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Shaun S. Garea School of Psychology, Massey University, Palmerston North 4424, Manawatu, New Zealand

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James D. Sauer School of Psychological Sciences, University of Tasmania, Australia

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Lauren C. Hall School of Psychology, Massey University, Palmerston North 4424, Manawatu, New Zealand

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Matt N. Williams School of Psychology, Massey University, Palmerston North 4424, Manawatu, New Zealand

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Aaron Drummond School of Psychology, Massey University, Palmerston North 4424, Manawatu, New Zealand
School of Psychological Sciences, University of Tasmania, Australia

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Abstract

Background and Aims

Loot boxes are digital containers of randomised rewards available in many video games. Individuals with problem gambling symptomatology spend more on loot boxes than individuals without such symptoms. This study investigated whether other psychopathological symptomatology, specifically symptoms of obsessive-compulsive behaviour and hoarding may also be associated with increased loot box spending.

Methods

In a large cross-sectional, cross-national survey (N = 1,049 after exclusions), participants recruited from Prolific, living in Aotearoa New Zealand, Australia, and the United States, provided self-reported loot box spending, obsessive-compulsive and hoarding symptomatology, problem gambling symptomatology, and consumer regret levels.

Results

There was a moderate positive relationship between loot box spending and obsessive-compulsive symptoms and hoarding. Additionally, greater purchasing of loot boxes was associated with increased consumer regret.

Discussion and Conclusion

Results identified that those with OCD and hoarding symptomatology may spend more on loot boxes than individuals without OCD and hoarding symptomatology. This information helps identify disproportionate spending to more groups of vulnerable players and may assist in helping consumers make informed choices and also aid policy discussions around the potentialities of harm.

Abstract

Background and Aims

Loot boxes are digital containers of randomised rewards available in many video games. Individuals with problem gambling symptomatology spend more on loot boxes than individuals without such symptoms. This study investigated whether other psychopathological symptomatology, specifically symptoms of obsessive-compulsive behaviour and hoarding may also be associated with increased loot box spending.

Methods

In a large cross-sectional, cross-national survey (N = 1,049 after exclusions), participants recruited from Prolific, living in Aotearoa New Zealand, Australia, and the United States, provided self-reported loot box spending, obsessive-compulsive and hoarding symptomatology, problem gambling symptomatology, and consumer regret levels.

Results

There was a moderate positive relationship between loot box spending and obsessive-compulsive symptoms and hoarding. Additionally, greater purchasing of loot boxes was associated with increased consumer regret.

Discussion and Conclusion

Results identified that those with OCD and hoarding symptomatology may spend more on loot boxes than individuals without OCD and hoarding symptomatology. This information helps identify disproportionate spending to more groups of vulnerable players and may assist in helping consumers make informed choices and also aid policy discussions around the potentialities of harm.

Introduction

Loot boxes are digital containers found in video games and purchased with real-world money that grant randomised in-game rewards (Drummond & Sauer, 2018; Griffiths, 2018; Lemmens, 2022). Due to the randomised nature of the rewards, concerns have been raised about the psychological and legal similarities between loot boxes and conventional forms of gambling (Brooks & Clark, 2019; Derevensky & Griffiths, 2019; Drummond & Sauer, 2018; Drummond, Sauer, Hall, Zendle, & Loudon, 2020; Garea, Drummond, Sauer, Hall, & Williams, 2021; Kristiansen & Severin, 2020; Zendle, Walasek, Cairns, Meyer, & Drummond, 2021). Further, there is a consistent association between loot box spending and problem gambling symptomatology, confirmed by meta-analysis (Garea et al., 2021; see also Brooks & Clark, 2019; Drummond, Sauer, Ferguson, & Hall, 2020; Lemmens, 2022; Zendle & Cairns, 2018, 2019; Zendle, Cairns, Barnett, & McCall, 2020).

Although research in this area has largely focused upon problem gambling populations, other personality or mental health traits may also have an association with increased spending on loot boxes. Despite compelling theoretical rationale to believe that certain groups, such as individuals who feel compelled to complete collections and item sets, may overspend on loot boxes, to date other potential populations and have largely gone unexplored (Garea et al., 2021). One area ripe for investigation is obsessive-compulsive symptomatology and related disorders. Loot boxes provide items that are often collected, that complete sets, that are available for limited times and/or exclusively through purchasing (Zendle, Meyer, & Over, 2019). Thus, people with predispositions for collectionism, compulsive buying, excessive organisation, and other potentially maladaptive behaviours for buying habits may be likely to spend more on loot boxes and other collectable in-game items. Research has identified a variety of motivations for engaging with loot box purchasing among adolescents (Zendle et al., 2019). One such motivation which may be relevant in the context of individuals with predispositions toward collectionism is that completing collections is demonstrable motivation for at least some adolescent loot box buyers; driving purchasing habits (Zendle et al., 2019). Here, we examined how spending on loot boxes relates to compulsions to buy, collect, keep, and organise items; symptoms often found at clinically disordered levels for individuals with obsessive-compulsive disorder (OCD) and/or hoarding.

Why might loot box purchasing be associated with obsessive compulsive and/or compulsive spectrum symptomatology? Although OCD is a clinical diagnosis, like many clinical diagnoses it exists on a continuum. For individuals displaying sub-clinical levels of OCD symptomatology, like hoarding, there is a spectrum of symptom severity, with individuals experiencing symptoms which range from mild to extremely severe. As many loot boxes provide exclusive and/or limited access to in-game items (Zendle et al., 2019), players predisposed towards collecting items may feel pressured to engage with loot boxes in order to complete their in-game virtual item sets. In turn, this may prompt individuals with more severe compulsive symptoms to spend more money on loot boxes to complete these sets. Collecting can become maladaptive when it negatively impacts one's personal, social, and psychical environments. Players with obsessive-compulsive/compulsive spectrum symptomatology may feel compelled to spend more (disproportionately) than other players.

OCD as designated by the DSM-5 is where the presence of obsessions and/or compulsions, that are both recurrent and persistent, result in marked distress (American Psychiatric Association, 2013). Hoarding, traditionally considered part of OCD symptomatology, has been moved to a related subcategorised disorder in the DSM-5 (now within the “Obsessive Compulsive and Related Disorders” section). Thus, although hoarding constitutes a related but distinct clinical phenomenon, many of the psychometrically validated instruments, such as the Revised Obsessive-Compulsive Inventory (OCI-R) and Dimensional Obsessive-Compulsive Scale (DOCS), retain hoarding and other key dimensions within their measurement of OCD. As such, these measures remain appropriate as screening and diagnostic tools to identify these symptom clusters and their clinical symptom severity (Abramowitz, Abramowitz, Reimann, & McKay, 2020; Wootton et al., 2015).

Although many of the dimensions of OCD symptomatology may be relevant to the psychological risk factors for disproportionate spending on loot boxes, hoarding - traditionally linked to OCD but now listed in a distinct ‘compulsive spectrum disorder’ category in the DSM-5 (Kalogeraki & Michopoulos, 2017) – might place individuals at particular likelihood of increased spending. Clinically significant hoarding or compulsive-hoarding occurs in the general population at up to four times the prevalence rate of rarer disorders such as bipolar or schizophrenia (Pertusa et al., 2010), and is often a condition that is concealed from others by the sufferer. It consists of both a desire to acquire, and a refusal to discard items (Nordsletten & Mataix-Cols, 2012). This refusal to discard is often associated with a strong personal-emotional connection to items of little or no economic (or objectively emotional) value, and these items are then acquired and retained in such large numbers that they impacts one's physical living environment and impair the individual's ability to engage in normal living activities. Additionally, whilst strong emotional attachment to items is often present, there is also often substantial emotional and personal distressed caused to the individual because of the accumulation of the items (Frost & Hartl, 1996).

