Reference list

All selected
publications

Every publication selected for this site in one list, newest first: the full reference, the DOI where one is registered, and one sentence on what the work found or covers. Each title links to the publication’s own page, with its summary, key findings, methods and data, and citation in five formats, and with definitions and limitations where the source gives them.

This is a curated selection, not a complete bibliography. The complete record is on ORCID, Scopus and Google Scholar. The same list is available as BibTeX, RIS and CSL-JSON.

2026

  1. Salahudeen, R., Fonkam, M., Vajjhala, N. R., Junaidu, S. B., & Garba, A. (2026). Talking Face Generation in Socio-Technical Systems: A Systematic Review of Deep Learning Architectures, Deployment Contexts, and Governance Frameworks. Systems, 14(10), Article 1198. https://doi.org/10.3390/systems14101198
    Across 92 primary studies (2016–2025), Diffusion Transformers and Gaussian Splatting account for about 36% of studies dated 2024 or later while GAN use has fallen below 3%; training data are demographically skewed and under-documented, and the dominant metrics (PSNR, SSIM, SyncNet) do not measure what deployment requires.
    Systematic reviewScopus Q1 (CiteScore 5.4)Web of Science SSCI Q1Impact Factor 3.8 (JCR 2025)Open accessSummary & key findings →
  2. Strang, K. D., & Vajjhala, N. R. (2026). Regulatory Volatility in Digital Supply Chains: An Information Systems Analytics Study of Decision-Maker Risk Perceptions and Sustainability-Related Outcomes. Sustainability, 18(18), Article 9359. https://doi.org/10.3390/su18189359
    Perceived regulatory-volatility risks do not form a single construct (KMO = 0.470), and labor and environmental risks were rated lowest of all (medians 0 and 1 on 0–5 scales) during a period of record forced-labor enforcement.
    Journal articleScopus Q1 (CiteScore 8.9)Web of Science Q2Impact Factor 4.1 (JCR 2025)Open accessSummary & key findings →
  3. Strang, K. D., & Vajjhala, N. R. (2026). Machine Learning and Data Science for ESG Compliance Measurement in Financial Software Engineering Projects: A Single-Case Socio-Technical Systems Analysis. Systems, 14(8), Article 990. https://doi.org/10.3390/systems14080990
    In 207 financial software project records, stakeholder-rated social and governance factors tracked the overall project score almost perfectly (r = +0.995 and +0.966) while the environmental factor was unrelated to it, and machine learning classifiers performed weakly (kNN AUC = 0.497; SVM accuracy 61.8%).
    Journal articleScopus Q1 (CiteScore 5.4)Web of Science SSCI Q1Impact Factor 3.8 (JCR 2025)Open accessSummary & key findings →
  4. Kumar, R., Vajjhala, N. R., Ramollari, E., & Joca, E. (2026). A Comparative Evaluation of Deep Learning Architectures for Weed Classification, with an Exploratory Out-of-Distribution Analysis of Albanian Field Images. Information, 17(8), Article 751. https://doi.org/10.3390/info17080751
    Fine-tuned DINOv2 and EfficientNet-B0 performed best on nine-class DeepWeeds data, but differences among several high-performing models were not consistently statistically significant across random seeds.
    Journal articleScopus Q1Web of Science ESCI Q2Impact Factor 4.3 (JCR 2025)Open accessSummary & key findings →
  5. Eappen, P., Vajjhala, N. R., Guo, R., Shinners, L., & Gunn, V. (Eds.). (2026). Enhancing Healthcare Informatics with Transparent and Explainable AI. Auerbach Publications (CRC Press). https://doi.org/10.1201/9781003604440
    The volume applies explainable and transparent AI across clinical decision-making, telehealth, electronic health records, EEG analysis, wearable monitoring and mental health, pairing ethical analysis with applied systems and strategies for building trust.
    Edited bookScopusSummary & key findings →
  6. Fonkam, M., & Vajjhala, N. R. (2026). A Spotlight on the Role of Functional Programming & Haskell in Computing & Software Development. In H. R. Arabnia (Ed.), Computational Science and Computational Intelligence (CSCE 2025) (pp. 56–63). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-032-22202-2_5
    A systematic review of 28 empirical studies (2015–2025) found mixed but promising outcomes: integrated approaches outperform purely functional ones, benefits for mathematical reasoning and abstract thinking are consistent, and transfer to mainstream object-oriented languages is selective.
    Conference paperScopusSummary & key findings →
  7. Haveri, K., & Vajjhala, N. R. (2026). Deep Learning for Medical Image Analysis: CNN-based Pneumonia Detection on Chest X-Rays. In 2026 6th International Conference on Pervasive Computing and Social Networking (ICPCSN) (pp. 156–161). IEEE. https://doi.org/10.1109/ICPCSN68523.2026.11543623
    A lightweight EfficientNetV2B0 model trained under CLAIM and TRIPOD-AI reporting standards achieved an AUC of 0.967 (95% CI 0.953–0.979) and pneumonia recall of 0.98 on paediatric chest X-rays, missing only about 2% of true cases.
    Conference paperScopusIEEE XploreSummary & key findings →
  8. Eappen, P., Gunn, V., Zikos, D., & Vajjhala, N. R. (2026). The Rise of AI in Healthcare: A Transparent Approach to Implementation. In P. Eappen, V. Gunn, D. Zikos, & N. R. Vajjhala (Eds.), AI in Healthcare: A Transparent Approach to Informatics (pp. 3–16). Chapman and Hall/CRC. https://doi.org/10.1201/9781003624875-2
    The chapter shows that successful healthcare AI depends on transparency, accountability and collaboration among patients, providers, developers, regulators and insurers, and identifies liability, algorithmic opacity and unclear accountability for AI-influenced outcomes as the key barriers to adoption.
    Book chapterScopusSummary & key findings →
  9. Eappen, P., Gunn, V., Zikos, D., & Vajjhala, N. R. (Eds.). (2026). AI in Healthcare: A Transparent Approach to Informatics. Chapman and Hall/CRC. https://doi.org/10.1201/9781003624875
    The volume combines conceptual, ethical, case-based and technical chapters — from transparent implementation and algorithmic scheduling to deep learning for abdominal trauma diagnosis and transparent drug-class prediction — into a framework for transparent healthcare AI.
    Edited bookScopusSummary & key findings →
  10. Strang, K. D., & Vajjhala, N. R. (2026). Verifying SDG ESG Compliance in Manufacturing Industry Projects by Surveying Sponsors. Information, 17(4), Article 311. https://doi.org/10.3390/info17040311
    A survey of 2,231 North American manufacturing project sponsors produced a validated seven-item, two-factor instrument — ESG planning and ESG monitoring and controlling — with excellent fit (CFI = 0.99, TLI = 0.98, RMSEA = 0.052, SRMR < 0.035).
    Journal articleScopus Q1Web of Science ESCI Q2Impact Factor 4.3 (JCR 2025)Open accessSummary & key findings →

