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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.

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.

The study aims to investigate how personal values (PVS) influence research self-efficacy (RSE) among academics in public universities in Tanzania.

A cross-sectional design was utilized by the study to gather data once through structured questionnaires administered to 247 academic staff from four public universities in Tanzania.

Generally, the results show that PVS positively and significantly influence RSE in universities. Specifically, OPC has β value of 0.284 and p < 0.001, SEFN has β = 0.352 and p < 0.001 and CONS has a β = 0.198 and p = 0.003.

University management should include PVS as among the criteria for recruitment of academic staff, as it determines their confidence in engaging in research.

The findings of this study broaden the applicability of Schwartz human values theory in Tanzania’s universities. Moreover, by carrying out empirical research on the influence of PVS on RSE in developing context such as Tanzania, the study contributes to the body of literature on PVS and RSE.