Paper summaries &
key findings

Question. Does a project team member’s willingness to disclose past performance evaluations during procurement improve team allocation and business process success — beyond established predictors such as budget, end-user community size, and certification?

Finding. Yes. After controlling for budget, end-user community size, and certification, team members’ willingness to share their past performance evaluations significantly improved project success, raising the explained variance in project success from 9.6% to 18.8%.

Why it matters. Projects still fail roughly half the time. The results suggest that transparency factors — specifically, willingness to share past performance — can outweigh traditional resource-allocation variables in predicting Fintech project outcomes, giving project sponsors and procurement teams a practical, low-cost signal to use when staffing projects.

Question. Is a project manager’s organisational commitment (measured as tenure with the same employer) in successful projects related to other project attributes, and which attributes predict that commitment tenure?

Finding. Analysing 439 successful US government (military) IT projects from 2015–2023 with 1,230 features, the authors found that linear regression was the best of three machine learning models, explaining 23.8% of the variance in project manager commitment tenure (MSE = 40.957, MAE = 5.3051), versus 10.8% for random forest and 4.7% for SVM. Line of business (relative relief 0.36), years of experience (0.29), team network collaboration type (0.28), cross-industry subject matter (0.27) and contract status (0.22) were the strongest predictors.

Why it matters. The study shows that secondary, unstructured big data about actual behaviour can be turned into predictive factors of project manager commitment, bypassing speculative survey-based perceptions. The authors argue this offers organisational decision-makers robust, data-driven strategies to improve talent retention and project success rates.

Question. Can project performance indicators in retrospective big data serve as a reliable, bias-free gauge of project manager organizational commitment, and which attributes of successful projects predict PM commitment tenure?

Finding. Analyzing 439 successful U.S. (primarily military) IT projects from 2015–2023 with supervised machine learning, the linear regression model best predicted PM commitment tenure (r² = 0.238, MAE = 5.31), ahead of random forest (r² = 0.108) and SVM (r² = 0.047). Line of business, PM experience, network collaboration tools and cross-industry team membership were the strongest features, supporting the proposition that higher PM commitment is associated with successful projects.

Why it matters. The study offers a more objective, big data-driven way to understand project manager commitment based on actual behavioral evidence rather than self-reported surveys, which suffer from bias and small effect sizes. Its insights are aimed at global stakeholders in projects and programs concerned with retaining experienced talent and reducing high project failure rates.

Question. Can machine learning explain why thousands of IT projects failed by mining hundreds of big data attributes, and which indicators are most likely associated with IT project failure?

Finding. Applying random forest machine learning to big data on about 17,430 U.S. government IT-related projects, the authors identified seven failure indicators with 79.9% precision, 81% recall, an F1 score of 0.798 and a ROC area of 0.849. A post-hoc logistic regression on those seven features was significant (χ2 = 76.287, p < .001, McFadden r2 = 0.267), confirming project manager experience, project budget and outsourced versus in-house project managers as significant predictors.

Why it matters. Roughly half of IT-related projects fail and earlier studies of success factors left 88–98% of the variation unexplained. The study shows that a pragmatic mixed-methods sequence — machine learning first, followed by logistic regression — can pull useful failure indicators out of messy project big data, and the authors urge researchers to use actual organizational project metrics rather than surveys of opinion.

Question. How much do cognitive bias and other individual factors (age, gender, education, experience, certification and competency) affect a project manager's risk management decision to cancel a failing project during a crisis?

Finding. In a repeated-measures controlled experiment with 24 experienced U.S.-based project managers, bias (a 400% bonus offered to keep a failing project going) significantly lowered the quality of decisions, while certification and competency were the only individual factors significantly related to good decisions (e.g., certification rho = 0.549 and 0.712; competency rho = 0.680 and 0.681). Competent and certified PMs made the best decisions in both the basic (M = 4.8) and biased (M = 4.2) conditions, whereas a certified but incompetent PM failed to cancel under bias (M = 1.7).

Why it matters. The authors argue that bias such as money and ego can override ethical risk management judgment even among certified project managers, which could be a problem for recruiters who rank certification highly without seeking evidence of competency. They suggest certified PMs receive additional risk management training, that certification courses include experiential exercises on quantifying and mitigating risks, and that employers offer short ethics or organizational refresher courses.

