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Several studies have shown that economic shock and crisis trigger companies to move forward innovatively. This paper aims to compliment this research topic by investigating how SMEs activate their organization resilience to adapt to changes generated by a crisis, with specific focus on how digitalization is used as an opportunity on this road. COVID-19 pandemic provided the context to investigate this situation.

The research approach combines literature review, quantitative data survey and data analysis and modeling using PLS-SEM. The quantitative data survey provided the database for building the structural equation model, exploring the structural relationships between the constructs and testing the hypotheses. Expert discussions contributed to the validation and interpretation of the results.

The model reveals that while organizational resilience has no direct effect on digitalization, combined with available resources, it realizes its indirect impact. Resilient companies require less external financial support to achieve their digitalization goals. The results also confirm that an uncertain environment encourages SMEs to go digital.

Several research studies highlighted the importance of SMEs in recovery from crises. Knowing more about how they can be supported and what capabilities they should develop is essential. This research explores the relationship between organizational resilience, resource availability and digitalization for SMEs in crises like the COVID-19 pandemic, revealing the self-reinforcing effect of organizational resilience and the level of digitalization that was not previously studied.

The purpose of this paper is to garner a deeper understanding of the site of influence of aspects of risk management for tax practitioners.

The research design is twofold. Phase one consisted of a wide-scale international survey with 1,061 tax experts across 59 jurisdictions. In phase two, the authors followed up with 68 semi-structured interviews with tax practitioners working in 11 different countries.

The findings recognise the importance of the firm as a significant “site of influence” for tax practitioners in shaping their risk appetite in their tax work. The firm eclipses other influences of risk such as professional body oversight, public interest and demographic markers such as gender and career stage. The authors show that firm is significant, irrespective of size of firm.

This work has practical implications as the findings highlight the importance of oversight of professional service firms by both the professional accountancy bodies and revenue authorities. The findings may have impact on the ethical training and guidance for trainee accountants in terms of an increased awareness on the employing firm as a site of influence for tax practitioners.

This research is important as it adds to the significant body of work on firm socialisation and highlights the important role that the firm holds in moderating (or exacerbating) the risk appetite of tax practitioners, which has significant implications in terms of pushing the boundaries of tax aggressive behaviours. The work aims to recognise the important role that tax practitioners can have in moderating aggressive tax practice, and, thus, reducing tax inequalities and shaping a better world of “Reduced Inequalities” (SDG10).

The purpose of this study was to develop a model of skilful surfing to aid understanding of how best to seek health information, online and offline, during pregnancy.

This study used an observational, descriptive design, using a purpose written questionnaire, factor analysis and structural equation modelling.

Analysis resulted in the factor loading of five components: online health information seeking behaviour, normalisation, offline support, trust and data saturation. These components were included as latent variables in an SEM to evaluate the credibility, and subsequently confirm the viability of the theory of skilful surfing.

To the authors' knowledge, this study is the first of its kind to successfully model and define what it is to skilfully surf health information online whilst pregnant, with empirical and theoretical underpinnings.

Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.

In order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.

Our model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.

Utilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.

Considering the potential of Collaborative International Online Learning (COIL) for cross-boundaries interacting and collaborating effectively, this study aims to explore the intercultural awareness of pre-service language teachers after participating in a COIL project.

Following a quantitative research approach and an exploratory cross-sectional method, the authors administered a 13-item questionnaire to unveil the perceptions of 64 future language teachers from Spain after their online experience with counterparts from the USA.

Participants consider that COIL may have enhanced their intercultural and global awareness and equipped them with valuable skills and knowledge for the future, being women more positive than men. Moreover, the results also suggest that those participants who have not traveled abroad consider COIL to be a good opportunity to compensate for the lack of knowledge or experience with other cultures resulting from not having had the opportunity to visit other countries.

COIL needs to be seen as a powerful tool to promote global learning, intercultural understanding and the development of skills among students that will be vital for success in today’s interconnected world. Nevertheless, universities and teacher training centers need to rethink the preparation of future teachers for the increasing demands to prepare students for the requirements of the global world, and to do so, they need to consider that COIL may offer them significant benefits.

This work offers an interesting exploration of teachers’ attitudes toward COIL, providing insights into the potential of online collaboration for developing intercultural awareness.

This study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding.

This paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction.

This work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools.

Given the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.