2025

  1. Gigov, L., Vajjhala, N. R., & Stoilov, A. (2025). Hyperlocal Temperature and Humidity Prediction Using Supervised Machine Learning. In 2025 2nd Global AI Summit – International Conference on Artificial Intelligence and Emerging Technology (AI Summit) (pp. 940–945). IEEE. https://doi.org/10.1109/AISummit66170.2025.11411099
    On ten years of daily data for Sofia, Berlin and Tokyo, linear regression matched random forest for temperature at a fraction of the run time and clearly outperformed it for humidity (R² 0.940–0.964), while decision trees performed worst.
    Conference paperScopusSummary & key findings →
  2. Strang, K. D., & Vajjhala, N. R. (2025). Can Project Team Members’ Willingness to Disclose Past Performance During Procurement Improve Organizational Business Process Success? Information, 16(11), Article 955. https://doi.org/10.3390/info16110955
    Team members’ willingness to disclose their past performance evaluations during procurement significantly improved project success after controlling for budget, end-user community size and certification, roughly doubling the explained variance in project success from 9.6% to 18.8%.
    Journal articleScopus Q1Web of Science ESCI Q2Impact Factor 4.3 (JCR 2025)Open accessSummary & key findings →
  3. Vajjhala, N. R., Rakshit, S., & Denga, E. M. (2025). Digital Retail Innovations: The Role of Artificial Intelligence (AI) and Machine Learning (ML). In A. Behl & B. Sampat (Eds.), Digital Transformation: A Business Imperative (pp. 75–86). Routledge India. https://doi.org/10.4324/9781003626640-8
    The chapter identifies seven technologies as imperative for digital business, explains each one’s applications, benefits and challenges, and shows how their interplay affects business strategy, operations and innovation.
    Book chapterScopusSummary & key findings →
  4. Vajjhala, N. R., & Strang, K. D. (Eds.). (2025). Cybersecurity in Knowledge Management: Cyberthreats and Solutions. CRC Press. https://doi.org/10.1201/9781003498094
    The volume connects knowledge management and cybersecurity through AI-based detection (including hybrid LSTM–XGBoost fake-news detection), malware classification, breach and insider-threat cases, supply chain resilience, and the human and organizational factors of social engineering.
    Edited bookScopusSummary & key findings →
  5. Martiri, E., Vajjhala, N. R., & Dalipi, F. (Eds.). (2025). AI-Enabled Threat Intelligence and Cyber Risk Assessment. CRC Press. https://doi.org/10.1201/9781003504979
    The volume maps current trends in AI-enabled threat intelligence and assembles sector and regional evidence — digital marketing fraud, healthcare compliance, workforce upskilling and Kazakh businesses — into guidance for building resilient AI-enabled cybersecurity frameworks.
    Edited bookScopusSummary & key findings →
  6. Strang, K. D., & Vajjhala, N. R. (2025). Exploring project manager commitment using machine learning on fuzzy big data. International Journal of Project Organisation and Management, 17(2), 135–152. https://doi.org/10.1504/IJPOM.2025.146727
    Across 439 successful US government IT projects with 1,230 features, linear regression outperformed random forest and SVM in predicting project manager commitment tenure.
    Journal articleScopusQ4Summary & key findings →
  7. Bole, D. K., & Vajjhala, N. R. (2025). Artificial Intelligence for Supply Chain Optimization: Benefits, Challenges, and Potential Solutions. In S. Dixit, M. Maurya, V. Jain, & G. Subramaniam (Eds.), Artificial Intelligence-Enabled Businesses: How to Develop Strategies for Innovation (pp. 81–93). Wiley. https://doi.org/10.1002/9781394234028.ch5
    The chapter assesses AI’s advantages for organizational supply chains, sets out methods for integrating AI into supply chain processes, and pairs each implementation challenge with potential solutions, arguing that AI adoption is a critical step toward competitiveness.
    Book chapterScopusSummary & key findings →
  8. Ajibesin, A. A., Vajjhala, N. R., Joel, E., & Rakshit, S. (2025). Predictive Web Prefetching: A Combined Approach Using Clustering Algorithms and WEKA in High-Traffic Settings. In F. Lin, D. Pastor, N. Kesswani, A. Patel, S. Bordoloi, & C. Koley (Eds.), Artificial Intelligence in Internet of Things (IoT): Key Digital Trends: Proceedings of 8th International Conference on Internet of Things and Connected Technologies (ICIoTCT 2023) (pp. 221–231). Springer Nature Singapore. https://doi.org/10.1007/978-981-97-5786-2_17
    A prefetching approach that builds an online navigation graph from server logs and clusters navigation behaviour with WEKA outperformed conventional prefetching techniques on domain client-group logs, particularly once server idle time allowed prefetching across all user predictions.
    Conference paperScopusSummary & key findings →