Whilst traditional hoarding has physical markers that can be identified (as above), digital hoarding presents differently. Digital hoarding does not result in the cluttering of one's physical environment, but excess digital accumulation can still negatively affect the accumulator (Neave, Briggs, McKellar, & Sillence, 2019). For example, obsessively collecting and organising thousands of digital images can impair an individual's ability to complete other (necessary) daily tasks, including those related to personal hygiene, due to the impact on their time/attention as opposed to their physical space (van Bennekom, Blom, Vulink, & Denys, 2015). Digital hoarding can be a relatively invisible condition: It is much more difficult for an observer to identify non-physical hoarding (cf. physical hoarding) and/or connect this behaviour to current distress/disorganisation, especially when the sufferer shows little-to-no insight into the issue (Sweeten, Sillence, & Neave, 2018). The nascent research into digital hoarding demonstrates the negative effects posed to individuals who hoard digitally, such as the inability to find relevant items due to excess clutter, the inability to resist taking or buying ‘everything’ (obligated collectionism), paralysis of choice, and neglect of work/social/personal hygiene (Neave et al., 2019; Pertusa et al., 2010; van Bennekom et al., 2015). However, digital hoarding in relation to video games specifically is an area in need of further study.

The current study

As the relationships between loot box spending and obsessive-compulsive/hoarding symptoms are largely unknown, we aimed to determine if any associations between OCD/related symptomatology and loot box spending could be identified. Our focus was largely upon loot box spending (because the randomised rewards from loot boxes reduce the volition that gamers with compulsive symptoms may have to complete sets in a targeted manner). However, collections of non-randomised rewards/digital items may plausibly be associated with this symptomatology. Thus, these variables were included for investigation.

Finally, although previous work has identified a small association between problem gambling symptoms and loot box spending, debate remains about whether loot boxes are harmful per se (e.g., McCaffrey, 2020). In response to such concerns, loot boxes themselves have been described to legislative bodies by game industry spokespeople as providing ‘surprise and delight’ to players, and as being entirely unrelated to gambling practices (Lum, 2018; Taylor, 2021). To test such claims, one potentially useful measure would be whether individuals experience regret at their previous purchasing decisions regarding loot boxes.

In relation to regret, existing research has identified in-game sales-mechanisms that ‘hide’ or obscure their potential financial impact (until players are psychologically ‘hooked’) as ‘predatory monetization’ (King & Delfabbro, 2018). These mechanisms often take the form of loot boxes and/or non-randomised rewards. Additionally, gamers have been found to report feelings of exploitation through exposure to such practices in their games (Petrovskaya & Zendle, 2022). Thus, we have included a scale of post-purchase regret to determine the extent to which people who spend more on loot boxes regret their purchases, and to help explore these concepts and their effects further. We do this using items included in the Post Purchase Regret scale (Lee & Cotte, 2009).

The present study looks for relationships between loot box spending and OCD and compulsive symptomatology, loot box spending and non-randomised reward spending, loot box spending and post purchase regret, and also whether hoarding (specifically) moderates the relationship between loot box spending and problem gambling.

Hypotheses

Based upon the reviewed literature, we pre-registered a number of hypotheses across three key categories: OCD and Hoarding, post purchase consumer regret, and replication hypotheses containing replications of previous problem gambling symptomatology and loot box spending associations.

OCD and hoarding hypotheses

With regard to OCD and Hoarding symptomology, we made several specific predictions. Specifically, we predicted:

  1. 1)That there will be a significant positive correlation between the amount of money participants report spending on purchasing loot boxes and scores of obsessive-compulsive symptomatology on the Obsessive-Compulsive Inventory Revised (OCI-R) scale.
  2. 2)That there will be a significant positive correlation between the amount of money participants report spending on purchasing loot boxes and the hoarding subscale scores of the OCI-R scale.
  3. 3)That hoarding, according to scores on the OCI-R subscale, will moderate the relationship between problem gambling symptomology as measured by the Problem Gambling Severity Index (PGSI) and loot box spending (See Fig. 1 below for diagram of all moderation hypotheses).
  4. 4)That the relationship between problem gaming symptomology (PGSI) and loot box spending will be more strongly positive for participants with higher OCI-R scores.
  5. 5)That there will be a significant positive relationship between the amount participants report spending on non-randomized rewards in video games and scores on the hoarding subscale of the OCI-R.
  6. 6)That there will be a significant relationship between the amount participants report spending on non-randomized rewards in video games and higher scores on the obsessing subscale of the OCI-R.
  7. 7)That there will be a significant relationship between the amount participants report spending on non-randomized rewards in video games and higher scores on the checking subscale of the OCI-R.
  8. 8)That there will be a significant relationship between the amount participants report spending on non-randomized rewards in video games and higher scores on the Symmetry and Completeness subscale of the Dimensional Obsessive-Compulsive Scale.
  9. 9)That symmetry and completeness, according to scores on the Dimensional Obsessive-Compulsive Scale subscale, will moderate the relationship between problem gambling symptomology; the relationship between problem gaming symptomology (PGSI) and loot box spending will be more strongly positive for participants with higher Dimensional Obsessive-Compulsive Scale scores.

Fig. 1.
Fig. 1.

Hypothesized moderation effects by hypothesis (Hx) of hoarding (using the hoarding subscale of the OCI-R), obsessive-compulsive symptomatology (using the full OCI-R scale), and symmetry and completeness (using the symmetry and completeness on the DOCS scale) on loot box monthly spending and problem gambling symptomatology (as measured by the PGSI)

Citation: Journal of Behavioral Addictions 12, 3; 10.1556/2006.2023.00038

Note: Hypotheses 3 and 4 were listed as-one in the pre-registration however they were intended to be separate and as such have been presented correctly here. Also, in our preregistration, the moderation hypotheses focusing on OCD, hoarding and Regret were listed as measuring both loot box spending & Risky Loot Box Index scores. This was an error and was intended to examine only loot box spending. Thus, analyses presented for these mentioned hypotheses report results using loot box spending as a variable only. The full dataset is openly available for reanalysis here:

https://osf.io/mwyvq/?view_only=70e70c1bb1f24f1ea66218136174356e.

Regret hypotheses

For post-purchasing consumer regret, we predicted that:

  1. 10)There will be a significant positive correlation between the amount of money participants report spending on purchasing loot boxes in the past month1 and their scores of regret on the post-purchase consumer regret scale.
  2. 11)That there will be a significant positive correlation between risky loot box use scores and scores of regret on the post-purchase consumer regret scale.

Replication hypotheses

Based upon the past literature, we predicted that:

  1. 12)There will be a significant positive correlation between the amount of money participants report spending on purchasing loot boxes in the past month and their problem gambling symptoms as measured by the Problem Gambling Severity Index (PGSI).
  2. 13)There will be a significant positive correlation between the amount of money participants report spending on purchasing loot boxes (scored from individual spending items) in the past month and their Risky Loot Box Index scores (Brooks & Clark, 2019).
  3. 14)There will be a significant relationship between problem gambling symptoms (scored on the PGSI) and the amount participants report spending on non-randomized rewards in video games, but this will be smaller than the association for loot boxes.

