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This paper aims to present a requirements analysis for the processing of water-based electrode dispersions in inkjet printing.

A detailed examination of the components and the associated properties of the electrode dispersions has been carried out. The requirements of the printing process and the resulting performance characteristics of the electrode dispersions were analyzed in a top–down approach. The product and process side were compared, and the target specifications of the dispersion components were derived.

Target ranges have been identified for the main component properties, balancing the partly conflicting goals between the product and the process requirements.

The findings are expected to assist with the formulation of electrode dispersions as printing inks.

Little knowledge is available regarding the particular requirements arising from the systematic qualification of aqueous electrode dispersions for inkjet printing. This paper addresses these requirements, covering both product and process specifications.

The COVID-19 pandemic has rapidly accelerated a shift to remote working for previously office-based employees in South Africa, impacting employee outcomes such as well-being. The remote work trend is expected to continue even post the pandemic, necessitating for organizational understanding of the factors impacting employee well-being. Using the Job Demands–Resources model as the theoretical framework, this study aims to understand the role of job demands and resources as predictors of employee well-being in the pandemic context.

A self-administered online survey questionnaire was used to gather quantitative data about remote workers’ (n = 204) perceptions of specifically identified demands, resources and employee well-being. Descriptive statistics, Pearson’s correlation and moderated hierarchical regression were used to analyse the data.

This study found that job demands in the form of work–home conflict were associated with reduced employee well-being. Resources, namely, job autonomy, effective communication and social support were associated with increased employee well-being. Job autonomy was positively correlated to remote work frequency, and gender had a significant positive association to work–home conflict. Social support was found to moderate the relationship between work–home conflict and employee well-being. Findings suggest that organizations looking to enhance the well-being of their remote workforce should implement policies and practices that reduce the demands and increase the resources of their employees. The significant association of gender to work–home conflict suggests that greater interventions are required particularly for women. This study advances knowledge on the role of demands and resources as predictors of employee well-being of remote workforces during COVID-19 and beyond.

This paper provides insight on employee well-being during COVID-19 remote work. Further, the findings suggest that organizations looking to enhance the well-being of their remote workforce should implement policies and practices that reduce the demands and increase the resources of their employees. The significant association of gender to work–home conflict suggests that greater interventions are required particularly for women. To the best of the authors’ knowledge, this is the first study carried out to explore the employee well-being during COVID-19 pandemic and will be beneficial to stakeholders for understanding the factors impacting employee well-being.

The construction industry is one of the least automated industries. In the aspect of automation, the technical understanding is very dominant. Focus has mostly been on tools, robots and industrialisation. sociomaterial design shows us that what may first appear technologically deterministic can be replaced and actually call for reinvisioning the traditional focus. The purpose of this study is to introduce the agency of a sociomaterial designer in construction.

This is a conceptual paper with an empirical example. To understand the sociomaterial complexity and dynamics of automation, practice theories are applied. To test this approach, the authors give an example from a Danish (global) supplier engaged in a development project about technical aid (tools) in mounting and assembling gypsum walls.

The sociomaterial-designer can help to understand and make innovation happen when doing automation in construction; as the centre of innovation in construction processes, she works all day with practice, together with practitioners, focusing on material arrangements as located not only in practice, but also in the artefacts. She can help the supplier of construction materials in understanding different professional practices and the transformation to use smarter tools.

This research is within a new practice domain “sociomaterial-design” and it has to follow up with an empirical study that covers a development project with a sociomaterial-design approach.

Developing competences (agency) as a sociomaterial-designer when linking the sociotechnical understanding of Automation with practice.

This research showcases how sociomaterial perspectives can inform automation in construction.

The purpose of this paper is to analyze this phenomenon and identify its determinants using data from Brazilian higher education institutions.