Question. Why do some competent project managers not terminate a failing project after a risk event, and how much does hidden cognitive (value prospect) bias affect the decision not to end a troubled project?

Finding. In a repeated measures experiment with 16 experienced US IT project managers, the mean likelihood of cancelling a failing project fell from 3.438 (SD = 1.672) in the basic condition to 2.438 (SD = 1.209) when a 400% bonus incentive was offered (t = 3.303, p = .005, Cohen’s d = 0.826). Risk competency was the significant factor (F = 7.861, p = .016, η² = 0.218, a 22% effect size), while certification was not (F = 1.956, p = .187).

Why it matters. Value prospect incentives (money and ego) can push even certified project managers to continue a doomed project against policy, which may explain some of the roughly 50% global project failure rate. The authors recommend that sponsors require both verified certification and tested competency when hiring project managers, and give existing project managers periodic refresher training in ethical decision making and risk estimation.

Question. Can a lightweight EfficientNetV2B0 convolutional neural network, trained with transfer learning under strict reproducibility protocols, accurately and reliably detect pneumonia in paediatric chest X-rays?

Finding. On the Kermany paediatric chest X-ray dataset (624-image test set), the EfficientNetV2B0 model achieved an AUC of 0.967 (95% CI 0.953–0.979; DeLong p < 0.001), accuracy of 0.894, F1-score of 0.920 and pneumonia recall of 0.98, missing only about 2% of true pneumonia cases. Calibration was moderate (ECE = 0.083, Brier Score = 0.091), and normal-class recall was lower at 0.752.

Why it matters. The results show that a compact model of about 7.1 million parameters can deliver clinically meaningful pneumonia detection without the heavy computation of larger networks, making it suited to edge devices, telehealth networks and low-resource healthcare settings. Its high sensitivity favours triage workflows, where missing pneumonia is costlier than an unnecessary follow-up, and the authors outline integration with PACS and CMMS platforms.

Question. How has data envelopment analysis (DEA) been applied in healthcare management, what has it contributed, and what are its limitations and challenges?

Finding. Reviewing a broad range of studies, the chapter finds that DEA has been applied extensively in hospital management, nursing and outpatient services, contributing to efficiency measurement, benchmarking, resource allocation and optimization, and performance evaluation. It also identifies six limitations: input/output selection, sensitivity to outliers, inability to handle statistical noise, lack of inherent uncertainty measures, the homogeneity assumption, and the static nature of traditional DEA models.

Why it matters. The limitations and challenges identified in the chapter underscore the need for further research and methodological advancements in applying DEA in healthcare management.

Question. What are the ground-truth agricultural governance and operational problems facing rural farmers in Nigeria, as seen by the agricultural extension workers who mentor them?

Finding. A grounded theory focus group with 16 agricultural extension workers in Northeast Nigeria found that rural farmers have significant problems 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/local security. Farmers also face fake farm inputs, poor roads, insecurity from the Boko Haram insurgency, limited access to credit and land, and too few extension workers.

Why it matters. The authors argue the government is the ‘keystone’ stakeholder: if the six core deficiencies were fixed—for example making good-quality inputs fairly available with modern-methods training—other operational problems might be mitigated through natural market growth. They recommend funding farmers’ cooperative associations, strengthening security, eliminating corruption, prioritizing the agricultural extension system, and greater involvement of private businesses and NGOs.

Question. Which demographic factors—such as age, gender, marital status, education, language, land size and software experience—predict farmers’ e-adoption of farm management information systems (FMIS) in central Nigeria?

Finding. In a survey of 105 FMIS-using farm owners, managers and laborers in the Jos Plateau region of central Nigeria, only marital status (Rho = −.225, p = .026), gender and language were correlated with FMIS e-adoption. A discriminant model using these three factors was significant (Wilks’ Lambda = 0.892, χ²(3) = 10.669, p = .014) and was reported to classify 100% of participants, with marital status the most important predictor.

Why it matters. Agriculture contributes over 24% of Nigeria’s GDP and employs 68% of the labor force, yet farmers are not adopting FMIS to improve productivity. The authors state that the results should generalize to other rural farm decision makers in Nigeria and suggest males were more influential in FMIS use on farms, while widowed women left to run farms after Boko Haram violence were less likely to adopt FMIS.