Method

This study was a survey investigation sampling populations across Aotearoa New Zealand, Australia, and the United States.

Pre-Registration

The complete pre-registration document (which includes exclusions, analyses plans and full questionnaires) can be accessed at the Open Science Framework website here: https://osf.io/g3d64/?view_only=312e46b91d93464cb29635650ad25cf8.

A-priori power analysis

We used the software program G*Power to conduct a power analysis. A sample size of 1,200 allows us to reliably detect correlations of r = 0.1 (the smallest correlation of interest) in the total sample with a target power of 0.8 at an alpha level of 0.05.

Design

We used a cross-sectional between-subjects correlational design featuring 68 questions hosted on Qualtrics' survey software. Primary measures were problem gambling symptoms on the Problem Gambling Severity Index (PGSI) which is a continuous scale, loot box spending in the past month (continuous), the Risky Loot Box Index which categorically measures loot box use on a 1–7 scale (between strongly agree and strongly disagree)., the Post-Purchase Consumer Regret scale which measures consumer regret on a Likert of 1–5 (strongly disagree-strongly agree) which can be collapsed into two main categories (regret of outcome, regret of process), the Obsessive-Compulsive Inventory Revised (OCI-R) which measures obsessive-compulsive behaviours/cognitions across six subscales (washing, obsessing, hoarding, ordering, checking, and neutralizing), and the Dimensional Obsessive-Compulsive Scale's subscale of ‘symmetry and completeness’ featuring 5 items on a Likert of 0–4 (with differing response options item-to-item). The present survey was modelled upon Drummond and Sauer's (2020) prior research. Thus, the internet gaming disorder symptomology (continuous) scale was also included for potential future exploratory analyses. However, no analyses utilising this scale were included in our pre-registration.

Participants

We recruited 1,201 participants across Aotearoa New Zealand, Australia, and the United States using Prolific. Participants (before exclusions were applied) had a mean age of 31.3 years (SD = 9.64) ranging from 18 to 74. Gender data revealed that 569 participants were male, 605 were female, 20 were non-binary, 2 preferred not to say, 4 reporting as ‘other’, and one participant not answering the question. Note that we had pre-registered the collection of 1,200 participants but received 1,201 responses due to software error. Analyses excluding the last recruited participant did not qualitatively alter the results.

Exclusions

Our pre-registered protocol included a number of exclusion criteria. We planned to exclude any mischievous response on the gender question, such as “Apache Attack Helicopter”. We inspected the gender data for non-serious answers and found no evidence for mischievous responding. Thus, no specific exclusions were made on this basis. We excluded data from 29 participants who failed to correctly answer our attention checks – specifically if they answered either a) anything other than 4 for the question “What is 2 + 2?”; b) anything other than 3 for the question “Please respond 3 to this question.”; or c) any participant who answered “true” to the question “I once owned a three-headed dog”. Ninety-one participants were excluded for indicating that they had not played video games within the last month (i.e., they answered “0, never”). Thirteen participants were excluded for indicating that they had spent more than $1,000 on loot boxes in the past month (which we pre-registered would be deemed either a non-serious and/or an extreme response). Following the above exclusions we excluded, based on Tabachnick, Fidell, and Ullman (2007), any participant who indicated that they had spent ± 3.29 Standard Deviations from the mean ($21.5USD, SD = $64.2USD) on loot boxes in the past month as outliers. Eighteen participants were excluded for exceeding this $232.718USD cut-off (set after examining the data). One participant was excluded for failing to respond to at least 75% of the PGSI questions, or 75% of the post purchased consumer regret questions, or 75% of the OCI-R questions. These exclusions resulted in a final sample size of n = 1, 049 (total exclusions equalling 152 from a 1,201 sample).

We made one minor deviation from our pre-registered exclusion criteria. We had initially pre-registered that we planned to exclude any participant who did not spend any money on loot boxes. However, this was a mistake in our pre-registration documentation and was inappropriate for two reasons. First, applying the ‘never spent money on loot boxes’ exclusion took our sample from n = 1, 049 to n = 454, drastically reducing our statistical power to detect differences. Second, part of the aim of our study was to investigate the associations between OCD symptomatology and loot box spending in the population, which includes people who do not purchase loot boxes. Thus, we did not apply this exclusion criteria to the analyses reported herein. Using the alternative exclusion criteria did not substantively alter the results. However, for transparency we provide the full dataset for reanalysis, and analyses employing this exclusion criteria in supplementary analyses. We also note any differences between those analyses in the main text below which exceeded a small difference in effect size magnitude, defined here and in the literature as being any difference between analyses of greater than r = 0.10 (Cohen, 1992), or if they altered the significance of the effect. To foreshadow, analyses were for the most part consistent across exclusion criteria, and there were relatively few instances where inconsistency occurred.

Measures

Problem Gambling Symptoms

Problem Gambling Symptoms were measured using the Problem Gambling Severity Index (PGSI). The PGSI is a 9 item scale which asks participants how frequently in the past 12 months they have engaged in potentially problematic gambling behaviours on a scale from 0 (never) to 3 (almost always). Higher scores indicating stronger problem gambling symptoms (Holtgraves, 2009). Scores totals tallied accounting for any reverse coding. Example items were “thinking about the last 12 months, have you bet more than you could really afford to lose?”; and “still thinking about the last 12 months, have you needed to gamble with larger amounts of money to get the same feel of excitement?”. Cronbach's α for the PGSI scale was 0.947.

Risky loot box engagement

Risky loot box engagement was measured by the Risky Loot Box Index. The Risky Loot Box Index consists of 5 items outlining risky loot box engagement with higher scores relating to higher risk engagement (Brooks & Clark, 2019) on a scale from 1 (strongly disagree) to 7 (strongly agree). Higher scores indicating stronger risky loot box engagement. Example items: “I frequently play games longer than I intend to, so I can earn loot boxes.” And “I have put off other activities, work, or chores to be able to earn or buy more loot boxes.” Cronbach's α for the Risky Loot Box Index scale was 0.915. Note that the Risky Loot Box Index has been amended from a 1–5 in its original form to a 1–7 scale here. This is due to the 1–7 scale being used in previous studies which were extended and replicated within the present study (e.g., Drummond et al., 2020). These changes were initially made in line with recommendations from Cox (1980), and Chyung, Roberts, Swanson, and Hankinson (2017), that employing a greater number of response options on Likert-type scales (ideally 7–9 items) can improve discrimination between participant responses.

Consumer regret

Consumer regret was measured by the Post Purchase Consumer Regret Scale. The Post Purchase Consumer Regret measures regret after spending across two key domains; regret of outcome, and regret of process, and is a 16 item instrument featuring a Likert scale of 1 (strongly disagree) to 5 (strongly agree). An additional response option was added to this measure for this study to screen for never having purchased loot boxes and which was coded as ‘0’. Higher scores indicate higher post-purchase consumer regret (Lee & Cotte, 2009). Example of (amended to include loot box) items include “I feel that I did not put enough consideration into buying Loot Boxes”, and “I regret purchasing as many Loot Boxes as I did in the past month.” Cronbach's α for the Post Purchase Consumer Regret scale was 0.987.

