A Modelling Relationship between Firm Strategic Advantages and Organizational Edge

The paper show the relationship between firm-specific-advantages (FSA) and how these can aid gaining an organization’s competitive edge in the present turbulent business environment. These firm specific advantages FSAs include Human Capital, Organization Culture, and Organization Structure (Verbeke 2009) and how these can lead towards organizational edge, and to provide a conceptual framework for the analysis of human capital management in learning organizations. Discourse on competitive advantage is of wide prevalence, clear definitions are rare and it is often used interchangeably with concepts like distinctive competence (Day and Wensley, 1988). Advantage is a relative concept (Hu, 1995; Kay, 1993), only meaningful when compared to another entity or set of entities. A competitive advantage, then, is an advantage one firm has over a competitor or group of competitors in a given market, strategic group or industry (Kay, 1993) and this Firm-Specific Advantages is discussed in this paper

A Modelling Relationship between Firm Strategic Advantages and Organizational Edge

The paper show the relationship between firm-specific-advantages (FSA) and how these can aid gaining an organization’s competitive edge in the present turbulent business environment. These firm specific advantages FSAs include Human Capital, Organization Culture, and Organization Structure (Verbeke 2009) and how these can lead towards organizational edge, and to provide a conceptual framework for the analysis of human capital management in learning organizations. Discourse on competitive advantage is of wide prevalence, clear definitions are rare and it is often used interchangeably with concepts like distinctive competence (Day and Wensley, 1988). Advantage is a relative concept (Hu, 1995; Kay, 1993), only meaningful when compared to another entity or set of entities. A competitive advantage, then, is an advantage one firm has over a competitor or group of competitors in a given market, strategic group or industry (Kay, 1993) and this Firm-Specific Advantages is discussed in this paper. Keywords: Human Capital, Competitive Edge, Organization culture, Organization structur

Measuring work engagement strategies and employees’ behavioural outcomes in Nigerian Universities

Institutions of higher learning in the modern and highly competitive academic environment compete to drive work engagement and explore possible means by which employees can develop and exhibit right attitudes and disposition to work. The main objective of this study was to examine the effect of work engagement strategies on employees’ behavioural outcomes. Few studies analysed how work engagement strategies could help in driving standard work behaviour particularly in higher institutions. In an attempt to bridge this gap, this study was carried out using descriptive research method and Structural Equation Model (AMOS 22) for the analysis of four hundred and forty-one (441) valid questionnaire which were completed by the faculty members of the six selected private universities in Nigeria using stratified and simple random sampling techniques. Factor model which shows high-reliability and good fit was generated, while construct validity was provided through convergent and discriminant analyses. The findings indicate that career opportunities, recognition of efforts, institution’s reputation, investment in employees and fun at work have positive influence on job satisfaction, job involvement and employees’ loyalty. This study contributes to the scientific knowledge in the area of Strategic Human Resources Management and the insight discovered from the study would help the management of institutions of higher learning to improve their employees’ level of engagement as well as their behavioural outcomes

Drivers and risk factors for circulating African swine fever virus in Uganda, 2012-2013

We explored observed risk factors and drivers of infection possibly associated with African swine fever (ASF) epidemiology in Uganda. Representative sub-populations of pig farms and statistics were used in a case-control model. Indiscriminate disposal of pig viscera and waste materials after slaughter, including on open refuse dumps, farm-gate buyers collecting pigs and pig products from within a farm, and retention of survivor pigs were plausible risk factors. Wire mesh-protected windows in pig houses were found to be protective against ASF infection. Sighting engorged ticks on pigs, the presence of a lock for each pig pen and/or a gate at the farm entrance were significantly associated with infection/noninfection; possible explanations were offered. Strict adherence to planned within-farm and communitybased biosecurity, and avoidance of identified risk factors is recommended to reduce infection. Training for small-scale and emerging farmers should involve multidimensional and multidisciplinary approaches to reduce human-related risky behaviours driving infection.National Agricultural Research Organization (NARO) (4760UG) and the Department of Production Animal Studies and the Faculty of Veterinary Science, Onderstepoort, Pretoria, South Africa.http://www.elsevier.com/locate/rvsc2015-10-31hb201

