The emergent community of esports fans in Japan: an analysis of motivations and preferences

Figures & data

Figure 1. Genshin Impact (原神)- poster advertisement in Tokyo’s subway station.

Table 1. Key sport spectator motivation measurement scales.

Measures	Authors, Year of Publication	Sample Size	Context	Motives
SFMS	Wann (1995)	272	Softball	Eustress Self-esteem Escape Entertainment Economic Aesthetic Group affiliation Family needs
SMS-II	Pelletier et al. (2013)	Study 1: 412 Study 2: 290	Study 1:Variety of sports Study 2: Basketball and swimming	Intrinsic Integrated Identified Introjected External Amotivated
MSSC	Trail and James (2001)	N/A	N/A	Achievement Aesthetic Acquisition of knowledge Drama Escapism Social interaction Physical attractiveness Physical skills
SII	Funk et al. (2001)	1,321	Women football	Soccer fan Vicarious achievement Excitement Team identification Supporting women’s opportunity in sport Aesthetics Socialisation National pride Drama Interest in player
N/A	Dwyer and Kim (2011)	Study 1: 23 Study 2: 235 Study 3: 201	Fantasy football	Competition Social interaction Entertainment/escape Competition

Figure 2. Concepts of esports players, spectators, and fans.

Table 2. A review of key factors influencing esports spectator motivation.

Measure Adopted/Developed	Authors, Year of Publication	Sample size	Game	Method	Motives
MSSC (adopted)	Hamari and Sjöblom (2017)	888	Not available	Online survey	Escapism Knowledge acquisition Novelty Aggressiveness
SII (adopted)	Pizzo et al. (2018)	517	K League soccer FIFA Online 3 StarCraft II	Stadium	Consumption motives for esports and traditional sports are similar
SESD (developed)		Phase 1: 8 interviews and 109 survey responses Phase 2: 1,309 online survey	CS: GO Overwatch DOTA 2 LoL StarCraft 2 WarCraft 3 Street Fighters 2K17	Mixed methods	Chat room Stream quality Commentary features Player characteristics Event attractiveness Streamer traits Virtual rewards Schedule convenience
MSES (developed)	Qian et al. (2020a)	Phase 1: 8 interviews and 207 survey Phase 2: 638	CS: GO Overwatch DOTA 2 LoL StarCraft 2 WarCraft 3 Street Fighters 2K17	Mixed methods	Skill improvement Skill appreciation Vicarious sensation Competition excitement Friends bonding Socialization opportunity Dramatic nature Entertaining nature Competitive nature Game knowledge
N/A	Qian et al. (2020b)	1,100	Not available	Online survey	Need fulfilment Competence Autonomy Relatedness

Table 3. Socio-demographic characteristics (N = 500).

Variable	Count	%
Gender
Male	341	68.20
Female	153	30.60
Non-binary	4	0.80
Other	2	0.04
Age
18–25	51	10.20
26–35	143	28.60
36–45	182	36.40
46–55	101	20.20
56 and above	23	4.60
Education
Lower than secondary school	11	2.20
Secondary school	109	21.80
College	60	12.00
Bachelor’s degree	21	4.20
Master’s degree	258	51.60
Professional degree	31	6.20
Doctorate	9	1.80
Prefer not to answer	1	0.20
Living Region
Kanto	231	46.20
Kansai	71	14.20
Kyushu	64	12.80
Shikoku	44	8.80
Chugoku	63	12.60
Others	52	10.40
Did not indicate	2	0.40
Employment
Student	28	5.60
Part-time Employee	37	7.40
Full-time Employee	347	69.40
Self-employed	38	7.60
Unemployed	41	8.20
Retired	4	8.80
Prefer not to answer	5	1.00
Personal annual income
No income	24	4.80
Less than 200万円	77	15.40
200万円 – 400万円	95	19.00
400万円 – 600万円	148	29.60
600万円 – 800万円	75	15.00
800万円 – 1000万円	29	5.8
1000万円 and above	25	5.00
Prefer not to answer	27	5.40

Note: “Kanto” includes Ibaraki, Kanagawa, and Tokyo; “Kansai” includes Aichi, Nara, and Osaka; “Kyushu” includes Kagoshima, Kumamoto, and Saga; “Shikoku” includes Kagawa and Tokushima; “Chugoku” includes Kouchi and Yamaguchi; and “Others” includes Niigata and Okinawa.

Table 4. Summary statistics of cluster analysis of esports play motivations.

	Cluster I (Mean/S.D.) (n = 161)	Cluster II (Mean/S.D.) (n = 259)	Cluster III (Mean/S.D.) (n = 80)	F-value	Scheffe post hoc test
					I–II	I–III	II–III
Hedonic motivation	6.15 (0.74)	5.30 (0.85)	3.63 (1.29)	207.72***	***	***	***
Habit	5.72 (0.72)	4.40 (0.74)	3.16 (0.86)	355.78***	***	***	***
Price value	5.64 (0.86)	4.36 (0.78)	3.12 (0.96)	258.86***	***	***	***
Effort expectancy	5.41 (0.87)	4.11 (0.79)	3.19 (0.98)	209.30***	***	***	***
Social influence	5.16 (1.07)	3.77 (0.91)	2.73 (1.18)	175.27***	***	***	***
Flow	5.94 (0.81)	4.53 (0.77)	3.17 (1.12)	305.75***	***	***	***
Cluster name	All-Rounder Players	Immersive Fun Seekers	Skill-based Players	Pillai’s Trace = 0.84 (p < 0.001) Wilks’ Lambda = 0.21 (p < 0.001) Hotelling’s Trace = 3.55 (p < 0.001) Roy’s Largest Root = 3.48 (p < 0.001)

***p < 0.001. Mean values measured on the basis of 7-point Likert scale (1: strongly disagree, 4: neutral, 7: strongly agree).

