MétaCan
Menu
Retour à la cohorte
Enregistrement W869171616 · doi:10.4324/9781315747972-11

Going the distance: Sport operations management in the public and third sectors

2015· book-chapter· en· W869171616 sur OpenAlexaboutno aff
David Bamford, Claire Moxham, Katri Kauppi, Benjamin Dehe

Notice bibliographique

RevueUniversity of Huddersfield Repository (University of Huddersfield) · 2015
Typebook-chapter
Langueen
DomaineSocial Sciences
ThématiqueSport and Mega-Event Impacts
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésBusinessOperations managementEngineering

Résumé

récupéré en direct d'OpenAlex

Sport is ubiquitous across the world. It is a ceremony, celebration, physical pursuit, leisure activity and a business (Chadwick, 2011). Sport can be amateur or professional, local or international and can be enjoyed as a participant or as a spectator. Sport is often funded by governments or from voluntary donations. It can therefore be classified as a public service, particularly in countries including the UK, Canada and Australia where there has been a steady increase in the allocation of public funds to sports activities (Houlihan, 2005). Sport is seen to have benefits at an individual level, which include fewer health care requirements and better quality of life, and at the collective social and economic level through lower crime rates and opportunities for urban renewal (Taylor and Godfrey, 2003). In addition, one of the fastest growing tourism segments is travel associated with sports or physical activities (Biddiscombe, 2004). It has been estimated that sport tourism accounts for 10 per cent (approximately $600 billion) of the international tourism market (Saltzman, 2011), mainly through sport mega-events such as the football World Cup, Olympic Games and other international tournaments. On an individual event basis, estimates of the total annual market size for sports and event tickets vary from $7 billion to $60 billion (Sainam et al., 2010).These figures indicate how sport and sport tourism can make a significant economic contribution to national and local economies. They also highlight the importance of public access to sport facilities for the health and social wellbeing of citizens. This chapter examines the application of operations management (OM) strategies to off-field sporting operations in the context of public and third sector sporting organisations. It highlights a clear requirement for the further development of off-field Operations Management and performance measurement within this increasingly important area. In considering sport, the big numbers are not limited to revenues: the Olympic Games is considered by experts to be the greatest, non-defence-related, world-wide logistics event (Minis et al., 2006). For the 30th Olympic Games, in London in 2012, nearly 11,000 athletes participated during July and August, followed by 4,278 athletes taking part in the 14th Paralympics Games in August and September (Bamford and Dehe, 2013).Recent Games have typically attracted 20,000 members of the media, are supported by 150,000 staff members and volunteers, host over 5.5 million ticketed spectators and are watched by billions of television viewers (Minis et al., 2006). Sport is not confined to large scale events with millions of spectators. It is broad and varied; as are the off-field operations that are required to support sports clubs and events, be they professional or amateur, local or international. It is therefore interesting to note that OM scholars have thus far paid limited attention to the sports industry despite calls for more research on sport OM (Machuca et al., 2007).The lack of interest from OM scholars is surprising as the consequences of operations failure have huge implications for the outcome of the on-field performance and are also visible to a range of stakeholders including fans, spectators and sponsors. Examples of failure include the 2010 Commonwealth Games held in Delhi, India, which was criticised for empty stands, collapsing scoreboards, poor transportation arrangements and failing technology (Gilmour, 2010).These instances of failure were predictable to a certain extent and, from an OM perspective, could therefore have been prevented; particularly with regards to these Commonwealth Games which cost an estimated $7.5 billion (Madhavan, 2012). Sporting events often attract criticism for operations issues, which suggests clear potential for improved off-field performance. Drawing on OM concepts, tools and techniques the application of improved layout, queuing methods, process and job design may be used to increase customer throughput and sales during half-time breaks with capacity management principles improving facility utilisation and spectators’ and athletes’ satisfaction during events. This is not to suggest that sport OM does not exist in practice; many sporting organisations currently utilise a number of service OM strategies and practices. For example, operations events management, the scheduling of sporting events and stadium management are common OM ‘good practices’ that organisations deploy purposefully as outlined in textbooks, such as that by Schwarz et al. (2010). It is therefore equally important to identify areas of good and best practice for managing off-field operations in the sport industry so that this knowledge can be shared by managers and OM scholars to identify which OM approaches work best in a range of different sporting contexts. To begin to understand the current application of OM strategies to off-field sporting operations, this chapter reports on the findings of interviews with international sporting practitioners. The chapter focuses on the OM strategies associated with planning, scheduling and control as these seemed the most appropriate for beginning an exploration of off-field sport OM. The sport industry value chain (Smith, 2008) suggests three defined categories of sport organisations: <br/><br/>• Category one is associated with government and public sectors, and includes institutions that are involved in the development of sport, determining sport policy, bolstering competitive performance and/or health promotion (e.g. Sport England).

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,779
Score d'incertitude au seuil0,997

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,001
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,032
Tête enseignante GPT0,223
Écart entre enseignants0,191 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeQualitatif
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2015
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueUniversity of Huddersfield Repository (University of Huddersfield)Même sujetSport and Mega-Event ImpactsTravaux en français237 207