Residents’ Perceptions of the Impacts of the Rio 2016 Olympic Games: Before, During and After the Mega-Event

She is a post-doctoral student in Civil Engineering (completion in 2019), Fluminense Federal University (UFF). She holds a PhD in Production Engineering (COPPE/UFRJ) and a Master’s Degree in Education, Federal University of Rio de Janeiro (UFRJ). She graduated in Administration, Getulio Vargas Foundation (FGV), Rio de Janeiro and in Law, University of the State of Rio de Janeiro (UERJ). She is a Professor of the Post-Graduation Programme in Administration, UNIGRANRIO and coordinator of the Centre for Tourism Research. Contribution: research design and discussion. E-mail: deborahzouain@gmail.com

Paola Bastos Lohmann,

She is a PhD student in Tourism, University of Aveiro, Portugal (completion in 2020). She holds a Master’s Degree in Business Management, Getulio Vargas Foundation (FGV). She graduated in Tourism, Fluminense Federal University (UFF). She is a researcher at UNIGRANRIO Centre for Tourism Research. She is a Reverse doctoral grant holder, FAPERJ - 2018. Contribution: data collection, data analysis and discussion E-mail: paolalohmann@gmail.com

Gabriela De Laurentis Cardoso,

She is a Master’s student in the Post-graduation Programme in Design, Superior School of Industrial Design, UERJ (PPDESDI). She attended a Master's Degree programme in Market Research, Media and Public Opinion, University of Entrepreneurial and Social Sciences (UCES), Buenos Aires. She graduated in Visual Programming by UniverCidade, Rio de Janeiro. She is a researcher at UNIGRANRIO Centre for Tourism Research. Contribution: literature review, data analysis and discussion E-mail: gabydelaurentis@yahoo.com

Kaarina Barbosa Virkki,

She completed post-graduate degrees in Business Administration, Getulio Vargas Foundation (FGV), and in Marketing, Brazilian Institute of Markets and Capitals (IBMEC). She graduated in Tourism, Fluminense Federal University (UFF). She is a researcher at UNIGRANRIO Centre for Tourism Research. Contribution: literature review, data collection and discussion E-mail: kaarina.virkki@gmail.com

Marcela Cohen Martelotte,

She completed post-doctoral studies, COPPE/UFRJ. She holds a PhD in Electrical Engineering, PUC-Rio, a Master’s Degree in Business Administration, Getulio Vargas Foundation (FGV), RJ and a Master's Degree in Electrical Engineering, PUC-Rio. She graduated in Statistics, National School of Statistics (ENCE). She is an Adjunct Professor at the Department of Production Engineering, Fluminense Federal University (EEIMVR) Contribution: data analysis and discussion E-mail: marcela.cohen@gmail.com

Impacts of events	Positive impacts	Negative impacts
	Shared experiences	Alienation of community
	Revitalisation of traditions	Manipulation of community
	Strengthening community pride	Negative community image
Social and cultural	Legitimizing community groups	Destructive behaviour
	Increase in community participation	Drug and alcohol abuse
	Presentation of new ideas and challenges	Social dislocation
	Expansion of cultural perspectives	Loss of amenity

	Showcasing the environment	Environmental damage
	Providing models for best practice	Pollution
Physical and	Increasing environmental awareness	Destruction of heritage
environmental	Infrastructural legacy	Noise disturbance
	Improved transport and communications	Traffic congestion
	Urban transformation and renewal

	International prestige	Risk of event failure
	Improved profile	Diversion of funds
Political	Promotion of investment	Lack of accountability
	Social cohesion	Misleading propaganda
	Development of administrative skills	Loss of community control
		Legitimation of ideology

	Promotion of destination and increased tourism	Community resistance to tourism
	Extended length of stay	Loss of authenticity
Tourism and economic	Higher yield	Damage to reputation
	Increased tax revenue	Exploitation
	Business opportunities	Inflated prices
	Business activity	Opportunity costs
	Job creation	Poor financial management
		Financial loss

