Research & Publications
A scholarly record across
technology and society
Peer-reviewed work indexed in Scopus and Web of Science — spanning machine learning, cybersecurity, healthcare informatics, project management, and the adoption of technology in transition economies. What follows is a selection rather than a full bibliography: up to two works per year in each category (2026 journal articles are listed in full), chosen for their closeness to computer science, project management, and information systems. Every entry links to the source; the complete record is on Scopus and ORCID.
19
h-index · Google Scholar
11
h-index · Scopus
6
h-index · Web of Science
h-index as reported by each author profile — Google Scholar · Scopus · Web of Science — as of October 2026. The three differ because each index covers a different slice of the literature: Google Scholar counts everything it can crawl, Scopus a curated set, and the Web of Science Core Collection narrower still. They measure the full record; the list below is a curated selection of it.
At a glance
The record in numbers
Selected output — journal articles, conference papers, books & chapters
Counts the works listed on this page — up to two per year in each category, plus every 2026 journal article — not the full indexed record shown in the Scopus and Web of Science figures above.
Citations per year · Scopus
Citations received each year, from the Scopus citation overview, captured October 2026. 2026 is year-to-date.
Publication summaries & key findings
Every publication listed here has its own page: a citable summary, numbered key findings, methods and data, definitions, the full abstract or publisher description, limitations, practical implications and ready-made citations — plus short, fully cited syntheses of each research focus area and longer evidence overviews.
Most cited · Scopus
Cited by 25
Applications of Artificial Intelligence in Small- and Medium-Sized Enterprises (SMEs)
2022
Cited by 22
Statistical modeling and visualizing Open Big data using a terrorism case study
2015
Cited by 21
A study of application of Web 3.0 technologies in Indian small and medium enterprises
2018
Cited by 18
Student resistance to a mandatory learning-management system in online supply-chain courses
2017
Refereed journal articles indexed in Scopus and/or Web of Science — every 2026 article, then up to two per year, listed most recent first. Impact Factor, CiteScore, and quartile reflect the Clarivate JCR 2025 and Scopus 2025 releases.
2026
Talking Face Generation in Socio-Technical Systems: A Systematic Review of Deep Learning Architectures, Deployment Contexts, and Governance Frameworks
Structured summary
- The Problem
- Talking face generation can now synthesise photorealistic, real-time speaking video from a single image and an audio clip and is entering healthcare, education, identity management and public communication, yet no prior review was at once systematic, current to 2024–2025, and comprehensive across its technical, ethical and socio-technical dimensions.
- The Methodology
- Systematic literature review (Kitchenham and Charters; reported to PRISMA 2020) of 92 primary studies published 2016–2025, screened from 18,343 records in six repositories over four search iterations, with a five-criterion quality assessment and qualitative narrative synthesis across six research questions.
- The Core Finding
- 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. Only one of the 92 systems embeds any technical safeguard — a non-compliant post hoc watermark — and none implements consent or provenance, even though EU AI Act Article 50 transparency obligations have applied since 2 August 2026.
- The Citation
- 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
2026
Regulatory Volatility in Digital Supply Chains: An Information Systems Analytics Study of Decision-Maker Risk Perceptions and Sustainability-Related Outcomes
Structured summary
- The Problem
- Supply chain decision-makers must manage fast-changing trade, labor and environmental regulation, yet it is unknown whether their perceptions of these regulatory-volatility risks form a coherent construct or predict whether a supply chain engagement succeeds.
- The Methodology
- Quantitative retrospective analysis of archival organisational records (N = 1,988): descriptive statistics, exploratory factor analysis with parallel analysis, and binary logistic regression with classification diagnostics.
- The Core Finding
- 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. A perfect in-sample classifier (AUC = 1.000) was an artefact of label leakage from near-duplicate items (r = 0.915), not genuine predictive skill.
- The Citation
- 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
2026
Machine Learning and Data Science for ESG Compliance Measurement in Financial Software Engineering Projects: A Single-Case Socio-Technical Systems Analysis
Structured summary
- The Problem
- Organizations increasingly want to measure Environmental, Social, and Governance (ESG) compliance at the level of individual software engineering projects, but it is unclear what machine learning can learn from the evaluation records firms actually produce.
- The Methodology
- Single-case socio-technical systems study of archival project records (N = 207) using an open-source data science workflow: correlation analysis, principal component analysis, common-method-bias diagnostics, and kNN and SVM classification.
- The Core Finding
- 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%). ESG measurement from such records reflects the firm’s rating practice as much as the algorithm — a socio-technical systems result.
