Abstract
Despite the popularity of Free-to-Play (F2P) games in recent years, the motivations behind players’ intention to purchase virtual goods in F2P games still require further investigations. This study aims to address this dilemma by investigating the antecedents of functional, emotional, and social values in shaping the purchase intention of virtual goods in F2P games. Using purposive sampling, data were collected through a survey from 352 F2P game participants in the United States. A hybrid PLS-SEM-Artificial Neural Network (ANN) modeling approach was employed to examine the impact of these factors on the intention to purchase virtual goods. The results reveal that perceived value positively influences the purchase intention of virtual goods. The findings also show that functional, emotional, and social values significantly impact the perceived value and purchase intention of virtual goods. Further, perceived value mediates the relationship between quality, achievement, enjoyment, aesthetics, customization, self-presentation, and the intention to purchase virtual goods. The ANN results reveal that quality and social presence are the most critical factors since they achieve the greatest normalized importance ratio compared to the others. The model illustrated considerable explanatory evidence for purchase intention in the context of F2P games. Additionally, this research significantly strengthens the marketing literature by developing an understanding of the intention to buy virtual goods in F2P games. The proposed model can provide insights for F2P game providers to design their games and marketing strategies.
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Appendices
Appendix 1: Demographic profile of players
Demographics | Frequency (n = 352) | Percentage (%) |
---|---|---|
Gender | ||
Male | 107 | 30.4 |
Female | 220 | 62.5 |
Non-binary/third gender | 21 | 6.0 |
Prefer not to say | 4 | 1.1 |
Age | ||
Between 18 and 20 | 69 | 19.6 |
Between 21 and 30 | 176 | 50.0 |
Between 31 and 40 | 75 | 21.3 |
Between 41 and 50 | 25 | 7.1 |
Between 51 and 60 | 5 | 1.4 |
Over 60 | 2 | 0.6 |
Education | ||
High school | 104 | 29.5 |
College | 126 | 35.8 |
Bachelor | 94 | 26.7 |
Master | 28 | 8.0 |
Income | ||
Less than 1000$ | 119 | 33.8 |
Between 1000$–1500$ | 51 | 14.5 |
Between 1500$–2500$ | 62 | 17.6 |
Between 2500$–3500$ | 49 | 13.9 |
Between 3500$–5000$ | 30 | 8.5 |
More than 5000$ | 41 | 11.6 |
How long are you playing the game? | ||
0–3 hours | 120 | 34.1 |
3–6 hours | 119 | 33.8 |
6–9 hours | 47 | 13.4 |
9–12 hours | 29 | 8.2 |
More than 12 hours | 37 | 10.5 |
How much, on average, are you willing to pay for virtual goods? | ||
Less than10 $ | 186 | 52.8 |
11–25 $ | 73 | 20.7 |
26–50 $ | 33 | 9.34 |
51–100 $ | 32 | 9.12 |
101–250 $ | 15 | 4.30 |
More than 250 $ | 13 | 3.74 |
Appendix 2: Measurement items
Price adapted from (Kim et al. 2011) |
PR1: The virtual goods sold in the F2P games are generally reasonably priced |
PR2: The virtual goods sold in the F2P games offer value for money |
PR3: The virtual goods sold in the F2P games are good products for the price |
PR4: The virtual goods sold in the F2P games are considered economical in terms of price |
Quality adapted from (Kim et al. 2011) |
QUAL1: The virtual goods Sold in the F2P games have an acceptable standard of quality |
QUAL2: The virtual goods sold in the F2P games are reliable in their performance |
QUAL3: The virtual goods sold in the F2P games are good in terms of their overall excellence |
QUAL: The virtual goods sold in the F2P games possess a degree of quality that is satisfactory |
