Perceived value and price of digital products:The contribution of consumer perception to pricing

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This paper proposes an adjustment to pricing processes of the paid version of a digital product, with the contribution of consumer perception through the collection and understanding of the perceived value of the free version. An instrument was adapted from a multiple factor construct of perceived value to investigate over a hypothetical paid version of WhatsApp. A total of 523 observations were obtained, which were subjected to correlation analysis and partial least square structured equation modeling to identify the contribution of perceived value to pricing. This made it possible to establish that consumers perceive WhatsApp has good value, and the perceived value has predictive strength for the consumer’s perception of the paid version; but the factors of perceived value have different impacts and informative levels, so each one will have different relevance to the pricing. By elucidating these dynamics, our findings may help managers gain essential insights for making informed pricing decisions and encourage them to innovate in pricing as a significant competitive advantage. digital products perceived value pricing PLS-SEM digital consumer behavior WhatsApp freemium Figures Figure 1 Figure 2 Figure 3 Introduction An enormous amount of digital goods and services exist in multiple markets, and most of them are free (Sai, 2010 ) and are used very frequently, regardless of whether there is awareness of their use. Markets of all kinds are created and stimulated around many digital products, and it is worth mentioning that most products could have a digital equivalent. Organizations—even the most traditional ones—find themselves having to produce digital goods and services, and those firms that produce physical products are being forced to provide a digital marketing service to market their physical products. It has also been observed that, in general, organizations have difficulty in choosing the model for making their digital products profitable (Lambrecht et al., 2014; Masuda & Whang, 2021 ; Sai, 2010 ; Van Angeren et al., 2022 ). Lambrecht et al. (2014) reviewed the documentation of the income generation models of companies offering digital goods or services and found that the most frequently used models include a free version of their products. Marketing models in which a product is offered totally or partially for free then lead consumers subsequently to pay for that product or spend money on additional content or updated, subsequent, or serialized versions. Deng et al. ( 2020 ) found that the existence of a free version has a strong positive correlation with an increase in consumption and sales of a subsequent paid version. As possible explanations for this result, they mention that there were changes to the free version when the paid version was issued, such as an update, design changes, or the announcement of the paid version; developers commonly advertised their free version by indirectly generating advertising for the paid version; and downloads of the free version caused the paid version to appear highlighted on the product acquisition service platform, among others. Laatikainen and Ojala ( 2019 ) examined the real problems faced by companies that develop and sell digital products, the most frequent being pricing. The importance of appropriate pricing lies in the fact that price is one of the most important factors that customers consider when purchasing goods (Albari & Safitri, 2018 ; Cauley, 2018 ; Hinterhuber & Liozu, 2014 ; Kotler et al., 2020 ; Kotler & Keller, 2016 ; Laatikainen & Ojala, 2019 ; Lambrecht et al., 2014; Runge et al., 2022 ; Sai, 2010 ; Solomon, 2013 ; Taleizadeh et al., 2021 ). For this reason, most companies tend to use strategies based on value to establish the prices of their digital products (Taleizadeh et al., 2021 ), including a self-assumed added value, the subjective value of the consumer, and value based on underlying technological characteristics (Laatikainen & Ojala, 2019 ), among others. Innovation in pricing can be a significant, but frequently unexplored, source of competitive advantage (Hinterhuber & Liozu, 2014 ). Also, the issue about the digital products pricing, has been highlighted, evidenced, and reviewed (Laatikainen and Ojala, 2019 ; Lambrecht et al., 2014), showing that traditional pricing process have not been fully functional in address it, nor have any solutions been offered. While some authors focus on why consumer buy paid versions or sub-products in freemium strategies (Chuang, 2020 ; Deng et al., 2020 ; Hamari & Keronen, 2017 ); and other ones just try to understand the consumer perspective (Arunrangsiwed, 2021 ; Kim et al., 2011 ; Kouhia, 2017 ; Kwon 2020); we try to functionally use the valuable information, we can get from the already widely spread freemium strategies, to propose a path to address the problem of pricing digital products, whether sub-products or paid versions. In a prototypical demand curve for any product, demand increases when the price decreases, so a price equal to zero would, in turn, result in demand tending to infinity. This, in practice, is not true with products that are of infinite supply, such as digital products, which tend to lose value quickly for consumers, so the simple offer of a free digital product does not guarantee its consumption. This research aims to serve as a foundational exploration, examining how consumer perception of the free version can contribute to pricing for digital products within a freemium business model. Integral to this endeavor is the comprehension of consumers' perceived value of digital goods, especially those distributed at no cost. Literature Review Online firms have difficulty in choosing a model to obtain income, and the question frequently boils down to whether or not to use advertising, keeping in mind that this way to obtain income is most frequently combined with other possible models. Under the possibility of selling content directly, companies usually decide to sell a subscription to access content, because it is difficult to control the resale and piracy of content sold directly (Lambrecht et al., 2014). There are other alternative ways to obtain income, such as the resale of consumer information, which suggest