2024

  1. Strang, K. D., & Vajjhala, N. R. (2024). Evaluating the Anti-Corruption Factor in Environmental, Social, and Governance Indices by Sampling Large Financial Asset Management Firms. Sustainability, 16(23), Article 10240. https://doi.org/10.3390/su162310240
    For 14 asset managers, each with USD 1 trillion or more in assets under management, no ESG score from MSCI, Sustainalytics or CSRhub was significantly correlated with misconduct or arbitration case counts, and MSCI and Sustainalytics scores for the same firms were uncorrelated (r = −0.069).
    Journal articleScopusWeb of Science Q2Impact Factor 4.1 (JCR 2025)Open accessSummary & key findings →
  2. Thandekkattu, S. G., & Vajjhala, N. R. (Eds.). (2024). Designing Sustainable Internet of Things Solutions for Smart Industries. IGI Global. https://doi.org/10.4018/979-8-3693-5498-8
    The volume shows how IoT can be designed for sustainability across education, waste reduction, agriculture and smart farming, disease surveillance, autonomous vehicles, Industry 5.0 manufacturing and SMEs, combining technical frameworks with sector reviews.
    Edited bookScopusSummary & key findings →
  3. Vajjhala, N. R., & Eappen, P. (2024). Smart Health: Advancements in Machine Learning and the Internet of Things Solutions. In P. Samui, S. S. Roy, W. Zhang, & Y.-H. Taguchi (Eds.), Machine Learning and IoT Applications for Health Informatics (pp. 31–51). CRC Press. https://doi.org/10.1201/9781003424987-3
    The chapter shows that combining machine learning and IoT supports enhanced patient care, early disease detection, operational efficiency and personalised treatment, and that data privacy, model interpretability, bias mitigation and secure connectivity are prerequisites for deployment.
    Book chapterScopusSummary & key findings →
  4. Fonkam, M. M., & Vajjhala, N. R. (Eds.). (2024). Revolutionizing Curricula Through Computational Thinking, Logic, and Problem Solving. IGI Global. https://doi.org/10.4018/979-8-3693-1974-1
    The volume reframes computational thinking as a discipline-independent skill set and provides lesson plans, case studies, tools and strategies for embedding it in fields well beyond computing, from educational robotics to digital marketing and literature.
    Edited bookScopusSummary & key findings →
  5. Vajjhala, N. R., & Strang, K. D. (2024). An Exploratory Big Data Approach to Understanding Commitment in Projects. In Á. Rocha (Ed.), WorldCIST 2024 (World Conference on Information Systems and Technologies) (pp. 66–75). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-031-60227-6_6
    Supervised machine learning on 439 successful US government IT projects showed linear regression best predicted project manager commitment tenure (r² = 0.238), with line of business, experience, network collaboration tools and cross-industry team membership as the strongest features — supporting an association between higher commitment and project success.
    Conference paperScopusWeb of ScienceSummary & key findings →
  6. Strang, K. D., & Vajjhala, N. R. (2024). Exploring Cybersecurity Risks in Higher Education Environments with Machine Learning. In 2024 4th International Conference on Pervasive Computing and Social Networking (ICPCSN) (pp. 1–6). IEEE. https://doi.org/10.1109/ICPCSN62568.2024.00008
    Unsupervised machine learning (t-SNE with radial and correspondence analysis) applied to hypertext from US higher-education websites (a sample of 848), containing over 4,000 potential indicators, scored breach likelihood on a 1–3 scale and flagged concentrations of higher-risk components; a combined control feature was the most useful risk signal.
    Conference paperScopusIEEE XploreWeb of ScienceSummary & key findings →
  7. Vajjhala, N. R., & Eappen, P. (2024). Data Envelopment Analysis in Healthcare Management: Overview of the Latest Trends. In Data Envelopment Analysis (DEA) Methods for Maximizing Efficiency (pp. 245–260). IGI Global. https://doi.org/10.4018/979-8-3693-0255-2.ch011
    The review shows that DEA is widely applied in hospital management, nursing and outpatient services for efficiency measurement, benchmarking, resource allocation and performance evaluation, and identifies six limitations, including input/output selection, outlier sensitivity, inability to handle statistical noise and the static nature of traditional models.
    Book chapterScopusSummary & key findings →
  8. Vajjhala, N. R., & Strang, K. D. (2024). Profitability, effectiveness, operational efficiency, and market growth of SMEs in Albania after piloting data analytics. International Journal of Services and Standards, 14(1), 51–64. https://doi.org/10.1504/IJSS.2024.140078
    Among 41 Albanian SMEs, data-analytics use correlated positively with operational efficiency, process optimisation, profitability and market growth (r = 0.540 to 0.769), with a significant multivariate effect (MANOVA F(1, 38) = 20.029, p < 0.001); a random forest model explained the outcomes best (R² = 0.661).
    Journal articleScopusQ4Summary & key findings →

