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To meet future healthcare needs, primary care is undergoing a transformation in which innovations and new ways of working play an important role. However, successful innovations depend on joint learning and rewarding collaborations between healthcare and other stakeholders. This study aims to explore how learning develops when entrepreneurs, healthcare professionals and older people collaborate in a primary care living lab.

The study had an action research design and was conducted at a clinically embedded living lab at a primary care centre on the west coast of Sweden. Data consisted of e-mail conversations, recordings from design meetings and three group interviews with each party (entrepreneurs, healthcare professionals and older people). Data were analysed with inductive qualitative content analysis.

An overarching theme, “To share each other’s worlds in an arranged space for learning”, was found, followed by three categories, “Prerequisites for learning”, “Strategies to achieve learning” and “To learn from and with each other”. These three categories comprise eight subcategories.

This research contributes to knowledge regarding the need for arranged spaces for learning and innovation in primary care and how collaborative learning can contribute to the development of practice.

This paper presents an experimental investigation in establishing the relationship between FDM process parameters and tensile strength of polycarbonate (PC) samples using the I-Optimal design.

I-optimal design methodology is used to plan the experiments by means of Minitab-17.1 software. Samples are manufactured using Stratsys FDM 400mc and tested as per ISO standards. Additionally, an artificial neural network model was developed and compared to the regression model in order to select an appropriate model for optimisation. Finally, the genetic algorithm (GA) solver is executed for improvement of tensile strength of FDM built PC components.

This study demonstrates that the selected process parameters (raster angle, raster to raster air gap, build orientation about Y axis and the number of contours) had significant effect on tensile strength with raster angle being the most influential factor. Increasing the build orientation about Y axis produced specimens with compact structures that resulted in improved fracture resistance.

The fitted regression model has a p-value less than 0.05 which suggests that the model terms significantly represent the tensile strength of PC samples. Further, from the normal probability plot it was found that the residuals follow a straight line, thus the developed model provides adequate predictions. Furthermore, from the validation runs, a close agreement between the predicted and actual values was seen along the reference line which further supports satisfactory model predictions.

This study successfully investigated the effects of the selected process parameters - raster angle, raster to raster air gap, build orientation about Y axis and the number of contours - on tensile strength of PC samples utilising the I-optimal design and ANOVA. In addition, for prediction of the part strength, regression and ANN models were developed. The selected ANN model was optimised using the GA-solver for determination of optimal parameter settings.

The proposed ANN-GA approach is more appropriate to establish the non-linear relationship between the selected process parameters and tensile strength. Further, the proposed ANN-GA methodology can assist in manufacture of various industrial products with Nylon, polyethylene terephthalate glycol (PETG) and PET as new 3DP materials.

This study aims to explore the management learning during a long-term crisis like a pandemic. The paper addresses both what health-care managers have learnt during the COVID-19 pandemic and how the management learning is characterized.

The paper is based on a qualitative case study carried out during the COVID-19 pandemic at two different public hospitals in Sweden. The study, conducted with semi-structured interviews, applies a combination of within-case analysis and cross-case comparison. The data were analyzed using thematic deductive analysis with the themes, i.e. sensemaking, decision-making and meaning-making.

The COVID-19 pandemic was characterized by uncertainty and a need for continuous learning among the managers at the case hospitals. The learning process that arose was circular in nature, wherein trust played a crucial role in facilitating the flow of information and enabling the managers to get a good sense of the situation. This, in turn, allowed the managers to make decisions meaningful for the organization, which improved the trust for the managers. This circular process was iterated with higher frequency than usual and was a prerequisite for the managers’ learning. The practical implications are that a combined management with hierarchical and distributed management that uses the normal decision routes seems to be the most successful management method in a prolonged crisis as a pandemic.

The gained knowledge can benefit hospital organizations, be used in crisis education and to develop regional contingency plans for pandemics.

This study has explored learning during the COVID-19 pandemic and found a circular process, “the management learning wheel,” which supports management learning in prolonged crises.

This paper presents findings from a qualitative study of healthcare professionals’ practice, where learning is taking place when a digital artefact is implemented for identification of patients’ cognitive impairment. The use of digital artefacts is increasing in various workplaces, to include professionals in healthcare. This paper aims to explore the following research question: How is the professional learning unfolding in patient-based work when a digital artefact transforms the practice?

