‘Play-to-Earn’ (P2E) is an emerging online-gaming model that presents an opportunity for players to receive monetary rewards during gameplay, with the exchange of digital goods facilitated by blockchain technology (Delfabbro et al., 2022; Vidal-Tomás, 2022). This gaming model usually requires an initial financial commitment such as the purchase of a crypto-currency and non-fungible tokens (NFTs) which can be in-game characters, tools or weapons or even part of the ecosystem (e.g., a segment of land in a virtual world). Players earn money by engaging in various forms of game play which may include competitions with other players that earns a tradable in-game currency or by upgrading and reselling their NFTs. Some games (e.g., Defiland on the Solana blockchain) may also enable players to stake their earned tokens into liquidity pools which earn an annualised return. In this sense, P2E provides brings together elements of speculative trading and gaming in the same business model. Players hope to gain money through the appreciation and quantity of the earned tokens, but also through the resale of their NFTs in designated marketplaces (e.g., Magic Eden on Solana). Examples of blockchain games where this model has been witnessed include the short-lived Cryptokitties game in late 2018 and Axie Infinity in 2021 (Aguila et al., 2022; De Jesus et al., 2022; Delic & Delfabbro, 2022). Both games involved competitive battles between players to earn in-game currencies, trading and breeding of NFTs and rapid a speculative-driven appreciation of the token prices followed by a significant fall (Jiang & Liu, 2021).

P2E represents the last iteration of increasingly monetized gaming models that have seen the evolution of gaming from a principally pay-to-own system (e.g., the CD, cartridge or downloaded game) to subscription-based models (Brock, & Johnson, 2021); Freemium models where people get the base game for free, but pay extra for the complete game; and, free-to-play models in which the game is free to download, but requires additional purchases to gain additional content or features ( Petrovskaya, & Zendle, 2021). Often termed micro-transactions, these additional purchases can be for game assets (cosmetic features such as skins or functional items such as weapons); or, access to additional content (Hamari et al., 2017). A class of activities which has received particular attention are loot boxes which are features which can be won or purchased in games (Drummond & Sauer, 2018). Loot boxes provide in-game rewards based on chance and have been likened to a form of gambling, with some studies showing that gamers who favour loot boxes tend to score higher on measures of problem gambling (Drummond et al., 2019; Griffiths, 2018; Macey & Hamari, 2019). Current estimates suggest that revenue from microtransactions was around $US67 billion annually in 2022 (The Business Research Company, 2022) and is now a major component of the gaming business model.

According to Davidovici-Nora (2013) and Hamari and Lehdonvirta (2010), gaming platforms that included a micro-transaction model emerged around 20 years ago as a way to gain greater control over the operation of the game, including transactions and how assets are traded. Such centralised control was seen to be needed to reduce the risk of cheating, piracy and copyright violations due to the operation of third-party trading sites beyond the platform’s control. New platforms (e.g., Valve’s Steam platform) emerged which now provide built-in mechanics (e.g., APIs or Application Platform Interfaces) that enable the greater interaction between different activities. People can play games, interact with the platform’s marketplace and trade assets such as skins on third party sites using custom-made integrations (Zanescu et al., 2021; Nieborg and Poell, 2018). Games also developed their own in-game currencies which has led, in some countries, to the phenomenon of “gold farming” in which large groups of usually young people would play games in factory-like conditions to earn game assets or currencies that could be sold elsewhere for real money (Dibbell, 2015; Nakamura, 2012; Tai & Hu, 2018).

In effect, these new gaming platforms have allowed for the greater monetization of gaming by facilitating the trading of assets, but also opportunities to use in-game assets for gambling on third party sites (Greer et al., 2022). In addition to concerns about in-game assets as potential facilitators of gambling (Abarbanel, & Macey, 2019), a number of authors have noted that these features can often be seen as ‘predatory’ from a consumer perspective (King & Delfabbro, 2018; Petrovskaya & Zendle, 2021). Consumers, when they play some F2P games, will often not know the final cost of participation and may feel compelled to continue making additional purchases to complete the game or maintain their performance against other players. Although it is unknown at the present time whether this might increase the addictive potential of these games, the increased monetary elements could lead to greater financial harm and greater behavioural persistence because of the desire to justify prior investments of time and effort (sunk-cost effect); or the need to keep up with the performance of other participants who are listed on leader-boards in these games or who are promoting their financial gains on related social media sites (King & Delfabbro, 2018).

