The authors used additional techniques, including radial analysis and correspondence analysis, to visualize the cybersecurity breach signals in the data. In this way, decision-makers should be able to spot anomalies that indicate cybercrime activity or potential cyber-attacks. This paper also exposes the problems of using ML and how to use methods and data triangulation to check reliability and validity.", "keyword": "unsupervised machine learning, cybersecurity breaches, higher education sector, hypertext data analysis, t-distributed stochastic neighbor embedding, pervasive computing, cyber-attack indicators, radial analysis, correspondence analysis, anomaly detection, data triangulation, cybersecurity, higher education, universities, machine learning, unsupervised learning, t-SNE, cyber risk assessment, website security, cybercrime, Python, United States", "language": "en" }, { "id": "10.1007/978-3-031-60227-6_6", "type": "paper-conference", "title": "An Exploratory Big Data Approach to Understanding Commitment in Projects", "author": [ { "family": "Vajjhala", "given": "Narasimha Rao" }, { "family": "Strang", "given": "Kenneth David" } ], "editor": [ { "literal": "Á. Rocha" } ], "container-title": "WorldCIST 2024 (World Conference on Information Systems and Technologies)", "collection-title": "Lecture Notes in Networks and Systems", "issued": { "date-parts": [ [ 2024 ] ] }, "volume": "989", "page": "66-75", "publisher": "Springer Nature Switzerland AG", "DOI": "10.1007/978-3-031-60227-6_6", "URL": "https://www.narasimharao.net/research/machine-learning-project-manager-organizational-commitment-big-data/", "abstract": "This study addresses the twin challenges of talent retention and high project failure rates (40–70%) by harnessing machine learning (ML) techniques to analyze retrospective big data. The study’s objective was to ascertain whether project performance indicators can be a reliable gauge of project manager (PM) organizational commitment. This approach sidesteps the inherent bias and small effect sizes associated with survey self-report responses. Our innovative methodology leverages secondary big data, transforming the values into structured features that predict PM organizational commitment. This study proposes a novel conceptual framework, focusing on actual behavioral evidence rather than traditional, self-reported attitudes to assess the fuzzy predictors of organizational commitment. Among the three developed ML models, one demonstrated a significant 24% effect size, uncovering key features correlating PM tenure and organizational commitment with success. The insights gained from this research have broad implications for global stakeholders in projects and programs, offering a more objective and big data-driven understanding of PM commitment.", "keyword": "Project Management, Organizational Commitment, Machine Learning, Big Data Analysis, Talent Retention, Project Failure Rates, Data-Driven Management, Predictive Modeling, Stakeholder Implications, Program Management, Performance Indicators, machine learning, big data, project management, organizational commitment, project manager tenure, talent retention, project success, IT projects, U.S. military projects, linear regression, random forest, support vector machine, multi-dimensional scaling, secondary data", "language": "en" }, { "id": "10.1145/3394788.3394790", "type": "paper-conference", "title": "Impact of Psycho-Demographic Factors on Smartphone Purchase Decisions", "author": [ { "family": "Vajjhala", "given": "Narasimha Rao" }, { "family": "Strang", "given": "Kenneth David" } ], "container-title": "Proceedings of the 2019 International Conference on Information System and System Management (ISSM 2019), Rabat, Morocco", "issued": { "date-parts": [ [ 2019, 10, 14 ] ] }, "page": "5-10", "publisher": "ACM", "DOI": "10.1145/3394788.3394790", "URL": "https://www.narasimharao.net/research/psycho-demographic-factors-smartphone-online-purchase-decisions/", "abstract": "The success of e-commerce depends significantly on having an understanding of online consumer decision making and behavior. The purpose of this study is to understand the influence of psycho-demographic factors on the purchasing decisions taken by young technology-literate consumers while purchasing smartphones. We used a structured questionnaire to collect data from a random sample of senior bachelor level students across a sample frame of international universities with online programs located in the USA and India. Nonparametric statistical techniques, such as Chi-square test of independence, cluster analysis, Spearman correlation, logistic regression, and discriminant analysis were used for testing the hypotheses. The three hypotheses on the association between gender, age, and income