# What drives user interest and purchase of virtual 3D assets? An empirical investigation of 3D model attributes and pricing dynamics

Jakob Korbel, Marc Riar, Thorsten Pröhl, Rüdiger Zarnekow

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Jakob Korbel, Marc Riar, Thorsten Pröhl, Rüdiger Zarnekow. What drives user interest and purchase of virtual 3D assets? An empirical investigation of 3D model attributes and pricing dynamics. Electronic Commerce Research and Applications, 2024, 68, pp.101452. 10.1016/j.elerap.2024.101452. hal-04833944

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# What drives user interest and purchase of virtual 3D assets? An empirical investigation of 3D model attributes and pricing dynamics

Jakob J. Korbel<sup>a, *,</sup> Marc Riar<sup>b</sup>, Thorsten Pröhl<sup>a, c</sup>, Rüdiger Zarnekow<sup>a</sup>

## ARTICLE INFO

### Keywords:

3D model

Virtual product

Virtual good

Virtual asset

Metaverse

Extended reality

Value-informed pricing

Competition-informed pricing

Cost-informed pricing

MAUT

## ABSTRACT

Virtual 3D assets, i.e., 3D models that form the basis of virtual environments, products, and goods, are essential for the creation of a future metaverse. However, we have limited knowledge about the market dynamics in which virtual 3D assets are traded and the indicators that influence their value and pricing – and thus the purchasing mechanisms. The present study draws on multi-attribute utility and value- and competition-informed pricing theory to determine what drives the purchase of virtual 3D assets using secondary data from the marketplace Sketchfab. The empirical analysis indicates that the sellers’ value perception of virtual 3D assets contradicts the users’ interest and that organizational sellers outperform individual sellers by relying to a higher degree on competition- and value-informed pricing. Based on our findings, we identify implications for both organizational and individual sellers to refine their pricing strategies in accordance with the unique dynamics of 3D virtual asset marketplaces.

## 1. Introduction

The metaverse is expected to have an incisive impact on our daily lives. We are meant to go about our work in virtual offices, spend our leisure time virtually, and purchase both physical and virtual products in the virtual world (Dwivedi et al., 2022). The foundation for a future metaverse lies in 3D models, which are both a necessity for building the virtual environments and for creating the products that consumers buy in virtual worlds, i.e., virtual goods (Lehdonvirta and Castronova, 2014). However, while various industries and companies are interested in participating in virtual environments or rely on extended reality (XR) technologies such as virtual reality (VR), augmented reality (AR) and virtual try-ons to extend our perceived environment (e.g., Heller et al., 2019; Javornik et al., 2016; Merle et al., 2012; Smink et al., 2019; Xi and Hamari, 2021), research emphasizes that a barrier to providing customers with a virtual experience is the unavailability of even such 3D models. Among other factors, this has to do with a lack of the required skills and knowledge to create 3D models or the associated resources (e. g., Korbel et al., 2022a; Riar et al., 2023).

One way to obtain the 3D models required for virtual environments or products is through virtual asset marketplaces, which are two-sided markets that allow the creation, offering, sharing and purchasing of

3D models, referred to as virtual 3D assets (Korbel et al., 2022b). Some of the most prominent platforms are the Unity Asset Store (Unity, 2024) and Sketchfab (Sketchfab, 2024). The Unity Asset Store focuses on 2D and 3D models, but also on software kits and packages specifically for building games and XR environments. The Sketchfab store on the other hand evolved from a platform that allowed technical artists to share, for example, 3D scans or virtual replicas of physical products. Both platforms show that 3D models are not only created by professional 3D studios and organizations but also by individual designers who strive to participate in the virtual 3D asset economy or need 3D models for private projects. In turn, the repertoire and pricing of virtual 3D assets is diverse, ranging from cultural heritage to plants to models of consumer products, such as Apple’s iPhone 15, which can be purchased for prices ranging from $4 to $125 (Sketchfab, 2024).

However, there is a dearth of knowledge about how these prices are determined and what attributes of 3D models attract customers to one model over another. The literature on value perception and pricing of virtual 3D assets is sparse. A broader consideration of literature on digital goods can help elucidate the core challenges in determining the value and price of 3D models. Digital goods are considered experience goods, which means that their quality can only be assessed once purchased. They are regarded as non-rival in consumption, which means

<footnote><sup>a</sup> Technische Universität Berlin, Department of Economics and Management, Chair of Information and Communication Management, 10623 Berlin, Germany</footnote>

<footnote><sup>b</sup> IESEG School of Management, Univ. Lille, CNRS, UMR 9221 – LEM - Lille Economie Management, F-59000 Lille, France</footnote>

<footnote><sup>c</sup> Trustami GmbH, Berlin, Germany</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: jakob.j.korbel@tu-berlin.de (J.J. Korbel).</footnote>
that compared to physical goods, their quantity does not decrease through consumption (Rayna, 2008). In addition, digital goods do not hold the same weight in terms of psychological ownership in comparison to physical goods and are thus perceived as less valuable (Atasoy and Morewedge, 2018). Lastly, since virtual 3D assets are digital goods, they have little to none marginal or distribution costs. Therefore, value assessment and pricing based on cost information loses its validity under these circumstances (Koppius, 1999; Rayna, 2008). These factors all contribute to the challenge of understanding pricing and value perception of virtual assets and ultimately what determines their purchase.

Therefore, the objective of this study is to shed light on what influences and drives the purchase of virtual 3D assets. We draw on multi-attribute utility (Schäfer, 2001; Wallenius et al., 2008) and value- and

competition information pricing theory (Ingenbleek et al., 2003) to investigate (1) the influence of 3D model attributes on the value perception of sellers and customers, (2) how the competitive environment affects the pricing decision, and (3) how value perception and pricing affect the purchase of virtual 3D assets. To address these research questions, we focus on an exploratory approach and secondary data from the virtual 3D asset platform Sketchfab, which includes the characteristics of 23.282 3D models and 3.220 seller profiles. The dataset is analyzed descriptively to provide an in-depth overview of the market structure of virtual 3D asset platforms, and quantitatively using structural equation modeling (SEM) to examine the value and pricing of virtual 3D assets and how the value perceptions and pricing decisions affect the purchase of 3D models. The focus of the quantitative analysis

<figure><img src="image_3.png" /><figcaption>Fig. 1. Sketchfab 3D model interface and virtual 3D asset attributes.</figcaption></figure>
is on the evaluation of the functional attributes of 3D models. In addition, a multi-group analysis (MGA) is conducted to investigate whether the two types of sellers active in virtual 3D asset markets, i.e., organizations and individuals, show differences in their value and pricing determination.

To achieve the research objective, the study is structured as follows: In Section 2, we synthesize the literature on digital goods, multi-attribute utility theory (MAUT), user interest, and value- and competition-informed pricing theory to derive hypotheses for the quantitative data analysis in Section 3. Section 4 highlights the methodological approach of this study, followed by an in-depth descriptive data analysis in the beginning of Section 5. Furthermore, the findings of the quantitative data analysis are summarized in Section 5, while Section 6 discusses the results of the study. Sections 7 and 8 conclude the study with a description of the limitations and future research avenues and a summary of the research results.

## 2. Literature Review

To provide an overview of the theoretical foundation, we emphasize current literature on the concepts and commonalities of digital goods and virtual 3D assets. In particular, we elaborate the theories which form the basis for the derivation of the hypotheses and the empirical model, i.e., multi-attribute utility theory, user interest as well as value- and competition-informed pricing.