Predictors of engagement in P2E gaming

Although blockchain P2E does not introduce features which are entirely new, the technology allows for even greater variety and flexibility in the trading and ownership of assets and integrates the speculative risks associated with crypto-currency into gaming. For this reason, it is important to understand what groups of people are most likely to be attracted to this form of gaming. One possibility is that it would be most attractive to people who are accustomed to playing using current F2P models; however, given the use of blockchain technology, it could also be that the financial elements of the activity appeal more to those already engaged in higher risk financial activities such as crypto-currency trading or gambling. In this paper, the existing literature was used as the basis for analysing potential individual difference factors likely to predict P2E engagement. The first was a person’s level of gaming risk. Those people who are classified as having an Internet Gaming Disorder (IGD) (see World Health Organisation, 2022) have been shown to be statistically more likely to be purchase loot boxes and make micro-transactions (Gibson et al., 2022; Macey et al., 2022; Raneri et al., 2022). A second predictor is gambling. Studies have also shown that scores on the Problem Gambling Severity Index (PGSI) (Ferris & Wynne, 2001) are often positively associated with loot box purchasing (Zendle & Cairns, 2018; Zendle et al., 2019, 2020). These findings lead to the expectation that those attracted to the highly monetized features of P2E would be hypothesised to score higher on measures of both problem gaming and gambling.

A third factor is motivation. Drawing upon Deci and Ryan’s (1985) Self-Determination Theory, it could be hypothesised that people who choose games because of the prospect of winning money may be more extrinsically motivated by the activity than those who play games just for enjoyment (intrinsic motivation). Several studies show that extrinsic motivation in online-gaming contexts has been associated with a greater risk of problem gaming (Kim et al., 2017; King & Delfabbro, 2009), poor self-control and other unhealthy gaming-related behaviours (Mills & Allen, 2020).

A fourth factor that was considered in this paper was impulsivity which refers to a person’s tendency to act without thinking or to prioritise short-term outcomes over longer-term consequences (Mills & Allen, 2020). This characteristic has been implicated as a vulnerability factor for problem gambling due to its effect on self-control and the delay of gratification (Maccallum et al., 2007) and also internet gaming disorder (Bargeron & Hormes, 2017). Studies (e.g., Kim et al., 2017) indicate that there is a positive association between impulsivity scores and microtransaction purchasing (Kim et al., 2017); the amount of time invested into online gaming (King et al., 2020); and how much money is spent on gambling (Ioannidis et al., 2019). P2E offers opportunities to engage in speculative investments via short-term crypto-currency trading and the hope of making rapid gains through gaming. For this reason, it may be attractive to people who score higher on measures of impulsivity as is the case for micro-transactions.

A fifth factor is to consider is players’ level of knowledge and experience. To engage in most P2E games, players must have some understanding of NFTs and cryptocurrencies (De Jesus et al., 2022). People must know how to buy cryptocurrencies on exchanges, send them to wallets, use codes and security passphrases and interact with the games. For this reason, it is likely that this new form of gaming may be more appealing to those with a pre-existing interest in digital assets.

Aims of the current study

Blockchain-based P2E games have the ability to significantly increase the level of monetisation within the modern videogaming industry. The P2E model provides a potential convergence between financial trading and gaming, as well as allow speculative elements similar to gambling. In this study, we examine the characteristics of those who are likely to be most attracted to P2E gaming, with comparative analyses included to examine whether this differs from the factors associated with existing monetised features in gaming. We examined whether engagement in monetised gaming, and P2E in particular, is associated with variables previously identified as potential risk factors in the literature; namely, a susceptibility to problems with gaming or gambling; more extrinsic motivations for gaming; and, greater impulsivity. We further predicted that greater P2E gaming would be associated with an existing involvement in higher risk financial activities (namely buying cryptocurrencies and NFTs). Our specific hypotheses were as follows:

  1. 1.