level were rejected while the remaining two hypotheses on the association of ethnicity and psycho-demographic factors were supported. The findings of this study will assist marketing managers in targeting segments of young technology-literate online consumers intending to purchase smartphones.", "keyword": "Technology Adoption, E-Commerce, Online Shopping, Motivation, Society, Culture, Demographic, Psychological, Information Systems, Acceptance, consumer behavior, online shopping, e-commerce, smartphone purchase intention, online trust, social media trust, demographic factors, ethnicity and culture, young consumers, USA, India, discriminant analysis, chi-square test, marketing", "language": "en" }, { "id": "online-purchase-intention-young-consumers-india", "type": "paper-conference", "title": "Examining Internet Behavior of Young Technology-Literate Consumers in India", "author": [ { "family": "Vajjhala", "given": "Narasimha Rao" }, { "family": "Strang", "given": "Kenneth David" } ], "container-title": "Twenty-fourth Americas Conference on Information Systems (AMCIS 2018), New Orleans", "issued": { "date-parts": [ [ 2018 ] ] }, "publisher": "Association for Information Systems", "URL": "https://www.narasimharao.net/research/online-purchase-intention-young-consumers-india/", "abstract": "In this study, we analyzed consumer Internet behavior in India since there were several unique cultural dimensions of interest. After reviewing the literature, we tested hypotheses that demographic and psychological factors such as happiness, excitement, satisfaction, positive feelings, pleasant feelings, gender, age, and income level could predict consumer Internet purchase behavior. We used Spearman correlation, binary logistic regression, and discriminant analysis techniques, which resulted in effect sizes ranging from 9.5% to 59.5%. Spearman correlation confirmed that gender, age, and income level were related to consumer Internet purchase behavior. Several binary logistic regression models with goodness-of-fit-tests revealed that all satisfaction, happiness, positive feelings and pleasant feelings, but not excitement, could predict consumer Internet purchase intention. A Discriminant Analysis model was able to correctly classify 87.3% of the sample respondents using two factors, and a second model with only one factor correctly categorized 90.5% of the consumers as willing to purchase on the Internet.", "keyword": "Consumer, Internet, behavior, risk-avoidance, culture, power, regression, purchase, consumer behavior, online shopping, e-commerce, purchase intention, India, Andhra Pradesh, Telangana, young consumers, national culture, Hofstede cultural dimensions, customer satisfaction, binary logistic regression, discriminant analysis, Spearman correlation", "language": "en" }, { "id": "10.1109/FiCloud.2015.15", "type": "paper-conference", "title": "Statistical Modeling and Visualizing Open Big Data Using a Terrorism Case Study", "author": [ { "family": "Vajjhala", "given": "Narasimha Rao" }, { "family": "Strang", "given": "Kenneth David" }, { "family": "Sun", "given": "Zhaohao" } ], "container-title": "2015 3rd International Conference on Future Internet of Things and Cloud (FiCloud), Rome, Italy", "issued": { "date-parts": [ [ 2015, 8 ] ] }, "page": "489-496", "publisher": "IEEE", "DOI": "10.1109/FiCloud.2015.15", "URL": "https://www.narasimharao.net/research/open-big-data-correspondence-analysis-global-terrorism/", "abstract": "This study addressed the literature gap, identified by other researchers, that there are too few examples of applied empirical open big data analytics. Using correspondence analysis as a big data analytical technique, this study demonstrates how qualitative big data type could be analyzed to identify hidden factor relationships that may assist strategic decision making. We use a 64MB open meta big dataset developed by summarizing terrorist activity as keyword frequencies collected from trillions of public news articles published during a 43 year period from 1970-2013 and readily available statistical software, SPSS, to visually summarize the findings on a global terrorism big dataset. The approach in this paper might facilitate the research and development of open big data, big data analytics against global terrorism.", "keyword": "open big data analytics, correspondence analysis, global terrorism open meta big dataset, statistical modeling, data mining, data warehouse, big data analytics, open big data, global terrorism, Global Terrorism Database, data visualization, SPSS, qualitative big data, nominal data, strategic decision making, terrorism risk", "language": "en" }, { "id": "10.4018/979-8-3693-0255-2.ch011", "type": "chapter", "title": "Data Envelopment Analysis in Healthcare Management: Overview