- The Citation
- 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
2026
A Comparative Evaluation of Deep Learning Architectures for Weed Classification, with an Exploratory Out-of-Distribution Analysis of Albanian Field Images
Structured summary
- The Problem
- Weed-classification studies usually rank deep learning architectures from single training runs, which can overstate differences between models and says nothing about transfer to new farming regions.
- The Methodology
- Comparative deep learning experiment on the DeepWeeds benchmark (nine classes) with leakage-checked splits, repeated multi-seed training of six architectures, statistical model comparison, and an exploratory out-of-distribution probe on unlabelled Albanian field images.
- The Core Finding
- 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. An exploratory test on Albanian field images revealed a clear geographic domain shift, so locally collected and labelled data are needed before deployment.
- The Citation
- 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
2026
Verifying SDG / ESG compliance in manufacturing-industry projects by surveying sponsors
Structured summary
- The Problem
- ESG frameworks measure firms and the SDGs measure nations, but no validated instrument existed for measuring ESG compliance in the individual projects where sustainability commitments are actually carried out.
- The Methodology
- Quantitative instrument development and validation with survey data from 2,231 project sponsors and decision-makers in North American manufacturing (NAICS 31–33): principal component analysis, exploratory factor analysis and structural equation modelling.
- The Core Finding
- 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). The instrument gives project offices a standardized, process-based benchmark for project-level ESG compliance.
- The Citation
- 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
2025
Can Project Team Members’ Willingness to Disclose Past Performance During Procurement Improve Organizational Business Process Success?
Structured summary
- The Problem
- Projects still fail roughly half the time, and established predictors such as budget, end-user community size and certification explain little of the difference between success and failure.
- The Methodology
- Post-positivist quantitative hypothesis testing on Fintech project data: correlation, conditioned correlation, t-tests and ordinary least squares regression with budget, end-user community size and certification as controls.
- The Core Finding
- 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%.
- The Citation
- 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
2025
Exploring project-manager commitment using machine learning on fuzzy big data
Structured summary
- The Problem
- Project manager organizational commitment is usually measured with self-report surveys, and little is known about which project attributes predict it in real project data.
- The Methodology
- Exploratory machine learning study of retrospective secondary big data (439 successful US government IT projects, 1,230 features): multidimensional scaling, silhouette scores, and linear regression, random forest and SVM regression with ten-fold cross-validation.
- The Core Finding
- Across 439 successful US government IT projects with 1,230 features, linear regression outperformed random forest and SVM in predicting project manager commitment tenure (R² = 0.238 versus 0.108 and 0.047). Line of business, years of experience, team network collaboration type, cross-industry subject matter and contract status were the strongest predictors.
- The Citation
- 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
2024
Evaluating the anti-corruption factor in environmental, social, and governance indices by sampling large financial asset-management firms
Structured summary
- The Problem
- Investors rely on ESG ratings to judge corporate governance, but it is untested whether those ratings reflect the anti-corruption record — misconduct and arbitration cases — of the largest asset management firms.
- The Methodology
- Quantitative archival study of 14 large asset management firms combining AI-assisted collection of legal, misconduct and arbitration records with ESG ratings from three providers, analysed with Bayesian correlation and bootstrapping.
- The Core Finding
- For 14 asset managers each holding more than USD 1 trillion, ESG scores from MSCI, Sustainalytics and CSRhub were unrelated to misconduct or arbitration case counts (all Bayes factors below 1), and the providers disagreed with each other (MSCI versus Sustainalytics r = −0.069). The ratings studied do not capture this governance evidence.
- The Citation
- 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
2024
Profitability, effectiveness, operational efficiency, and market growth of SMEs in Albania after piloting data analytics
Structured summary
- The Problem
- Small and medium-sized enterprises in transition economies such as Albania are adopting data analytics, but evidence on whether it improves their performance is scarce.
- The Methodology
- Quantitative post-positivist survey of 41 Albanian SMEs: descriptive statistics, Pearson and Spearman correlation, generalised linear model, MANOVA, and random forest, SVM, linear regression and neural network models.
- The Core Finding
- 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).
- The Citation
- 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
2023
Comparative analysis of bit-parallel string pattern-matching algorithms for biological sequences
Structured summary
- The Problem
- Bioinformatics needs fast pattern matching over DNA, RNA and protein sequences, but the many bit-parallel string-matching algorithms have not been compared side by side for biological data.
- The Methodology
- Comparative literature review of bit-parallel exact and approximate string matching algorithms (Shift-Or/Shift-And, BNDM, TNDM, SBNDM and parameterized variants) with a tabulated comparison of 11 algorithms by complexity, drawbacks and application area.
- The Core Finding
- 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. The BNDM family is most efficient within that limit, and optimal performance requires designing algorithms around the nature of the target sequence.