Variables	Before (N = 426)		During (N = 391)		After (N = 394)		Total
	Freq.	Percent (%)	Freq.	Percent (%)	Freq.	Percent (%)	Freq.	Percent (%)
Gender
Male	182	42.7	164	41.9	187	47.5	533	44.0
Female	244	57.3	227	58.1	207	52.5	678	56.0
Age
18-25 years	89	20.9	153	39.1	79	20.1	321	26.5
26-35 years	112	26.3	71	18.2	107	27.2	290	23.9
36-45 years	72	16.9	67	17.1	83	21.1	222	18.3
46-55 years	76	17.8	59	15.1	72	18.3	207	17.1
56-64 years	37	8.7	28	7.2	35	8.9	100	8.3
65 years and above	40	9.4	13	3.3	18	4.6	71	5.9
Education level
Primary or below	24	6	23	6	11	3	58	5
Secondary	104	24	113	29	100	25	317	26
University	244	57	200	51	210	53	654	54
Postgraduate	54	13	55	14	73	19	182	15
Monthly household income
Below R$ 725	25	5.9	6	1.5	6	1.5	37	3.1
R$ 725-1,448	29	6.8	40	10.2	28	7.1	97	8.0
R$ 1,449-2,172	68	16.0	51	13.0	35	8.9	154	12.7
R$ 2,173-3,620	90	21.1	83	21.2	96	24.4	269	22.2
R$ 3,621-7,240	105	24.6	66	16.9	88	22.3	259	21.4
R$ 7,241-14,480	56	13.1	67	17.1	69	17.5	192	15.9
R$ 14,481 or higher	17	4.0	24	6.1	26	6.6	67	5.5
Don't know/don't want to declare	36	8.5	54	13.8	46	11.7	136	11.2


	Yes (percent valid)	No (percent valid)	p-value (binary logistic regression)	EXP(B)
Before	54.5	45.5	(a)	(a)
During	59.3	40.7	0.160	1.220
After	54.3	45.7	0.967	0.994

Questions and attributes	Before		During		After		ANOVA F-test
	Mean	SD	Mean	SD	Mean	SD
How much do you consider yourself to be interested in the World Cup?	5.94	3.13	6.77	2.43	6.73	2.47	12.067***
How much do you consider yourself to be interested in the Olympic Games?	6.53	2.87	7.42	2.33	7.43	2.10	18.046***

	Decreased (percent valid)	Remained the same (percent valid)	Increased (percent valid)	Mean (SD)	ANOVA F-test
Before	7.0	71.8	21.1	0.14 (0.51)	18.046***
During	5.1	55.2	39.6	0.34 (0.57)
After	2.0	78.4	19.9	0.18 (0.43)

	Yes (percent valid)	No (percent valid)	p-value (binary logistic regression)	EXP(B)
Before	38.3	61.7	(a)	(a)
During	34.5	65.5	0.268	0.851
After	22.3	77.7	0.000***	0.464

	Yes (percent valid)	No (percent valid)	p-value (binary logistic regression)	EXP(B)
Before	53.3	46.7	(a)	(a)
During	54.0	46.0	0.846	1.028
After	33.0	67.0	0.000***	0.432

Questions and attributes	Before		During		After		ANOVA F-test
	Mean	SD	Mean	SD	Mean	SD
The Olympic Games generates economic gains for the city of Rio de Janeiro.	4.30	1.22	4.28	1.06	2.90	1.42	164.450***
There is an increase in prices due to the Olympic Games.	4.52	1.13	4.51	1.06	3.15	1.43	167.870***
The Olympic Games stimulates business activity in the host city.	4.60	1.00	4.60	.74	3.04	1.42	273.413***
There is financial liability for the host city.	3.20	1.76	3.16	1.56	4.15	1.19	53.899***
The Olympic Games provides business opportunities for the host city.	4.45	1.16	4.42	.90	3.14	1.41	160.259***
The Olympic Games generates jobs in the host city.	4.28	1.22	4.34	1.04	2.56	1.45	258.224***
The Olympic Games promote the tourist activities of the host city.	4.73	.84	4.78	.59	3.23	1.32	334.429***
The Olympic Games damage the reputation of the host city.	2.60	1.69	2.38	1.44	3.21	1.50	30.101***
There will be resistance from the population towards tourists arriving for the Olympic Games.	1.68	1.30	2.06	1.47	1.90	1.19	8.761***
The Olympic Games increase the number of tourists coming to the host city.	4.88	.55	4.94	.26	3.33	1.28	503.796***
Urban mobility	3.24	1.74	3.60	1.46	4.04	1.30	28.238***
Airport infrastructure	3.24	1.73	3.73	1.24	3.78	1.27	17.515***
Public safety	1.99	1.44	2.52	1.59	1.37	.81	74.311***
Tourism	4.02	1.49	4.34	1.03	3.33	1.27	63.867***
Sport	3.77	1.60	4.06	1.23	3.06	1.33	53.515***

	Yes, in a negative way (percent valid)	Does not influence (percent valid)	Yes, in a positive way (percent valid)	Mean (SD)	ANOVA F-test
Before	87.6	5.9	6.6	-0.81 (0.53)	17.449***
During	86.4	4.3	9.2	-0.77 (0.60)
After	97.5	1.5	1.0	-0.96 (0.23)

[TFN1] Source: Allen et al. (2008)

Residents’ Perceptions of the Impacts of the Rio 2016 Olympic Games: Before, During and After the Mega-Event

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