of the Latest Trends", "author": [ { "family": "Vajjhala", "given": "Narasimha Rao" }, { "family": "Eappen", "given": "Philip" } ], "container-title": "Data Envelopment Analysis (DEA) Methods for Maximizing Efficiency", "collection-title": "Advances in Business Information Systems and Analytics", "issued": { "date-parts": [ [ 2024, 1, 16 ] ] }, "page": "245-260", "publisher": "IGI Global", "ISSN": "2327-3275 (print); 2327-3283 (electronic)", "ISBN": "9798369302552 (print); 9798369302576 (electronic)", "DOI": "10.4018/979-8-3693-0255-2.ch011", "URL": "https://www.narasimharao.net/research/data-envelopment-analysis-healthcare-management-review/", "abstract": "This chapter explores the applications, contributions, limitations, and challenges of data envelopment analysis (DEA) in healthcare management. DEA, a non-parametric method used for evaluating the efficiency of decision-making units, has found extensive applications in healthcare sectors such as hospital management, nursing, and outpatient services. The review consolidates findings from a broad range of studies, highlighting DEA's significant contributions to efficiency measurement, benchmarking, resource allocation and optimization, and performance evaluation. However, despite DEA's robust applications, the chapter also identifies several limitations and challenges, including the selection of inputs and outputs, sensitivity to outliers, inability to handle statistical noise, lack of inherent uncertainty measures, homogeneity assumption, and the static nature of traditional DEA models. These challenges underscore the need for further research and methodological advancements in applying DEA in healthcare management.", "keyword": "data envelopment analysis, DEA, healthcare management, efficiency measurement, benchmarking, resource allocation, performance evaluation, hospital management, nursing, outpatient services, non-parametric methods, literature review", "language": "en" }, { "id": "10.1201/9781003319887-1", "type": "chapter", "title": "Why Cyberattacks Disrupt Society and How to Mitigate Risk", "author": [ { "family": "Strang", "given": "Kenneth David" }, { "family": "Vajjhala", "given": "Narasimha Rao" } ], "editor": [ { "literal": "Narasimha Rao Vajjhala" }, { "literal": "Kenneth David Strang" } ], "container-title": "Cybersecurity for Decision Makers", "issued": { "date-parts": [ [ 2023 ] ] }, "page": "1-28", "publisher": "CRC Press / Taylor & Francis", "DOI": "10.1201/9781003319887-1", "URL": "https://www.narasimharao.net/research/cyberattacks-disrupt-society-mitigate-risk-industry-4-0/", "keyword": "cybersecurity, cyberattacks, cyber terrorism, ransomware, cyber extortion, data breaches, DDoS attacks, Industry 4.0, Internet of Things, cyber risk management, Russia–Ukraine war, bitcoin, critical infrastructure, decision makers", "language": "en" }, { "id": "10.1007/978-981-16-9158-4_3", "type": "chapter", "title": "Statistical Relational Learning for Genomics Applications: A State-of-the-Art Review", "author": [ { "family": "Biba", "given": "Marenglen" }, { "family": "Vajjhala", "given": "Narasimha Rao" } ], "editor": [ { "literal": "S. S. Roy" }, { "literal": "Y.-H. Taguchi" } ], "container-title": "Handbook of Machine Learning Applications for Genomics", "collection-title": "Studies in Big Data", "issued": { "date-parts": [ [ 2022 ] ] }, "volume": "103", "page": "31-42", "publisher": "Springer Nature Singapore Pte Ltd.", "DOI": "10.1007/978-981-16-9158-4_3", "URL": "https://www.narasimharao.net/research/statistical-relational-learning-genomics-review/", "abstract": "This paper aims to review the state-of-the-art statistical relational learning models (SRL) in genomics. SRL deals with machine learning and data mining in relational domains where observations may be missing, partially observed, and noisy. This chapter introduces a background overview of various models, including probabilistic graphical models, Bayesian networks, dependency networks, Markov networks, first-order logic, and probabilistic inductive logic programming. This chapter also discusses the various statistical relational learning approaches, including probabilistic relational models, stochastic logic programs, Bayesian logic programs, relational dependency networks, relational Markov networks, and Markov logic networks. Finally, the last part of the paper focuses on the practical application of statistical relational learning techniques in genomics. The chapter concludes with a discussion on the limitations of current methods.", "keyword": "Genomic, Artificial intelligence, Machine learning, Probabilistic, Bayesian, Markov, Dependency, Genetics, Bioinformatics, statistical relational learning, genomics, bioinformatics, machine learning, probabilistic graphical models, Bayesian networks, Markov