Bibliographic record
Abstract
Even before the initial spread of COVID-19 in Canada in early 2020, nearly one in 10 Canadians worked from home in some capacity (Conference Board of Canada, 2020). The COVID-19 pandemic has significantly impacted the way organizations work (Gallacher & Hossain, 2020), making it critically urgent to understand how to build high-performing virtual teams, and to learn the basics of virtual team leadership. Suddenly, virtual teams are the norm in many sectors and organizations, a significant change for the labour market. With approximately five-million Canadians now working from home in response to the pandemic, the \nnational total of work-from-home employees has risen to 6.8 million, or almost 40 per cent of Canada’s workforce (St. Denis, 2020). Meaning, 6.8 million employees are likely working across time and space, with interdependent virtual teams that communicate and collaborate through internet-based communications (Maduka, Edwards, Greenwood, Osborne, & Babatunde, 2018). \nWith change comes opportunity (McCallum & O’Connell, 2009), in this case, to clarify and evolve in our use of virtual teams, creating potentially long-lasting benefits for organizations (i.e. Clancy, 2020; Gottfredson, 2020; Ruiller, Heijden, Chedotel, & Dumas, 2019). Of course, virtual teams are unique from physically co-located ones. With the lost ability to communicate in person, virtual teams face intense communication challenges that on-site teams do not (Sproull & Kiesler, 1986, as cited by Martins, Gilson, & Maynard, 2004), but strong leadership can coordinate teams into collectives (Ziek & Smulowitz, 2014). \nTowards the achievement of strong leadership, organizations should ensure leaders have the precise skills needed to navigate the unique virtual environment (Byrd, 2019). Even for experienced leaders, the virtual work environment carries new complexities that would indicate the need for virtual team leadership training. It is up to the leaders of teams to help facilitate the relational environment, rich in social capital, that is needed to build the trust, satisfaction and collectivism that virtual teams need to succeed (i.e. Ceri-Booms, 2020; Peterman, 2019; Spurk & Straub, 2020). This social capital, based in relationships, mutual obligations and reciprocated trust and respect can be achieved via strategic and targeted leadership development (Day, \n2000). \nThis paper explores the concepts of remote work and virtual teams, and examines the role of the leader in developing high-performing virtual teams. Suggesting a need for openness to new ways of leading, the value of shared leadership, linked to social capital and transformational leadership in virtual settings, is explored (Liu, Hu, Li, Wang, & Lin, 2014; Muethel, Gehrlein, & Hoegl, 2012; Robert & You, 2018). This paper contributes to the literature on virtual team leadership by suggesting that organizations need to undertake intense leadership development activities to increase team social capital (Day, 2000) and the use of shared leadership behaviours (Shuffler, Wiese, Salas, & Burke, 2010). It also examines considerations for leadership development in the virtual context. \nEspecially now, amidst the global pandemic creating lockdown-type conditions in many parts of the world, the emphasis needs to shift from building effective leaders, to building effective teams of leaders. If employees and leaders learn together as a team, they will be better equipped to succeed (Panteli & Sockalingam, 2005, as cited by King, Fielke, Bayne, Klerkx, & Nettle, 2019). With the right training and development, leaders and teams can better face challenges, including those associated with the rapid onset of a “global health emergency” (Schumaker, 2020, para. 1), like the COVID-19 pandemic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".