Obsessive-compulsive symptoms

Obsessive-compulsive symptoms were measured using the Obsessive-Compulsive Inventory (Revised). The OCI-R scores across several subscales focusing on distinct aspects of obsessive-compulsive behaviours; washing, obsessing, hoarding, ordering, checking, and neutralizing (Foa et al., 2002). This scale features 18 items on a Likert-type scale of 0 (not at all) to 4 (extremely). Scores on this scale are tallied for a total score. Examples of some items are “I have saved up so many things that they get in the way.”, and “I check things more often than necessary.” Cronbach's α for the OCI-R scale was 0.929.

Additional obsessive-compulsive symptoms

Additional obsessive-compulsive symptoms as identified by the ‘symmetry and completeness’ subscale from Dimensional Obsessive-Compulsive Scale (DOCS) were also included. The DOCS subscale features 5 items using a 5 point Likert-type scale from 0 to 4 with differing response options item to item (Abramowitz et al., 2010). Scores from this measure were tallied (as subgroup only). Example items include “About how much time have you spent each day with unwanted thoughts about symmetry, order, or balance and with behaviours intended to achieve symmetry, order or balance?” and “To what extent have you been avoiding situations, places or objects associated with feelings that something is not symmetrical or “just right?”. Cronbach's α for this DOCS (Symmetry subscale) was 0.878.

Loot box spending in the past month

Loot box spending in the past month was recorded by using a continuous measure asking how much money participants had spent on loot boxes in the last month (in their native currency).

Non-randomised reward spending in the past month

Spending was recorded by using a continuous measure asking how much money participants had spent on loot boxes in the last month (in their native currency).

NOTE: All currencies not reported in US dollars (USD; assessed by linking with the survey country item) were converted to US dollars using the exchange rates from XE.com and PountsterlingLive.com using the mid-day average (the difference between these two resources was minimal; for New Zealand currency, 1NZD = either 0.7093 or 0.7109 USD's, and for Australian currency 1AUD = 0.7390 or 0.7400 USD's). Rates were taken and applied for the date and afternoon that participants completed the survey (2 September 2021).

Ethics

Approval for survey data collection for this study was granted by Massey University's Human Ethics Committee, Approval number: SOB 21/08. All subjects were informed about the study and provided informed consent.

Results

The data for this study is open for public access and analysis here: https://osf.io/mwyvq/

Our pre-registration document outlined that we would exclude participants who had not spent real money on loot boxes (See point 22.1.5 of pre-registration document). However, as noted earlier, this criterion was a mistake in our pre-registration document and we did not apply this exclusion criterion for several reasons. When this filter was used, despite reducing the overall sample size, most effects were found to be qualitatively similar or slightly stronger. Where results across analyses are substantially different – identified by a difference of an r larger than 0.1 or a change in statistical significance – then both sets of results will be presented herein.

As loot box spending data showed strong skewness/kurtosis, all hypotheses are reported in the main text body using Spearman's rho correlations as specified in our preregistration document (Spearman's were used over Pearson's when spending data showed a high degree of skewness or kurtosis >2). Analyses employing Pearson's correlations can be found in the supplementary materials available online.

OCD & hoarding hypotheses

Correlation hypotheses

Table 1 shows the associations between loot box spending, OCD symptomatology scores, and hoarding. Associations were small-to-moderate in size for loot box spending and OCI-R scores (r. = 0.324, p < 0.001 (H1)) and loot box spending and the hoarding subscale of the OCI-R (r. = 0.227, p ≤ 0.001 (H2)).

Table 1.

Associations (Spearman's Rho) between loot box spending, OCD symptomatology scores, hoarding, and consumer regret

MeasureLoot Box Monthly Spendingp
OCI-R0.324<0.001
Hoarding*0.227<0.001
Post Purchase Consumer Regret0.437<0.001

* The hoarding subscale of the OCI-R

Table 2 shows the associations between non-randomised reward spending and the compulsive symptomatology scores of hoarding, obsessing and checking, and symmetry and completeness. There were consistent, significant small-to-moderate associations between our individual difference variables and non-randomised reward spending. Additionally, a medium relationship was found with problem gambling symptoms which interestingly was slightly larger than what was observed between loot box spending and problem gambling (See Section 3.3).

Table 2.

The association (Spearman's Rho) between Non-randomised reward spending, OCD symptomatology scores, and hoarding

MeasureNon-Randomised Reward Monthly Spendingp
Hoarding10.288<0.001
Obsessing20.180<0.001
Checking30.319<0.001
Symmetry & Completeness0.243<0.001

1 The hoarding subscale of the OCI-R.

2 The obsessing subscale of the OCI-R.

3 The checking subscale of the OCI-R

We also found small-to-moderate associations between non-randomized reward spending and a) the hoarding subscale of the OCI-R, r. = 0.288, p ≤ 0.001 (H5); b) the obsessing subscale of the OCI-R, r. = 0.180, p ≤ 0.001 (H6); c) the checking subscale of the OCI-R, r. = 0.319, p ≤ 0.001 (H7); and d) the completeness subscale of the Dimensional Obsessive-Compulsive scale, r. = 0.243, p ≤ 0.001 (H8).

Moderation hypotheses

All three hypothesised moderating relationships were supported by the data with the hoarding subscale of the OCI-R moderating the relationship between PGSI and loot box spending (H3); full OCI-R scores moderating the relationship between PGSI and loot box spending (H4); and the symmetry and completeness subscale of the Dimensional Obsessive-Compulsive scale moderating the relationship between PGSI and loot box spending (H9). Table 3 shows the inferential statistics for each analysis.

Table 3.

Moderation results by hypothesis in relation to loot box monthly spending including main and interaction effects plus median-split results

VariablesbpAbove MedianBelow Median
rprp
Hypothesis H3:
PGSI Total2.303<0.001
OCIR Hoarding Subscale1.583<0.001
PGSI * OCI-R Hoarding0.444<0.0010.475<0.0010.227<0.001
Hypothesis H4:
PGSI Total1.731<0.001
OCI-R Total0.420<0.001
PGSI * OCI-R Total0.089<0.0010.5<0.0010.166<0.001
Hypothesis H9:
PGSI Total3.291<0.001
DOCS Total−0.1170.652
PGSI * Symmetry & Completeness0.1140.0090.482<0.0010.266<0.001

Note. Median split controlling for age and gender. * = Interaction.

Figure 2 shows that for OCI-R hoarding (H3/Panel A), total OCI-R (H4/Panel B) and DOCS scores (Panel C), the relationship between PGSI symptomatology and loot box spending was stronger for participants above the median score on the moderator variable. However, in all cases, when participants who never purchased loot boxes were excluded from analyses, these moderating effects became non-significant, b < 0.208, p > 0.152.

Fig. 2.
Fig. 2.

Three simple slope plots outlining: Panel A) Moderation effects between loot box spending, problem gambling symptoms (PGSI) and hoarding subscale scores from the OCI-R. Panel B) Moderation effects between loot box spending, problem gambling symptoms (PGSI) and total scores from the OCI-R. Panel C) Moderation effects between loot box spending, problem gambling symptoms (PGSI) and DOCS symmetry and completeness scores

Citation: Journal of Behavioral Addictions 12, 3; 10.1556/2006.2023.00038

Regret hypotheses

As previously reported in Table 1, there was a significant, moderate association between loot box spending and consumer regret, rs = 0.437, p < 0.001 (H10). When participants who had never purchased loot boxes were excluded from the analyses, the association between consumer regret and loot box spending remained significant but was reduced in magnitude, rs = 0.250, p < 0.001. This may be due to participants who did not purchase loot boxes being unable to regret said loot box purchases (i.e., exaggerating the association between lack of regret and low levels of spending).