Question. What are the key problems hindering agriculture advancement and information systems adoption by farmers in West Africa, particularly Nigeria, and what probable solutions does the empirical literature suggest?

Finding. A critical review of empirical agriculture literature from 2015–2020 found that about half the studies (50.81%) spanned multiple levels of analysis and roughly 40% addressed psychology-domain subjects, led by attitude change (22.58%) and training (16.94%). Key problems included lack of modern-method training, corrupt and broken input dissemination, poor infrastructure and logistics, insecurity from terrorism, lack of credit and weak strategic planning, with only weak evidence of significant information system use in agriculture.

Why it matters. The authors propose integrating training, knowledge sharing and attitude change – including via social media – and combining infrastructure, technology provision and anti-corruption efforts led by government as probable solutions. Their keyword themes and conceptual models are intended for agriculture software developers, practitioners, extension professionals and researchers, and they recommend mixed-methods studies that pair empirical evidence with problem-solving by local subject matter experts.

Question. What do rural farmers in North-East Nigeria see as the ground-truth causes of, and remedies for, the agriculture crisis, as voiced by the agricultural extension workers who mentor them?

Finding. A consensual qualitative research focus group with 16 agricultural extension workers from three local government areas of Adamawa State produced themes largely consistent with the literature but with several new issues. Rural farmers face significant problems with government support in six core areas: farm input quality and dissemination, fair input subsidization, training, market facilitation, corruption and insecurity.

Why it matters. The authors argue that if these six core areas were addressed, other problems might be indirectly mitigated through natural market growth, and that the government is the keystone stakeholder for all solutions. Policymakers should recognize the realities rural farmers face and urgently review the current process of farm input dissemination.

Question. What localized, data-driven and prioritized solutions, grounded in the knowledge of agriculture extension workers, could resolve the food security crisis facing rural farmers in Nigeria?

Finding. A 6-hour focus group of 16 agriculture extension workers from three North-East Nigeria LGAs (representing about 900 farmers) produced ideas that multiple correspondence analysis organized into a significant two-dimension model (dimensions explaining 19% and 17% of inertia; r² = 0.05, p < 0.05). The most strategically urgent ideas were access to affordable seeds and fertilizer, modern farming knowledge and training, market information, road and transport infrastructure, affordable credit, and agricultural mentors.

Why it matters. The authors argue that food security solutions in Nigeria fail unless they are data-driven and customized by local experts to local sociocultural conditions. Their prioritized model is intended to generalize to rural farmers in Nigeria and to government policymakers in developing countries, pointing to fixes such as restoring input distribution, supporting extension workers and creating farmers’ cooperative unions.

Question. Which factors — confirmation experience, perceived usefulness and satisfaction — explain whether Nigerian farmers intend to continue using agricultural information system (AIS) software for farm management and crop planning?

Finding. Using structural equation modeling on 97 valid survey responses from farm owners, managers and labourers on the Jos Plateau, Nigeria, the study found that confirmation experience significantly influenced satisfaction (path = 0.524, p < .001) and perceived usefulness (path = 0.383, p < .001). No factor significantly influenced continuance intention, even though farmers reported a very high intention to keep using AIS (median 5, mean 4.49 on a 1–5 scale).

Why it matters. The results raise controversial issues about the AIS education given to Nigerian farmers and the effectiveness of government agriculture technology funding. The authors suggest policy makers provide more educational seminars on alternative AIS products, including local vendor demonstrations, and recommend replication in other developing countries in Western Africa and elsewhere.

Question. How could socio-cultural big data be collected and analyzed to measure organizational-fit factors relevant for human resourcing, partnering and other organizational decisions?

Finding. From a literature review the authors propose redefining a big data 'V' – viability – as a socio-cultural dimension, a lens for whether big data interpretations will generalize meaningfully to the intended population. They propose a two-phase approach: first sample big data to reduce volume and velocity challenges and apply data reduction (e.g. factor and cluster analysis); then formulate and test specific hypotheses with parametric or nonparametric statistics to make inferential generalizations for decision-making.

Why it matters. Collecting and analyzing big data requires large economic and time investments, so the authors argue it makes no sense to do so if the results will not be socio-culturally meaningful for the population of interest. Publicly available social networking data could let researchers test cultural dimensions such as uncertainty avoidance and individualism–collectivism, informing decisions on hiring, partnering and technology acceptance, though researchers must be cautious about big data quality.