- The Citation
- 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
2023
Mining project-failure indicators from big data using machine-learning mixed methods
Structured summary
- The Problem
- Thousands of IT projects fail every year, yet the indicators most strongly associated with failure have not been mined systematically from large project datasets.
- The Methodology
- Pragmatic recursive mixed-methods design on U.S. Government Accountability Office big data (about 17,430 IT projects): structured data cleaning, random forest classification of project breach/failure, and post-hoc logistic regression in SPSS.
- The Core Finding
- Random forest analysis of about 17,430 US government IT projects identified seven failure indicators with 79.9% precision, 81% recall and a ROC area of 0.849. Post-hoc logistic regression confirmed project manager experience, project budget and outsourced versus in-house project managers as significant predictors of failure.
- The Citation
- 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
2022
Testing risk-management decision-making competency of project managers in a crisis
Structured summary
- The Problem
- Failing projects are often not cancelled in a crisis, and it is unclear how much cognitive bias and individual factors such as certification, competency and experience drive project managers’ risk decisions.
- The Methodology
- Repeated-measures controlled quasi-experiment with 24 experienced U.S. project managers (competency test, basic decision, biased decision) analysed with Spearman correlation, MANOVA, repeated-measures ANOVA, Friedman test and post-hoc t-tests.
- The Core Finding
- 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). Competent, certified project managers made the best decisions under bias, whereas a certified but incompetent project manager failed to cancel.
- The Citation
- 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
2022
How bias impacted the project-manager decision to not terminate a failing project
Structured summary
- The Problem
- Competent project managers sometimes keep failing projects alive after a risk event, and the role of hidden value-prospect bias in that decision had not been measured experimentally.
- The Methodology
- Online repeated-measures controlled experiment with 16 experienced U.S. IT project managers in a simulated military software project, using a PERT competency test, basic and incentive-biased decision conditions, paired t-test and repeated-measures ANOVA.
- The Core Finding
- 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). Risk competency, not certification, was the significant protective factor (η² = 0.218).
- The Citation
- 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
2021
Thematic analysis of agricultural government policy and operational problems
Structured summary
- The Problem
- Nigerian agricultural policy is designed far from the farm, and the ground-truth governance and operational problems facing rural farmers are rarely documented.
- The Methodology
- Qualitative constructivist grounded theory study: one-day focus group with 16 agricultural extension workers in Northeast Nigeria, member checking, and discourse analysis coded into thematic concept maps in NVivo.
- The Core Finding
- Extension workers representing about 900 rural farmers in Northeast Nigeria identified six core failures of government support: input quality and dissemination, fair subsidization, training, market facilitation, corruption and local security. Fake farm inputs, poor roads, insurgency-driven insecurity, and limited credit and land compound these problems.
- The Citation
- 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
2020
Voice of farmers in the agriculture crisis in North-East Nigeria: focus-group insights from extension workers
Structured summary
- The Problem
- The voices of rural farmers in conflict-affected North-East Nigeria are largely missing from research on the region’s agriculture crisis.
- The Methodology
- Sequential mixed-methods constructivist design: grounded theory literature review followed by a consensual qualitative research focus group with 16 extension workers (nominal group technique, discourse analysis, thematic concept mapping in NVivo 12).
- The Core Finding
- 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.
- The Citation
- 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
2020
Predictors of e-service consumption in a highly productive Brazil–Russia–India–China–South Africa region sample
Structured summary
- The Problem
- Online retailers targeting fast-growing BRICS markets such as India know little about which social, demographic and emotional factors drive young consumers’ e-service purchase intentions.
- The Methodology
- Exploratory post-positivist quantitative survey of 63 young educated consumers in Andhra Pradesh and Telangana, India: chi-square tests, Spearman correlation, binary logistic regression and discriminant analysis in SPSS.
- The Core Finding
- Among 63 young educated Indian consumers, satisfaction, happiness, positive feelings and pleasant feelings predicted online purchase intention while excitement did not. Satisfaction alone correctly classified 90.5% of consumers, and satisfied consumers were almost 28 times more likely to intend to buy (odds ratio 27.91).
- The Citation
- 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
2019
Factors impacting farm-management decision-making software adoption
Structured summary
- The Problem
- Agricultural information systems can improve farm decisions in Nigeria, but little is known about why farmers continue or stop using such software.
- The Methodology
- Quantitative positivist survey grounded in expectation confirmation theory with 97 valid responses from farm owners, managers and labourers on the Jos Plateau, Nigeria, analysed with confirmatory factor analysis and structural equation modelling.
- The Core Finding
- 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.
- The Citation
- 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
2019
Urgently strategic insights to resolve the Nigerian food-security crisis
Structured summary
- The Problem
- Nigeria’s food security crisis needs localized, prioritized solutions, but policy rarely draws on the practical knowledge of the extension workers who mentor rural farmers.