Achievement adapted from (Li et al. 2015) |
ACH1: I can beat/surpass other players in the F2P games due to purchasing virtual goods |
ACH2: I gain more power than others in the F2P games due to purchasing virtual goods |
ACH3: I get a higher status/degree than other players in the F2P games due to purchasing virtual goods |
Enjoyment adapted from (Li et al. 2015) |
ENJ1: I feel the activity of purchasing virtual goods is interesting |
ENJ2: I am happy to purchase virtual goods in the F2P games |
ENJ3: I am happy to use virtual goods that I purchased in the F2P games |
ENJ4: I enjoy using virtual goods that I purchased in the F2P games |
Aesthetics adapted from (Kim et al. 2011) |
AES1: The virtual goods sold in the F2P games are lovely |
AES2: The virtual goods sold in the F2P games reflect the beauty |
AES3: The virtual goods sold in the F2P games are aesthetically appealing |
AES4: The virtual goods sold in the F2P games have an attractive aesthetic feature |
Customization adapted from (Teng 2010) |
CZ1: I have more items in the game because I purchased virtual goods |
CZ2: I can modify the appearance and many goods in the F2P game because I purchased virtual goods |
CZ3: I can change many things about my game following my preferences because I purchased virtual goods |
Social presence adapted from (Li et al. 2015) |
SOC1: I can offer more help to others using the Virtual goods I purchased in the F2P games |
SOC2: I can be myself and show what kind of player/person I am by purchasing virtual goods in the F2P games |
SOC3: I feel like I am a member of the F2P games community because of the virtual goods I purchased |
SOC1: I feel connected to other players in the F2P games due to using virtual goods |
Perceived scarcity adapted from (Chen and Sun 2014) |
SC1: In my opinion, the limited virtual goods are going to be sold out soon |
SC2: I think the limited virtual goods surely attract more people to buy than the available virtual items |
SC3: The number of limited virtual goods is very limited |
SC4: It is difficult to acquire the limited virtual goods |
SC5: The limited virtual goods in the F2P game are scarce |
Self-presentation adapted from (Lee et al. 2012) |
SL1: I use virtual goods in the game because it helps other players to perceive me as competent |
SL2: I use virtual goods in the game because it helps other players to perceive me as socially desirable |
SL3: I use virtual goods in the game because it helps other players to perceive me as likable |
SL4: I use virtual goods in the game because it helps other players to perceive me as friendly |
SL5: I use virtual goods in the game because it helps other players to perceive me as skilled |
SL6: I use virtual goods in the game because it helps me to make a good impression |
SL7: I use virtual goods in the game because it helps me to tell others a little bit about myself |
Perceived value adapted from (Yang et al. 2016) |
SL1: Using virtual goods in the F2P games is a good deal |
SL2: Compared to the effort I make, using virtual in the F2P games is beneficial to me |
SL3: Compared to the time I spend, virtual goods in the F2P games are worthwhile |
SL4: Overall, using virtual goods in the F2P games delivers good value |
Purchase Intention adapted from (Guo and Barnes 2012) |
PI1: I intend to purchase virtual goods for my characters in online games |
PI2: My willingness to buy advanced virtual goods in online games is high |
PI3: The likelihood that I would purchase advanced goods in online games is high |
Appendix 3: ULMC test results