### 2.1. Digital goods and virtual 3D assets

Virtual 3D assets are 3D models that are used within companies, for example, to facilitate physical product development and simulation processes, to create virtual environments for games, or to generate profit within virtual economies by transforming the assets into virtual goods (Korbel, 2021; Korbel et al., 2022b). In essence, virtual 3D assets belong to the category of digital assets (Horodyski, 2006; Toygar et al., 2013) and possess three main attributes which can be determined in and depend on the 3D model file format (e.g., Eck, 2023; Unity Manual, 2024): their geometry, appearance, and animations (Fig. 1). The geometry defines the shape of the virtual 3D asset. A 3D model is made up of polygons, points, edges and vertices that form the surface of the object (Chang et al., 2015). The more detailed a 3D model, the higher is the complexity of the 3D mesh, i.e., the number of polygons, points, edges, and vertices. The appearance-based features – textures and materials – not only enhance the aesthetics of the model, but elucidate what the object is (e.g., an oval-shaped 3D model cannot be identified as a gemstone solely based on its geometry). Textures are 2D images that envelop the 3D object (Culbertson et al., 2014). Materials, on the other hand, are used to imitate or simulate the characteristics of physical materials and thus define how the surface of the 3D model reacts to environmental influences. Basic material settings include light reflection, bumpiness, or transparency (Li et al., 2022), while physical product simulations require additional information on, for example, the robustness of a material (Rädel et al., 2021). The material can be based on either a color (e.g., green painted metal) or one or more texture files. Finally, animations can be used to simulate the movement of a 3D model. Prominent application areas for animations are the simulation of character movements (Pan et al., 2009) or sequences that illustrate the functionality of a physical product (Costa et al., 2018). Complex animations, as in the case of the former, require the rigging of the 3D model, i.e., the creation of internal structures, often referred to as “bones”, which are manipulated to simulate the movement of a person or animal (Arshad et al., 2019).

Apart from the use of 3D models for in-house processes, virtual 3D assets can also be traded as digital goods on virtual 3D asset marketplaces (e.g., Turbosquid, 2024; Sketchfab, 2024). As digital goods, virtual 3D assets are intangible, durable, non-rival in consumption, non-excludable and do not induce marginal costs (Rayna, 2008). However,

unlike most traditional digital goods, such as software or music, which can be consumed and experienced directly (Koppius, 1999; Rayna, 2008), the value of virtual 3D assets unfolds through their incorporation into business processes and subsequent use (Korbel et al., 2022b), which has implications for the value perception of consumers. The consumer value perception, in turn, influences the consumers' willingness to purchase goods (Ingenbleek et al., 2013). Therefore, in order to derive insights into what drives the purchase of virtual 3D assets, we need to understand what influences the consumers' value perception of 3D models. However, literature on the value of virtual 3D assets is sparse, both from the customer and creator perspective, which induces the necessity to determine their value exploratively. A promising and suitable approach to assess consumer value perception is through multi-attribute utility theory and user interest.

### 2.2. Multi-Attribute utility theory and user interest

Multi-attribute utility theory (MAUT) (von Winterfeldt and Fischer, 1975; Wallenius et al., 2008) implies that each product consists of multiple attributes associated with a particular value dimension, which form the product utility for customers. In turn, the customer utility can be assessed by weighting the relative importance of the product attributes based on user evaluations (von Winterfeldt and Fischer, 1975). While MAUT has been adapted in various fields of business research for physical goods, ranging from cost estimation (e.g., Dean Ting et al., 1999; Dong et al., 2003) to organizational performance (e.g., Young-blood and Collins, 2003) and manufacturing systems (e.g., Gumasta et al., 2011), one way to evaluate the user utility for goods in digital markets is to derive implications for the user value based on the expressed user interest in product attributes (Chang, 2011; Schäfer, 2001). Relying on user data in digital markets to assess the user utility has the advantage that various information about the user, such as purchasing habits, are digitally available. However, similar to offline settings, understanding the user's interest is perceived as a highly complex task due to the variance of influencing factors.

User interest can be either explicit or implicit (Zhang et al., 2019; Zhu et al., 2019): Explicit user interest refers to direct user feedback, i.e., asking the user directly for an evaluation of a product. To collect user feedback, websites provide options, amongst others, for viewing, liking, and sharing content, rating products, or writing comments. Consequently, the extent (e.g., number of likes) and the form (e.g., average user rating) of user feedback can serve as a basis for estimating the user's interest in a product (Dhelim et al., 2020; Hu et al., 2020). Assessing the implicit user interest, however, is more complex and requires advanced analytical approaches. Implicit user interest is not expressed through active feedback on products, but through the user's behavior on one or more websites. This includes, for example, the user's browsing behavior, dwell time on a particular product page, or customer comments. Therefore, a common approach to measure implicit user interest is either to analyze clickstream data (e.g., Volk et al., 2017) or to extract information from user comments that lead to a deeper understanding of the personal, i.e., individual user interests (Kim and Chan, 2003). Advanced approaches include sentiment analyses of user reviews (e.g., Hu et al., 2020; Sun et al., 2020) or user interest graphs (e.g., Zhu et al., 2019) to granularly define and assess customers' interests and value perceptions of a product. Thereby, the customer value has a precise impact on the pricing decision of sellers. Based on the individual customer value and utility, marketers are able to pursue different pricing strategies which allow for dynamic pricing, the exploitation of profits in the customers' interest, and recommender systems which make pricing decisions automatically based on the customer's preferences (Han et al., 2024; Sheng et al., 2023; Zhou et al., 2022). Hence, practitioners often focus on the customer value (Alt, 2016), which is the most prominent of the three pricing principles.
## 2.3. Value- and Competition-Informed pricing

The three pricing principles comprise value-, competition- and cost-informed pricing (Ingenbleek et al., 2003). Understanding and focusing on the customer value perception in pricing decisions is emphasized in the literature as the most promising pricing approach, while relying on cost information is considered as the weakest (Hinterhuber, 2008; Ingenbleek et al., 2003). The weakness of building pricing decisions on the costs involved is even more evident in the case of digital goods, because digital goods do not induce marginal or distribution costs (Koppius, 1999; Rayna, 2008), which are often considered as the basis for cost-informed pricing decisions. The costs associated with the production of a good are emphasized in the literature as the price floor, which is thus considerably low for digital goods (Monroe, 2003).

In contrast, value-informed pricing is described in the literature as the extent to which firms rely on information about the customers' value perception, i.e., the trade-off between the benefits that the product provides to the customer and its price (Ingenbleek et al., 2013, 2010). The product benefit is mainly determined by the product quality (Rao and Monroe, 1989; Zeithaml, 1988). A high product quality leads to increasing benefits for the customer and thus to the acceptance of higher prices. In terms of the price discretion paradigm of Monroe (2003), an increase in product quality leads to a higher value for the customer and thereby lift the price ceiling. In turn, a higher product quality allows marketers to set higher prices for their products. Therefore, focusing on the customer value perception can lead to higher sales, market share and profits (Ingenbleek, 2007) and is therefore a key goal of practitioners (Alt, 2016).