networks, probabilistic relational models, relational dependency networks, relational Markov networks, Markov logic networks, non-i.i.d. data, genome-wide association studies, big data", "language": "en" }, { "id": "10.1007/978-3-030-50244-7_7", "type": "chapter", "title": "Ideologies and Issues for Teaching Blockchain Cybersecurity in Management and Computer Science", "author": [ { "family": "Strang", "given": "Kenneth David" }, { "family": "Che", "given": "Ferdinand" }, { "family": "Vajjhala", "given": "Narasimha Rao" } ], "editor": [ { "literal": "K. Daimi" }, { "literal": "G. Francia III" } ], "container-title": "Innovations in Cybersecurity Education", "issued": { "date-parts": [ [ 2020 ] ] }, "page": "109-126", "publisher": "Springer", "DOI": "10.1007/978-3-030-50244-7_7", "URL": "https://www.narasimharao.net/research/teaching-blockchain-cybersecurity-management-computer-science/", "keyword": "blockchain, cybersecurity education, higher education, curriculum design, teaching ideology, management science education, computer science education, Internet of Things (IoT), risk management, distributed ledger technology, cryptocurrency, emerging economies, pedagogy and andragogy, conceptual typology", "language": "en" }, { "id": "10.4018/978-1-7998-3045-0.ch005", "type": "chapter", "title": "Contemporary Usage of Farm Management Information Systems in Nigeria", "author": [ { "family": "Vajjhala", "given": "Narasimha Rao" }, { "family": "Strang", "given": "Kenneth David" }, { "family": "Bitrus", "given": "Nankyer Sarah" } ], "editor": [ { "literal": "O. Yildiz" } ], "container-title": "Recent Developments in Individual and Organizational Adoption of ICTs", "issued": { "date-parts": [ [ 2021 ] ] }, "page": "82-95", "publisher": "IGI Global", "ISBN": "9781799830450", "DOI": "10.4018/978-1-7998-3045-0.ch005", "URL": "https://www.narasimharao.net/research/farm-management-information-systems-adoption-nigeria/", "abstract": "Agriculture is a critical sector in the Nigerian economy, contributing significantly to GDP as well as employment generation. Agricultural technology has evolved substantially over the last decade with significant advancements in farm management information systems (FMIS) as well as agricultural information systems (AIS). FMIS have evolved from addressing simple production tasks to handling complex across multifunctional sectors in farming enterprises. However, the adoption rates of FMIS have been low in Nigeria. In this chapter, the contemporary usage of farm management information systems in central Nigeria is examined, and the various constraints leading to the low adoption rates are explored. In this study, the factors impacting the FMIS adoption by rural farmers in central Nigeria were examined. The findings of this study indicated that some of the demographic factors were influencing the FMIS adoption by rural farmers in central Nigeria. The results of this study in this chapter should help policymakers in framing policies intended to improve FMIS adoption rates in Nigeria.", "keyword": "farm management information systems, FMIS, technology adoption, e-adoption, agriculture, Nigeria, Jos Plateau, rural farmers, smallholder farmers, demographic factors, marital status, gender, discriminant analysis, Spearman correlation", "language": "en" }, { "id": "10.4018/978-1-7998-4849-3.ch003", "type": "chapter", "title": "Agriculture Business Problems: Analysis of Research and Probable Solutions in Africa", "author": [ { "family": "Strang", "given": "Kenneth David" }, { "family": "Che", "given": "Ferdinand Ndifor" }, { "family": "Vajjhala", "given": "Narasimha Rao" } ], "container-title": "Opportunities and Strategic Use of Agribusiness Information Systems", "collection-title": "Advances in Business Information Systems and Analytics", "issued": { "date-parts": [ [ 2021 ] ] }, "page": "33-58", "publisher": "IGI Global", "ISBN": "9781799848493", "DOI": "10.4018/978-1-7998-4849-3.ch003", "URL": "https://www.narasimharao.net/research/agriculture-business-problems-solutions-west-africa-nigeria/", "abstract": "Researchers need to investigate global life-threatening problems tied to agriculture such as food insecurity and malnutrition pandemics. This chapter reviews empirical fact-based state-of-the-art literature underlying the agri-business adoption barriers and the agriculture food insecurity crises. The authors focus their effort on identifying the hot spots of global agriculture problems, in developing nations. They use critical analysis to identify the most pressing issues and controversies surrounding West Africa. They then explore empirical literature suggesting possible remedies and future research needs to resolve the agriculture problems, in a way that these concepts would generalize globally and