This paper aims to propose a new method to derive custom dynamic cyber risk metrics based on the well-known Goal, Question, Metric (GQM) approach. A framework that complements it and makes it much easier to use has been proposed too. Both, the method and the framework, have been validated within two challenging application domains: continuous risk assessment within a smart farm and risk-based adaptive security to reconfigure a Web application firewall.

The authors have identified a problem and provided motivation. They have developed their theory and engineered a new method and a framework to complement it. They have demonstrated the proposed method and framework work, validating them in two real use cases.

The GQM method, often applied within the software quality field, is a good basis for proposing a method to define new tailored cyber risk metrics that meet the requirements of current application domains. A comprehensive framework that formalises possible goals and questions translated to potential measurements can greatly facilitate the use of this method.

The proposed method enables the application of the GQM approach to cyber risk measurement. The proposed framework allows new cyber risk metrics to be inferred by choosing between suggested goals and questions and measuring the relevant elements of probability and impact. The authors’ approach demonstrates to be generic and flexible enough to allow very different organisations with heterogeneous requirements to derive tailored metrics useful for their particular risk management processes.

This study aims to explore an emerging collection of smart building technologies, known as smart workplace solutions (SWS), in the context of facilities management (FM).

This study is based on semi-structured interviews with facility managers in Finland, Norway and Sweden who have deployed SWSs in their organizations. SWS features, based on empirical data from a previous study, were also used to further analyse the interviews.

It analyses the benefits that SWSs bring from the facility management point of view. It is clear that the impetus for change and for deploying SWS in the context of FM is primarily driven by cost savings related to reductions in office space.

This research has been conducted with a focus on office buildings only. However, other building types can learn from the benefits that facility managers receive in the area of user-centred smart buildings.

SWSs are often seen as employee experience solutions that are only related to “soft” elements such as collaboration, innovation and learning. Understanding the FM business case can help make a more practical case for their deployment.

SWSs are an emerging area, and this study has collected data from facility managers who use them daily.

This study aims to assess the information-seeking behaviors, attitudes and beliefs about pregnancy-related nutrition, food-related myths and taboos and supplementation among pregnant women in different communities of Pakistan.

A cross-sectional quantitative survey method was used in this study, which included 150 pregnant women from various locations in Lahore and Faisalabad.

Most participants were between the ages of 25 and 29, having 14 years of education. Regardless of age or number of children, all selected women had the same dietary awareness. Regarding myths and taboos, highly significant values (p = 0.001) were found in various studied variables. No significant difference was observed in knowledge level between age and number of children. It was observed that food myths and taboos significantly (p = 0.001) impact pregnant women’s dietary choices.

This survey experienced limited representativeness; many participants provided incomplete food and nutritional information. This research was conducted in two major districts of Pakistan, i.e. Lahore and Faisalabad; therefore, the results can be generalized for a population of pregnant women living in the northeastern region of Pakistan.

This study can provide helpful insight for health-care professionals to improve pregnant women’s nutritional status and knowledge.

The outcomes of this study can help guide how pregnant women might be educated and have better nutritional awareness at domestic and professional levels.

In Pakistan, there is a lack of research on pregnant women’s nutritional knowledge, and this research can assist health-care professionals in providing diversified knowledge to promote maternal health.

This research investigates the impact of business analytics (BA) on corporate entrepreneurship (CE) and open innovation (OI), considering the moderated mediation analysis in the context of Iran as a developing country. The study was conducted in various industries, including food, chemicals, agriculture, automobile, and service industries, with 207 observations.

Through an in-depth review of the extant literature a conceptual model was developed and the proposed hypotheses were tested using Structural Equation Modeling technique (PLS-SEM).

The results indicate that business analytics has significant effects on corporate entrepreneurship and open innovation. Open innovation has a significant effect on corporate entrepreneurship, with open innovation serving as a suitable mediator. Furthermore, the moderated mediation analysis shows the positive impact of Business Analytics on Open Innovation-Corporate Entrepreneurship relationship.

As this study was conducted in Iran, one of the main limitations can be attributed to the specific characteristics of the country which may affect how and how much the variables influence each other.

The study highlights the importance of promoting Open Innovation in organizations and utilizing Business Analytics to make strategic decisions and foster innovation in entrepreneurial activities.

This study fills the gap in the literature by exploring how BA contributes to corporate entrepreneurship of the Iranian organizations in various industries, given open innovation as a mediator under dynamic market conditions.