However, the extent to which a higher product quality allows for higher prices depends on the competitive environment. The analysis and incorporation of information about the competitive environment is referred to in the literature as competition-informed pricing (Hinterhuber, 2008; Ingenbleek et al., 2003). Relevant pricing indicators are thereby the pricing behavior of competitors and the degree of competition in the market (Ingenbleek et al., 2013). Pricing on the basis of the competitors' pricing behavior requires to observe or anticipate the price levels of competitors (Hinterhuber, 2008). Sellers are advised to draw on the competitor prices as a reference point and set their prices accordingly to attract customers (Ingenbleek et al., 2013). The degree of competition, however, can lower the perceived value of customers in terms of the price that the customer is willing to pay for the same product quality. Referring to price discrimination, competition may thus lower the overall price ceiling (Ingenbleek et al., 2003), because customers are provided with more product choices with comparable quality levels. Thus, markets with a higher degree of competition are associated with lower product prices (Bresnahan and Reiss, 1991).

## 3. Hypotheses

MAUT and value- and competition-informed pricing theory form the basis for the derivation of the hypotheses to investigate the factors that influence the purchase of virtual 3D assets. Important constructs for investigating what drives the purchase of virtual 3D assets are the value perception of products by both customers and seller, as well as the market conditions under which products are sold. A key criterion for the former is the perceived overall product quality which depends on the product attributes. According to MAUT, a product can consist of either single or multiple product attributes, depending on the complexity of its functionality, which form the value dimensions of a product. For example, the operating time of a digital camera can be considered as a single attribute dimension, whereas the picture quality dimension consists of multiple attributes such as picture resolution, or sharpness (Schäfer, 2001). Similarly, user interest in a product depends on the relevance of the different product attributes, which can be extracted, for example, by feature scores (Hu et al., 2020). Hence, measuring the user

interest in product attributes allows to draw conclusions on the overall product quality from the user perspective. As emphasized in Section 2.1, virtual 3D assets are comprised of a geometry, appearance, and animations which can be described as their basic characteristics. Thus, we consider these basic characteristics of virtual 3D assets as value dimensions, that are represented by one or more attributes. Improving the quality of the attributes in the value dimension is associated with a higher overall utility, which users can express by increasing their interest in the product. Hence, we argue that more advanced 3D model characteristics, i.e., value dimensions, increase the user interest in a virtual 3D asset:

*   **H1a: A more advanced geometry has a positive effect on the user interest.**

*   **H2a: A more advanced appearance has a positive effect on user interest.**

*   **H3a: More advanced animations have a positive effect on user interest.**

Besides the positive effect of the advanced product attributes on the user interest, we assume a positive relationship between higher product quality and product price. According to value-informed pricing theory, customers perceive the quality and price of a product as a trade-off (Ingenbleek et al., 2013, 2010). Therefore, customers are willing to accept higher prices for a higher quality product. In addition, achieving higher product quality requires more resources to be invested in a product, thereby raising the price floor of products (Monroe, 2003). Since virtual 3D assets are digital goods, there is little or no costs for the reproduction of the 3D model. However, the creation of 3D models requires human resources while the level of investment in a 3D model depends on the quality of the 3D model attributes. Thus, we argue, that a higher quality of virtual 3D assets, in terms of advanced 3D model attributes – geometry, appearance, and animations – lead to higher virtual 3D asset prices:

*   **H1b: An advanced geometry is positively related with higher prices of 3D assets.**

*   **H2b: An advanced appearance is positively related with higher prices of 3D assets.**

*   **H3b: Advanced animations is positively related with higher prices of 3D assets.**

In addition to the influence of the virtual 3D asset characteristics on user interest, we expect a positive effect of user interest on the purchase decision due to digital market mechanisms and emotional effects. Today, most digital markets rely on product reviews and ratings due to their relevance for the customer's purchase decision (Baum and Spann, 2014; Jiménez and Mendoza, 2013; Kollmer et al., 2022). Furthermore, the users' interest in a product is often affected by social influence, i.e., the customers' interest increases when they observe that other customers like, buy, or recommend a product in various ways (Yang et al., 2013). Thus, digital goods that receive high levels of attention are likely to become more popular as more and more users are exposed to the good, e.g., by filtering for popularity or likes and views (Szabo and Huberman, 2010). In turn, popular content leads to a higher degree of user interest and to higher purchases of popular goods due to conformity (Szabo and Huberman, 2010; Zheng et al., 2021), while the user interest expressed through the number of reviews and positive reviews promotes the purchase intention (Jiménez and Mendoza, 2013; Park et al., 2007). Therefore, we argue that a higher user interest has a positive effect on the purchase:

*   **H4: A higher user interest leads to higher virtual 3D asset purchase.**

Apart from the influence of the behavior of other customers on the marketplace, the behavior of other sellers on the platform is an important influencing factor. Due to the transparent nature of digital markets, aggregating information about competitors' prices and products is easier to accomplish than in offline markets (Gerpott and Berends, 2022). Therefore, aligning product prices with competitors' prices, which is necessary to attract customers (Ingenbleek et al., 2013), can be considered as more convenient in electronic markets. Hence, pricing products dynamically and adjusting the price to increasing or decreasing competitor prices in competitive pricing strategies is a common procedure in e-commerce (e.g., Li et al., 2023; Zhao and Peng, 2023), which
can even be automated due to the availability of online data (e.g., Faehnle and Guidolin, 2021; Kastius and Schlosser, 2022). However, this increasing transparency also applies to the customers. While the transparency per se does not necessarily lead to lower overall product prices (Lee et al., 1999), the degree of competition in the market has the potential to lower the prices due to the increased availability of comparable products. Raising the number of providers on the platform may increase the customer surplus but lower the surplus of providers (Fu and Lyu, 2022). Thus, a higher degree of competition can have a negative effect on the overall price ceiling (Bresnahan and Reiss, 1991; Ingenbleek et al., 2003), and vice versa, lower degrees of competition can lead to increasing prices. Based on this rationale, we argue that a lower degree of competition and higher competitor prices lead to higher virtual 3D asset prices:

**H5: A lower degree of competition leads to higher virtual 3D asset prices.**

**H6: Higher competitor pricing leads to higher virtual 3D asset prices.**

However, it is to be expected that the extent to which the participants in virtual 3D asset markets rely on competition-informed pricing is influenced by the type of seller. Both individual sellers and companies are active on virtual 3D asset platforms. Individual designers may compare the price of their 3D models with competitive products but are unlikely to possess the resources to investigate the entire market and competition structure and to incorporate those information into their pricing decisions. Organizations, on the other hand, have both the resources and the economic interest to investigate the market in depth in order to make a more informed pricing decision that will positively affect their market performance. We therefore suggest that the organizational marketers consider the degree of competition and the competitors' pricing behavior to a greater extent in their pricing decisions than individual sellers:

**H7: The positive effect of lower degree of competition on the virtual 3D asset price is higher for organizations than for individual sellers.**

**H8: The positive effect of the competitor pricing on the virtual 3D asset price is higher for organizations than for individual sellers.**

Eventually, the price resulting from the pricing decision process affects the user's interest and customer's purchase intention. The customer's purchasing process is emphasized to comprise five stages: problem recognition, information search, evaluation of alternatives, choice and post-sales services (Huang and Benyoucef, 2017; Liang and Lai, 2002). During the evaluation of alternatives, customers form a consideration set by identifying several products that meet their needs, with the product quality being of paramount importance (Jang et al., 2012). At this stage, higher-priced products may arouse the user interest to a greater extent than lower-priced products, since the price can be considered as a quality indicator for customers (Erdem et al., 2008; Rao and Monroe, 1989; Wolinsky, 1983; Zeithaml, 1988). However, once the customers make the final purchase decision, i.e., choose and purchase one of the products from the consideration set, the price becomes the dominant decision criterion (Jang et al., 2012). Due to the trade-off between perceived benefit and price (Ingenbleek et al., 2013; Ingenbleek et al., 2003) and the loss aversion of customers (Tversky and Kahneman, 1991), customers tend to purchase the product with the lower price if the quality and benefits are comparable. Thus, higher-priced 3D models might lead to a higher user interest in the evaluation and consideration phase, while the customers purchase lower-priced 3D models once they have built a consideration set with virtual 3D assets of comparable quality. Therefore, we argue that higher virtual 3D asset prices have a positive effect on user interest, while the purchase decision is negatively correlated with increasing 3D model prices:

**H9: An increasing virtual 3D asset price has a positive effect on user interest.**

**H10: An increasing virtual 3D asset price has a negative effect on virtual 3D asset purchase.**

However, as for competition-informed pricing, we expect different results for the effect of price on purchase for the two types of sellers. Organizations have the resources to gather information that benefit the

value- and competition-informed pricing process and to set prices that are more responsive to market conditions than individual sellers who cannot rely on these resources. Therefore, we expect the negative effect of higher prices on purchasing to be less pronounced for organizational sellers than for individual sellers:

**H11: The negative effect of the virtual 3D asset price on the purchase is lower for organizational than for individual sellers.**

The constructs and hypotheses are illustrated in Fig. 2. To test the hypotheses, we draw on secondary data from the virtual 3D asset marketplace Sketchfab and an empirical data analysis based on SEM.

# 4. Methodology

To achieve the research objectives, this study relies on secondary data on virtual 3D asset characteristics and an empirical data analysis approach based on structural equation modelling (SEM). SEM is a prevailing methodological approach to investigate relationships between latent variables. This approach is fitting for the present study because it enables the analysis of complex models consisting of several independent and dependent variables, sets of causal effects including indirect effects (Lowry and Gaskin, 2014).

In the course of a pre-screening of virtual 3D asset markets, the asset store Sketchfab was identified as a suitable website to aggregate research data for hypothesis testing due to three reasons: First, Sketchfab is the largest platform for virtual 3D assets with over 10 million users and more than 5 million 3D models (Sketchfab, 2022). Second, the platform provides a wealth of information about the 3D model at sale, ranging from user likes to 3D model characteristics and categorizations. Thirdly, and most importantly, the platform provides information on the number of downloads, which can be used to derive insights into what drives the purchase of 3D models. The Sketchfab platform was founded in 2012, acquired by Epic Games in 2021, and offers both freely available and purchasable 3D models. While the vast majority of 3D models is "view only", over 600.000 models are downloadable for free and only a fraction is available for purchase in the Sketchfab store (Sketchfab, 2022). For the analysis, all the models available in the Sketchfab store and their associated data were acquired via web scraping and stored in a database for the subsequent analysis.

## 4.1. Data Collection, Cleaning, and Selection

For the empirical analysis, two data sets were obtained from Sketchfab in August 2021. The first data set contains records with information on all 3D models in the Sketchfab store, including the characteristics required to test the hypotheses, such as model category, number of animations, or number of triangles (Fig. 1). The second data set contains user profile information on all creators that uploaded models to the Sketchfab store. The web scraping process resulted in a data set containing 99.564 3D models and their characteristics, as well as 5.168 user profiles (Fig. 3).

Both data sets were subsequently inspected, cleaned, and prepared for analysis. First, the data set containing the virtual 3D asset characteristics was inspected for false entries and null values. The initial analysis resulted in 1.580 incorrect entries which were excluded from the data set. In addition, 8 variables in the dataset showed null values ranging from 872 and 925 to 53.208. For the latter, the null values were replaced with the value "none", as they were found in the column containing information on whether the seller had assigned a second category to the 3D model. The remaining entries with null values were excluded from the dataset (total: 1.047 entries). The resulting dataset of 96.937 3D models and user profiles contained neither null values nor false entries.

To ensure that the data analysis met the research objectives, two qualitative exclusion criteria were established. First, the Sketchfab store has existed since 2018. Prior to this, 3D models could be uploaded to the platform, but not sold. Therefore, models could have collected likes and
<figure><img src="image_4.png" /><figcaption>Fig. 2. Research model.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Fig. 3. Data crawling, cleaning, and preparation.</figcaption></figure>

views, as well as downloads, before being added to the store in 2018. Hence, models uploaded before 2018 were excluded from the dataset (criterion 1: 11.279). Second, since the aim of the study is to examine what drives the purchase of virtual 3D assets, all models that have not yet been downloaded were excluded to avoid negative effects on the attribute utility assessment (criterion 2: 60.365). Finally, the data set was checked for outliers that could have a negative impact on the data

analysis. A univariate analysis of the numeric variables showed that the distribution of all variables was either positively or negatively skewed and of leptonic nature. To reduce the outlier effect, a 99%-quantile was set for all numeric variables. This process resulted in the exclusion of 2.011 3D models and 163 user profiles. The resulting data set was transformed by $log + 1$ transformation and scaled between 0 and 1 based on min-max scaling to normalize the data for the data analysis.
## 4.2. Data analysis

Prior to the hypothesis testing and interpretation of the data, measures for the data analysis are determined and validity and reliability tests are conducted.

### 4.2.1. Measures

The measures comprise the virtual 3D asset attributes geometry, appearance, and animations as well as the user interest, competition related constructs, and price and purchase.

**Geometry:** The geometry is measured by the complexity of the 3D model mesh. The 3D mesh defines the shape of 3D models and is made up of multiple lines, points, edges and vertices that form polygons. The quality and complexity of the geometric shape can therefore be described by the total number of polygons. Sketchfab provides a “total triangles” variable that expresses the total number of polygons the 3D model consists of. If the model consists of other types of polygons (e.g., quads), Sketchfab recalculates the number of quads for the total number triangles. As the geometry is an observable characteristic, a single-item measure (Sarstedt et al., 2022) is used to describe the construct.

**Appearance:** The appearance of a 3D model is defined by materials and textures. The number of textures and materials used in a 3D model increases the complexity of the appearance and therefore its quality. Furthermore, the quality of the appearance can be defined by the complexity of the texture, i.e., the 2D image that is wrapped around the 3D model. To measure the texture quality, we rely on the average size of the texture files per model (in kilobytes). While material settings can be adjusted, the quality of the material highly depends on the texture files. Therefore, the size of the material file does not necessarily correspond to a higher quality of the material and is hence neglected as a variable for the construct.

**Animations:** The animation quality is measured by the number of animations associated with a 3D model. Animations for 3D models in asset stores are rarely long sequences, but rather short sequences for, e. g., specific movements. Thus, more complex animation schemes, such as a running, climbing, or dancing character, are included in the model as single sequences. Hence, we argue that the construct animation can be fully described by the number of animations, since each attached animation induces an additional movement scheme. We therefore measure the construct animation by the single item “number of animations”.

**User Interest:** The user interest is expressed by user activities, which can be measured based on implicit or explicit information (Dhelim et al., 2020; Hu et al., 2020; Zhang et al., 2019). Since the aim in this study is to measure the general interest rather than an in-depth analysis of the users' attitude (e.g. positive or negative) towards the virtual 3D assets, we rely on the latter. Hence, we measure the user interest based on explicit information in the form of views and likes of virtual 3D assets, and the number of comments on the 3D model.