2023

  1. Strang, K. D., & Vajjhala, N. R. (2023). Why Cyberattacks Disrupt Society and How to Mitigate Risk. In N. R. Vajjhala & K. D. Strang (Eds.), Cybersecurity for Decision Makers (pp. 1–28). CRC Press / Taylor & Francis. https://doi.org/10.1201/9781003319887-1
    An analysis of 25 major cyberattacks (2020–2022) classified 44% as extortion, 32% as service disruption and 24% as data theft, with 36% involving bitcoin ransom.
    Book chapterScopusSummary & key findings →
  2. Vajjhala, N. R., & Strang, K. D. (Eds.). (2023). Cybersecurity for Decision Makers. CRC Press. https://doi.org/10.1201/9781003319887
    Contributors from 11 countries integrate theory with evidence-based practice on cyberattack causes, human factors, awareness, cybercrime trends, emerging domains such as space and cyber-physical systems, and business continuity, giving decision-makers actionable mitigation strategies.
    Edited bookScopusSummary & key findings →
  3. Olaniyan, R., Rakshit, S., & Vajjhala, N. R. (2023). Application of User and Entity Behavioral Analytics (UEBA) in the Detection of Cyber Threats and Vulnerabilities Management. In P. Chatterjee, D. Pamucar, M. Yazdani, & D. Panchal (Eds.), Computational Intelligence for Engineering and Management Applications: Select Proceedings of CIEMA 2022 (pp. 419–426). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-8493-8_32
    The paper positions user and entity behavioural analytics (UEBA) as an AI approach that scans large volumes of system and network data to discover where attacks originate and to recommend responses to organizational decision-makers.
    Conference paperScopusSummary & key findings →
  4. Muhammad, M. Y., Fonkam, M., Thandekatu, S. G., Rakshit, S., & Vajjhala, R. N. (2023). Comparative Analysis of Bit-Parallel String Pattern Matching Algorithms for Biological Sequences. Operational Research in Engineering Sciences: Theory and Applications, 6(1), 322–331. https://oresta.org/article-view/?id=554
    A comparison of 11 bit-parallel algorithms shows that bit-parallelism is versatile for biological sequence analysis but is limited mainly by the requirement that pattern length not exceed the computer word length.
    Journal articleScopusQ2Open accessSummary & key findings →
  5. Vajjhala, N. R., & Eappen, P. (2023). The Role of 5G Networks in Healthcare Applications. In A. Bajpai & A. Balodi (Eds.), Applications of 5G and Beyond in Smart Cities (pp. 87–98). CRC Press. https://doi.org/10.1201/9781003227861-5
    The chapter maps 5G technologies to smart healthcare applications, sets out the challenges of deploying them, and gives leadership and policy recommendations for designing 5G-enabled healthcare strategies.
    Book chapterScopusSummary & key findings →
  6. Strang, K. D., & Vajjhala, N. R. (2023). Mining Project Failure Indicators From Big Data Using Machine Learning Mixed Methods. International Journal of Information Technology Project Management, 14(1), 1–24. https://doi.org/10.4018/IJITPM.317221
    Random forest analysis of about 17,430 US government IT projects identified seven failure indicators with 81% recall and a ROC area of 0.849.
    Journal articleScopusWeb of ScienceQ3Open accessSummary & key findings →
  7. Imoh, N., Vajjhala, N. R., & Rakshit, S. (2023). Experimental Face Recognition Using Applied Deep Learning Approaches to Find Missing Persons. In S. Basu, D. K. Kole, A. K. Maji, D. Plewczynski, & D. Bhattacharjee (Eds.), Proceedings of International Conference on Frontiers in Computing and Systems: COMSYS 2021 (pp. 3–11). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-0105-8_1
    The authors built an experimental face-recognition system that combines a convolutional neural network with facial calibration and modelling to extract face encodings and match them against a query image, as a faster tool for identifying missing persons.
    Conference paperScopusSummary & key findings →