    Higher scores on a measure of Internet Gaming Disorder Scale (IGD) and the Problem Gambling Severity Index (PGSI) will be associated with increased purchases of microtransactions and greater engagement in ‘Play-to-Earn’ games.

  2. 2.

    Motivation will be related to monetised gaming: higher scores of extrinsic motivation and lower scores on intrinsic motivation will be positively associated with monetised gaming engagement and P2E.

  3. 3.

    Higher scores of impulsivity measures on impulsivity will be positively associated with monetised gaming engagement and P2E.

  4. 4.

    Individuals who own or have invested in cryptocurrency and NFTs will be more likely to engage in P2E than those that do not.



Table 1 summarises the demographic characteristics of the sample. The majority were male and were aged under 25 years (M = 28.3, SD = 8.3). Participants were recruited using the online recruitment platform Prolific (https://www.prolific.co/) in June 2022. Inclusion criteria required all participants to be fluent in English, at least 18 years of age and participate in 13 hours or more of online gaming per week. While the sample was representative of the six major continents, most participants resided in North America (43.9%) and Europe (40.6%). Nearly half (48.7%) of the participants reported a stable flow of income, with most having completed high school (33.7%) or a Bachelor’s degree (38%) and employed full-time (35.5%).

Table 1 Demographic Characteristics of the Sample


Demographic Information

Basic demographic information was collected to provide the details in Table 1. This included: gender; age; region; education level; employment status; financial stability; and, general gaming habits.

Engagement in Monetised Gaming

Two dimensions of monetised gaming were captured: existing forms present in current gaming models and then those specific to P2E gaming. Participants were asked (Yes/ No) if they had made gaming microtransactions / in-game purchases and if they had engaged in P2E gaming. Those who endorsed the first question, were then asked about the: frequency (times per week) and financial investment of in-game purchases (amount spent); the type/s of in-game content purchased; and, their future intention to engage in these monetised activities. Participants were also asked if they intended to try P2E gaming in the future (Yes/ No).

Internet Gaming Disorder Scale

Petry et al.’s (2014) 9-item Internet Gaming Disorder (IGD) scale was used. This nine-item questionnaire, developed by an international team, addresses each of the nine Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria for IGD using a previous 12-month framing. Scores of 5 or higher indicate IGD. This measure had only modest internal reliability (α = .69) in the current study.

Problem Gambling

Participants completed the 9-tem Problem Gambling Severity Index (PGSI) (Ferris & Wynne, 2001) using a last 12 months time-frame. Each response was measured on a four-point Likert scale ranging from 0 (Never) to 3 (Almost always). Total scores range from 0 to 27 and are used to classify respondents in to one of four categories: non-problem gamblers (0), low-risk gamblers (1-2), moderate risk gamblers (3-7) or problem gamblers (8+). Overall, the PGSI had very good internal reliability within the present sample (α = .92).

Gaming Motivation Scale

The Gaming Motivation Scale (GAMS) (Lafrenière et al., 2012) is an 18-item questionnaire based on Deci and Ryan’s (1985) Self-Determination Theory of motivation. The GAMS includes six dimensions assessing intrinsic motivation, amotivation and each of the four kinds of extrinsic motivation (interrelated regulation, identified regulation, introjected regulation and external regulation) (Deci & Ryan, 1985). Each item is prompted by the statement “Why do you play videogames…?” and includes phrases such as “For the feeling of efficacy I experience when I play” measured on a seven-point Likert scale ranging from 1 (do not agree at all) to 7 (very strongly agree). Integrated regulation refers to making gaming part of one’s life and habits; Introjected regulation relates principally to using gaming for emotional needs; Identified regulation is about finding personal meaning, whereas External regulation is about achieving tangible rewards such as items, better scores. Overall, each of the six dimensions are interpreted separately on a scale from 3-21, with higher scores indicating a stronger presentation of the motivational type. The GAMS demonstrated good internal reliability (α = .84) within the current study.