Based on a data set comprehending 2,230 university students from 70 different institutions across the country, the authors develop five Probit models to assess impacts related to individual traits and systemic conditions on five dependent dimensions: entrepreneurial activity, potential entrepreneurs, high-impact entrepreneurship, serial entrepreneurship and innovation-driven entrepreneurship.

The lack of significance in many of the variables included in estimations suggests that student entrepreneurship seems to be a rather random phenomenon in Brazil.

Findings pose challenges for student entrepreneurship, as targets for intervention are not clear.

Over the past decades, universities have been receiving an increasing demand to go beyond their role of producing science and technology to explore its knowledge potential to produce novel commercial applications. However, while there is a growing interest in ways to foster scientific academic entrepreneurship, universities also serve as a positive environment for student entrepreneurship training, knowledge sharing, testing ideas and learning. So far, the importance of student entrepreneurship has received far less attention than it likely deserves.

This paper aims to propose a system for generating actionable knowledge from Big Data and use this system to construct a comprehensive knowledge base (KB), called GrandBase.

In particular, this study extracts new predicates from four types of data sources, namely, Web texts, Document Object Model (DOM) trees, existing KBs and query stream to augment the ontology of the existing KB (i.e. Freebase). In addition, a graph-based approach to conduct better truth discovery for multi-valued predicates is also proposed.

Empirical studies demonstrate the effectiveness of the approaches presented in this study and the potential of GrandBase. The future research directions regarding GrandBase construction and extension has also been discussed.

To revolutionize our modern society by using the wisdom of Big Data, considerable KBs have been constructed to feed the massive knowledge-driven applications with Resource Description Framework triples. The important challenges for KB construction include extracting information from large-scale, possibly conflicting and different-structured data sources (i.e. the knowledge extraction problem) and reconciling the conflicts that reside in the sources (i.e. the truth discovery problem). Tremendous research efforts have been contributed on both problems. However, the existing KBs are far from being comprehensive and accurate: first, existing knowledge extraction systems retrieve data from limited types of Web sources; second, existing truth discovery approaches commonly assume each predicate has only one true value. In this paper, the focus is on the problem of generating actionable knowledge from Big Data. A system is proposed, which consists of two phases, namely, knowledge extraction and truth discovery, to construct a broader KB, called GrandBase.

Cybersecurity vulnerabilities are often due to human users acting according to their own ethical priorities. With the goal of providing tailored training to cybersecurity professionals, the authors conducted a study to uncover profiles of human factors that influence which ethical principles are valued highest following exposure to ethical dilemmas presented in a cybersecurity game.

The authors’ game first sensitises players (cybersecurity trainees) to five cybersecurity ethical principles (beneficence, non-maleficence, justice, autonomy and explicability) and then allows the player to explore their application in multiple cybersecurity scenarios. After playing the game, players rank the five ethical principles in terms of importance. A total of 250 first-year cybersecurity students played the game. To develop profiles, the authors collected players' demographics, knowledge about ethics, personality, moral stance and values.

The authors built models to predict the importance of each of the five ethical principles. The analyses show that, generally, the main driver influencing the priority given to specific ethical principles is cultural background, followed by the personality traits of extraversion and conscientiousness. The importance of the ingroup was also a prominent factor.

Cybersecurity professionals need to understand the impact of users' ethical choices. To provide ethics training, the profiles uncovered will be used to build artificially intelligent (AI) non-player characters (NPCs) to expose the player to multiple viewpoints. The NPCs will adapt their training according to the predicted players’ viewpoint.

Crises such as the Coronavirus pandemic pose extraordinary challenges to the decision making in management teams. Teams need to integrate available information quickly to make informed decisions on the spot and update their decisions as new information becomes available. Moreover, making good decisions is hard as it requires sacrifices for the common good, and finally, implementing the decisions made is not easy as it requires persistence in the face of strong counterproductive social pressures.