Additionally, results for risky loot box use scores and consumer regret showed a significant and strong positive correlation with a Spearman's rho of rs = 0.610, p < 0.001 (H11). Thus, participants who engaged with loot boxes with greater risk tended to also more strongly regret their purchases. Like the association between consumer regret and spending above, this effect was markedly reduced (though remained significant) when participants who had not purchased loot boxes were excluded from the analysis, rs = 0.340, p < 0.001. Again, this difference may be explained in part due to a lack of regret for loot box purchases among those who did not purchase them.

Replication hypotheses

We also undertook analyses to replicate previously found associations between problem gambling symptomatology, risky loot box engagement and monthly loot box spending. Replicating prior work, we found a significant moderate-to-strong association between monthly loot box spending and PGSI, rs = 0.418, p < 0.001 (H12). We also replicated the previously found moderate-strong association between Risky Loot Box Index scores and monthly loot box spending, rs = 0.529, p < 0.001 (H13). Monthly spending on non-randomised rewards were also significantly associated with problem gambling symptoms, with a moderate-to-strong effect size, rs = 0.465, p < 0.001 (H14).

Additional analyses

Controlling for age and gender produced negligible differences in core correlations. Thus, the associations did not appear to be due to age or gender. This adds confidence that our core associations of interest are not spuriously produced by demographic characteristics (Wysocki, Lawson, & Rhemtulla, 2022).

Exploratory analyses

As identified, non-randomised reward monthly spending matched or exceeded some effect sizes in relation to loot box spending and other measures. However this was not listed as an investigation avenue in our pre-registration. Accordingly, to present a clearer picture, a full correlation table is presented here for all non-randomised reward spending results (See Table 4). Additionally, please see the supplementary materials (Supp. Tables S1 and S2 for complete correlation matrix’ across all measures).

Table 4.

The association (Spearman's Rho) between Non-randomised reward spending and all other measures for exploratory analyses

MeasureNon-randomised Reward Monthly Spendingp
LB Monthly Spending0.594<0.001
PGSI Scores0.465<0.001
RLBI Scores0.497<0.001
OCIR Scores0.358<0.001
Hoarding Scores10.288<0.001
Obsessing Scores20.180<0.001
Checking Scores30.319<0.001
Symmetry and Completeness0.243<0.001

1 The hoarding subscale of the OCI-R.

2 The obsessing subscale of the OCI-R.

3 The checking subscale of the OCI-R

Discussion

This study investigated the relationships between loot box spending, problem gambling symptoms, consumer regret, non-randomised reward spending, and OCD and hoarding symptomology in samples from Aotearoa New Zealand, Australia, and the United States. In addition to replicating the relationship between Problem Gambling Symptomatology and loot box spending found in other studies, our study found novel associations between OCD symptomatology, Hoarding symptomatology, and loot box spending. Participants with higher OCD/compulsive symptomatology appear to also disproportionately engage in higher loot box and non-randomised reward spending. We also identified a novel association between loot box spending and consumer regret, showing that people who spend more on loot boxes also regret their spending more than those who spend less on loot boxes. Notably, the relationship between risky loot box engagement and regret was stronger still. The effect sizes for these associations all exceeded guidelines for the minimum effect sizes of interest (Ferguson, 2009). Being the first demonstration of this association, however, this finding obviously warrants further replication.

Hoarding (measured by the OCI-R scale) and obsessive-compulsive symptomatology (measured by both the OCI-R and Symmetry and Completeness scales) moderated the associations between problem gambling symptoms and loot box spending. The relationship between problem gambling symptoms and loot box spending was stronger for those high in hoarding/OCD scores compared to those low in hoarding scores. This indicates that individuals with comorbidities of problem gambling and OCD/compulsive symptomatology may be particularly likely to spend more on loot boxes.

It is important to acknowledge that a major limitation of the current research is that it is correlational, and we therefore cannot determine the directionality of the effect/s. Perhaps those with OCD are actually more likely to purchase loot boxes (perhaps to hoard items or due to a desire for completeness or symmetry in their collections). Alternatively, exposure to loot boxes and item ‘collection’ in video games may perhaps increases/creates OCD symptoms and/or symptom intensity. Given the typically small effects of media on psychological outcomes, we suspect that the latter direction of the relationship may be less likely. However, it is also possible that a third variable may be affecting both OCD symptoms and loot box spending habits.

Another important, novel finding of the present research is that participants who spend more money purchasing loot boxes tended to regret their purchases more than those who spend less purchasing loot boxes: Loot box spending is associated with regret. This finding suggests that due to loot boxes awarding players with mostly low value, common items (Zendle et al., 2020), players who are spending in order to attain high rarity items will be often regretting their purchases. The finding that increased regret was associated with increased loot box spending highlights that consumers are not always happy with their loot box purchases. This may be due to the inability to know precisely what it is that they are getting prior to purchase, and this in part may be driven by many of the mechanisms of sale being engineered to encourage reflexive and emotive (conditioned) spending over considered and reasoned decision making (Derevensky & Griffiths, 2019). The associations between spending and regret, and risky loot box engagement and regret, strongly contrasts with gaming-industry descriptions of loot boxes as “surprise and delight” mechanisms, since the emotion of delight is seemingly antithetical to the affective response of regret (Lum, 2018; Taylor, 2021). After opening a loot box, and perhaps due to the rarity of highly desirable items (and the corresponding increased probability of receiving non-desired items), consumers are perhaps likely to find items they did not desire, and this results in immediate disappointment, and thus regret. Further research investigating whether consumer regret is similar or divergent across different spending domains or applications of randomised and non-randomised items is also a worthy goal. However, in order to accurately compare across domains and cultures, a wider understanding of the psychometric properties of, and norms for, the PPCR would likely be required.

Non-randomised reward purchasing was not a primary focus of the current study. However, it is worth noting that somewhat unexpectedly, exploratory analyses comparing post purchase consumer regret findings with non-randomised reward spending we found an almost identical result to regret/loot box spending. This suggests that non-randomised in game purchases may also be associated with regret for consumers. There are several reasons why this might occur. First, there appears to be some collinearity in the data – with those with higher non-randomised reward spending also scoring higher on problem gambling symptomology as well as on loot box spending. Such overlap in symptomatology requires further research to fully understand. Second, it is possible that participants with OCD symptomatology may overspend on in-game purchases to collect items irrespective of whether such items are randomised or not. Further research is required to replicate and further examine this finding of regret and non-randomised reward spending.

Whilst existing studies have found clear positive small relationships between problem gambling symptomology and loot box use, this study broadens our understanding of this phenomenon as it relates to OCD symptoms. People who score higher on obsessive compulsive scales spend more money on loot boxes, and those who spend more money on loot boxes, and those that spend more money on non-randomised reward purchases tend to regret those purchases more. In turn, this spending appears to be associated with increased consumer regret. Further research to examine this phenomenon using greater control variables and clinical diagnoses would be helpful, as would longitudinal designs.

