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We conceptualized the novel phenomenon of COVID-induced virtual teams and its implications and provided researchers with the required information on how to conduct a phenomenon-based study for conceptualizing novel phenomena in relevant ways.

This article stems from phenomenon-based and, thus, theory-building and grounded qualitative research in the German industrial sector. We conducted 47 problem-centered interviews in two phases (February–July 2021 and February–July 2022) to understand how team members and team leaders experienced COVID-induced virtual teamwork and its subsequent developments.

Empirically, we found COVID-induced virtual teams to be characterized by a high relevance of shaping positive team dynamics via steering internal moderators; crisis is a novel external moderator and transformation becomes the key output factor to be leveraged. Work-from-home leads to specific configuration needs and interrelations between work-from-home and on-site introduce additional dynamics. Methodologically, the phenomenon-based approach is found to be highly suitable for studying the effects of such novel phenomena.

This article is explorative. Thus, we advocate further research on related novel phenomena, such as post-COVID-hybrid and work-from-home teams. A model of how to encourage positive dynamics in post-COVID-hybrid teams is developed and lays the groundwork for further studies on post-COVID teamwork. Concerning methodology, researchers are provided with information on how to conduct phenomenon-based research on novel phenomena, such as the COVID-induced virtual teams that we studied.

Companies receive advice on how to encourage positive dynamics in post-COVID teamwork, e.g. on identifying best practices and resilient individuals.

In a country such as Germany that faces labor shortages, our insights might facilitate better labor-market integration for those with care-work obligations and international workers.

We offer a first conceptualization of a relevant novel phenomenon, namely COVID-induced virtual teams. We exemplify the phenomenon-based approach as a suitable methodology that serves to build relevant theory using active categorization.

This paper aims to investigate the entrepreneurial behaviours exhibited by commercial smallholder farmers in Zimbabwe, focusing on their socio-economic characteristics, and considers their implication for outcomes of livelihood resilience in a resource-constrained and turbulent rural context.

The study used survey data collected from 430 smallholder farmers in Masvingo province, Zimbabwe. Using a two-step cluster analysis, the study constructed a typology of farmers based on their entrepreneurial behaviour and socio-economic characteristics.

The results revealed that commercial smallholder farmers are heterogeneous in terms of their entrepreneurial behaviours. Four clusters were identified: non-entrepreneurial, goal-driven, means-driven and ambidextrous. Beyond their entrepreneurial behaviours, these clusters significantly differ in the socio-economic characterises (gender, age, education levels, farm size, proximity to the market and social connection) and farm performance (seasonal sales per hectare and farm income per hectare).

The typology framework relating farmers’ entrepreneurial behaviours to their socio-economic characteristics and business performance is important to tailor and therefore improve the effectiveness of farmer entrepreneurship programmes and policies. In particular, tailoring farmer entrepreneurship education is crucial to distribute land, finance and market resources in purposive ways to promote a combination of smallholder farmers’ effectual and causal behaviours at an early stage of their farm ventures.

Researchers still know little about which farmers’ behaviours are entrepreneurial and how these behaviours manifest in action during their commercial farm activities. This research leverages effectuation and causation theory to unveil previously overlooked distinctions on farmers’ entrepreneurial behaviours, thereby enhancing a more grounded understanding of farmer entrepreneurship in a resource-constrained context.

This article informs school leaders and staffs about existing research findings on the use of data-driven decision-making in creating class rosters. Given that teachers are the most important school-based educational resource, decisions regarding the assignment of students to particular classes and teachers are highly impactful for student learning. Classroom compositions of peers can also influence student learning.

A literature review was conducted on the use of data-driven decision-making in the rostering process. The review addressed the merits of using various quantitative metrics in the rostering process.

Findings revealed that, despite often being purposeful about rostering, school leaders and staffs have generally not engaged in data-driven decision-making in creating class rosters. Using data-driven rostering may have benefits, such as limiting the questionable practice of assigning the least effective teachers in the school to the youngest or lowest performing students. School leaders and staffs may also work to minimize negative peer effects due to concentrating low-achieving, low-income, or disruptive students in any one class. Any data-driven system used in rostering, however, would need to be adequately complex to account for multiple influences on student learning. Based on the research reviewed, quantitative data alone may not be sufficient for effective rostering decisions.