2022

  1. Vajjhala, N. R., & Eappen, P. (Eds.). (2022). Health Informatics and Patient Safety in Times of Crisis. IGI Global. https://doi.org/10.4018/978-1-6684-5499-2
    The volume documents how health informatics performed during the pandemic — machine learning for predicting spread, telemedicine, clinical decision support design, big data analytics, registries and national emergency responses — and shows how improved informatics can protect patient safety in future crises.
    Edited bookScopusSummary & key findings →
  2. Strang, K. D., Che, F., & Vajjhala, N. R. (2022). Thematic Analysis of Agricultural Government Policy and Operational Problems. Agricultural Research, 11(3), 549–556. https://doi.org/10.1007/s40003-021-00588-2
    Extension workers representing about 900 rural farmers in Northeast Nigeria identified six core failures of government support, among them input quality and dissemination, fair subsidization, training, market facilitation and corruption.
    Journal articleScopusWeb of ScienceQ3Summary & key findings →
  3. Strang, K. D., & Vajjhala, N. R. (2022). How Bias Impacted the Project Manager Decision to Not Terminate a Failing Project. International Journal of Information Technology Project Management, 13(1), 1–17. https://doi.org/10.4018/IJITPM.304059
    A 400% bonus incentive significantly lowered 16 experienced IT project managers’ likelihood of cancelling a failing project (from 3.438 to 2.438 on a 1–5 scale; p = .005, Cohen’s d = 0.826).
    Journal articleScopusWeb of ScienceQ3Summary & key findings →
  4. Biba, M., & Vajjhala, N. R. (2022). Statistical Relational Learning for Genomics Applications: A State-of-the-Art Review. In S. S. Roy & Y.-H. Taguchi (Eds.), Handbook of Machine Learning Applications for Genomics (pp. 31–42). Springer Nature Singapore Pte Ltd. https://doi.org/10.1007/978-981-16-9158-4_3
    The review shows that statistical relational learning — probabilistic relational models, relational dependency networks and relational Markov networks — suits genomics tasks from sequence annotation to GWAS, and identifies the computational complexity of inference, followed by graph size, as the main limitation shared by most methods.
    Book chapterScopusEI CompendexSummary & key findings →
  5. Vajjhala, N. R., & Strang, K. D. (Eds.). (2022). Global Risk and Contingency Management Research in Times of Crisis. IGI Global. https://doi.org/10.4018/978-1-6684-5279-0
    The volume compiles evidence on risk and contingency management across nations and industries — from petroleum and automotive risk models to cybersecurity, healthcare informatics and educational inequality — offering business leaders lessons for future crises.
    Edited bookScopusSummary & key findings →
  6. Borah, S., Kama, C., Rakshit, S., & Vajjhala, N. R. (2022). Applications of Artificial Intelligence in Small- and Medium-Sized Enterprises (SMEs). In P. K. Mallick, A. K. Bhoi, P. Barsocchi, & V. H. C. de Albuquerque (Eds.), Cognitive Informatics and Soft Computing: Proceeding of CISC 2021 (pp. 717–726). Springer Nature Singapore. https://doi.org/10.1007/978-981-16-8763-1_59
    The paper sets out the AI applications relevant to SMEs together with the challenges, solutions and advantages of implementation, concluding that SMEs depend on AI and cloud-based solutions for growth despite the risks.
    Conference paperScopusSummary & key findings →
  7. Muhammad, M. Y., Thandekkattu, S. G., Rakshit, S., & Vajjhala, N. R. (2022). Efficient Structural Matching for RNA Secondary Structure Using Bit-Parallelism. In Ch. Satyanarayana, D. Samanta, X.-Z. Gao, & R. K. Kapoor (Eds.), High Performance Computing and Networking: Select Proceedings of CHSN 2021 (pp. 399–409). Springer Nature Singapore. https://doi.org/10.1007/978-981-16-9885-9_33
    The authors developed a bit-parallel structural matching (s-matching) algorithm that extends Shift-Or with encoding techniques for s-strings, solving exact s-matching on RNA secondary structure with assumed average-time optimality.
    Conference paperScopusSummary & key findings →
  8. Rakshit, S., Clement, N., & Vajjhala, N. R. (2022). Exploratory Review of Applications of Machine Learning in the Finance Sector. In S. Borah, S. K. Mishra, B. K. Mishra, V. E. Balas, & Z. Polkowski (Eds.), Advances in Data Science and Management: Proceedings of ICDSM 2021 (pp. 119–125). Springer Nature Singapore. https://doi.org/10.1007/978-981-16-5685-9_12
    The exploratory review surveys state-of-the-art machine learning applications, algorithms and techniques used in finance and sets out how machine learning can maximise productivity in financial institutions.
    Book chapterScopusEI CompendexSummary & key findings →
  9. Strang, K. D., & Vajjhala, N. R. (2022). Testing Risk Management Decision Making Competency of Project Managers in a Crisis. The Journal of Modern Project Management, 10(1), 52–71. https://journalmodernpm.com/manuscript/index.php/jmpm/article/view/JMPM02904
    In a controlled experiment with 24 experienced US project managers, a biasing incentive significantly lowered decision quality, and certification and risk competency were the only individual factors significantly related to good decisions (rho = 0.549 to 0.712).
    Journal articleScopus (at publication)Open accessSummary & key findings →