Construct | Item | Substantive method loading (R1) | R12 | Method factor loading (R2) | R22 |
---|---|---|---|---|---|
Price (FCT=1.286) | PR1 | 0.785** | 0.616 | − 0.047 | 0.002 |
PR2 | 0.717** | 0.514 | 0.087 | 0.008 | |
PR3 | 0.841** | 0.707 | 0.077 | 0.006 | |
PR4 | 0.732** | 0.536 | 0.091 | 0.008 | |
Quality (FCT=1.093) | QUAL1 | 0.737** | 0.543 | − 0.003 | 0.000 |
QUAL2 | 0.778** | 0.605 | − 0.016 | 0.000 | |
QUAL3 | 0.767** | 0.588 | 0.046 | 0.002 | |
QUAL4 | 0.815** | 0.664 | − 0.029 | 0.001 | |
Achievement (FCT=1.284) | ACH1 | 0.880** | 0.774 | − 0.033 | 0.001 |
ACH2 | 0.911** | 0.830 | 0.002 | 0.000 | |
ACH3 | 0.767** | 0.588 | 0.034 | 0.001 | |
Enjoyment (FCT=1.075) | ENJ1 | 0.603** | 0.364 | 0.075** | 0.006 |
ENJ2 | 0.524** | 0.275 | 0.259* | 0.067 | |
ENJ3 | 0.881** | 0.776 | − 0.131* | 0.017 | |
ENJ4 | 0.878** | 0.771 | − 0.171** | 0.029 | |
Aesthetics (FCT=1.684) | AES1 | 0.724** | 0.524 | 0.016 | 0.000 |
AES2 | 0.681** | 0.464 | 0.130* | 0.017 | |
AES3 | 0.885** | 0.783 | − 0.132* | 0.017 | |
AES4 | 0.815** | 0.664 | − 0.013 | 0.000 | |
Customization (FCT=1.356) | CZ1 | 0.727** | 0.529 | 0.078 | 0.006 |
CZ2 | 0.866** | 0.750 | − 0.047 | 0.002 | |
CZ3 | 0.818** | 0.669 | − 0.027 | 0.001 | |
Social presence (FCT=1.036) | SOC1 | 0.509** | 0.259 | 0.060 | 0.004 |
SOC2 | 0.723** | 0.523 | 0.054 | 0.003 | |
SOC3 | 0.854** | 0.729 | − 0.049 | 0.002 | |
SOC4 | 0.848** | 0.719 | − 0.042 | 0.002 | |
Perceived scarcity (FCT=1.101) | SC1 | 0.668** | 0.446 | 0.178 | 0.032 |
SC2 | 0.569** | 0.324 | − 0.064 | 0.004 | |
SC3 | 0.785** | 0.616 | − 0.088 | 0.008 | |
SC4 | 0.793** | 0.629 | − 0.019 | 0.000 | |
SC5 | 0.828** | 0.686 | 0.034* | 0.001 | |
Self-presentation (FCT=1.219) | SL1 | 0.777** | 0.604 | − 0.025 | 0.001 |
SL2 | 0.750** | 0.563 | 0.012 | 0.000 | |
SL3 | 0.821** | 0.674 | − 0.018 | 0.000 | |
SL4 | 0.670** | 0.449 | 0.100 | 0.010 | |
SL5 | 0.712** | 0.507 | − 0.017 | 0.000 | |
SL6 | 0.852** | 0.726 | − 0.053 | 0.003 | |
SL7 | 0.665** | 0.442 | 0.006 | 0.000 | |
Perceived value (FCT=1.516) | PV1 | 0.766** | 0.587 | − 0.025 | 0.001 |
PV2 | 0.791** | 0.626 | 0.008 | 0.000 | |
PV3 | 0.817** | 0.667 | − 0.079 | 0.006 | |
PV4 | 0.850** | 0.723 | 0.038* | 0.001 | |
Purchase Intention (FCT=1.667) | PI1 | 0.594** | 0.353 | − 0.002 | 0.000 |
PI2 | 0.935** | 0.874 | − 0.018 | 0.000 | |
PI3 | 0.955** | 0.912 | − 0.122 | 0.015 | |
Average | 0.604 | 0.006 |
Appendix 4: Discriminant validity—Fornell–Larcker criterion
Construct | PR | QUAL | ACH | ENJ | AES | CZ | SOC | SC | SL | PV | PI |
---|---|---|---|---|---|---|---|---|---|---|---|
PR | 0.588 | ||||||||||
QUAL | 0.042 | 0.599 | |||||||||
ACH | 0.047 | 0.107 | 0.730 | ||||||||
ENJ | 0.112 | 0.143 | 0.057 | 0.520 | |||||||
AES | 0.097 | 0.179 | 0.116 | 0.252 | 0.602 | ||||||
CZ | 0.056 | 0.084 | 0.097 | 0.159 | 0.201 | 0.646 | |||||
SOC | 0.070 | 0.289 | 0.102 | 0.123 | 0.127 | 0.141 | 0.555 | ||||
SC | 0.092 | 0.045 | 0.033 | 0.046 | 0.050 | 0.029 | 0.098 | 0.532 | |||
SL | 0.057 | 0.200 | 0.146 | 0.155 | 0.208 | 0.170 | 0.246 | 0.055 | 0.564 | ||
PV | 0.128 | 0.359 | 0.210 | 0.299 | 0.312 | 0.226 | 0.265 | 0.037 | 0.374 | 0.650 | |
PI | 0.176 | 0.297 | 0.117 | 0.265 | 0.304 | 0.179 | 0.338 | 0.138 | 0.319 | 0.399 | 0.701 |
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Mkedder, N., Özata, F.Z. I will buy virtual goods if I like them: a hybrid PLS-SEM-artificial neural network (ANN) analytical approach. J Market Anal 12, 42–70 (2024). https://doi.org/10.1057/s41270-023-00252-4
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DOI: https://doi.org/10.1057/s41270-023-00252-4