Given the rich data available to school leaders and staffs, data-driven decision-making could inform rostering and contribute to more efficacious and equitable classroom assignments.

This article is the first to summarize relevant research across multiple bodies of literature on the opportunities for and challenges of using data-driven decision-making in creating class rosters.

The purpose of this study is to investigate benefits tourist seek when visiting a nature-based tourism destination to develop a benefit segmentation framework.

The study used quantitative research methods, with 400 self-administered survey administered to a sample of 400 tourists visiting the Kruger, Panorama, and Lowveld areas in Mpumalanga.

Cluster analysis produced two benefit segments. Binary logistic regression benefits that emerged from the cluster analysis were statistically significant predictors of the attractions tourists visited and the activities in which they participated during their stays in Mpumalanga. Factor-cluster analysis and binary logistic regression results were used to develop a benefit segmentation framework as a marketing planning tool.

The study was only based on Mpumalanga Province and therefore, the results cannot be generalised. The study was conducted over one season, the Easter period

The proposed benefit segmentation framework provides a tool that destination management organisations can use to plan effectively for marketing.

Effective marketing may lead to increased tourism growth which can have a multiplier effect on the destination.

This article is based on a master’s study conducted in Mpumalanga and results are presented on this paper.

The study aimsto analyze the main elements associated with the evolution of Brazilian agtechs from the initial conception of the business model to becoming companies in the scale-up stage.

The exploratory research was conducted based on data collected through in-depth interviews. The answers were analyzed quantitatively using descending hierarchical classification (DHC) and correspondence factor analysis (CFA) and qualitatively using content analysis.

Five main elements were identified as responsible for the evolution of the companies up to their entering the scale-up phase: (1) governance, (2) decisions inherent to resource allocation, (3) monitoring of strategic, tactical and operational activities, (4) fostering human capital development and (5) business model validation. Each element presents a set of performance indicators that show the scalability of these companies.

The model developed can help companies that have not yet advanced from the conception of the business model to the scalability of different sectors, in addition to agribusiness.

Proposal of a model that presents the main elements that impact on scalability and respective indicators that contributed to the scalability process of Brazilian agtechs.

This study contributed to advancing the knowledge on the organizational life cycle (OLC) of agricultural startups, particularly regarding the factors responsible for their scalability.

The purpose of this paper is to describe a thriving partnership between Frostburg State University and the Garrett County Public Schools that aims to improve teacher effectiveness and retention through the implementation of a robust induction program. The initiative includes sustained, strategic mentoring; extensive professional development; and validated, competency-based microcredentials aligned to high-leverage practices.

The study included surveys and structured interviews with teaching fellows and their instructional coaches.

Having ample support and mentoring can make a significant difference for novice teachers. Partnerships between universities and local school districts can provide this critical support.

A limitation that cannot be ignored is the small number of participants in this program, all of whom are teaching in a rural school system. However, researchers working with larger school districts would add valuable knowledge to the field of study.

This paper includes implications for designing new induction programs or improving existing ones.

Mentoring, a major component of high-quality induction programs, has the potential of providing important benefits to beginning teachers including increased motivation, self-confidence, growth in professional identity, and reduced stress and anxiety.

As school systems are struggling to retain qualified teachers, high-quality induction programs are necessary.

Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on climate conditions, which fluctuate over time. In this research, we propose a hybrid model that combines machine-learning methods with Theta statistical method for more accurate prediction of future solar power generation from renewable energy plants. The machine learning models include long short-term memory (LSTM), gate recurrent unit (GRU), AutoEncoder LSTM (Auto-LSTM) and a newly proposed Auto-GRU. To enhance the accuracy of the proposed Machine learning and Statistical Hybrid Model (MLSHM), we employ two diversity techniques, i.e. structural diversity and data diversity. To combine the prediction of the ensemble members in the proposed MLSHM, we exploit four combining methods: simple averaging approach, weighted averaging using linear approach and using non-linear approach, and combination through variance using inverse approach. The proposed MLSHM scheme was validated on two real-time series datasets, that sre Shagaya in Kuwait and Cocoa in the USA. The experiments show that the proposed MLSHM, using all the combination methods, achieved higher accuracy compared to the prediction of the traditional individual models. Results demonstrate that a hybrid model combining machine-learning methods with statistical method outperformed a hybrid model that only combines machine-learning models without statistical method.