Impulsivity was assessed using the 20-item version of the UPPS-P Impulsive Behaviour Scale (Billieux et al., 2012; McCredie et al., 2021). The scale captures: (1) negative urgency, (2) lack of perseverance, (3) lack of premeditation, (4) sensation seeking, and (5) positive urgency. Items are scored on a 4-point Likert scale ranging from 1 (Agree Strongly) to 4 (Disagree Strongly). Final scores range from 20-80, with higher ratings indicating lower impulsivity (although this was reversed to aid interpretation). Overall, internal reliability within the present sample was good (α = .84).

Investment in Digital Assets

Participants were asked to indicate if they owned crypto-currencies or NFTs. Those who said yes, were then asked about the types of cryptocurrencies or NFTs they owned; the amount of money they invested; and, the primary reason for investment. Participants were also asked if they would purchase a form of cryptocurrency in the future?”, measured on a four-point Likert scale ranging from 1 (very likely) to 4 (very unlikely).


The present study utilised an online survey methodology. Prior to the commencement of research, ethics approval was granted by the University of X Human Research Ethics Subcommittee (approval number 22/27). Initially, the study was advertised via the online survey platform Prolific (https://www.prolific.co/) that provided a brief overview of the research’s purpose and a pre-screening question. Consenting participants were directed to a survey hosted on Qualtrics (https://www.qualtrics.com/) and this took around 15-20 minutes. A small remuneration was provided for participation.

Analytical Approach

Analyses were completed using SPSS v.28. Data screening removed 36 participants who failed to meet validity checks, who responded too quickly (under 5 minutes) or produced patterned responses. Mean comparisons of psychometric scores across groups defined by their microtransaction purchases and/ or engagement in P2E gaming were made using independent samples t-tests and chi-squared tests to examine associations between groups and the purchase of digital assets. Each test was conducted after assumption testing, inspections of the data to ensure the robustness of testing (e.g., pooled estimates were used for t-test interpretation if Levene’s tests were significant).

Finally, given the interest in profiling gamers and the likely interdependence of characteristics, an exploratory cluster analysis was conducted. This proceeded in 2-stages following the recommendations of Hair et al. (1995). A hierarchical analysis was run initially to identify the number of clusters using inspection of the agglomeration values. This was followed by a K-means cluster analysis was conducted using Ward’s Method and squared Euclidian distance as the proximity measures to profile the clusters. The SPSS Analysis of Variance (ANOVA) profiling method was applied to examine how the resultant clusters differed on key variables in the study.


Overall descriptive statistics

An initial descriptive analysis was undertaken to examine participants’ level of engagement in various gaming related activities and provides a comparison between those who made microtransactions, played P2E, or engaged in both activities (as compared to those who did neither) (Table 2). Table 2 shows that over a third of respondents reported gaming at least 30 hours or more per week. A total of 29.4% were categorised as problem gamers on the Petry’s Internet Gaming Disorder (IGD) measure and 6.4% were classed as problem gamblers according to the (PGSI). There were 4.3% who scored positively on both measures. Half of the participants (49.1%) reported owning cryptocurrency, but only 7.7% owned non-fungible tokens (NFTs). The majority of respondents (82%) reported spending $20 or less per month on micro-transactions; 14% spent between $20-50; and around 4% spent more than $50. Of those who had spent money on NFTs, 75% had spend $200 at most on a single NFT, but 11% reported spending more than $500 on a single purchase.

Table 2 Engagement in Monetised Gaming and Gambling Activities

Table 3 summarises participant scores on individual difference measures. Participants generally scored higher on intrinsic motivation (M = 16.8) were generally than in validation studies (e.g., Billieux et al., 2012). Mean item scores for intrinsic motivation were 4.38 in the validation sample suggesting a total scale mean of 13.14, whereas. Participants also generally scored higher on impulsivity (M = 51.8) compared to a recent validation study of a similar cohort (Billieux et al., 2012) which reported a total mean value of 46.21 across the subscales. Mean scores for the externalising motivation subscales were also higher: in Billieux et al. (2012 mean item scores multiplied by 3 indicated subscale scores of 7.68 for Internalised regulation; 8.43 for Identified regulation; 6.24 for Introjected regulation and 10.62 for External regulation.