We provide a “psychology of action” perspective on making team-based management decisions in crisis by introducing collective implementation intentions (We-if-then plans) as a theory-based intervention tool to improve decision processes. We discuss our program of research on forming and acting on We-if-then plans in ad hoc teams facing challenging situations.

Teams with We-if-then plans consistently made more informed decisions when information was socially or temporally distributed, when decision makers had to make sacrifices for the common good, and when strong social pressures opposed acting on their decisions. Preliminary experimental evidence indicates that assigning simple We-if-then plans had similar positive effects as providing a leader to steer team processes.

Our analysis of self-regulated team decisions helps understand and improve how management teams can make and act on good decisions in crises such as the Coronavirus pandemic.

Entrepreneurial trait and behaviour approaches are used to identify differing entrepreneurial profiles. Specifically, this study aims to determine which entrepreneurial competencies (ECs) can predict entrepreneurial action (EA) for distinct profiles, such as male versus female, start-up versus established and for entrepreneurs within different age groups and educational levels.

The research was conducted using a survey method on a large sample of 1,150 South African entrepreneurs. Chi-squared automatic interaction detection (CHAID) algorithms were used to build decision trees to illustrate distinct entrepreneurial profiles.

Each profile has a different set of ECs that predict EA, with a growth mindset being the most significant predictor of action. Therefore, this study confirms that a “one-size-fits-all” approach cannot be applied when profiling entrepreneurs.

From a pedagogical standpoint, different combinations of these ECs for each profile provide priority information for identification of appropriate candidates (e.g. the highest potential for success) and training initiatives, effective pedagogies and programme design (e.g. which individual ECs should be trained and how should they be trained).

Previous work has mostly focused on demographic variables and included a single sample to profile entrepreneurs. This study maintains much wider applicability in terms of examining profiles in a systematic way. The large sample size supports quantitative analysis of the comparisons between different entrepreneurial profiles using unconventional analyses. Furthermore, as far as can be determined, this represents the first CHAID conducted in a developing country context, especially South Africa, focusing on individual ECs predicting EA.

This paper aims to examine tourists' behavioral changes in response to health crises, this study examines the individual's uncertainty and adaptability to the challenges using behavioral coping strategies.

The study combines the theory of planned behavior (TPB) and protection motivation theory. Using the PLS-SEM technique, this study examines the relationship between the destination's competitive profits and travel intention of Iranian tourists in the post-Covid-19 pandemic.

The social-support coping (Instrumental) does not incorporate tourists' adaptive behaviors. Vulnerable vaccination significantly affects the extremeness of an individual's problem-focused coping, which affects tourist's adaptive behaviors in crisis time, indicating the effectiveness of the Covid-19 vaccination on travel intention.

The findings may assist tourism authorities and planners develop unique tourism products and services based on tourist behavior following the health crises.

This study contributes to development of the TPB method, indicating that visa exemption and competitive profits of a destination would motivate travel intention existing inefficacy of local government and its negative background, reshaping and thus influencing changing behavior.

This study aims to establish the shape of investment dynamics in equity crowdfunding to better understand backer behavior.

This study provides insights into when backers invest in successful funding campaigns. It uses t-tests to compare differences in means between observation windows during successful funding campaigns. It is based on 4,938 transactions from 61 campaigns, focusing on the first and last tail ends.

In contrast to previous findings, the current investment dynamics seem more U-shaped than L-shaped. This supports previous findings about a strong start but also suggests a late collective attention effect. The strength is higher at the first tail end. However, differences in the later tail ends are statistically significant and emphasize the presence of late investment activities, especially in crowded or less complex campaigns.

These findings emphasize the importance of signaling during the entire funding window. This encourages platforms to invest in user-friendly functionalities that guide entrepreneurs and help backers when investing in successful campaigns.

This study improves the understanding of backer behavior and suggests changing investment dynamics in equity crowdfunding. In addition, this pattern contrasts with previous findings on dynamic collective attention effects in rich digitally informative markets, implying two attention effects when uncertainty is high.