Table 5. Summary statistics of cluster analysis of esports watching motivations.

	Cluster I (Mean/S.D.) (n = 135)	Cluster II (Mean/S.D.) (n = 142)	Cluster III (Mean/S.D.) (n = 223)	F-value	Scheffe post hoc test
					I–II	I–III	II–III
Competitive nature	4.00 (0.76)	6.14 (0.63)	5.13 (0.68)	335.50***	***	***	***
Socialisation opportunity	3.69 (0.83)	5.87 (0.73)	4.52 (0.80)	271.32***	***	***	***
Friends bonding	3.66 (0.85)	5.93 (0.76)	4.63 (0.95)	238.04***	***	***	***
Game knowledge	3.79 (0.84)	5.92 (0.70)	4.87 (0.63)	309.79***	***	***	***
Skill appreciation	3.96 (0.86)	6.20 (0.68)	5.26 (0.65)	334.15***	***	***	***
Entertaining nature	4.00 (0.73)	6.30 (0.57)	5.28 (0.64)	437.46***	***	***	***
Dramatic nature	3.78 (0.82)	6.18 (0.68)	5.07 (0.71)	370.39***	***	***	***
Competition excitement	4.01 (0.73)	6.31 (0.55)	5.14 (0.63)	456.83***	***	***	***
Vicarious sensation	3.58 (0.85)	5.90 (0.76)	4.59 (0.84)	279.05***	***	***	***
Cluster name	Casual Viewers	Entertainment-Excitement Seekers	Skill Appreciation Seekers	Pillai’s Trace = 0.87 (p < 0.001) Wilks’ Lambda = 0.18 (p < 0.001) Hotelling’s Trace = 4.46 (p < 0.001) Roy’s Largest Root = 4.41 (p < 0.001)

***p < 0.001. Mean values measured on the basis of 7-point Likert scale (1: strongly disagree, 4: neutral, 7: strongly agree).

Figure 3. Hours of playing and watching esports games per week.

Figure 4. Types of esports games most played and watched.

Table 6. Esports play clusters differences by esports fans’ characteristics.

	Cluster I (n = 161)	Cluster II (n = 259)	Cluster III (n = 80)
	All-Rounder Players	Immersive Fun Seekers	Skill-based Players
Hours of playing esports game per week				Total
Less than 1 h	20	54	26	100	χ^2 = 33.77, df = 10, p < .001
1–3 h	65	126	35	226
3–5 h	33	44	14	91
5–7 h	19	18	1	38
7–10 h	8	10	2	20
Over 10 h	16	7	2	25
Number of years of being an esports fan
less than 1 year	24	44	24	92	χ^2 = 15.68, df = 6, p < .05
1–3 years	69	128	31	228
3–5 years	30	49	16	95
over 5 years	38	38	9	85
Main platform of esports gameplay
Mobile phone	33	73	36	142	χ^2 = 23.33, df = 8, p < .01
PC/Laptop	46	60	23	129
Tablet/iPad	11	24	5	40
Console/Switch	70	100	15	185
Other	1	2	1	4
Types of esports games most played
First Person Shooter	14	26	8	48	χ^2 = 20.99, df = 22, p = 0.52 (n.s.)
Third Person Shooter	20	39	9	68
Real Time Strategy	4	4	0	8
MOBA	1	4	1	6
Fighting	23	28	12	63
Sports	27	50	12	89
Racing	12	16	5	33
Puzzle	41	57	22	120
Trading Card	5	22	6	33
MMORPG	2	0	2	4
Online Strategy	9	7	1	17
Other	3	6	2	11

Table 7. Esports watching clusters differences by esports fans’ characteristics.

	Cluster I (n = 135)	Cluster II (n = 142)	Cluster III (n = 223)
	Casual Viewers	Entertainment-Excitement Seekers	Skill Appreciation Seekers
Gender				Total
Male	90	100	151	341	χ^2 = 10.17, df = 4, p < .05
Female	40	42	71	153
Prefer not to say	5	0	1	6
Hours of watching esports game per week
Less than 1 h	62	29	84	175	χ^2 = 38.06, df = 10, p < .001
1–3 h	43	59	88	190
3–5 h	17	29	27	73
5–7 h	7	7	14	28
7–10 h	4	6	8	18
Over 10 h	2	12	2	16
Number of years of being an esports fan
Less than 1 year	35	13	44	92	χ^2 = 20.09, df = 6, p < .01
1–3 years	58	62	108	228
3–5 years	22	32	41	95
over 5 years	20	35	30	85
Main platform of watching esports
Live streaming platforms (e.g. YouTube, Twitch)	93	74	165	332	χ^2 = 29.47, df = 6, p < .001
Online game platform (e.g. Steam)	34	52	44	130
Onsite (e.g. in person in stadium)	4	16	12	32
Other	4	0	2	6
Types of esports games most watched
First Person Shooter	19	27	35	81	χ^2 = 13.31, df = 22, p = 0.92 (n.s.)
Third Person Shooter	18	24	41	83
Real Time Strategy	3	1	5	9
MOBA	1	3	3	7
Fighting	25	28	32	85
Sports	24	23	41	88
Racing	7	10	14	31
Puzzle	25	20	33	78
Trading Card	6	3	8	17
MMORPG	0	0	1	1
Online Strategy	5	2	5	12
Other	2	1	5	8

Figure 5. Puyo Puyo Esports.

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