**Degree of Competition & Competitor Pricing:** For the purpose of this study, we refer to the different categories in the Sketchfab store as different market segments, e.g., Cars & Vehicles, Animal & Creatures, or Architecture. The Sketchfab store allows a maximum of two categories to be assigned to each virtual 3D asset. Therefore, the market segment size is represented by the number of 3D models in that category if the 3D model is assigned to only one category, while the market segment for 3D models assigned to two categories is represented by the combined number of 3D models in both categories. The degree of competition is expressed by the size of the market segment(s) divided by the number of competitors who sell 3D models in the market segment(s) (1). Hence, the higher the values of the variable “degree of competition”, the lower is the degree of competition in the market segment. In terms of competitor pricing, we refer to the average competitor prices in the market segment, calculated individually for each seller in the market. To do this, we subtract the number of 3D models and prices of each vendor from the total number of 3D models and prices in the market segment. The remaining prices are divided by the remaining number of 3D models to

derive an individual competitor price indicator for each seller in the respective market segment (2).

$$ (1) \textit{ Degree of Competition : } D_v = \frac{n_j+n_k}{C_j+C_k} $$

$$ (2) \textit{ Competitor Pricing : } R_v = \frac{\sum_{i \in S_j} p_i + \sum_{i \in S_k} p_i}{n_j + n_k} - \frac{\sum_{v_j \in S_j} p_{j+} + \sum_{v_k \in S_k} p_{k+}}{n_{j+} + n_{k+}} $$

$$ S = \textit{market segment} \quad | \quad C_j = \textit{number of competitors in market segment} \\ S_j = |P_i| = \textit{price of asset i}| $$

$$ n_j = \textit{number of assets in market segment } S_j \quad | \quad n_{v,j} = \textit{number of assets by } v \textit{ in market segment } S_j $$

**Price & Purchase:** The price represents the actual 3D model price chosen by the creator and seller of the virtual 3D asset. Similarly, we rely on the number of 3D model downloads to measure the virtual 3D asset purchase. As for the constructs geometry and animation, we rely on single item measures, since the model price and downloads are observable characteristics. While the 3D model price is apparent on the Sketchfab website, the number of downloads is not visibly displayed but is recorded in the website's source code. In order to verify the validity of the variable “downloads”, a test purchase and download of a 3D model was carried out, which resulted in an increase of the variable by 1. We therefore consider the variable to be valid.

### 4.2.2. Validity and reliability

The research model was assessed using PLS-SEM in SmartPLS 4 (Ringle et al., 2022). The model consists of formative measures only, as the items for all constructs are non-interchangeable and the causal relationships lead from the indicators to the constructs (Petter et al., 2007). In line with the recommendations for assessing formative constructs, we inspected the weights, variance inflation factors (VIFs) and t-values (Cenfetelli and Bassellier, 2009). As noted above, several constructs consist of a single item because they are one-dimensional and measure observable characteristics (Sarstedt et al., 2022). The weights for the single item measures have a perfect value because they do not compete for explanatory power, whereas t-values cannot be calculated for single items. The weights and t-values of the multidimensional constructs reveal strong significance of their corresponding items (i.e., t > 1.96) (see Table 1).

The VIFs were reflected on to control for potential multicollinearity between the items. As opposed to reflective measures, the items of formative measures should explain different dimensions of the construct. Thus, collinearity should be low. The VIFs for single item measures have the lowest possible value (i.e., 1). The VIFs for the multidimensional measures are all well below the recommended threshold of 3.3 (Petter et al., 2007). Taken together, the assessment of the formative measures demonstrates adequate validity and reliability.

## 5. Results

The results comprise a descriptive data analysis and hypothesis

<table><caption>**Table 1**

Validity and reliability of formative measures.</caption><thead><tr><th>Construct</th><th>Item</th><th>Weight</th><th>T-Value</th><th>VIF</th></tr></thead><tbody><tr><td>Geometry</td><td>total_trangles</td><td>1.000</td><td>—</td><td>1.000</td></tr><tr><td rowspan="3">Appearance</td><td>materials</td><td>0.751</td><td>29.025</td><td>1.434</td></tr><tr><td>texture quality</td><td>0.268</td><td>7.579</td><td>1.809</td></tr><tr><td>textures</td><td>0.347</td><td>9.049</td><td>2.058</td></tr><tr><td>Animation</td><td>animations</td><td>1.000</td><td>—</td><td>1.000</td></tr><tr><td>Degree of Competition</td><td>competition</td><td>1.000</td><td>—</td><td>1.000</td></tr><tr><td>Competitor Pricing</td><td>average_competitor_price</td><td>1.000</td><td>—</td><td>1.000</td></tr><tr><td rowspan="3">User Interest</td><td>comments</td><td>0.124</td><td>10.162</td><td>1.354</td></tr><tr><td>likes</td><td>0.294</td><td>17.362</td><td>2.619</td></tr><tr><td>views</td><td>0.687</td><td>45.024</td><td>2.511</td></tr><tr><td>Price</td><td>price</td><td>1.000</td><td>—</td><td>1.000</td></tr><tr><td>Purchase</td><td>downloads</td><td>1.000</td><td>—</td><td>1.000</td></tr></tbody></table>
testing based on SmartPLS SEM and Multi-Group Analysis (MGA). First, we provide insights into the market structure, participants, and types of virtual 3D assets in the overall Sketchfab store, followed by an in-depth analysis of the sample data set. Second, the results from the hypothesis testing are emphasized to allow an interpretation and discussion of the results in Section 5.

## 5.1. Descriptive data analysis

The year-wise distribution of virtual 3D assets and contributors in the Sketchfab store shows a steadily growing platform since 2016 with an annual growth rate ranging between 50 % and 270 % (Sketchfab store), and 50 % to 100 % (sample dataset) (Fig. 4). An increase in contribution can be witnessed in 2018, when Sketchfab launched the store. Thus, only a fraction of the 3D models in the store were available on the platform before the launch. The growth of the store decreases slightly in 2019, while the growth in the sample set remains constant. Similar to the number of virtual 3D assets, the number of contributors increases over time. However, the increase in contributors is less than the increase in virtual 3D assets. Hence, while new contributors joined the platform, the number of virtual 3D assets per contributor in the store increases over time (from about 5 3D models per seller in 2018 to 7 in 2020).

The 3D models on the platform can be segmented into (1) consumer products such as furniture, electronics, fashion and food, (2) animals, creatures and people, (3) science, art and culture, (4) architecture, (5) cars and vehicles, (6) weapons and military, (7) nature and plants, and (8) leisure time activities (Fig. 5). The contributors to the categories are both individual designers and organizations. Individual sellers are in the majority on the platform and provide most of the virtual 3D assets in both the Sketchfab store (cleaned dataset) and the sample set (sample dataset) (Fig. 5). Furthermore, individual artists are the dominant contributors, offering around 28 % to almost 50 % of the 3D models in all market segments, i.e., categories.

The majority of 3D models (1) represent consumer products (Furniture & Home, Electronics & Gadgets, Fashion & Style, Food & Drinks – 26,68 %). As with any category in the Sketchfab store, individual artists are the main contributors to this category, with a segment share of around 40 %, followed by individual game developers (13,4%) and

designers (12 %). In the organizational domain, 3D design agencies are the most prominent contributors, offering about 44 % of all organizational models in the market segment, followed by 3D creation tool providers. 3D creation tool vendors seem to focus on this category, as 56 % of their models are found in the consumer product segment. Interestingly, although no major brand sells 3D models on the platform, a number of virtual 3D assets on the platform are virtual representations of physical products such as Bose headphones, Nike sneakers or Apple iPhone models.