Funding sources

Supported by the Marsden Fund Council from Government funding, managed by Royal Society Te Apārangi; MAU1804.

Authors' contribution

Study concept and design: SSG, JDS, AD. Data analysis: SSG, AD. Interpretation of Data: SSG, JDS, LCH, MNW, AD. Obtained the funding: AD, JDS. Data collection: SSG, LCH. Statistical analysis: SSG, AD. Study supervision, AD, MNW, JDS. Wrote the Manuscript: SSG. Edited the Manuscript: SSG, JDS, LCH, MNW, AD. The authors of this study take responsibility for the integrity of data and data interpretation in this paper.

Conflicts of interest

The authors declare no conflicts of interest.

Data availability

The data from this research is freely available for viewing and re-analyses here: https://osf.io/mwyvq/.

Acknowledgements

The authors would like to thank Christopher J. Ferguson for their feedback on an earlier version of this paper.

Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1556/2006.2023.00038.

References

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    • Search Google Scholar
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    • Search Google Scholar
    • Export Citation
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    • Search Google Scholar
    • Export Citation
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    • Search Google Scholar
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    • Search Google Scholar
    • Export Citation
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    • Search Google Scholar
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  • Pertusa, A., Frost, R. O., Fullana, M. A., Samuels, J., Steketee, G., Tolin, D., … Mataix-Cols, D. (2010). Refining the diagnostic boundaries of compulsive hoarding: A critical review. Clinical Psychology Review 30(4), 371386. https://doi.org/10.1016/j.cpr.2010.01.007.

    • Search Google Scholar
    • Export Citation
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    • Search Google Scholar
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  • Sweeten, G., Sillence, E., & Neave, N. (2018). Digital hoarding behaviours: Underlying motivations and potential negative consequences. Computers in Human Behavior, 85, 5460. https://doi.org/10.1016/j.chb.2018.03.031.

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Supplementary Materials

  • Abramowitz, A., Abramowitz, J. S., Reimann, B. C., & McKay, D. (2020). Severity benchmarks and contemporary clinical norms for the Obsessive-Compulsive Inventory-Revised (OCI-R). Journal of Obsessive-Compulsive and Related Disorders, 27, 100557. https://doi.org/10.1016/j.jocrd.2020.100557.

    • Search Google Scholar
    • Export Citation
  • Abramowitz, J. S., Deacon, B. J., Olatunji, B. O., Wheaton, M. G., Berman, N. C., Losardo, D., … Hale, L. R. (2010). Assessment of obsessive-compulsive symptom dimensions: Development and evaluation of the dimensional obsessive-compulsive scale. Psychological Assessment, 22(1), 180198. https://doi.org/10.1037/a0018260.

    • Search Google Scholar
    • Export Citation
  • American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). https://doi.org/10.1176/appi.books.9780890425596.dsm05.

    • Search Google Scholar
    • Export Citation
  • Brooks, G. A., & Clark, L. (2019, Sep). Associations between loot box use, problematic gaming and gambling, and gambling-related cognitions. Addictive Behaviors, 96, 2634. https://doi.org/10.1016/j.addbeh.2019.04.009.

    • Search Google Scholar
    • Export Citation
  • Chyung, S. Y., Roberts, K., Swanson, I., & Hankinson, A. (2017). Evidence‐based survey design: The use of a midpoint on the Likert scale. Performance Improvement, 56(10), 1523. https://doi.org/10.1002/pfi.21727.

    • Search Google Scholar
    • Export Citation
  • Cohen, J. (1992). A power primer. Psycholigcal Bulletin, 112(1), 155159. https://doi.org/10.1037/0033-2909.112.1.155.

  • Cox, E. P., III (1980). The optimal number of response alternatives for a scale: A review. Journal of Marketing Research, 17(4), 407422. https://doi.org/10.2307/3150495.

    • Search Google Scholar
    • Export Citation
  • Derevensky, J. L., & Griffiths, M. D. (2019). Convergence between gambling and gaming: Does the gambling and gaming industry have a responsibility in protecting the consumer? Gaming Law Review, 23(9), 633639. https://doi.org/10.1089/glr2.2019.2397.

    • Search Google Scholar
    • Export Citation
  • Drummond, A., & Sauer, J. D. (2018, Aug). Video game loot boxes are psychologically akin to gambling. Nature Human Behaviour, 2(8), 530532. https://doi.org/10.1038/s41562-018-0360-1.

    • Search Google Scholar
    • Export Citation
  • Drummond, A., Sauer, J. D., Ferguson, C. J., & Hall, L. C. (2020). The relationship between problem gambling, excessive gaming, psychological distress and spending on loot boxes in Aotearoa New Zealand, Australia, and the United States—A cross-national survey. Plos One, 15(3), e0230378. https://doi.org/10.1371/journal.pone.0230378.

    • Search Google Scholar
    • Export Citation
  • Drummond, A., Sauer, J. D., Hall, L. C., Zendle, D., & Loudon, M. R. (2020). Why loot boxes could be regulated as gambling. Nature Human Behaviour. https://doi.org/10.1038/s41562-020-0900-3.

    • Search Google Scholar
    • Export Citation
  • Ferguson, C. J. (2009). An effect size primer: A guide for clinicians and researchers. Professional Psychology: Research and Practice, 40(5), 532538. https://doi.org/10.1037/a0015808.

    • Search Google Scholar
    • Export Citation
  • Foa, E. B., Huppert, J. D., Leiberg, S., Langner, R., Kichic, R., Hajcak, G., & Salkovskis, P. M. (2002). The Obsessive-Complusive Inventory: Development and validation of a short version. Psychological Assessment, 14(4), 485495. https://doi.org/10.1037//1040-3590.14.4.485.

    • Search Google Scholar
    • Export Citation
  • Frost, R. O., & Hartl, T. L. (1996). A cognitive-behavioral model of compulsive hoarding. Behaviour Research and Therapy, 34(4), 341350. https://doi.org/10.1016/0005-7967(95)00071-2.

    • Search Google Scholar
    • Export Citation
  • Garea, S. S., Drummond, A., Sauer, J. D., Hall, L. C., & Williams, M. N. (2021). Meta-analysis of the relationship between problem gambling, excessive gaming and loot box spending. International Gambling Studies, 21(3), 460479. https://doi.org/10.1080/14459795.2021.1914705.

    • Search Google Scholar
    • Export Citation
  • Griffiths, M. D. (2018). Is the buying of loot boxes in video games a form of gambling or gaming? Gaming Law Review, 22(1), 5254. https://doi.org/10.1089/glr2.2018.2216.

    • Search Google Scholar
    • Export Citation
  • Holtgraves, T. (2009, Mar). Evaluating the problem gambling severity index. Journal of Gambling Studies, 25(1), 105120. https://doi.org/10.1007/s10899-008-9107-7.

    • Search Google Scholar
    • Export Citation
  • Kalogeraki, L., & Michopoulos, I. (2017). Hoarding disorder in DSM-5: Clinical description and cognitive approach. Psychiatriki, 28(2), 131141. https://doi.org/10.22365/jpsych.2017.282.131.

    • Search Google Scholar
    • Export Citation
  • King, D. L., & Delfabbro, P. H. (2018). Predatory monetization schemes in video games (e.g. ‘loot boxes’) and internet gaming disorder. Addiction, 113(11), 19671969. https://doi.org/10.1111/add.14286.