- The Methodology
- Pragmatic inductive study using consensual qualitative research with embedded nominal brainstorming in a six-hour focus group of 16 extension workers, prioritised with multiple correspondence analysis in SPSS 25.
- The Core Finding
- 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.
- The Citation
- 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
2018
A study of application of Web 3.0 technologies in Indian small and medium enterprises
Structured summary
- The Problem
- Indian small and medium enterprises are under pressure to adopt Web 3.0 technologies, but the opportunities and obstacles their managers perceive had not been documented.
- The Methodology
- Qualitative study of managers from 40 Indian SMEs in five economic sectors using semi-structured questionnaires with open-ended questions, content analysis and thematic coding in NVivo 11.
- The Core Finding
- Interviews with managers from 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.
- The Citation
- 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
2017
Student resistance to a mandatory learning-management system in online supply-chain courses
Structured summary
- The Problem
- Universities impose mandatory learning management systems on students, yet it is unclear which Technology Acceptance Model 3 factors explain resistance and whether they predict actual performance.
- The Methodology
- Post-positivist within-group survey of 65 senior supply chain management students at SUNY using the TAM3 instrument, with hierarchical, surface response and multiple regression on behavioural intention and actual course grade.
- The Core Finding
- Among 65 senior supply chain students using a new mandatory LMS, perceived usefulness, enjoyment, voluntariness and results demonstrability explained 52.3% of behavioral intention, and behavioral intention predicted actual course grade — confirming a key TAM3 tenet with a performance outcome rather than self-report alone.
- The Citation
- 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
2017
Measuring Organizational-Fit Through Socio-Cultural Big Data
Structured summary
- The Problem
- Organizations make hiring and partnering decisions on cultural fit, but no published studies had used big data to measure the relationship between culture and organizational fit.
- The Methodology
- Conceptual paper based on a literature review of big data and national and organisational culture models, proposing a socio-cultural viability dimension and a two-phase sampling, data-reduction and hypothesis-testing approach.
- The Core Finding
- The paper proposes “viability” as a socio-cultural sixth V 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.
- The Citation
- 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
2015
Influence of cultural factors on knowledge sharing in medium-sized enterprises within transition economies
Structured summary
- The Problem
- Knowledge-sharing initiatives in transition-economy firms often stall, and the cultural factors behind employee participation in Albanian enterprises were not understood.
- The Methodology
- Qualitative multisite exploratory case study: 27-question open-ended interviews with 20 managers from ten medium-sized enterprises across five Albanian sectors, coded in NVivo 9 following Creswell’s procedure until six themes emerged.
- The Core Finding
- 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.
- The Citation
- 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
2014
Collaboration strategies for a transition economy: measuring culture in Albania
Structured summary
- The Problem
- Foreign organizations collaborating with Albania had no empirical measure of its national culture, because Albania was absent from Hofstede’s cultural-dimension data.
- The Methodology
- Non-experimental post-positivist survey of 73 business professionals in Tirana validating a five-factor Hofstede-based instrument through exploratory and confirmatory factor analysis, with national culture indexes computed and benchmarked against Hofstede data.
- The Core Finding
- 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). Albania’s profile is closest to its Balkan and Turkish neighbours rather than to Western cultures such as the USA.
- The Citation
- 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
Recognition
Awards & honours
2021
Outstanding Paper, Emerald Literati Awards
2023
Best Researcher Award
2023
Best Paper Award — Data-Driven Sustainability
2023
Best Paper Award — ML & DL in Healthcare Management
2012
Best Professor of the Year
Keynote addresses
2026
“Orchestrating Human-AI Synergy in Educational Ecosystems: From Transparent Collaboration to Sustainable Innovation”
Invited talks, panels & policy roundtables
2026
“AI in a Real Organization”
Co-moderated a discussion with Irvinder Singh, Director and Chief Technology Officer of the World Bank Group, Ryan Bunney, its Manager for Enterprise Service Management and Autonomous Platforms, and Jason Pellmar, its Country Manager for Bulgaria, on what it takes to put AI to work inside a large organisation — where adoption is worth it and where conventional automation is enough, measuring benefit against a baseline, accountability and human oversight as AI agents take on actions, AI adoption and Bulgaria’s competitiveness, and the skills computer-science students will need.
2025
“AI and disinformation: The challenge of LLM poisoning”
Presented on the poisoning of large language models as a vector for information manipulation, alongside an OSINT analysis of the Pravda network’s methodology and an assessment of reflexive-control techniques, then took part in the closing roundtable on countermeasures and policy recommendations.
Conference scientific and technical committee service, and sessions chaired, are listed with the editorial & reviewing roles.