be of interest to other scholars. They produce several conceptual models to assist future agriculture research scholars including keyword thematic diagrams, cross-case subject analysis, topic contingency analysis, and literature topic synthesis. They then focus on probable solutions and they create several conceptual models to summarize those. They close with recommendations for future research.", "keyword": "agriculture, agribusiness, food security, food insecurity crisis, West Africa, Nigeria, literature review, agricultural information systems adoption, agricultural extension workers, corruption, Boko Haram terrorism, farmer training, smallholder farmers, Pareto analysis", "language": "en" }, { "id": "10.4018/978-1-5225-0013-1.ch002", "type": "chapter", "title": "Communities of Practice in Transition Economies: Innovation in Small- and Medium-Sized Enterprises", "author": [ { "family": "Vajjhala", "given": "Narasimha Rao" } ], "container-title": "Organizational Knowledge Facilitation through Communities of Practice in Emerging Markets", "collection-title": "Advances in Knowledge Acquisition, Transfer, and Management", "issued": { "date-parts": [ [ 2016 ] ] }, "page": "31-44", "publisher": "IGI Global", "ISSN": "2326-7607 (print); 2326-7615 (electronic)", "ISBN": "9781522500131 (print); 9781522500148 (electronic)", "DOI": "10.4018/978-1-5225-0013-1.ch002", "URL": "https://www.narasimharao.net/research/communities-of-practice-transition-economies-sme-innovation/", "abstract": "Communities of Practice (CoPs) are informal groups of individuals sharing knowledge and experience within or outside an organization. CoPs can help organizations, especially Small- and Medium-sized Enterprises (SMEs) with limited financial and human resources improve efficiency and productivity by leveraging knowledge resources in the organization. Transition economies have different social and economic conditions as compared to developing and developed countries. The success of CoPs in SMEs located in transition economies depends to a certain extent on the social and cultural factors in transition economies. This chapter explores the factors contributing to the success of CoPs as well as challenges that CoPs face in transition economies. This chapter explores the role of national and organizational culture on the functioning of CoPs in SMEs in transition economies. The objective of this chapter is to develop a framework that could be applied to CoPs in transition economies. This chapter also identifies the factors that might limit the work of CoPs in the context of innovation in SMEs in transition economies.", "keyword": "communities of practice, CoPs, knowledge management, knowledge sharing, SMEs, small and medium-sized enterprises, transition economies, national culture, organizational culture, innovation, conceptual framework", "language": "en" }, { "id": "10.4018/978-1-5225-0489-4.ch012", "type": "chapter", "title": "Visual Data Mining for Collaborative Filtering: A State-of-the-Art Survey", "author": [ { "family": "Biba", "given": "Marenglen" }, { "family": "Vajjhala", "given": "Narasimha Rao" }, { "family": "Nishani", "given": "Lediona" } ], "container-title": "Collaborative Filtering Using Data Mining and Analysis", "issued": { "date-parts": [ [ 2017 ] ] }, "page": "217-235", "publisher": "IGI Global", "DOI": "10.4018/978-1-5225-0489-4.ch012", "URL": "https://www.narasimharao.net/research/visual-data-mining-collaborative-filtering-survey/", "abstract": "This book chapter provides a state-of-the-art survey of visual data mining techniques used for collaborative filtering. The chapter begins with a discussion on various visual data mining techniques along with an analysis of the state-of-the-art visual data mining techniques used by researchers as well as in the industry. Collaborative filtering approaches are presented along with an analysis of the state-of-the-art collaborative filtering approaches currently in use in the industry. Visual data mining can provide benefit to existing data mining techniques by providing the users with visual exploration and interpretation of data. The users can use these visual interpretations for further data mining. This chapter dealt with state-of-the-art visual data mining technologies that are currently in use apart. The chapter also includes the key section of the discussion on the latest trends in visual data mining for collaborative filtering.", "keyword": "visual data mining, collaborative filtering, recommender systems, data visualization, information visualization, data mining, memory-based collaborative filtering, model-based collaborative filtering, hybrid collaborative filtering, content-based filtering, visual analytics, movie recommendation, state-of-the-art survey", "language": "en" } ]