    • Search Google Scholar
    • Export Citation
  • Kristiansen, S., & Severin, M. C. (2020, Apr). Loot box engagement and problem gambling among adolescent gamers: Findings from a national survey. Addictive Behaviors, 103, 106254. https://doi.org/10.1016/j.addbeh.2019.106254.

    • Search Google Scholar
    • Export Citation
  • Lee, S. H., & Cotte, J. (2009). Post-purchase consumer regret: Conceptualization and development of the PPCR scale. Advances in Consumer Research, 36, 456462.

    • Search Google Scholar
    • Export Citation
  • Lemmens, J. S. (2022). Play or pay to win: Loot boxes and gaming disorder in FIFA ultimate team. Telematics and Informatics Reports, 8, 100023. https://doi.org/10.1016/j.teler.2022.100023.

    • Search Google Scholar
    • Export Citation
  • Lum, P. (2018, 16th Aug). Video game loot boxes addictive and a form of ‘simulated gambling’, Senate inquiry told. The Guardian. https://www.theguardian.com/games/2018/aug/17/video-game-loot-boxes-addictive-and-a-form-of-simulated-gambling-senate-inquiry-told.

    • Search Google Scholar
    • Export Citation
  • McCaffrey, M. (2020). A cautious approach to public policy and loot box regulation. Addictive Behaviours, 102, Article 106136. https://doi.org/10.1016/j.addbeh.2019.106136.

    • Search Google Scholar
    • Export Citation
  • Neave, N., Briggs, P., McKellar, K., & Sillence, E. (2019). Digital hoarding behaviours: Measurement and evaluation. Computers in Human Behavior, 96, 7277. https://doi.org/10.1016/j.chb.2019.01.037.

    • Search Google Scholar
    • Export Citation
  • Nordsletten, A. E., & Mataix-Cols, D. (2012, Apr). Hoarding versus collecting: Where does pathology diverge from play? Clinical Psychology Review, 32(3), 165176. https://doi.org/10.1016/j.cpr.2011.12.003.

    • Search Google Scholar
    • Export Citation
  • Pertusa, A., Frost, R. O., Fullana, M. A., Samuels, J., Steketee, G., Tolin, D., … Mataix-Cols, D. (2010). Refining the diagnostic boundaries of compulsive hoarding: A critical review. Clinical Psychology Review 30(4), 371386. https://doi.org/10.1016/j.cpr.2010.01.007.

    • Search Google Scholar
    • Export Citation
  • Petrovskaya, E., & Zendle, D. (2022). Predatory monetisation? A categorisation of unfair, misleading and aggressive monetisation techniques in digital games from the player perspective. Journal of Business Ethics, 181, 10651081. https://doi.org/10.1007/s10551-021-04970-6.

    • Search Google Scholar
    • Export Citation
  • Sweeten, G., Sillence, E., & Neave, N. (2018). Digital hoarding behaviours: Underlying motivations and potential negative consequences. Computers in Human Behavior, 85, 5460. https://doi.org/10.1016/j.chb.2018.03.031.

    • Search Google Scholar
    • Export Citation
  • Tabachnick, B. G., Fidell, L. S., & Ullman, J. B. (2007). Using multivariate statistics (Vol. 5). Pearson.

  • Taylor, M. (2021). FIFA's Ultimate Team is a 'long way' from gambling, says former EA Sports president. https://www.pcgamer.com/au/fifas-ultimate-team-is-a-long-way-from-gambling-says-former-ea-sports-president/.

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  • van Bennekom, M. J., Blom, R. M., Vulink, N., & Denys, D. (2015, Sep). A case of digital hoarding. British Medical Journal Case Reports, 14. https://doi.org/10.1136/bcr-2015-210814.

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  • Wootton, B. M., Diefenbach, G. J., Bragdon, L. B., Steketee, G., Frost, R. O., & Tolin, D. (2015). A contemporary psychometric evaluation of the obsessive compulsive inventory—Revised (OCI-R). Psychological Assessment, 27(3), 874882. https://doi.org/10.1037/pas0000075.

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  • Wysocki, A. C., Lawson, K. M., & Rhemtulla, M. (2022). Statistical control requires causal justification. Advances in Methods and Practices in Psychological Science, 5(2). https://doi.org/10.1177/25152459221095823.

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  • Zendle, D., & Cairns, P. (2018). Video game loot boxes are linked to problem gambling: Results of a large-scale survey. Plos One, 13(11), e0206767. https://doi.org/10.1371/journal.pone.0206767.

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  • Zendle, D., Cairns, P., Barnett, H., & McCall, C. (2020). Paying for loot boxes is linked to problem gambling, regardless of specific features like cash-out and pay-to-win. Computers in Human Behavior, 102, 181191. https://doi.org/10.1016/j.chb.2019.07.003.

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  • Zendle, D., Walasek, L., Cairns, P., Meyer, R., & Drummond, A. (2021). Links between problem gambling and spending on booster packs in collectible card games: A conceptual replication of research on loot boxes. Plos One, 16(4), e0247855. https://doi.org/10.1371/journal.pone.0247855.

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The author instruction is available in PDF.
Please, download the file from HERE

Dr. Zsolt Demetrovics
Institute of Psychology, ELTE Eötvös Loránd University
Address: Izabella u. 46. H-1064 Budapest, Hungary
Phone: +36-1-461-2681
E-mail: jba@ppk.elte.hu

Indexing and Abstracting Services:

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  • EBSCO
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  • PubMed Central
  • SCOPUS
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  • CABI
  • CABELLS Journalytics

2022  
Web of Science  
Total Cites
WoS
5713
Journal Impact Factor 7.8
Rank by Impact Factor

Psychiatry (SCIE) 18/155
Psychiatry (SSCI) 13/144

Impact Factor
without
Journal Self Cites
7.2
5 Year
Impact Factor
8.9
Journal Citation Indicator 1.42
Rank by Journal Citation Indicator

Psychiatry 35/264

Scimago  
Scimago
H-index
69
Scimago
Journal Rank
1.918
Scimago Quartile Score Clinical Psychology Q1
Medicine (miscellaneous) Q1
Psychiatry and Mental Health Q1
Scopus  
Scopus
Cite Score
11.1
Scopus
Cite Score Rank
Clinical Psychology 10/292 (96th PCTL)
Psychiatry and Mental Health 30/531 (94th PCTL)
Medicine (miscellaneous) 25/309 (92th PCTL)
Scopus
SNIP
1.966

 

 
2021  
Web of Science  
Total Cites
WoS
5223
Journal Impact Factor 7,772
Rank by Impact Factor Psychiatry SCIE 26/155
Psychiatry SSCI 19/142
Impact Factor
without
Journal Self Cites
7,130
5 Year
Impact Factor
9,026
Journal Citation Indicator 1,39
Rank by Journal Citation Indicator

Psychiatry 34/257

Scimago  
Scimago
H-index
56
Scimago
Journal Rank
1,951
Scimago Quartile Score Clinical Psychology (Q1)
Medicine (miscellaneous) (Q1)
Psychiatry and Mental Health (Q1)
Scopus  
Scopus
Cite Score
11,5
Scopus
CIte Score Rank
Clinical Psychology 5/292 (D1)
Psychiatry and Mental Health 20/529 (D1)
Medicine (miscellaneous) 17/276 (D1)
Scopus
SNIP
2,184