that, in models that include advertising, this advertising can be understood as a resale of the consumer’s eyes to advertisers (Lambrecht et al., 2014; Van Angeren et al., 2022 ). It has also been found that using several approaches to income generation with the same product is advantageous, but also makes it possible to take action to adjust the parameters of the different versions of the product over its useful life in a dynamic market (Appel et al., 2020 ). Appel et al. ( 2020 ) presented a way to characterize the influence of digital products on the consumer using four concepts: Sampling— there is uncertainty about the consumer remaining unsure of the idiosyncratic utility they can expect from product use; Advertising— the free version of the product includes advertising, and the intensity of advertising decreases the utility for the consumer; Price— consumers can choose to eliminate the hassle of ads, for a price; and Satiety— digital products tend to draw people out more quickly compared to traditional products, which reduces their life cycle. Motivating persistence in the use of the product has also been found to be valuable for the producer, regardless of whether advertising is used in the generation model, because the effectiveness and persistence of advertisements can, in turn, increase the optimal price and advertising revenue in each scenario. In the payment-based scenario, advertising increases uncertainty, so the user is willing to pay more for the version without advertising. Likewise, in the model with ads, there is a decrease in uncertainty and the user will generate more advertising revenue (Appel et al., 2020 ). Digital products Cauley ( 2018 ) proposed that producers and marketers should focus on clarifying the existence and potential appropriation for the consumer of digital products, because the power of personalization in digital goods is key to their massification. More and better ways must be found to make the consumer perceive ownership over the given good, because whether these goods reach more people are dependent on this (Kwon, 2021 ). To this end, the attractiveness of digital products for consumers must be maintained, and their customizable nature must be supported and enriched to guarantee that the consumer perceives some type of real belonging to the purchased product (Catapano et al., 2022 ; Cauley, 2018 ; Kwon, 2021 ; Runge et al., 2022 ). Yu-Wei Chuang ( 2020 ) related perceived value to satisfaction and clarified that a very satisfactory digital product still will not be very profitable; products must therefore satisfy, but leave a room for the consumer, seeking greater satisfaction, to make purchases. Chuang showed that the satisfaction offered by the product is not what affects purchase intention most, but rather loyalty, continuity, and consistency of participation in the associated virtual environment activate purchase intention. This importance of loyalty to the virtual environment was empirically tested and suggested an essential mediator between satisfaction and purchase intention. Digital goods are less valued than their physical equivalents, perhaps because a different psychological sense of ownership of the products is generated in the consumer (Atasoy & Morewedge, 2018 ; Catapano et al., 2022 ; Kwon, 2021 ). This sense would be a consequence of the possibility of manipulating and having physical control over the products. People create a property relationship with the goods they acquire, and this property relationship gives the goods they acquire greater perceived value due to positive self-perception (Atasoy & Morewedge, 2018 ). The nature of digital products affects their consumption, which is why they are copied and pirated more than physical goods (Taleizadeh et al., 2021 ). Because digital goods are basically information, their consumption could be influenced by, for example, the way in which that information is presented, whether on a large screen of a desktop computer or on the small screen of a mobile phone, and its use could modify its consumption (Catapano et al., 2022 ; Lambrecht et al., 2014; Van Angeren et al., 2022 ). Piracy has a significant impact on retail prices and order amounts. Piracy in the supply chain leads to lower retail prices, fewer total orders, and reduced profit margins for all members of the supply chain. When the traditional channel and the digital channel grow in the market, however, and each one has a market share, competition is created between the two channels (Taleizadeh et al., 2021 ). There are several forms of digital products, including virtual goods, which are highly contextualized goods associated with a specific virtual environment (Hamari & Keronen, 2017 ), such as items to decorate the user’s profile on a social network or accessories purchased within a video game. In general, such products only have meaning, usefulness, and usability exclusively in their context; their existence is apparent and equally linked to the existence of their virtual environment. These goods have no use or function outside the virtual context for which they were created, and one might think that their consumption is mainly hedonic, but there are complete, complex, and well-defined markets in which virtual goods are traded (Hsieh & Tseng, 2018 ; Runge et al., 2022 ), and content creators, distributors, commercial mediators, consumers of different levels of purchasing power, and varied interests participate in those markets (Hamari & Keronen, 2017 ). Digital consumption The meta-analysis carried out by Hamari and Keronen ( 2017 ) revealed that the reasons why people buy virtual goods are closely related to the platform on which they are sold. They found that there is a strong positive correlation between the attitude toward virtual goods and purchase intention. They also found that most of the factors that were significant predictors of purchasing behavior are directly related to the aspects and design of the platform, beyond general attitudes toward the virtual goods themselves. The consumer’s attitude toward virtual worlds and virtuality itself is also an important factor shaping purchase intention for virtual goods. The differences between the free and paid versions that are most valued by consumers are, in order: the offer of more content, followed by alternative modes or themes, then other additional functions, the