Designing industry 4.0 implementation from the initial background and context of companies

Industry 4.0 is a promising concept that allows industries to meet customers’ demands with flexible and resilient processes, and highly personalised products. This concept is made up of different dimensions. For a long time, innovative digital technology has been thought of as the only dimension to succeed in digital transformation projects. Other dimensions have been identified such as organisation, strategy, and human resources as key while rolling out digital technology in factories. From these findings, researchers have designed industry 4.0 theoretical models and then built readiness models that allow for analysing the gap between the company initial situation and the theoretical model. Nevertheless, this purely deductive approach does not take into consideration a company’s background and context, and eventually favours one single digital transformation model. This article aims at analysing four actual digital transformation projects and demonstrating that the digital transformation’s success or failure depends on the combination of two variables related to a company’s background and context. This research is based on a double approach: deductive and inductive. First, a literature review has been carried out to define industry 4.0 concept and its main dimensions and digital transformation success factors, as well as barriers, have been investigated. Second, a qualitative survey has been designed to study in-depth four actual industry digital transformation projects, their genesis as well as their execution, to analyse the key variables in succeeding or failing. 46 semi-structured interviews were carried out with projects’ members; interviews have been analysed with thematic content analysis. Then, each digital transformation project has been modelled regarding the key variables and analysed with regards to succeeding or failing. Investigated projects have consolidated the models of digital transformation. Finally, nine digital transformation types have been identified.

A data transformation process for using Benford’s Law with bounded data

Benford’s Law is an empirical observation about the frequency of digits in a variety of naturally occurring data sets. Auditors and forensic scientists have used Benford’s Law to detect erroneous data in accounting and legal usage. One well-known limitation is that Benford’s Law fails when data have clear minimum and maximum values. Many kinds of education data, including assessment scores, typically include hard maximums and therefore do not meet the parametric assumptions of Benford’s Law. This paper implements a transformation procedure which allows for assessment data to be compared to Benford’s Law. As a case study, a data quality assessment of oral language scores from the Early Childhood Longitudinal Study, Kindergarten (ECLS-K) study is used and higher risk data segments detected. The same method could be used to evaluate other concerns, such as test fraud, or other bounded datasets.

Designing industry 4.0 implementation from the initial background and context of companies

Industry 4.0 is a promising concept that allows industries to meet customers’ demands with flexible and resilient processes, and highly personalised products. This concept is made up of different dimensions. For a long time, innovative digital technology has been thought of as the only dimension to succeed in digital transformation projects. Other dimensions have been identified such as organisation, strategy, and human resources as key while rolling out digital technology in factories. From these findings, researchers have designed industry 4.0 theoretical models and then built readiness models that allow for analysing the gap between the company initial situation and the theoretical model. Nevertheless, this purely deductive approach does not take into consideration a company’s background and context, and eventually favours one single digital transformation model. This article aims at analysing four actual digital transformation projects and demonstrating that the digital transformation’s success or failure depends on the combination of two variables related to a company’s background and context. This research is based on a double approach: deductive and inductive. First, a literature review has been carried out to define industry 4.0 concept and its main dimensions and digital transformation success factors, as well as barriers, have been investigated. Second, a qualitative survey has been designed to study in-depth four actual industry digital transformation projects, their genesis as well as their execution, to analyse the key variables in succeeding or failing. 46 semi-structured interviews were carried out with projects’ members; interviews have been analysed with thematic content analysis. Then, each digital transformation project has been modelled regarding the key variables and analysed with regards to succeeding or failing. Investigated projects have consolidated the models of digital transformation. Finally, nine digital transformation models have been identified. Industry practitioners could design their digital transformation project organisation and strategy according to the right model.

Teachers as educational change agents: what do we currently know? findings from a systematic review

Change agents are individuals who can successfully transform aspects of how organisations operate. In education, teachers as change agents are increasingly seen as vital to the successful operation of schools and self-improving school systems. To date, however, there has been no systematic investigation of the nature and role of teacher change agents. To address this knowledge gap, we undertook a systematic review into five key areas regarding teachers as change agents. After reviewing 70 outputs we found that current literature predominantly positions teacher change agents as the deliverers of top-down change, with the possibility of bottom-up educational reform currently neglected.