Table 3 Descriptive Statistics for the Motivation and Impulsivity Scales

Hypothesis testing

The first hypothesis was that individuals who made microtransactions and engaged in P2E games would score higher on Petry’s IGD Scale and the PGSI. T-test comparisons presented in Table 4 show that those who engaged in existing forms of monetisation (microtransactions) scored higher on Petry’s IGD and the PGSI than those who did not. Those who engaged in P2E gaming scored higher on the PGSI, but lower on Petry’s IGD as compared to non-P2E gamers. In other words, support for Hypothesis 1 was only confirmed for microtransaction purchasing, but not P2E. A second hypothesis was that both forms of monetisation would coincide with higher scores on intrinsic and extrinsic motivation (Hypothesis 2) as well as impulsivity (Hypothesis 3) Table 4 shows that intrinsic motivation scores did not differ based on whether a person had engaged in either form of monetised gaming. Three of the extrinsic motivational subscales also did not differ depending upon whether the person had made microtransaction purchases , but the External Regulation which relates to obtaining rewards in the game was higher for those who had made microtransactions which is generally consistent with Hypothesis 2. Comparisons based on whether respondents had engaged in P2E gaming showed significantly higher Introjected and External regulation scores for P2E gamers. As expected, impulsivity was significantly higher amongst those who engaged in microtransactions and P2E gaming. All effect sizes were small to moderate (see Table 4). These findings supported Hypothesis 3, but again showed a different pattern of results for P2E gaming. All of these results were found to hold after conducting logistic regressions (P2E and MT) as dependent variables or extrinsic or intrinsic motivation and with the number of hours spent gaming per week as a control variable entered in hierarchical models.

Table 4 T-test Comparisons of Psychometric Scores Defined by Monetised Gaming Involvement

NFTs, Cryptocurrency Ownership and P2E Gaming

The fourth aim was to determine if interest in digital assets differed depending on monetised gaming engagement. It was expected that individuals who currently own or have previously invested into NFTs and Cryptocurrency will be more likely to engage in P2E games than those who have not. In a sense, this was a validation test, as both of these asset types are usually required for engagement in P2E gaming. As predicted, statistically significant associations were observed between P2E engagement and owning Cryptocurrency, χ 2(1, N = 560) = 17.94, p < .01, as well as NFTs, χ 2(1, N = 560) = 62.13, p < .01. A greater proportion of individuals who engage in P2E owned Cryptocurrency (63.6%) and NFTs (22.1%) when compared to those who do not (Cryptocurrency = 26.2%, NFTs = 0%).

Further t-test analyses were undertaken to capture a more refined measurement of engagement in these activities. The results indicated that individuals who engage in P2E own more types of cryptocurrencies (M = 3.22, SD = 4.73) as compared to those who do not (M = 1.34, SD = 1.82), t (560) = 6.84, p < .01, d = 0.65. Similarly, those who engage in P2E spend more (or hypothetically would spend more) on NFTs (M = 88.96, SD = 104.36) as compared to those who do not (M = 57.40, SD = 37.11), t (560) = 5.28, p < .01, d = 0.50. Taken together, Hypothesis 4 was supported in that prior ownership of NFTs and cryptocurrencies, as well as the amount invested into these digital assets and future likelihood to invest, were all higher amongst individuals who engaged in P2E.

Cluster analysis

In phase two, the specific characteristics of these two groups were identified via a non-hierarchical (K-means) cluster analysis. The two clusters contained a substantial number of cases and captured much of the sample (Cluster 1, n = 236, Cluster 2, n = 308; comparisons of these two groups on the variables are provided in Table 5. As indicated, Cluster 1 appears to represent individuals who engaged in more P2E gaming, scored higher on problem gambling and gaming and were prepared to spend more on NFTs when compared to Cluster 2. Cluster 1 also generally had higher external regulation scores indicating that they were more motivated to play games because of tangible rewards.

