Restarting live events in Greece: an industry analysis of post-pandemic strategies
Bibliographic record
Abstract
PURPOSE: Prior to the Covid-19 pandemic, tourism arrivals in Greece in 2019 had reached 31.3 million per year with revenues generated of €17.7 billion (INSETE, 2020:6), contributing over a quarter of Greece’s direct and indirect Gross Domestic Product (GDP) (INSETE, 2019). Greece is primarily known as a “sun, sea, and sand” tourism destination, underpinned archaeological sites and museums. Relatively little research focus has been given to live events such as business conventions, trade exhibitions, academic conferences, sports, cultural events and festivals which can drive economic growth (Lundberg et al, 2017) and urban and regional development (Richards, 2017; Clark and Misener, 2015). The main purpose of this paper is to examine how events-related firms in Greece have responded to the Covid-19 pandemic, and their plans for future recovery. We posed the following research questions: 1. How has the Covid-19 affected the live events industry in Greece? How have these firms reacted? 2. How quickly do event firms in Greece believe business will resume? Will there be a turn to online or hybrid events? 3. What type of support would Greek event firms prefer to assist with the recovery? 4. How coherent and coordinated is the Greek events industry qua industrial sector? METHOD: Targeted stratified sampling was used to contact middle and senior level managers in events-related firms and organisations (pilot sample n = 33). The population was drawn from events-related firms listed in business telephone directories, professional associations, and targeted web searches, and word of mouth networking. The research instrument was designed to probe the management of business disruption across four stages: disruption recognition, disruption diagnosis, response development, and response implementation (Bode and Macdonald, 2017). An online survey was employed using 36 scaled and two open-ended questions. Data were analysed using statistical software (SPSS). Thematic coding was used for the qualitative results. FINDINGS: The results suggest caution for 2021 but more optimism from 2022 onwards. Recovery is a long term 3 to 5 year prospect. Post-Covid, the main challenges are (a) safety and security (b) cashflow, and (c) marketing. Views are more polarised with regards to whether clients will come back and whether the supply chain will be resilient enough. Financial support would be preferred as short term tax waivers rather than long term interest free loans. Whilst there is interest in incorporating some hybrid and virtual components post-Covid, the dominant preference is to return to live events. The events industry dominated by Athens and Thessaloniki, then dispersed across the regions and islands. Conversely, second and third tier cities not as active. Particular sub-segments such as ‘destination weddings’ may lead the way in re-building the event industry due to their flexible cost and operations base. IMPLICATIONS: Public policy makers should take note of the preferred types and timing of support desired by the events industry. Embracing of online technology could help to extend events into the main tourism off-season. Second- and third-tier cities should consider developing their events profile. The live events industry in Greece remains fragmented and demonstrates several characteristics of an ‘emerging industry’ (Monfardini et al, 2012) still developing its distinct self-identity. Despite much previous research on tourism in Greece, little attention has previously been given specifically to live events in Greece. As the country seeks to re-balance its reliance on ‘sun and sand’ tourism Campana, 2020), the paper offers timely new knowledge of how the events industry operates in Greece and its future needs to achieve growth post-Covid and beyond.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".