Growing your own in times of crisis: the role of home food growing in perceived food insecurity and well-being during the early COVID-19 lockdown

Household food insecurity and poor well-being have increased during the coronavirus disease 2019 (COVID-19) pandemic and resulting lockdown measures. Home food growing has been associated with improved food access and well-being, but it is unknown what role it plays during food supply crises and lockdown. It is also unclear how home food growing and social restrictions may affect opinions about growing food in urban areas (i.e., urban agriculture [UA]). A cross-sectional online survey was conducted during the UK national lockdown in March-April 2020 to measure home food growing, perceived food insecurity, well-being, and opinions of UA. The participants were 477 UK-based adults (369 female, mean age 39.57 years ± 13.36); 152 participants were engaged in home food growing prior to the pandemic. Responses were compared to data collected from a separate sample of participants before the pandemic (N = 583) to explore potential shifts in opinions about UA. Participants who engaged in home food growing had lower levels of food insecurity (U ­= 19894.50, z = -3.649, p<.001, r = -.167) and higher well-being (U = 19566.50, z = -3.666, p<.001, r = -.168) than those not engaged in home food growing. Perceived food insecurity partially mediated the relationship between home food growing and well-being; home food growing was associated with less food insecurity, which in turn was associated with better well-being. There were no differences in opinions of UA compared to the sample of participants from before the pandemic. Home food growing may have had a protective effect over perceived food security and well-being in the early stages the pandemic. Opinions of UA were positive and unchanged compared to data collected pre-pandemic. Policies that support home food growing and access to suitable growing spaces and resources may be beneficial for food system resilience and well-being.

Reflections on developing a collaborative multi-disciplinary approach to embedding education for sustainable development into higher education curricula

As higher education institutions (HEIs) have increasingly turned to consider sustainability over the last decade, education for sustainable development (ESD) has emerged as a way of imbuing students with the skills, values, knowledge, and attributes to live, work, and create change in societies facing complex and cross-cutting sustainability challenges. However, the question of how HEIs can actively embed ESD more broadly in and across curricula is one that continues to challenge institutions and the HE sector as a whole. While traditional teaching practices and methods associated with subject-based learning may be suitable for educating students about sustainable development, a re-orientation towards more transformational, experiential and action-oriented methods is required to educate for sustainable development. The need for educators to share their practices and learn lessons from each other is essential in this transformation. This paper presents a selection of practical examples of how to embed a range of interactive, exploratory, action-oriented, problem-based, experiential and transformative ESD offerings into HE teaching practice and curricula. Presented by a group of academics and professional services staff at the University of Strathclyde who lead key modules and programmes in the institution’s ESD provision, this paper reflects on five approaches taken across the four faculties at Strathclyde (Humanities and Social Sciences, Science, Engineering and the Strathclyde Business School) and examines the challenges, practicalities and opportunities involved in establishing a collaborative programme of ESD.

Effects of hedgerow enhancement as a net zero strategy on farmland biodiversity: a rapid review

In order to mitigate the effects of climate change, the UK government has set a target of achieving net zero greenhouse gas (GHG) emissions by 2050. Agricultural GHG emissions in 2017 were 45.6 million tonnes of carbon dioxide equivalent (CO2e; 10% of UK total GHG emissions). Farmland hedgerows are a carbon sink, storing carbon in the vegetation and soils beneath them, and thus increasing hedgerow length by 40% has been proposed in the UK to help meet net zero targets. However, the full impact of this expansion on farm biodiversity is yet to be evaluated in a net zero context. This paper critically synthesises the literature on the biodiversity implications of hedgerow planting and management on arable farms in the UK as a rapid review with policy recommendations. Eight peer-reviewed articles were reviewed, with the overall scientific evidence suggesting a positive influence of hedgerow management on farmland biodiversity, particularly coppicing and hedgelaying, although other boundary features, e.g. field margins and green lanes, may be additive to net zero hedgerow policy as they often supported higher abundances and richness of species. Only one paper found hedgerow age effects on biodiversity, with no significant effects found. Key policy implications are that further research is required, particularly on the effect of hedgerow age on biodiversity, as well as mammalian and avian responses to hedgerow planting and management, in order to fully evaluate hedgerow expansion impacts on biodiversity.