2021

  1. Saheed, Y. K., Longe, O., Baba, U. A., Rakshit, S., & Vajjhala, N. R. (2021). An Ensemble Learning Approach for Software Defect Prediction in Developing Quality Software Product. In M. Singh, V. Tyagi, P. K. Gupta, J. Flusser, T. Ören, & V. R. Sonawane (Eds.), Advances in Computing and Data Sciences: 5th International Conference, ICACDS 2021, Nashik, India, April 23–24, 2021, Revised Selected Papers, Part I (pp. 317–326). Springer International Publishing. https://doi.org/10.1007/978-3-030-81462-5_29
    A seven-model ensemble of boosted and bagged learners, evaluated with AUC, precision, recall, F-measure and Matthews correlation coefficient on NASA defect datasets, outperformed a logistic regression baseline, and the ensemble CatBoost model gave outstanding performance on all three datasets reported.
    Conference paperScopusWeb of Science (CPCI)Summary & key findings →
  2. Vajjhala, N. R., Strang, K. D., & Bitrus, N. S. (2021). Contemporary Usage of Farm Management Information Systems in Nigeria. In O. Yildiz (Ed.), Recent Developments in Individual and Organizational Adoption of ICTs (pp. 82–95). IGI Global. https://doi.org/10.4018/978-1-7998-3045-0.ch005
    In a survey of 105 farm owners, managers and labourers on the Jos Plateau, only marital status, gender and language correlated with FMIS e-adoption; age, education, land size and software experience did not, and a discriminant model using the three significant factors was statistically significant (p = .014).
    Book chapterScopusSummary & key findings →
  3. Strang, K. D., Che, F. N., & Vajjhala, N. R. (2021). Agriculture Business Problems: Analysis of Research and Probable Solutions in Africa. In Opportunities and Strategic Use of Agribusiness Information Systems (pp. 33–58). IGI Global. https://doi.org/10.4018/978-1-7998-4849-3.ch003
    A critical review of 2015–2020 empirical studies identifies lack of modern-method training, corrupt input dissemination, poor infrastructure, terrorism-driven insecurity, lack of credit and weak strategic planning as the key problems, and finds only weak evidence of significant information-system use in agriculture.
    Book chapterScopusSummary & key findings →
  4. Vajjhala, N. R., Rakshit, S., Oshogbunu, M., & Salisu, S. (2021). Novel User Preference Recommender System Based on Twitter Profile Analysis. In S. Borah, R. Pradhan, N. Dey, & P. Gupta (Eds.), Soft Computing Techniques and Applications: Proceeding of the International Conference on Computing and Communication (IC3 2020) (pp. 85–93). Springer Singapore. https://doi.org/10.1007/978-981-15-7394-1_7
    A recommender built on IBM Watson that mines a user’s Twitter profile and timeline predicted the category of goods and services the user is most likely to consume, and the authors report a strong correlation between consumed categories and tweet content.
    Conference paperScopusSummary & key findings →

