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This study investigated the effects of reward system (RS) and job conditions (JC) on employee retention (ER). In particular, this study addressed the mediating effect of employee engagement (EE) on the relationship between RS, JC and ER.

This paper employed descriptive survey approach and the unit of analysis consisted of public hospital nursing staff. Data were collected using questionnaires with a sample of 370 nurse respondents. Structural equation modelling with Smart-Partial Least Squares (PLS) 3.3.8 was used in a statistical analysis.

The results revealed that RS and JC significantly related to ER. The study also showed the direct effect of RS and JC on EE. These findings indicate that (EE) has a partial mediating role in the relationship between RS, JC and ER.

The study offers important policy insights for public nursing stakeholders who seek to increase retention of skills among their nursing staff. The findings are also crucial because they may help the health sector improve their ER strategies, especially in dynamic and competitive business situations where organisations are challenged to retain personnel from a limited skilled workforce.

The findings of this study contribute to the literature on retention of nursing employees by enhancing the understanding of the influences of EE, RS and JC on ER among public hospitals.

The purpose of the study is to analyse municipal solid waste (MSW) disposed of in Jimeta-Yola metropolis for landfill gas (LFG), methane and project viability potential.

The data was collected daily from landfills for four weeks. About 7,329.55 Mg/year of waste was analysed. These waste were separated into bio-degradable components i.e. paper and textile (263.66 Mg), non-food organic (681.45 Mg), wood and straw (189.50 Mg) and food and kitchen waste (1797.20 Mg). Non-degradable components include plastics, polythene bags, metals, sand, stones, cans etc. (4397.73 Mg). The component's characteristics such as a number of samples, weight, volume, landfill age etc. were measured. The waste, methane (CH4) and energy potential were also analysed using LFG energy cost model.

The landfills received 15 Gg/year of MSW and emit 0.31 Gg/year of LFG having CH4 content of 82.95 Mg in 2016. These can produce 33.78 GWh of heat energy equivalent to 10.14 GWh of electricity analytically. Therefore, between 2016 and 2022, about 2.24 Gg CH4 and 5201.32 MWh of electricity were wasted. Henceforth, proper management of these waste substances can produce 186.4 Gg CH4 which will generate 432.52 GWh of electricity. The most economically viable project is an electricity project generating 418 kW/year at a sale price of $1.14/kWh (58.38/kWh) and a payback period of 11 years.

Raw LFG collected can be used in heating brick kilns, boilers, furnaces and greenhouses. When treated, the LFG can produce renewable natural gas (RNG), which is used in energy generation and various domestic, vehicle and industrial applications.

The analytical energy generation can provide gross revenue of ₦19.46bn at an average of ₦192.71million/year. Using Landfill Gas Emissions Model (LandGEM) model, the gross and net revenue will be $0.42m and $0.28m yearly, respectively. The project can provide jobs and economic boost to the immediate community through associated ripple effect.

The research is a pre-feasibility study for LFG to gas or electricity projects in Jimeta-Yola. The study contributed to the body of knowledge as a source of literature for further studies locally and globally.

In this study, the authors examine artificial knowledge as a fundamental stream of knowledge management for sustainable and resilient business models in supply chain management (SCM). The study aims to provide a comprehensive overview of artificial knowledge and digitalization as key enablers of the improvement of SCM accountability and sustainable performance towards the UN 2030 Agenda.

Using the SCOPUS database and Google Scholar, the authors analyzed 135 English-language publications from 1990 to 2022 to chart the pattern of knowledge production and dissemination in the literature. The data were collected, reviewed and peer-reviewed before conducting bibliometric analysis and a systematic literature review to support future research agenda.

The results highlight that artificial knowledge and digitalization are linked to the UN 2030 Agenda. The analysis further identifies the main issues in achieving sustainable and resilient SCM business models. Based on the results, the authors develop a conceptual framework for artificial knowledge and digitalization in SCM to increase accountability and sustainable performance, especially in times of sudden crises when business resilience is imperative.

The study results add to the extant literature by examining artificial knowledge and digitalization from the resilience theory perspective. The authors suggest that different strategic perspectives significantly promote resilience for SCM digitization and sustainable development. Notably, fostering diverse peer exchange relationships can help stimulate peer knowledge and act as a palliative mechanism that builds digital knowledge to strengthen and drive future possibilities.

This research offers valuable guidance to supply chain practitioners, managers and policymakers in re-thinking, re-formulating and re-shaping organizational processes to meet the UN 2030 Agenda, mainly by introducing artificial knowledge in digital transformation training and education programs. In doing so, firms should focus not simply on digital transformation but also on cultural transformation to enhance SCM accountability and sustainable performance in resilient business models.

This study is, to the authors' best knowledge, among the first to conceptualize artificial knowledge and digitalization issues in SCM. It further integrates resilience theory with institutional theory, legitimacy theory and stakeholder theory as the theoretical foundations of artificial knowledge in SCM, based on firms' responsibility to fulfill the sustainable development goals under the UN's 2030 Agenda.