Assessing audience’s willingness to curb digital piracy: A gender perspective

Rising incidences of piracy in the entertainment/creative industry in Nigeria are increasing concerns about the sustainability of the industry and the livelihood of content creators. The United Nations’ Sustainable Development Goal 1 (SDG 1) aims to end poverty at all levels and in all places by the year 2030, but this goal faces a challenge if personal and corporate investments of moviemakers are lost to piracy. Studies have shown that profit-seeking pirates are not the only ones who do damage to the industry, but end-users also share unauthorised digital contents. The purpose of this research is to investigate the connection between gender and willingness of the audience, who in this case are undergraduate students of a government-owned university in Lagos, to see piracy curbed. Multistage sampling was used to cluster the population into faculties and departments. A sample of 199 was selected purposively based on the respondents’ knowledge of digital piracy, and a 20-item questionnaire was used for data gathering. Using t-test to analyse the data, the result shows that there was no significant difference between the views of female and male respondents. Cohen’s d analysis also indicates that there is a negligible effect size. While respondents participated in digital file-sharing, they did not consider their stoppage of the habit relevant to curbing piracy in Nigeria. Hence, the study recommends proper enlightenment of end-users to understand their significant role in digital piracy

Latent class evaluation of the performance of serological tests for exposure to Brucella spp. in cattle, sheep, and goats in Tanzania

Background: Brucellosis is a neglected zoonosis endemic in many countries, including regions of sub-Saharan Africa. Evaluated diagnostic tools for the detection of exposure to Brucella spp. are important for disease surveillance and guiding prevention and control activities. Methods and findings: Bayesian latent class analysis was used to evaluate performance of the Rose Bengal plate test (RBT) and a competitive ELISA (cELISA) in detecting Brucella spp. exposure at the individual animal-level for cattle, sheep, and goats in Tanzania. Median posterior estimates of RBT sensitivity were: 0.779 (95% Bayesian credibility interval (BCI): 0.570–0.894), 0.893 (0.636–0.989), and 0.807 (0.575–0.966), and for cELISA were: 0.623 (0.443–0.790), 0.409 (0.241–0.644), and 0.561 (0.376–0.713), for cattle, sheep, and goats, respectively. Sensitivity BCIs were wide, with the widest for cELISA in sheep. RBT and cELISA median posterior estimates of specificity were high across species models: RBT ranged between 0.989 (0.980–0.998) and 0.995 (0.985–0.999), and cELISA between 0.984 (0.974–0.995) and 0.996 (0.988–1). Each species model generated seroprevalence estimates for two livestock subpopulations, pastoralist and non-pastoralist. Pastoralist seroprevalence estimates were: 0.063 (0.045–0.090), 0.033 (0.018–0.049), and 0.051 (0.034–0.076), for cattle, sheep, and goats, respectively. Non-pastoralist seroprevalence estimates were below 0.01 for all species models. Series and parallel diagnostic approaches were evaluated. Parallel outperformed a series approach. Median posterior estimates for parallel testing were ≥0.920 (0.760–0.986) for sensitivity and ≥0.973 (0.955–0.992) for specificity, for all species models. Conclusions: Our findings indicate that Brucella spp. surveillance in Tanzania using RBT and cELISA in parallel at the animal-level would give high test performance. There is a need to evaluate strategies for implementing parallel testing at the herd- and flock-level. Our findings can assist in generating robust Brucella spp. exposure estimates for livestock in Tanzania and wider sub-Saharan Africa. The adoption of locally evaluated robust diagnostic tests in setting-specific surveillance is an important step towards brucellosis prevention and control

Latent class evaluation of the performance of serological tests for exposure to Brucella spp. in cattle, sheep, and goats in Tanzania