Various data collection methods are used for this study, consisting of dialogue meetings, interviews and a reference-group meeting. Thematic analysis is used to inductively bring forth the themes of the collected data.

Professionals’ knowledge and experience are of vital importance in learning and changing work practices. Together with their ability to reflect on changes, their knowledge and experience constitute the prefiguration when the introduction of a digital application brings about indeterminacy in the work practice.

This paper makes a contribution to practice-based research as it consolidates previous research and identifies professionals knowledge and learning in a healthcare context. This can be used to further explore and advance the field, as well as to establish the evidence-based importance of transforming practices based on implementation of digital artefacts.

The purpose of the study is to highlight the need for academic librarians to incorporate effective methodologies in their delivery of information literacy instruction.

The researchers conducted a qualitative research using a case study approach. A nonprobability or purposive sampling method was employed in this research to select five participants. Semistructured interviews and observation were used to garner data from the sample.

The findings of the study revealed that the support required by distance education and face-to-face students is typically the same. An examination of the findings pointed to the fact that some students may be demotivated in information literacy instruction sessions because of an overload of information, which leads to frustration and poor performance.

The findings of the study highlight the need for Caribbean academic librarians to incorporate effective methodologies in their delivery of information literacy instruction and provide an analytical view of how these methodologies may impact performance, understanding and the overall work produced by both students and faculty.

Research on the topic specific to the Caribbean is limited; therefore, research of this nature provides useful strategies that academic librarians may use in developing stellar information literacy programs in the Caribbean to help both students and faculty members achieve excellence.

This study explored use of big data analytics (BDA) to analyse data of a large number of construction firms to develop a construction business failure prediction model (CB-FPM). Careful analysis of literature revealed financial ratios as the best form of variable for this problem. Because of MapReduce’s unsuitability for iteration problems involved in developing CB-FPMs, various BDA initiatives for iteration problems were identified. A BDA framework for developing CB-FPM was proposed. It was validated by using 150,000 datacells of 30,000 construction firms, artificial neural network, Amazon Elastic Compute Cloud, Apache Spark and the R software. The BDA CB-FPM was developed in eight seconds while the same process without BDA was aborted after nine hours without success. This shows the issue of not wanting to use large dataset to develop CB-FPM due to tedious duration is resolvable by applying BDA technique. The BDA CB-FPM largely outperformed an ordinary CB-FPM developed with a dataset of 200 construction firms, proving that use of larger sample size with the aid of BDA, leads to better performing CB-FPMs. The high financial and social cost associated with misclassifications (i.e. model error) thus makes adoption of BDA CB-FPMs very important for, among others, financiers, clients and policy makers.

The purpose of this paper is to evaluate the possibility of rating the credit worthiness of a firm’s quarterly financial report using a dynamic anomaly detection method.

The study uses a data set containing financial statements from Quarter 1 – 2001 to Quarter 4 – 2016 of 937 Vietnamese listed firms. In sum, 24 fundamental financial indices are chosen as control variables. The study employs the Mahalanobis distance to measure the proximity of each data point from the centroid of the distribution to point out the extent of the anomaly.

The finding shows that the model is capable of ranking quarterly financial reports in terms of credit worthiness. The execution of the model on all observations also revealed that most financial statements of Vietnamese listed firms are trustworthy, while almost a quarter of them are highly anomalous and questionable.

The study faces several limitations, including the availability of genuine accounting data from stock exchanges, the strong assumptions of a simple statistical distribution, the restricted timeframe of financial data and the sensitivity of the thresholds for anomaly levels.

The study opens an avenue for ordinary users of financial information to process the data and question the validity of the numbers presented by listed firms. Furthermore, if fraud information is available, similar research can be conducted to examine the tendency for companies with anomalous financial reports to commit fraud.

This is the first paper of its kind that attempts to build an anomaly detection model for Vietnamese listed companies.

This chapter demonstrates that social business models do not meaningfully prioritize or impose accountability to “social good” over other purposes in ways that (a) best protect against owners changing their minds or entry of new owners with different priorities and (b) enable reliable accountability over time and across circumstances. This chapter further suggests a model – a “social primacy company” – that actually prioritizes “social good” and meaningful accountability to it. This chapter thus clarifies circumstances under which existing models might be most useful and are not particularly useful, especially as investors, entrepreneurs, employees, regulators, and others pursue shared, common understandings about purposes, priorities, and accountability.