Question. What factors contribute to the success of communities of practice (CoPs) in SMEs in transition economies, what challenges do they face, and how do national and organizational culture shape their functioning?

Finding. The chapter argues that the success of CoPs in transition-economy SMEs depends to a certain extent on the social and cultural factors of transition economies, which differ from those of developing and developed countries. It explores success factors, challenges, and the role of national and organizational culture, and develops a framework that could be applied to CoPs in transition economies.

Why it matters. According to the chapter, CoPs can help SMEs with limited financial and human resources improve efficiency and productivity by leveraging the organization's knowledge resources. The proposed framework and the factors identified as limiting CoPs are aimed at supporting innovation in SMEs in transition economies.

Question. How do cultural factors influence employees’ perceptions of knowledge-sharing initiatives in medium-sized enterprises in transition economies such as Albania?

Finding. Interviews with 20 managers from ten medium-sized Albanian enterprises produced six themes showing that national and organizational culture shape employee participation in knowledge sharing. 85% said Albanian national culture influences employee behavior, 80% said communism had a negative influence, 70% cited lack of trust as a key barrier, and 90% said top management support is essential.

Why it matters. Medium-sized firms in transition economies often adopt generic, Western-developed knowledge-sharing models without considering culture. The authors advise Albanian SME leaders to account for national culture, build a supportive organizational culture led by top management, introduce incentives and training, address fears and clustering, and adapt information systems to national and organizational cultural requirements rather than buying generic knowledge management systems.

Question. What is Albania's national culture profile on Hofstede's five dimensions, and how does it compare with other countries that foreign organizations might collaborate with?

Finding. Surveying 73 business professionals in Tirana and validating a five-factor multicultural model (CFI = 0.94, TLI = 0.93, RMSEA = 0.05, overall Cronbach's α = 0.76), the study estimated Albania's indexes as PDI = 78.7, ICI = 42.7, MFI = 72.9, UAI = 64.6 and LTI = 52.2. Albania was most similar to its Balkan and Turkish neighbors (Czech Republic, Slovakia, Turkey) rather than to Asian or Western cultures such as the USA.

Why it matters. The Albanian indexes give foreign governments, corporations and nonprofits general indicators of national beliefs, norms and values to benchmark against their own culture profile when collaborating in this emerging transition economy. Large gaps between two nations' indexes suggest the approaching entity must customize its approach, and the validated instrument can be reused to profile other countries.

Question. How do bit-parallel string pattern matching algorithms work, and how do they compare in performance, drawbacks and application areas when used to analyze biological sequences such as DNA, RNA and protein?

Finding. The review explains the Shift-Or/Shift-And, BNDM, TNDM and SBNDM families and their parameterized variants, and compares 11 published bit-parallel algorithms by time complexity, drawbacks and application areas (Table 1). It concludes that bit-parallelism is versatile for biological sequence analysis but is mostly hampered by the requirement that the pattern length be less than or equal to the computer word length, and that optimal performance requires designing algorithms around the nature of the target sequence.

Why it matters. Because biological sequence data is generated rapidly and matching involves large amounts of computation, choosing an appropriate algorithm is difficult; the comparison is intended to help researchers select the most appropriate method for a particular application area. The authors note that benchmark algorithms applied to RNA without modification perform poorly, pointing to structure-aware bit-parallel methods as future work.

Question. What statistical relational learning (SRL) models exist, and how can they be applied to the relational, noisy and incomplete data found in genomics?

Finding. The chapter reviews the background of SRL (probabilistic graphical models, Bayesian networks, Markov networks) and the main SRL approaches — probabilistic relational models, relational dependency networks and relational Markov networks — and surveys their genomics applications such as annotating genomic sequence elements, handling missing microarray values, identifying drought- and disease-resistance genes in plants, GWAS and mining electronic health records. It identifies the computational complexity of inference, followed by graph size, as the most significant limitations shared by most SRL methods.

Why it matters. Genomic data is being generated faster than existing methods can analyze it, and annotating all sequences manually is unfeasible and costly, so the authors argue SRL methods will be essential to automatically annotate sequences and handle large genomic data sets. They expect demand for machine learning methods that can adapt to these big data sets to increase over the next decade.