2020  
Total Cites 4024
WoS
Journal
Impact Factor
6,756
Rank by Psychiatry (SSCI) 12/143 (Q1)
Impact Factor Psychiatry 19/156 (Q1)
Impact Factor 6,052
without
Journal Self Cites
5 Year 8,735
Impact Factor
Journal  1,48
Citation Indicator  
Rank by Journal  Psychiatry 24/250 (Q1)
Citation Indicator   
Citable 86
Items
Total 74
Articles
Total 12
Reviews
Scimago 47
H-index
Scimago 2,265
Journal Rank
Scimago Clinical Psychology Q1
Quartile Score Psychiatry and Mental Health Q1
  Medicine (miscellaneous) Q1
Scopus 3593/367=9,8
Scite Score  
Scopus Clinical Psychology 7/283 (Q1)
Scite Score Rank Psychiatry and Mental Health 22/502 (Q1)
Scopus 2,026
SNIP  
Days from  38
submission  
to 1st decision  
Days from  37
acceptance  
to publication  
Acceptance 31%
Rate  

2019  
Total Cites
WoS
2 184
Impact Factor 5,143
Impact Factor
without
Journal Self Cites
4,346
5 Year
Impact Factor
5,758
Immediacy
Index
0,587
Citable
Items
75
Total
Articles
67
Total
Reviews
8
Cited
Half-Life
3,3
Citing
Half-Life
6,8
Eigenfactor
Score
0,00597
Article Influence
Score
1,447
% Articles
in
Citable Items
89,33
Normalized
Eigenfactor
0,7294
Average
IF
Percentile
87,923
Scimago
H-index
37
Scimago
Journal Rank
1,767
Scopus
Scite Score
2540/376=6,8
Scopus
Scite Score Rank
Cllinical Psychology 16/275 (Q1)
Medicine (miscellenous) 31/219 (Q1)
Psychiatry and Mental Health 47/506 (Q1)
Scopus
SNIP
1,441
Acceptance
Rate
32%

 

Journal of Behavioral Addictions
Publication Model Gold Open Access
Submission Fee none
Article Processing Charge 990 EUR/article for articles submitted after 30 April 2023 (850 EUR for articles submitted prior to this date)
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%.
Subscription Information Gold Open Access

Journal of Behavioral Addictions
Language English
Size A4
Year of
Foundation
2011
Volumes
per Year
1
Issues
per Year
4
Founder Eötvös Loránd Tudományegyetem
Founder's
Address
H-1053 Budapest, Hungary Egyetem tér 1-3.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 2062-5871 (Print)
ISSN 2063-5303 (Online)

Senior editors

Editor(s)-in-Chief: Zsolt DEMETROVICS

Assistant Editor(s): Csilla ÁGOSTON

Associate Editors

  • Stephanie ANTONS (Universitat Duisburg-Essen, Germany)
  • Joel BILLIEUX (University of Lausanne, Switzerland)
  • Beáta BŐTHE (University of Montreal, Canada)
  • Matthias BRAND (University of Duisburg-Essen, Germany)
  • Ruth J. van HOLST (Amsterdam UMC, The Netherlands)
  • Daniel KING (Flinders University, Australia)
  • Gyöngyi KÖKÖNYEI (ELTE Eötvös Loránd University, Hungary)
  • Ludwig KRAUS (IFT Institute for Therapy Research, Germany)
  • Marc N. POTENZA (Yale University, USA)
  • Hans-Jurgen RUMPF (University of Lübeck, Germany)

Editorial Board

  • Max W. ABBOTT (Auckland University of Technology, New Zealand)
  • Elias N. ABOUJAOUDE (Stanford University School of Medicine, USA)
  • Hojjat ADELI (Ohio State University, USA)
  • Alex BALDACCHINO (University of Dundee, United Kingdom)
  • Alex BLASZCZYNSKI (University of Sidney, Australia)
  • Judit BALÁZS (ELTE Eötvös Loránd University, Hungary)
  • Kenneth BLUM (University of Florida, USA)
  • Henrietta BOWDEN-JONES (Imperial College, United Kingdom)
  • Wim VAN DEN BRINK (University of Amsterdam, The Netherlands)
  • Gerhard BÜHRINGER (Technische Universität Dresden, Germany)
  • Sam-Wook CHOI (Eulji University, Republic of Korea)
  • Damiaan DENYS (University of Amsterdam, The Netherlands)
  • Jeffrey L. DEREVENSKY (McGill University, Canada)
  • Naomi FINEBERG (University of Hertfordshire, United Kingdom)
  • Marie GRALL-BRONNEC (University Hospital of Nantes, France)
  • Jon E. GRANT (University of Minnesota, USA)
  • Mark GRIFFITHS (Nottingham Trent University, United Kingdom)
  • Anneke GOUDRIAAN (University of Amsterdam, The Netherlands)
  • Heather HAUSENBLAS (Jacksonville University, USA)
  • Tobias HAYER (University of Bremen, Germany)
  • Susumu HIGUCHI (National Hospital Organization Kurihama Medical and Addiction Center, Japan)
  • David HODGINS (University of Calgary, Canada)
  • Eric HOLLANDER (Albert Einstein College of Medicine, USA)
  • Jaeseung JEONG (Korea Advanced Institute of Science and Technology, Republic of Korea)
  • Yasser KHAZAAL (Geneva University Hospital, Switzerland)
  • Orsolya KIRÁLY (Eötvös Loránd University, Hungary)
  • Emmanuel KUNTSCHE (La Trobe University, Australia)
  • Hae Kook LEE (The Catholic University of Korea, Republic of Korea)
  • Michel LEJOXEUX (Paris University, France)
  • Anikó MARÁZ (Humboldt-Universität zu Berlin, Germany)
  • Giovanni MARTINOTTI (‘Gabriele d’Annunzio’ University of Chieti-Pescara, Italy)
  • Astrid MÜLLER  (Hannover Medical School, Germany)
  • Frederick GERARD MOELLER (University of Texas, USA)
  • Daniel Thor OLASON (University of Iceland, Iceland)
  • Nancy PETRY (University of Connecticut, USA)
  • Bettina PIKÓ (University of Szeged, Hungary)
  • Afarin RAHIMI-MOVAGHAR (Teheran University of Medical Sciences, Iran)
  • József RÁCZ (Hungarian Academy of Sciences, Hungary)
  • Rory C. REID (University of California Los Angeles, USA)
  • Marcantanio M. SPADA (London South Bank University, United Kingdom)
  • Daniel SPRITZER (Study Group on Technological Addictions, Brazil)
  • Dan J. STEIN (University of Cape Town, South Africa)
  • Sherry H. STEWART (Dalhousie University, Canada)
  • Attila SZABÓ (Eötvös Loránd University, Hungary)
  • Ferenc TÚRY (Semmelweis University, Hungary)
  • Alfred UHL (Austrian Federal Health Institute, Austria)
  • Róbert URBÁN  (ELTE Eötvös Loránd University, Hungary)
  • Johan VANDERLINDEN (University Psychiatric Center K.U.Leuven, Belgium)
  • Alexander E. VOISKOUNSKY (Moscow State University, Russia)
  • Aviv M. WEINSTEIN  (Ariel University, Israel)
  • Kimberly YOUNG (Center for Internet Addiction, USA)

 

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