The second highest number of 3D models are related to (2) characters, animals, and creatures (Characters & Creatures, Animals & Pets, People – 20,28 %). Organizations are more active in this market segment than in other categories, especially in the “People” 3D model category, where a quarter of all 3D models come from organizations. The high participation of organizations in this category is mainly driven by 3D scanning studios, which not only create about 25 % of their 3D models for this category, but also have the highest market share in the organizational domain, offering 80 % of all organizational 3D models in this category. This category also shows a high level of participation by 3D printing enthusiasts. Although their market segment share is comparatively low (1,28 %), – due to the overall low number of 3D printing enthusiasts active in Sketchfab store – they concentrate about 40 % of their 3D models in this category. This implies that a large proportion of the character models on the platform are scanned rather than modelled 3D humans. In addition, creators from the educational context (schools, education projects, museums) seem to focus on this market segment, creating about 21–23 % of their 3D models for this category.

Third, there are a number of models related to (3) science, art and culture (Art & Abstract, Science & Technology, Cultural Heritage & History – 16,09 %). As with the previous categories, this category is dominated by individual artists (37,39 %), followed by individual game designers (13,4%). Looking at the models created by organizations in this category, 3D scanning studios, design studios, and science and education related organizations contribute to the category in roughly equal proportions (25–27 % each). Unsurprisingly, contributors belonging to the latter category create most of their 3D models for this segment: educational projects (62,94 %), non-profit organization (73,39 %), and Museums and Scientific Organization with around 40 %

<figure><img src="image_6.png" /><figcaption>Fig. 4. Year-wise distribution of virtual 3D assets and contributors (cumulated).</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Fig. 5. 3D model seller categories and distribution in the cleaned and sample data set.</figcaption></figure>

of their models. However, although scientific and educational institutions concentrate their efforts on this category, only about 3 % of the models in this category come from educational institutions.

(4) Architectural models are the second largest single category with a market share of 12,40 %. This is particularly interesting as only around 1,5% of the 3D model sellers identify themselves as architects. Unsurprisingly, the individual architects focus on this market segment (40 % of all 3D models created by architects can be found in the category "Architecture"). However, the main contributors in this category, apart from individual artists, albeit with their lowest participation among the categories (28,65 %). The other main contributors in this category are game developers (around 15 %) and 3D scanning enthusiasts (around

11 %). Interestingly, animators focus on this category, creating about a third of their 3D models for the architecture category. Since architectural buildings do not necessarily require movement, this is surprising. In the organizational domain, the segment is dominated by 3D design agencies (around 40 %).

Cars & Vehicles (7,26 %), Weapon & Military (6,32 %) and Nature & Plant (5,94 %) models each account for around 6–8 % of the 3D models in the Sketchfab store, while models related to leisure time activities such as music, sport and fitness, news and politics, and travel / places together account for only 5 % of the 3D models in the marketplace (Activities – 5,04 %). The segment (5) Cars & Vehicles is dominated by individual sellers. 46 % of the 3D models originate from individual
artists, while 19 % are offered by individual game developers (second highest value for game developers). In the organizational domain, the category shows almost the lowest value for 3D scanning studios and the highest for game studios (about 7,5%). However, the category is dominated by 3D design agencies (66,28 %). Schools create a fifth of their 3D models for this category, whereas other scientific and educational institutions mostly neglect this category. The market segment (6) Military & Weapons shows the highest participation of game developers (around 25 %) and the lowest level of 3D scanning participation (1,39 %). Similarly, the highest value for game studios (organizations) can be observed in this category (around 10 %), leading to 35 % of the models coming from both individual and organizational game developers,

presumably due to widespread use of weapon and military models in game environments. Unsurprisingly, scientific and educational institutions mostly refrain from contributing to this segment, with the exception for schools, which create about 10 % of their models for this category. In the category (7) Nature & Plants, 3D scanning enthusiasts outnumber game developers and individual designers in contributing 3D models (14,68 %). In the organizational domain, 3D creation tool providers show a high participation in this market with 9,89 % of all 3D models, compared to rather low rates for the remaining organizational participants. In addition, 3D creation tool providers focus on this market segment (31,18 % of the models), as do museums, which contribute 15,25 % of their models to this category.

<figure><img src="image_8.png" /><figcaption>Fig. 6. Hypotheses testing.</figcaption></figure>
The segment with the lowest overall participation is (8) activity-related (Sport & Fitness, Music, Travel & Places, News and politics – 5,04 %). Again, this category is dominated by individual artists (38,13 % of all models in this category), followed by game developers (14,52 %) and 3D scanning enthusiasts (12,83 %). In the organizational domain, scientific organizations have the highest internal focus on 3D models in this category (13,24 %), while most of the models are created by 3D scanning (about 37 %) and design studios (about 28 %). Interestingly, the scientific organizations do not focus on news & politics models (about 2,63 %), but rather on music 3D models (about 23 %) in this category.

## 5.2. Hypotheses testing

The research model (Fig. 6) accounts for 25.6 % of the variance of purchase, 19.9 % of price, and 12.1 % of user interest. The results (Table 2) indicate significant positive relationships between appearance and user interest (β = 0.071, p < 0.01), and animations and user interest (β = 0.124, p < 0.01), whereas the relationship between geometry and user interest indicates a slight positive effect which is, however, borderline insignificant (β = 0.011, p > 0.1). Accordingly, H1a could not be supported, whereas H2a and H3a are supported. Significant positive relationships are also found between geometry and price (β = 0.300, p < 0.01), appearance and price (β = 0.0181, p < 0.01), and animations and price (β = 0.055, p < 0.01). Thus, H1b-H3b could all be supported. Moreover, a significant positive relationship could be encountered between competitor pricing and price (β = 0.209, p < 0.01), whereas contrary to our expectation, the degree of competition did not show a significant effect on price (β = 0.009, p > 0.1). Respectively, H6 could be supported whereas H5 could not be supported. A substantial and significant effect is encountered between price and user interest (β = 0.291, p < 0.01) and the strongest observed effect is observed between user interest and purchase (β = 0.524, p < 0.01), supporting H4 and H9. As expected, and in support of H10, an increasing product price is negatively related to purchase (β = -0.262, p < 0.01).

In terms of the modeled partial mediation effects, we observed the following indirect effects (Fig. 7): Price was found to be a partial mediator for the relationships between appearance and user interest (β = 0.053, p < 0.01), animations and user interest (β = 0.016, p < 0.01), competitor pricing and user interest (β = 0.061, p < 0.01) and a full mediator for the relationship between geometry and user interest (β = 0.087, p < 0.01). Price also served as a partial mediator for the relationships between geometry and purchase (β = -0.079, p < 0.01), appearance and purchase (β = -0.047, p < 0.01), animation and purchase (β = -0.014, p < 0.01), and competitor pricing and purchase (β = -0.055, p < 0.01). In addition, user interest was found to be a partial mediator for the relationships between appearance and purchase (β = 0.037, p < 0.01), animation and purchase (β = 0.065, p < 0.01), and price and purchase (β = 0.152, p < 0.01).

