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Record W869171616 · doi:10.4324/9781315747972-11

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

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

Bibliographic record

VenueUniversity of Huddersfield Repository (University of Huddersfield) · 2015
Typebook-chapter
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessOperations managementEngineering

Abstract

fetched live from 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).

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.779
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.032
GPT teacher head0.223
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreOther

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".

Quick stats

Citations4
Published2015
Admission routes1
Has abstractyes

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