Table 5 K-means Cluster Analysis


Overview of findings

This study examined the behavioural and individual difference profile of those with a greater interest in monetised gaming, with a particular focus on in-game microtransactions and Play-to-Earn (P2E) gaming. Several hypotheses were investigated which were partially or fully supported, but which highlighted potential differences between existing monetised gaming involvement and P2E. First, while we showed that microtransaction purchasing was associated with both higher problem gambling and IGD scores, engagement in P2E was only associated with problem gambling. Second, while differences in intrinsic motivation were not found to be associated with engagement in either activity, we observed that particular forms of extrinsic motivation (most notably External regulation based on a desire to obtain tangible rewards was higher for those who engaged in either form of monetised gaming). P2E gamers also scored higher on Introjected regulation which refers to a need to play regulators and the establishment of habitual behaviour. Third, the study confirmed that impulsivity would be higher amongst those who engaged in any form of monetised gaming. Fourth, P2E gamers were more likely to own and invest into non-fungible tokens (NFTs) and cryptocurrencies, as well as indicate a greater likelihood of future investment into these assets. Overall, it appeared that gamers fell into two distinct categories. In contrast to regular gamers, those who engaged in P2E scored higher on measures of problem gambling, were more extrinsically motivated and more involved in speculative, high-risk investment activities (e.g., NFTs and Cryptocurrency).

Analysis of findings

The findings in Hypothesis 1 relating to IGD and problem gambling scores are generally consistent with the current literature (Drummond & Sauer, 2018; Gibson et al., 2022; Zendle & Cairns, 2018; Zendle et al., 2020). For example, a recent systematic review indicated that the frequency and financial outlay of in-game microtransactions is positively associated with IGD and PGSI scores (Raneri et al., 2022). Considering that microtransactions are designed to enhance game play, and can involve gambling-like elements (e.g., loot boxes) (Zendle et al., 2019), it is unsurprising that this activity appeals to both highly active gamers and those inclined to problem gambling. On the other hand, the finding that only P2E gamers scored higher on the PGSI, but lower on Petry’s IGD when compared to non-P2E gamers appears inconsistent with linking IGD and monetised gaming engagement more broadly (Gibson et al., 2022). A potential explanation for this may be the structure of P2E games, which have a strong focus on monetised elements that may detract from genuine gaming enjoyment (Delic & Delfabbro, 2022), and, therefore, discourage those gamers who dislike this type of development in gaming.

The findings obtained in relation to motivation (Hypothesis 2) indicated that extrinsic motivation scores on the Gaming Motivation Scale (GAMS) were generally higher for both groups of monetised gamer with the largest differences observed for external regulation which relates to tangible goal seeking (Peracchia et al., 2019; Reid, 2012). Respondents appear to engage in monetised gaming out of a stronger to obtain tangible rewards of the game and this is consistent with findings from recent qualitative studies involving P2E gaming. A desire to earn money and additional in-game assets are often a reason for playing P2E games (De Jesus et al., 2022; Delic & Delfabbro, 2022) because this is one of the selling points for this activity. The lack of an association between monetised gaming and intrinsic motivation is also generally consistent with some qualitative observations relating to P2E gaming (Delic & Delfabbro, 2022). If the activity involves repetitive “grinding” (i.e., repeating the same actions) to obtain rewards, then it might not be surprising to find that players who commit large amounts of time to these activities are not necessarily doing so because they are finding it intrinsically enjoyable. Delic and Delfabbro note, for example, that many of the early blockchain games that have attracted periods of popularity (e.g., Cryptokitties or Axie Infinity) often feature quite rudimentary graphics and features compared to high quality conventional games built on modern development engines and which feature regular content updates and expansions.

Strong support was found for Hypothesis 3. Impulsivity, as measured by the Short UPPS-P Impulsive Behaviour Scale, was higher amongst those who engaged in microtransaction purchases when compared to those who did not. This finding directly supports Kim et al. (2017) who also observed higher impulsivity scores amongst gamers that made in-game purchases. These results can be attributed to the structure of microtransactions that often include predatory design features (King and Delfabbro, 2018) and dark patterns (Zagal et al., 2013), which serve to incite feelings of urgency and necessity amongst consumers. Further, considering that high impulsivity is associated with an impaired ability to cope with delayed gratification (Gassen et al., 2019), it is likely that such individuals may be more prone to making in-game purchases (rather than waiting for their in-game lives to recharge etc.). Similar arguments could be advanced for the positive correlation between impulsivity and interest in P2E gaming. Blockchain technology enables players to connect their digital wallets directly to the game which is not dissimilar to credit card to an online gambling site (López-Torres et al., 2021). This enables fast transactions which likely appeal to impulsive gamers who are already attracted to high-risk activities (e.g., gambling) (Zendle et al., 2019). However, unlike online gambling sites which often impose caps on the amount of money that can be spent or transferred into betting accounts (Auer et al., 2020; Broda et al., 2008), P2E currently exists without any regulatory limits. Therefore, cohorts with already low impulse control (i.e., adolescents) may be particularly attracted to this type of activity in the future.