Firms increasingly rely on both external and internal crowdsourcing to capture ideas more broadly and enhance innovative problem-solving. Especially in internal crowdsourcing, knowledge sharing that contributes to develop or further the understanding of the problem the idea is focused on solving can take place between critical employees, and in that way improve ideas generated by others. This far, most crowdsourcing practices have focused on identifying solutions to proposed problems, whereas much less is known about how crowds can be used to share problem-related knowledge. There is thus an untapped potential in leveraging crowds not just to generate solution-oriented ideas but also to share knowledge to improve ideas and even to reframe problems. This paper aims to explore the effect of problem- and solution-related knowledge sharing in internal crowdsourcing for idea development.

Data on ideas and comments were collected from an idea management system in a Swedish multinational company. The investigation captures the influences of the problem- and solution-related knowledge sharing on ideas based on content analysis and logistic regression analysis.

The results from this study show that sharing knowledge related to solutions in idea development impacts idea acceptance positively, whereas sharing knowledge related to problems in idea development has a negative effect on the likelihood of idea acceptance and these effects of knowledge sharing are moderated by the active author responses.

This research provides managerial implications for firms to deliberately manage knowledge sharing in peer communities in internal crowdsourcing, especially by providing suggestions on problem reframing and solution refining for ideas.

The results contribute to existing theory in terms of extending the view of crowdsourcing in ideation to include how crowds contribute to the development of the problem and the solution during the development of ideas and providing new insights on knowledge sharing in internal crowdsourcing based on problem-solving theory.

Exclusive breastfeeding (EBF) rates continue to be low in Vietnam. This study aimed to determine the factors predicting 6-month EBF among mothers in Ho Chi Minh City, Vietnam.

A cross-sectional study was conducted with 259 mothers of infants aged between six to nine months at well-baby clinics in Ho Chi Minh City. The questionnaires used for data collection included personal background questionnaire, perceived benefits of breastfeeding scale, breastfeeding self-efficacy scale-short form, perceived barriers to breastfeeding scale and the family support of breastfeeding scale. Descriptive statistics, bivariate and multiple logistic regression were used for data analysis.

About 32% of the Vietnamese mothers practiced 6-month EBF. By increasing one unit of perceived benefits of breastfeeding, perceived self-efficacy in breastfeeding and family support, the mothers' likelihood to give 6-month EBF would increase 19% (AOR = 1.19, 95% CI = 1.08, 1.31), 12% (AOR = 1.12, 95% CI = 1.04, 1.19) and 10% (AOR = 1.10, 95% CI = 1.04, 1.16), while previous breastfeeding experience, maternal age and maternal education could not significantly contribute to the 6-month EBF.

This is the first study in Vietnam using a nursing model, the health promotion model, as a framework to identify factors predicting 6-month EBF. An effective program for promoting EBF could be developed by manipulating and tailoring the predicting factors to fit the Vietnamese mothers' needs through a mother class, lactation clinic or individual approach.

This paper aims to explore the impact of brand authenticity dimensions (i.e. aesthetic, symbolism, heritage, originality, quality commitment and virtue) on consumer engagement in the context of social media. This study answers to the need of scholars to understand consumer behaviour towards family and non-family firms’ brand authenticity constructs and for practitioners to find the correct levers to increase consumer engagement.

Top 10 European family firms with a retrievable Facebook (FB) page from the Global Family Business Index have been selected. Then, the study analysed family firms’ social media consumer engagement versus their non-family business direct competitors on a sample of 21.664 FB posts over a four-year period, leveraging multi-group analysis.

The results outline that three out of six brand authenticity dimensions posted on FB are statistically arousing more interactions respect to non-authenticity-related contents when posted by family firms. However, there are no statistically significant findings when brand authenticity content is posted by the non-family competitors.

This research is helpful for practitioners and entrepreneurs who might want to strengthen their social media brand strategies. With this regard, the study provides insights on which elements of brand authenticity are perceived by consumers as more engaging and which levers to use when communicating the familiness of the company.

To the best of authors’ knowledge, this is one of the earliest studies crosscutting the family business and brand authenticity literature streams to conduct an empirical analysis based on official FB data with a data set of over 20,000 observations. Moreover, this study assesses that not every dimension of the brand authenticity construct is relevant in the context of social media and that its effectiveness depends on the firms’ familiness.