<table><caption>**Table 2**
Hypotheses summary and effects.</caption><thead><tr><th rowspan="2">Hypothesis / Path</th><th rowspan="2"></th><th colspan="2">Original</th><th colspan="2">Individual</th><th colspan="2">Organization</th></tr><tr><th>β</th><th>p</th><th>β</th><th>p</th><th>β</th><th>p</th></tr></thead><tbody><tr><td>H1a</td><td>Geometry → User Interest</td><td>0.011</td><td>0.103</td><td></td><td></td><td></td><td></td></tr><tr><td>H1b</td><td>Geometry → Price</td><td>0.300*</td><td>0.000</td><td>0.303*</td><td>0.000</td><td>0.240*</td><td>0.000</td></tr><tr><td>H2a</td><td>Appearance → User Interest</td><td>0.071*</td><td>0.000</td><td></td><td></td><td></td><td></td></tr><tr><td>H2b</td><td>Appearance → Price</td><td>0.181*</td><td>0.000</td><td>0.172*</td><td>0.000</td><td>0.300*</td><td>0.000</td></tr><tr><td>H3a</td><td>Animation → User Interest</td><td>0.124*</td><td>0.000</td><td></td><td></td><td></td><td></td></tr><tr><td>H3b</td><td>Animation → Price</td><td>0.055*</td><td>0.000</td><td>0.042*</td><td>0.000</td><td>0.090*</td><td>0.000</td></tr><tr><td>H5</td><td>Degree of Com. → Price</td><td>0.009</td><td>0.133</td><td>-0.005</td><td>0.491</td><td>0.120*</td><td>0.000</td></tr><tr><td>H6</td><td>Competitor Pri. → Price</td><td>0.209*</td><td>0.000</td><td>0.201*</td><td>0.000</td><td>0.377*</td><td>0.000</td></tr><tr><td>H9</td><td>Price → User Interest</td><td>0.291</td><td>0.000</td><td>0.305*</td><td>0.000</td><td>0.271*</td><td>0.000</td></tr><tr><td>H4</td><td>User Interest → Purchase</td><td>0.524</td><td>0.000</td><td></td><td></td><td></td><td></td></tr><tr><td>H10</td><td>Price → Purchase</td><td>-0.262*</td><td>0.000</td><td>-0.273*</td><td>0.000</td><td>-0.147*</td><td>0.000</td></tr></tbody></table>

## 5.3. Moderating effects

To test the hypothesized moderating effects (Table 3 & Fig. 6), we performed an MGA in SmartPLS 4 (Ringle et al. 2022). The relationships between degree of competition and price (individual: β = -0.005, organization: β = 0.120) as well as competitor pricing and price (individual: β = 0.201, organization: β = 0.377) are significantly stronger for organizations than for individual sellers, hence, supporting hypotheses H7 and H8. Moreover, in support of H11, the negative relationship between price and purchase indicates a less significant effect for organizations compared to individual sellers. Overall, the results thus indicate significant path coefficient differences for all hypothesized moderating effects between individual sellers and organizations (p < 0.01).

# 6. Discussion and implications

With the transcendence of human lives into virtual space and the looming emergence of a metaverse, it becomes increasingly important to understand the dynamics of virtual asset marketplaces through which virtual 3D assets can be offered, shared and purchased. Given the unique nature of virtual 3D assets, which can be categorized as digital goods, one particular challenge and knowledge gap exists in understanding their pricing, value perception and purchase on 3D asset marketplaces. To address this gap, the present study draws on multi-attribute utility (Schäfer, 2001; Wallenius et al., 2008) and value- and competition-informed pricing theory (Ingenbleek et al., 2003) to empirically explore (1) the relationship of 3D model attributes on the value perception of sellers (i.e., their pricing decisions) and customers (i.e., user interest), (2) how the competitive environment affects the pricing decision, and (3) how user interest and pricing impact the purchase of virtual 3D assets. Results from performing PLS-SEM based on secondary data (i.e., 23.282 3D models and 3.220 user profiles) from the virtual 3D asset platform Sketchfab allow for different interpretations regarding the indicators that influence the purchase of 3D models.

First and foremost, the evaluation shows that the value perception of the sellers regarding the attributes of virtual 3D assets contradicts the user interest. Whereas the results indicate that an advanced geometry is not relevant to arouse the users' interest, advanced appearance and animations do have an impact on the user interest, although the latter seems to attract more user attention than the appearance-related attributes. On the contrary, the geometry of the 3D models has the strongest impact on the pricing decisions of virtual 3D asset creators, followed by the appearance, while advanced animations seem to be only marginally considered. Thus, the relevance of the attributes is perceived inversely by the sellers and customers which implies that the sellers misunderstand what drives the user interest. The discrepancy in the value perception of the attributes may be due to the tendency of virtual 3D asset creators to consider those attributes as most relevant that are associated with the greatest amount of work. Therefore, in line with the

<footnote>* = p < 0.01</footnote>
<figure><img src="image_9.png" /><figcaption>Fig. 7. Research model without mediators.</figcaption></figure>

<table><caption>**Table 3**
Moderating effects of seller type.</caption><thead><tr><th rowspan="2">Hypothesis / Path</th><th rowspan="2"></th><th colspan="2">Individual</th><th colspan="2">Organization</th><th colspan="2">Difference</th></tr><tr><th>β</th><th>p</th><th>β</th><th>p</th><th>β</th><th>p</th></tr></thead><tbody><tr><td>H7</td><td>Degree of Com. → Price</td><td>-0.005</td><td>0.491</td><td>0.120*</td><td>0.000</td><td>0.124*</td><td>0.000</td></tr><tr><td>H8</td><td>Competitor Pri. → Price</td><td>0.201*</td><td>0.000</td><td>0.377*</td><td>0.000</td><td>0.176*</td><td>0.000</td></tr><tr><td>H11</td><td>Price → Purchase</td><td>-0.273*</td><td>0.000</td><td>-0.147*</td><td>0.000</td><td>-0.126*</td><td>0.000</td></tr></tbody></table>

pricing recommendations of virtual 3D asset marketplace providers (Sketchfab, 2023), 3D model creators need to better understand their customers' needs and price their models according to the benefits that they expect from the different attributes, rather than according to the amount of work they invested in the models.

This seems to be particularly evident for individual creators. An additional result of the MGA revealed differences between individual and organizational sellers in the weighting of the model attributes in relation to the anticipated customer value. The MGA shows that individual contributors anticipate the user value inversely (Geometry → Price: Ind: β = 0.303 | Appearance → Price: Ind: β = 0.172 | Animation → Price: Ind: 0.042), whereas organizations evaluate the appearance-based attributes as most important for their pricing decision, followed by the geometry (Geometry → Price: Org: β = 0.240 | Appearance → Price: Org: β = 0.300). Moreover, organizations assess animations significantly higher than individual contributors (Animation → Price: Org: β = 0.090 | path coefficient difference between Ind and Org: p =

-0.048*). Thus, while both types of sellers fail to adapt to the customer's interest in terms of the weighted importance assigned to the product attributes, organizations perform better by including animations in their pricing decision and perceive the geometry of their models as less important. Nevertheless, both organizations and specifically individuals need to strengthen their efforts to understand and address the customer's value perception of 3D model attributes, starting with a higher consideration of animations in their pricing decisions.

However, it must be noted that the overall effect of the 3D model attributes on user interest is lower than expected. One reason for this may be that the attributes play a divergent role in the different product categories of virtual 3D assets. Whereas a highly complex geometry may increase the user interest in the market segment of architectural 3D models, advanced animations may arouse the user interest in 3D models that require movement, such as characters or creatures. Furthermore, the user interest in specific attributes of virtual 3D asset is likely to vary depending on the intended use. For example, customers who need

<footnote>* = p < 0.01</footnote>
moving characters for game environments have different requirements for 3D models than companies who need the 3D model for static 2D renderings. Therefore, other influencing factors that explain the user interest in virtual 3D assets, such as aesthetics or marketing efforts, need to be identified and assessed, while the effect of the model attributes may need to be re-examined based on single products and product category.