2020

  1. Strang, K. D., Che, F., & Vajjhala, N. R. (2020). Ideologies and Issues for Teaching Blockchain Cybersecurity in Management and Computer Science. In K. Daimi & G. Francia III (Eds.), Innovations in Cybersecurity Education (pp. 109–126). Springer. https://doi.org/10.1007/978-3-030-50244-7_7
    A literature search found only five relevant peer-reviewed papers, which — with the authors’ teaching experience — led to a two-dimensional teaching typology (stakeholder role from programmer to managerial decision-maker; teaching ideology from theoretical to hands-on).
    Book chapterScopusSummary & key findings →
  2. Strang, K. D., Che, F., & Vajjhala, N. R. (2020). Urgently strategic insights to resolve the Nigerian food security crisis. Outlook on Agriculture, 49(1), 77–85. https://doi.org/10.1177/0030727019873012
    A focus group of extension workers representing about 900 farmers produced a significant two-dimension model of solutions, ranking affordable seeds and fertilizer, modern farming training, market information, roads and transport, affordable credit and agricultural mentors as the most strategically urgent.
    Journal articleScopusWeb of ScienceQ2Summary & key findings →
  3. Che, F. N., Strang, K. D., & Vajjhala, N. R. (2020). Voice of farmers in the agriculture crisis in North-East Nigeria: Focus group insights from extension workers. International Journal of Development Issues, 19(1), 43–61. https://doi.org/10.1108/IJDI-08-2019-0136
    Extension workers from three local government areas of Adamawa State confirmed themes from the literature and added new ones, showing that rural farmers face significant problems with government support in six areas: farm input quality and dissemination, fair input subsidization, training, market facilitation, corruption and insecurity.
    Journal articleOutstanding Paper2021 Emerald Literati AwardsScopusQ2Summary & key findings →
  4. Strang, K. D., & Vajjhala, N. R. (2020). Predictors of e-service Consumption in a Highly Productive Brazil-Russia-India-China-South Africa Region Sample. International Journal of E-Services and Mobile Applications, 12(1), 39–56. https://doi.org/10.4018/IJESMA.2020010103
    Among 63 young educated Indian consumers, satisfaction, happiness, positive feelings and pleasant feelings predicted online purchase intention while excitement did not.
    Journal articleScopusQ3Summary & key findings →
  5. Nasir, S., & Vajjhala, N. R. (2020). Evaluating Information Security Awareness and Compliance in Sub-Saharan Africa: An Interpretivist Perspective. In M. B. Nunes, P. Isaías, P. Powell, & B. Bontchev (Eds.), Proceedings of the 13th IADIS International Conference Information Systems 2020 (IS 2020) (pp. 187–190). IADIS Press. https://www.iadisportal.org/digital-library/evaluating-information-security-awareness-and-compliance-in-sub-saharan-africa-an-interpretivist-perspective
    The paper establishes an interpretivist, multisite case-study design — in-depth interviews with 50 managers in 20 medium-sized Nigerian companies across ten economic sectors — for identifying the non-technical drivers of information security compliance in SMEs.
    Conference paperScopusSummary & key findings →

2019

  1. Vajjhala, N. R., & Strang, K. D. (2019). Impact of Psycho-Demographic Factors on Smartphone Purchase Decisions. In Proceedings of the 2019 International Conference on Information System and System Management (ISSM 2019), Rabat, Morocco (pp. 5–10). ACM. https://doi.org/10.1145/3394788.3394790
    Among 89 young technology-literate consumers in the USA and India, gender, age and income did not influence online smartphone purchase decisions.
    Conference paperScopusSummary & key findings →
  2. Strang, K. D., Bitrus, S. N., & Vajjhala, N. R. (2019). Factors impacting farm management decision making software adoption. International Journal of Sustainable Agricultural Management and Informatics, 5(1), 1–14. https://doi.org/10.1504/IJSAMI.2019.10019819
    Structural equation modelling of 97 Jos Plateau farm owners, managers and labourers showed that confirmation experience significantly drove satisfaction (path = 0.524) and perceived usefulness (path = 0.383), yet no factor significantly predicted continuance intention, which was uniformly very high.
    Journal articleScopusQ3Web of ScienceSummary & key findings →

2018

  1. Potluri, R. M., & Vajjhala, N. R. (2018). A Study on Application of Web 3.0 Technologies in Small and Medium Enterprises of India. The Journal of Asian Finance, Economics and Business, 5(2), 73–79. https://doi.org/10.13106/jafeb.2018.vol5.no2.73
    Responses from managers in 40 Indian SMEs across five sectors produced 12 subthemes: advantages centred on service integration and new functionalities, while challenges centred on privacy and security, financial and technological constraints, and organizational barriers.
    Journal articleScopus and Web of Science (indexed at publication)Open accessSummary & key findings →
  2. Vajjhala, N. R., & Strang, K. D. (2018). Sociotechnical Challenges of Transition Economy SMEs During EU Integration. In A. M. Dima (Ed.), Doing Business in Europe: Economic Integration Processes, Policies, and the Business Environment (pp. 295–313). Springer International Publishing. https://doi.org/10.1007/978-3-319-72239-9_14
    Interviews with 20 managers in ten Albanian medium-sized enterprises identified five critical success factors: investment in ICT for work processes, investment in employee training, perceived usefulness of the technology, employee self-efficacy, and openness toward new technology.
    Book chapterScopusWeb of Science (BKCI)Summary & key findings →
  3. Vajjhala, N. R., & Strang, K. D. (2018). Examining Internet Behavior of Young Technology-Literate Consumers in India. In Twenty-fourth Americas Conference on Information Systems (AMCIS 2018), New Orleans. Association for Information Systems. https://aisel.aisnet.org/amcis2018/
    Among 63 Indian online shoppers, happiness, satisfaction, positive and pleasant feelings — but not excitement — predicted purchase intention.
    Conference paperScopusWeb of ScienceSummary & key findings →