BACKGROUND : Brucellosis is a neglected zoonosis endemic in many countries, including regions of sub-Saharan Africa. Evaluated diagnostic tools for the detection of exposure to Brucella spp. are important for disease surveillance and guiding prevention and control activities. METHODS AND FINDINGS : Bayesian latent class analysis was used to evaluate performance of the Rose Bengal plate test (RBT) and a competitive ELISA (cELISA) in detecting Brucella spp. exposure at the individual animal-level for cattle, sheep, and goats in Tanzania. Median posterior estimates of RBT sensitivity were: 0.779 (95% Bayesian credibility interval (BCI): 0.570–0.894), 0.893 (0.636–0.989), and 0.807 (0.575–0.966), and for cELISA were: 0.623 (0.443–0.790), 0.409 (0.241–0.644), and 0.561 (0.376–0.713), for cattle, sheep, and goats, respectively. Sensitivity BCIs were wide, with the widest for cELISA in sheep. RBT and cELISA median posterior estimates of specificity were high across species models: RBT ranged between 0.989 (0.980–0.998) and 0.995 (0.985–0.999), and cELISA between 0.984 (0.974–0.995) and 0.996 (0.988–1). Each species model generated seroprevalence estimates for two livestock subpopulations, pastoralist and non-pastoralist. Pastoralist seroprevalence estimates were: 0.063 (0.045–0.090), 0.033 (0.018–0.049), and 0.051 (0.034–0.076), for cattle, sheep, and goats, respectively. Non-pastoralist seroprevalence estimates were below 0.01 for all species models. Series and parallel diagnostic approaches were evaluated. Parallel outperformed a series approach. Median posterior estimates for parallel testing were ≥0.920 (0.760-0.986) for sensitivity and ≥0.973 (0.955-0.992) for specificity, for all species models. CONCLUSIONS : Our findings indicate that Brucella spp. surveillance in Tanzania using RBT and cELISA in parallel at the animal-level would give high test performance. There is a need to evaluate strategies for implementing parallel testing at the herd- and flock-level. Our findings can assist in generating robust Brucella spp. exposure estimates for livestock in Tanzania and wider sub-Saharan Africa. The adoption of locally evaluated robust diagnostic tests in setting-specific surveillance is an important step towards brucellosis prevention and control.The UK Biotechnology and Biological Sciences Research Council (BBSRC), Department for International Development (DFID), the Economic & Social Research Council (ESRC), the Medical Research Council (MRC), the Natural Environment Research Council (NERC) and the Defence Science & Technology Laboratory under the Zoonoses and Emerging Livestock Systems programme, Zoonoses and Emerging Livestock Systems – Associated Studentship programme, US National Institutes of Health-National (NIH) Science Foundation Ecology and Evolution of Infectious Disease program, UK BBSRC , the US NIH, National Institute of Allergy and Infectious Diseases through Investigating Febrile Deaths in Tanzania (INDITe) and the BBSRC Institute Strategic Programme Grants.https://journals.plos.org/plosntdspm2022Veterinary Tropical Disease

The evolving SARS-CoV-2 epidemic in Africa: insights from rapidly expanding genomic surveillance