In addition to the 3D model attributes, the information about the
competitive environment, i.e., the degree of competition and the
competitor pricing, were emphasized as influencing the pricing decision.
Contrary to the hypotheses, the results suggest that the degree of
competition does not have a significant impact on the price. Hence,
either the 3D model sellers do not inform themselves about the com-
petitors and the number of 3D models in the market segment or they do
not consider the degree of competition as relevant for their pricing de-
cision. This is surprising given the benefits of considering the market
structure for the market performance and the prevalence of the
approach (Hinterhuber, 2008; Ingenbleek et al., 2013). However, the
MGA shows that the negligence of the degree of competition is mainly
driven by individual contributors. Organizations do incorporate infor-
mation about the competitive environment in their pricing decisions.
One reason for the divergence between the different groups of sellers
may be that individual sellers consider the Sketchfab store as a whole
marketplace rather than a segmented market with varying conditions.
Organizations, on the other hand, analyze the market structures and
competitive conditions to gain a competitive advantage based on their
market information. Similarly, the relevance of the competitor pricing is
evaluated differently by the two groups. Overall, the impact of
competitor pricing on the price is highly significant and therefore plays a
major role in the pricing decision. However, organizations ascribe the
competitor prices a higher relevance than individual designers. Thus,
both individual and organizational sellers compare their virtual 3D as-
sets with similar models in the market segment in order not to under- or
overprice their 3D model and set prices in line with their competitors;
but for organizations, the competitor pricing seems to be the most
relevant pricing criterion.

Our findings further indicate that the pricing has a direct effect on the user interest and purchase. Higher-priced models have a higher chance of attracting the users’ interest, which was expected since higher prices can serve as an indicator for product quality. Furthermore, the overall importance of arousing the users’ interest can be observed in the high positive correlation between user interest and purchase. As expected, 3D models that arouse the user interest are more likely to be purchased. However, higher product prices have a negative effect on the purchase decision. This supports the assumption, emphasized in the derivation of the hypotheses, that customers show interest in higher-priced 3D models in the evaluation phase, while they choose the lower-priced option once they have assembled a set of alternative virtual 3D assets in which they are interested. Therefore, 3D model sellers need to strengthen their efforts to convert the user interest in higher-priced products into actual purchases.

Finally, the results imply that organizations are able to access and
use value and competition information to a greater extent than indi-
vidual seller, which in turn leads to a less negative effect of higher vir-
tual 3D asset prices on purchase. Thus, the differences between the two
groups of sellers provide evidence that a higher consideration of value-
and competition-informed pricing has a positive effect on the overall
market performance.

7. Limitations and future research

Besides the theoretical and practical findings, the limitations of the
study and current technological developments suggest three future
research avenues.

7.1. Virtual 3D asset types, attributes, and purpose

Overall, the attributes of the 3D models investigated in this study can
only explain user interest to a limited extent. One reason for the low
explainability may be related to the limitation that the (1) types, (2)
subjective indicators, and the (3) anticipated usage of the virtual 3D
assets have been neglected in this study. It can be assumed that the (1)
value perception of the 3D model attributes is different for, e.g., 3D
models that are “static” (e.g., architecture), that require movement (e.g.,
characters), and for which the appearance is of utmost importance (e.g.,
design objects or decor). Furthermore, (2) subjective indicators, such as
perceived aesthetics, are thought to play an important role in the cus-
tomers’ value perception (Hekkert and Leder, 2008; Yamamoto and
Lambert, 1994) but are not the focus of the study. Lastly, (3) customers
who need virtual 3D assets to create 3D game environments may have
different requirements for certain 3D model attributes than customers
who aim at using the asset for 2D renderings. Accordingly, different
customer requirement may affect the value perception of the specific
attributes. Therefore, future research could focus on (1) re-evaluating
the attributes of 3D models for specific market segments, taking into
account additional aspects that build the overall user utility, such as
psychological factors (Benoit et al., 2019), (2) investigating subjective
indicators that influence the user interest, and (3) nurturing our un-
derstanding of the application context and the different user needs that
drive purchase for specific target groups.

7.2. Intellectual property and user integration

Besides the limitation of the study, the descriptive analysis revealed that most of the virtual 3D assets in the Sketchfab store are created by individuals, as in a number of alternative 3D model marketplaces, while no major brand participates in the store. However, a variety of virtual 3D assets in the store are 3D models of brand products which are thus created by individuals, raising intellectual property concerns which are largely unsolved and unexplored. Hence, intellectual property and pi- racy, which are well-known issues in the realm of information goods (e. g., Avinadav et al., 2014; Cremer and Pestieau, 2009; Wu and Chen, 2008), may become a more important issue for 3D models in the future that require further investigation. On the contrary, the non-availability of product 3D models is today one of the main hurdles for companies to participate in the growing trend of virtual environments. Thus, com- panies may even benefit from collaborations with virtual 3D asset platforms that allow users to create 3D models of their physical prod- ucts. This opportunity has already been recognized by firms such as Ford (Turbosquid, 2015), while other companies focus on removing user- created 3D models from online marketplaces (e.g., BMW lawsuit (SolidSmack, 2016)). Thus, a relevant avenue for future research exists in exploring user integration models for virtual 3D assets.

7.3. AI-based 3D model and texture generation

Finally, recent developments in the realm of artificial intelligence (AI) have led to the emergence of tools that allow the users to automatically generate 3D models and attributes. While earlier approaches focused on automatically generating 2D images based on text input, tools such as Google's DreamFusion (Poole et al., 2022), Luma AI (LumaLabs, 2024) or BariumAI (BariumAI, 2022) – acquired by Unity in 2023 – allow users to automatically generate 3D models and materials. The ability to create 3D models and materials in a matter of minutes based on a short text input will certainly have a major impact on the value and pricing of virtual 3D assets. However, there is little knowledge of the areas in which automatically generated 3D models and materials can be useful, as well as of the prevalence of 3D models created based on AI tools in virtual 3D asset marketplaces.
## 8. Conclusion

The findings provide support that the user interest and the pricing of virtual 3D assets have a high impact on the purchase of virtual 3D assets. However, the results imply that sellers misunderstand what drives the user interest as they weigh the relevance of the virtual 3D asset attributes (geometry, appearance, and animations) inversely, even though organizations seem to have a better understanding of the user interest, as they consider the appearance-based features of the 3D models as the highest pricing principle and animations (marginally). Apart from the attributes of 3D models, the inclusion of competition information (competition-informed pricing) influences the pricing decision and consequently the purchase of virtual 3D assets. As for the relevance weighting of the 3D model attributes, differences can be observed between individual and organizational sellers. For organizations, both the degree of competition and the competitor pricing are highly relevant for setting their prices, whereas individual sellers seem to neglect information about the market structure and consider the competitor prices less than organizations. As a result, the negative effect of higher prices on purchases is significantly less pronounced for organizations, since companies weigh the relevance of the 3D model attributes more according to the user interest (value-informed pricing) and, in particular, seem to pay more attention to the competitive environment (competition-informed pricing).

## Funding

Open Access funding enabled and organized by Project DEAL.

## CRediT authorship contribution statement

**Jakob J. Korbel:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.
**Marc Riar:** Writing – review & editing, Writing – original draft, Validation, Methodology, Investigation, Formal analysis.
**Thorsten Pröhl:** Software, Resources, Data curation.
**Rüdiger Zarnekow:** Writing – review & editing, Supervision.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Data availability

The data allows conclusions to be drawn about the business data and performance of the organization and is therefore not made publicly accessible.

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