Sustainability assessment of energy production: A critical review of methods, measures and issues.

Sustainable operations of energy production systems have become an increasingly important policy agenda globally because of the massive pressure placed on energy resources needed to support economic development and population growth. Due to the increasing research interest in examining the operational impacts of energy production systems on the society and the environment, this paper critically reviews the academic literature on the clean, affordable and secure supply of energy focussing on methods of assessments, measures of sustainability and emerging issues in the literature. While there have been some surveys on the sustainability of energy production systems they have either tended to focus on one assessment approach or one type of energy generation technology. This study builds on previous studies by providing a broader and comprehensive examination of the literature across generation technologies and assessment methods. A systematic review of 128 scholarly articles covering a 20-year period, ending 2018, and gathered from ProQuest, Scopus, and manual search is conducted. Synthesis and critical evaluation of the reviewed papers highlight a number of research gaps that exist within the sustainable energy production systems research domain. In addition, using mapping and cluster analyses, the paper visually highlights the network of dominant research issues, which emerged from the review.

The use of digital technologies by small and medium enterprises during COVID-19: Implications for theory and practice.

Scholars have highlighted the role of Digital Technologies (DT) in enhancing productivity and performance in Small and Medium Enterprises (SMEs). However, there is limited evidence on the use of DT for dealing with the consequences of extreme events, such as COVID-19. We discuss this gap by (i) outlining potential research avenues and (ii) reflecting on the managerial implications of using DT within SMEs to deal with the repercussions of COVID-19 and securing business continuity.

Psychometric validation of the needs assessment tool: progressive disease in interstitial lung disease.

The inter-rater/test-retest reliability and construct validity of a palliative care needs assessment tool in interstitial lung disease (NAT:PD-ILD) were tested using NAT:PD-ILD-guided video-recorded consultations, and NAT:PD-ILD-guided consultations, and patient and carer-report outcomes (St George's Respiratory Questionnaire (SGRQ)-ILD, Carer Strain Index (CSI)/Carer Support Needs Assessment Tool (CSNAT)). 11/16 items reached at least fair inter-rater agreement; 5 items reached at least moderate test-retest agreement. 4/6 patient constructs demonstrated agreement with SGRQ-I scores (Kendall's tau-b, 0.24-20.36; P<0.05). 4/7 carer constructs agreed with the CSI/CSNAT items (kappa, 0.23-20.53). The NAT:PD-ILD is reliable and valid. Clinical effectiveness and implementation are to be evaluated.

Examining the impact of deep learning technology capability on manufacturing firms: moderating roles of technology turbulence and top management support.

Data science can create value by extracting structured and unstructured data using an appropriate algorithm. Data science operations have undergone drastic changes because of accelerated deep learning progress. Deep learning is an advanced process of machine learning algorithm. Its simple process of presenting data to the system is sharply different from other machine learning processes. Deep learning uses advanced analytics to solve complex problems for accurate business decisions. Deep leaning is considered a promising area for creating additional value in firms' productivity and sustainability as they develop their smart manufacturing activities. Deep learning capability can help a manufacturing firm's predictive maintenance, quality control, and anomaly detection. The impact of deep learning technology capability on manufacturing firms is an underexplored area in the literature. With this background, the purpose of this study is to examine the impact of deep learning technology capability on manufacturing firms with moderating roles of deep learning related technology turbulence and top management support of the manufacturing firms. With the help of literature review and theories, a conceptual model has been prepared, which is then validated with the PLS-SEM technique analyzing 473 responses from employees of manufacturing firms. The study shows the significance of deep learning technology capability on smart manufacturing systems. Also, the study highlights the moderating impacts of top management team (TMT) support as well as the moderating impacts of deep learning related technology turbulence on smart manufacturing systems.

The role of resource orchestration in humanitarian operations: a COVID-19 case in the US healthcare.

This paper investigates the role of resource allocation in alleviating the impact on from disruptions in healthcare operations. We draw on resource orchestration theory and analyse data stemming from US healthcare to discuss how the US healthcare system structured, bundled and reconfigured resources (i.e. number of hospital beds, and vaccines) during the COVID-19 pandemic. Following a comprehensive and robust econometric analysis of two key resources (i.e. hospital beds and vaccines), we discuss its effect on the outcomes of the pandemic measured in terms of confirmed cases and deaths, and draw insights on how the learning curve effect and other factors might influence in the efficient and effective control of the pandemic outcomes through the resource usage. Our contribution lies in revealing how different resources are orchestrated ('structured', 'bundled', and 'leveraged') to help planning responses to and dealing with the disruptions to create resilient humanitarian operations. Managerial implications, limitations and future research directions are also discussed.

A planetary boundaries perspective on the sustainability: resilience relationship in the Kenyan tea supply chain.

The purpose of this paper is to examine whether agricultural supply chains (ASC) can be simultaneously sustainable and resilient to ecological disruptions, using the Planetary Boundaries theory. The nine different Planetary Boundaries i.e. climatic change, biodiversity loss, biogeochemical, ocean acidification, land use, freshwater availability, stratosphere ozone depletion, atmospheric aerosols and chemical pollution are examined in relation to ASC sustainability and resilience. Kenya's tea upstream supply chain sustainability and resilience from the ecological point of view is questioned. This study adopts a multi-case study analysis approach of nine producer organisations from Kenya's tea supply chain. The data from the in-depth semi-structured interviews and a focus group discussion are analysed using thematic analysis. The Kenyan tea supply chain producers are not aware of all the nine planetary boundaries, although these impact on their resilience practices. They are engaged in pursuing both sustainability and resilience practices. They implement mainly environmental practices in relation to sustainability, while only a few of them are implementing resilience practices. The sustainability and resilience concepts were found to be interrelated, but resilience does not improve at the same pace as sustainability. It is suggested that the relationship between sustainability and resilience is non-linear. Limitations and future research avenues are also provided.