Hypothesis 4 was supported as P2E gamers were more likely to own and invest more into NFTs and cryptocurrencies, as well as indicate a greater likelihood of future investment. This follows from the work of Delic and Delfabbro (2022) which reported that a high degree of market awareness and prior interest in the cryptocurrency market was a common thread amongst P2E gamers. These findings may be attributed to two key factors. First, as a majority of blockchain based P2E games require the user to link their digital wallet and purchase the in-game currency/NFT, proficiency in these tasks may act as a pre-requisite for new players (Vidal-Tomás, 2022). Second, those with stakes in digital investments are likely more interested in P2E gaming, as this activity allows assets such as NFTs to have a higher level of functionality (e.g., character NFTs). Taken together, these findings indicate that one of the main paths to P2E engagement is likely pre-existing involvement in digital asset investments.


From a theoretical perspective, research of this kind contributes to the ongoing debate within the literature as to whether monetised games should be considered a form of gaming or gambling. A total of 9.1% of P2E gamers scored in the problem range on the PGSI and these findings align with Zendle et al. (2020) and Macey and Hamari (2022), who have drawn attention to the increasing ‘gamblification’ of online games and the risks of features such as loot boxes to higher risk gamblers. The practical implications of this are twofold. First, given that P2E is a new class of activity, it is important for researchers to profile the behavioural characteristics of this population to better target consumer information or strategies to reduce the potential for harm. These strategies should consider: the risks associated with both gambling and speculative investment (King et al., 2019), including the potential to spend more than can be afforded; the high volatility of cryptocurrency prices; problems with the internal economics of these projects (e.g., inflationary tokens, over-supply of NFTs); and the potential for scams and asset loss (e.g., losing digital wallet keys). Second, these findings emphasise the need for greater regulatory attention towards the monetised gaming industry. The introduction of regulations surrounding P2E engagement (e.g., age requirements and disclosure of consumer information) could minimise the potential for harm and allow consumers safer access to the benefits of P2E.

Strengths and Limitations

Several limitations should be considered when interpreting the results. First, due to the study’s reliance on a self-report methodology, it is possible that participants may have provided a biased appraisal of their behaviour. For example, it is common for problem gamblers to exhibit denial and/or social desirability bias and misrepresent the amount of money they spend on gambling, or the extent to which their behaviours are problematic (Kuentzel et al., 2008). Second, the study’s cross-sectional design is limited in its ability to confidently draw causal inferences. Third, the analysis was limited by the significant overlap between the general monetised gaming and P2E gaming within this sample. This prevented the analysis of those who engaged in P2E or microtransactions exclusively, as well as the combination of these two groups, to develop an overall ‘monetised gamer’ variable. In future, participants should be recruited by cohort, ensuring an even representation of all gamer types.


The current study provides evidence that P2E gaming may have some specific features that set it apart from existing free-to-play models that include monetisation. In particular, we found P2E gamers to be significantly more extrinsically motivated and, as with those who are attracted to loot-boxes, to score higher on measures of problem gambling. The evidence here suggests that the most likely pathway into this activity is through an existing interest in cryptocurrency and NFTs (an activity which is correlated with gambling involvement, Mills & Nower, 2019). As a result, there is a need for education and awareness within the gaming community of the risks associated with these activities; in particular, whether P2E might lead to greater financial harm than conventional forms of gambling. Important insight may therefore need to be drawn from the existing gambling literature in practical and policy responses such as the importance of setting budgets, not chasing losses, avoiding highly risky gambles or trades (e.g., purchasing a highly volatile in-game currency) and not financially overcommitting oneself to the activity.