Which forest-risk commodities imported to the UK have the highest overseas impacts? A rapid evidence synthesis

Background: Commodity-driven deforestation is a major driver of forest loss worldwide, and globalisation has increased the disconnect between producer and consumer countries. Recent due-diligence legislation aiming to improve supply chain sustainability covers major forest-risk commodities. However, the evidence base for specific commodities included within policy needs assessing to ensure effective reduction of embedded deforestation. Methods: We conducted a rapid evidence synthesis in October 2020 using three databases; Google Scholar, Web of Science, and Scopus, to assess the literature and identify commodities with the highest deforestation risk linked to UK imports. Inclusion criteria include publication in the past 10 years and studies that didn’t link commodity consumption to impacts or to the UK were excluded. The development of a review protocol was used to minimise bias and critical appraisal of underlying data and methods in studies was conducted in order to assess the uncertainties around results. Results: From a total of 318 results, 17 studies were included in the final synthesis. These studies used various methodologies and input data, yet there is broad alignment on commodities, confirming that those included in due diligence legislation have a high deforestation risk. Soy, palm oil, and beef were identified as critical, with their production being concentrated in just a few global locations. However, there are also emerging commodities that have a high deforestation risk but are not included in legislation, such as sugar and coffee. These commodities are much less extensively studied in the literature and may warrant further research and consideration. Conclusion: Policy recommendations in the selected studies suggests further strengthening of the UK due diligence legislation is needed. In particular, the provision of incentives for uptake of policies and wider stakeholder engagement, as well as continual review of commodities included to ensure a reduction in the UK’s overseas deforestation footprint.

Methodology to categorize the research output of academic institutions, through the Sustainable Development goals (SDGs)

Background: This article presents a methodology to categorize scientific publications according to the targets of the 17 Sustainable Development Goals (SDGs) of the United Nations. For the above, a dataset with bibliographic and descriptive attributes of 2,379 articles from 2017 by co-authors affiliated to the Pontificia Universidad Católica de Chile, indexed in the Web of Science and Scopus databases, was used. Methods: The methodology considered three relevant and consecutive milestones: establishment of the reading level that was applied for each publication record, which considers a proportional amount of information; assignment of one of the 18 categories identified for the analysis of the information, which include the 17 SDGs and the option "unclassified" and one of the 169 subcategories corresponding to the specific goals; and, finally, recording the status of the review process carried out, which allowed control of the progress and quality of the cross-review. Results: The results show that 58.6% of the articles contribute to a primary target, of these 233 contribute to a secondary target; goals 3, 4, 9, and 11 are the most frequent in the process of assigning SDGs. There is an 81% increase in the use of alphanumeric targets when they are assigned as secondary targets. At the same time, cross-checking is shown to be beneficial when allowing the reclassification of 190 articles to some of the targets. Finally, it is established that levels 2 and 3 enabled better classification, given that the contents considered provide more information; however, it is significant that through level 1, 355 articles were categorized as "unclassified". Conclusions: It is concluded that the methodology allows for a conclusive, exhaustive, rigorous, extensive, and varied classification through the different milestones and actions carried out, providing strategic information for decision making and research management in the academy-society relationship.

Success of the ‘first-time first-line manager’: model development and validation

Purpose: The purpose of this paper is to identify the factors involved in the success of a ‘first-time first-line Manager’ (FTFLM), and establish a model for the success of a FTFLM using these identified factors. Methods: Empirical factors influencing the success of a first-line manager were gathered through the literature review and an experience survey of managers. Based on these factors, a questionnaire for primary research was developed and used for the survey. An exploratory factor analysis of the collected data yielded a nine-factor model which was validated using confirmatory factor analysis. Findings: A major portion (74%) of the FTFLM’s success factors are under the direct control of the FTFLM. The remaining portion (26%) are influenced by the organization’s procedures and policies for grooming the FTFLM and the attitude and behaviour of management towards the FTFLM.