2017

  1. Vajjhala, N. R., & Strang, K. D. (2017). Measuring Organizational-Fit Through Socio-Cultural Big Data. New Mathematics and Natural Computation, 13(2), 145–158. https://doi.org/10.1142/S179300571740004X
    The paper proposes “viability” as a socio-cultural dimension of big data and a two-phase method — sampling and data reduction, followed by hypothesis testing — for turning socio-cultural big data into generalizable organizational-fit evidence.
    Journal articleScopusQ3Web of ScienceESCISummary & key findings →
  2. Strang, K. D., & Vajjhala, N. R. (2017). Student Resistance to a Mandatory Learning Management System in Online Supply Chain Courses. Journal of Organizational and End User Computing, 29(3), 49–67. https://doi.org/10.4018/JOEUC.2017070103
    Among 65 senior supply chain students using a new mandatory LMS, perceived usefulness, enjoyment, voluntariness and results demonstrability predicted behavioral intention, and behavioral intention predicted actual course grade — confirming a key TAM3 tenet with a performance outcome rather than self-report alone.
    Journal articleScopusQ2Web of ScienceSSCI/SCIESummary & key findings →
  3. Biba, M., Vajjhala, N. R., & Nishani, L. (2017). Visual Data Mining for Collaborative Filtering: A State-of-the-Art Survey. In Collaborative Filtering Using Data Mining and Analysis (pp. 217–235). IGI Global. https://doi.org/10.4018/978-1-5225-0489-4.ch012
    The survey organizes visual data mining techniques, tools and collaborative filtering approaches, and concludes that combining visual data mining with collaborative filtering has led to substantial improvement and can alleviate the shortcomings of traditional collaborative filtering.
    Book chapterScopusSummary & key findings →

2016

  1. Vajjhala, N. R. (2016). Communities of Practice in Transition Economies: Innovation in Small- and Medium-Sized Enterprises. In Organizational Knowledge Facilitation through Communities of Practice in Emerging Markets (pp. 31–44). IGI Global. https://doi.org/10.4018/978-1-5225-0013-1.ch002
    The chapter shows that the success of communities of practice in transition-economy SMEs depends partly on social and cultural factors that differ from those in developed and developing countries, and develops a framework for applying communities of practice in that context.
    Book chapterScopusSummary & key findings →

2015

  1. Vajjhala, N. R., Strang, K. D., & Sun, Z. (2015). Statistical Modeling and Visualizing Open Big Data Using a Terrorism Case Study. In 2015 3rd International Conference on Future Internet of Things and Cloud (FiCloud), Rome, Italy (pp. 489–496). IEEE. https://doi.org/10.1109/FiCloud.2015.15
    Simple correspondence analysis in standard statistical software, applied to 125,087 global terrorism records (1970–2013), related nine attack methods to 13 world regions with two dimensions capturing 94.5% of inertia — demonstrating an accessible method for visualizing open big data.
    Conference paperScopusWeb of ScienceSummary & key findings →

2014

  1. Vajjhala, N. R., & Strang, K. D. (2014). Collaboration strategies for a transition economy: measuring culture in Albania. Cross Cultural Management, 21(1), 78–103. https://doi.org/10.1108/CCM-02-2013-0023
    A validated five-factor survey of 73 Tirana business professionals produced the first Hofstede-style indexes for Albania (power distance 78.7, individualism 42.7, masculinity 72.9, uncertainty avoidance 64.6, long-term orientation 52.2).
    Journal articleWeb of ScienceSSCIQ1ABDCERASummary & key findings →
  2. Vajjhala, N. R., & Baghurst, T. (2014). Influence of cultural factors on knowledge sharing in medium-sized enterprises within transition economies. International Journal of Knowledge Management Studies, 5(3/4), 304–321. https://doi.org/10.1504/IJKMS.2014.067235
    Interviews with 20 managers in ten Albanian medium-sized enterprises showed that national and organizational culture shape knowledge sharing: 85% said national culture influences employee behavior, 70% named lack of trust as a key barrier, and 90% said top management support is essential.
    Journal articleScopusQ3Summary & key findings →

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