Investment in SARS-CoV-2 sequencing in Africa over the past year has led to a major increase in the number of sequences generated, now exceeding 100,000 genomes, used to track the pandemic on the continent. Our results show an increase in the number of African countries able to sequence domestically, and highlight that local sequencing enables faster turnaround time and more regular routine surveillance. Despite limitations of low testing proportions, findings from this genomic surveillance study underscore the heterogeneous nature of the pandemic and shed light on the distinct dispersal dynamics of Variants of Concern, particularly Alpha, Beta, Delta, and Omicron, on the continent. Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve, while the continent faces many emerging and re-emerging infectious disease threats. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background: Understanding the health consequences associated with exposure to risk factors is necessary to inform public health policy and practice. To systematically quantify the contributions of risk factor exposures to specific health outcomes, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 aims to provide comprehensive estimates of exposure levels, relative health risks, and attributable burden of disease for 88 risk factors in 204 countries and territories and 811 subnational locations, from 1990 to 2021. Methods: The GBD 2021 risk factor analysis used data from 54 561 total distinct sources to produce epidemiological estimates for 88 risk factors and their associated health outcomes for a total of 631 risk–outcome pairs. Pairs were included on the basis of data-driven determination of a risk–outcome association. Age-sex-location-year-specific estimates were generated at global, regional, and national levels. Our approach followed the comparative risk assessment framework predicated on a causal web of hierarchically organised, potentially combinative, modifiable risks. Relative risks (RRs) of a given outcome occurring as a function of risk factor exposure were estimated separately for each risk–outcome pair, and summary exposure values (SEVs), representing risk-weighted exposure prevalence, and theoretical minimum risk exposure levels (TMRELs) were estimated for each risk factor. These estimates were used to calculate the population attributable fraction (PAF; ie, the proportional change in health risk that would occur if exposure to a risk factor were reduced to the TMREL). The product of PAFs and disease burden associated with a given outcome, measured in disability-adjusted life-years (DALYs), yielded measures of attributable burden (ie, the proportion of total disease burden attributable to a particular risk factor or combination of risk factors). Adjustments for mediation were applied to account for relationships involving risk factors that act indirectly on outcomes via intermediate risks. Attributable burden estimates were stratified by Socio-demographic Index (SDI) quintile and presented as counts, age-standardised rates, and rankings. To complement estimates of RR and attributable burden, newly developed burden of proof risk function (BPRF) methods were applied to yield supplementary, conservative interpretations of risk–outcome associations based on the consistency of underlying evidence, accounting for unexplained heterogeneity between input data from different studies. Estimates reported represent the mean value across 500 draws from the estimate's distribution, with 95% uncertainty intervals (UIs) calculated as the 2·5th and 97·5th percentile values across the draws. Findings: Among the specific risk factors analysed for this study, particulate matter air pollution was the leading contributor to the global disease burden in 2021, contributing 8·0% (95% UI 6·7–9·4) of total DALYs, followed by high systolic blood pressure (SBP; 7·8% [6·4–9·2]), smoking (5·7% [4·7–6·8]), low birthweight and short gestation (5·6% [4·8–6·3]), and high fasting plasma glucose (FPG; 5·4% [4·8–6·0]). For younger demographics (ie, those aged 0–4 years and 5–14 years), risks such as low birthweight and short gestation and unsafe water, sanitation, and handwashing (WaSH) were among the leading risk factors, while for older age groups, metabolic risks such as high SBP, high body-mass index (BMI), high FPG, and high LDL cholesterol had a greater impact. From 2000 to 2021, there was an observable shift in global health challenges, marked by a decline in the number of all-age DALYs broadly attributable to behavioural risks (decrease of 20·7% [13·9–27·7]) and environmental and occupational risks (decrease of 22·0% [15·5–28·8]), coupled with a 49·4% (42·3–56·9) increase in DALYs attributable to metabolic risks, all reflecting ageing populations and changing lifestyles on a global scale. Age-standardised global DALY rates attributable to high BMI and high FPG rose considerably (15·7% [9·9–21·7] for high BMI and 7·9% [3·3–12·9] for high FPG) over this period, with exposure to these risks increasing annually at rates of 1·8% (1·6–1·9) for high BMI and 1·3% (1·1–1·5) for high FPG. By contrast, the global risk-attributable burden and exposure to many other risk factors declined, notably for risks such as child growth failure and unsafe water source, with age-standardised attributable DALYs decreasing by 71·5% (64·4–78·8) for child growth failure and 66·3% (60·2–72·0) for unsafe water source. We separated risk factors into three groups according to trajectory over time: those with a decreasing attributable burden, due largely to declining risk exposure (eg, diet high in trans-fat and household air pollution) but also to proportionally smaller child and youth populations (eg, child and maternal malnutrition); those for which the burden increased moderately in spite of declining risk exposure, due largely to population ageing (eg, smoking); and those for which the burden increased considerably due to both increasing risk exposure and population ageing (eg, ambient particulate matter air pollution, high BMI, high FPG, and high SBP). Interpretation: Substantial progress has been made in reducing the global disease burden attributable to a range of risk factors, particularly those related to maternal and child health, WaSH, and household air pollution. Maintaining efforts to minimise the impact of these risk factors, especially in low SDI locations, is necessary to sustain progress. Successes in moderating the smoking-related burden by reducing risk exposure highlight the need to advance policies that reduce exposure to other leading risk factors such as ambient particulate matter air pollution and high SBP. Troubling increases in high FPG, high BMI, and other risk factors related to obesity and metabolic syndrome indicate an urgent need to identify and implement interventions

Emergence and spread of two SARS-CoV-2 variants of interest in Nigeria.

Identifying the dissemination patterns and impacts of a virus of economic or health importance during a pandemic is crucial, as it informs the public on policies for containment in order to reduce the spread of the virus. In this study, we integrated genomic and travel data to investigate the emergence and spread of the SARS-CoV-2 B.1.1.318 and B.1.525 (Eta) variants of interest in Nigeria and the wider Africa region. By integrating travel data and phylogeographic reconstructions, we find that these two variants that arose during the second wave in Nigeria emerged from within Africa, with the B.1.525 from Nigeria, and then spread to other parts of the world. Data from this study show how regional connectivity of Nigeria drove the spread of these variants of interest to surrounding countries and those connected by air-traffic. Our findings demonstrate the power of genomic analysis when combined with mobility and epidemiological data to identify the drivers of transmission, as bidirectional transmission within and between African nations are grossly underestimated as seen in our import risk index estimates