possibility for social interactions, and finally, the removal of app advertising and better technical or post-acquisition support. It has also been found that paid products are judged more harshly in user ratings than those that are free (Deng et al., 2020 ). Consumers’ social groups effectively influence the purchase intention of digital products, regardless of whether the social circle involves other consumers of the same type of product who congregated in the same virtual context or if the social circle is external to the virtual environment. In either case, there is a considerable influence on purchase intention (Ding & Li, 2019 ; Hsieh & Tseng, 2018 ; Kim et al., 2011 ). There is, however, a mediating factor: the happiness in the social group studied, something that is relatively linked to the feeling of belonging to a social group, as well as the level of satisfaction and coverage of the social needs that the group brings to the individual environment (Hsieh & Tseng, 2018 ). Perceived Value The concept of value is fundamental in marketing and has been heavily researched (Kotler et al., 2005 ; Kotler & Armstrong, 2013 ; Zeithaml, 1988 ). Perceived value is a key determinant of purchase intention (Chuang, 2020 ). Hannu Kouhia ( 2017 ) studied the perceived value of digital goods; she mentions it as one of the main notions for understanding consumer thinking about marketing in general and digital products, and that there are different approaches to this concept (e.g., Babin et al., 1994; Mattsson, 1991 ; Monroe 1991 ; Sheth et al., 1991 ; Sweeney & Soutar, 2001 , cited in Kouhia, 2017 ). Zeithaml ( 1988 , p. 14) defines perceived value as: “the consumer’s overall assessment of the utility of a product based on perceptions of what is received and what is given” and points out the subjective and individual nature of perceived value, which varies among consumers. Consumers may evaluate the same products in different ways on different occasions. On the other hand, Kotler and Armstrong ( 2013 ) noted that customers do not objectively or accurately judge the costs of products and services, and they establish perceived value as the “customer’s evaluation of the difference between all benefits and all the costs of a market offer, in relation to competing offers” (p. 13). Perceived value is presented in different ways in the literature: as a unidimensional (Agarwal & Teas 2002; Brady & Robertson 1999; Chang & Wildt 1994; Dodds 1991; Hartline & Jones 1996; Kerin et al., 1992; Sweeney et al., 1999, cited in Kouhia 2017 ) or a multidimensional construct, with different attributes, variations, and sub-dimensions that form a holistic representation of a complex phenomenon (Kouhia, 2017 ), such as those seen in Table 1 . Table 1 Perceived Value Constructs Author Dimensions Hartman ( 1967 , 1973) Intrinsic value Extrinsic value Mattsson ( 1991 ) Emotional Practical Logical Wohlfeil and Whelan ( 2006 ) Utilitarian Economic hedonic Sheth et al. ( 1991 ) Emotional Functional Social Monroe ( 1991 ) Perceived benefit / Perceived sacrifice (Ratio) Sweeney and Soutar ( 2001 ) Emotional Social Functional by price Functional by performance Measuring value through factors or components does not necessarily show how these goods are valued, nor is the characterization of each of these factors necessarily well established (Kim et al., 2011 ). So, to test the usefulness of the perceived value construct in the pricing of a paid version of a cost-free digital product, the scale developed by Sweeney and Soutar ( 2001 ; Chuang, 2020 ; Kouhia, 2017 ; Valencia, 2021 ) was used, because it is sufficiently rigorous and robust (Gil & Gonzales, 2008). Of the four factors that constitute perceived value based on constructs, it could be thought that each one, as a contributor to the perception of value by the consumer, could equally contribute to the pricing process from the consumer perspective. To evaluate the perceived value, must evaluate the social perceived value of the free digital product, as well as its possible relation with pricing, while bearing in mind that social influence can have a positive impact in increasing the price of the paid version. The first hypothesis of this paper is thus: H1: The perceived social value of the product is positively related to pricing. The emotional perceived value, “derived from the feelings or affective states that a product generates” (Sweeney & Soutar, 2001 , p. 211) could have a positive relation with pricing, so the second hypothesis is: H2: The perceived emotional value of the product is positively related to pricing. Both functional sub-dimensions could have a positive relation to pricing, because they must be understood, along with the other dimensions, as an increasingly substantial parts of the perceived value. The perceived functional value is related to the price and, in general, to the costs associated with the product, which yield the third hypothesis: H3: The perceived value connected to the product’s free cost is positively related to pricing. The perceived functional value related to the quality, performance, and usability of the product must be taken as being positively related alongside the other dimensions, which pleads to the fourth and final hypothesis of this study: H4: The perceived value connected to the product’s quality is positively related to pricing. It is worth clarifying that pricing here does not refer to the entire pricing process for a new product, where there are factors from various states of the organization to consider. The approach taken here to pricing is in terms of the consumer’s perspective and what this information can contribute to the pricing process. To evaluate the hypotheses, we proposed an analysis using SEM to establish the relationships between the constructs of perceived value and pricing, using the PLS-SEM technique to analyze the model shown in Fig. 1. Method An abductive approach was applied in which hypothesis ware established based on a review of the literature on digital products, given their particular characteristics and how to approach the pricing of paid versions of products previously distributed for free, with the information that these insights can provide about the consumer. Design It is necessary to determine the perceived value of a free digital product, so a survey instrument was established as a data collection strategy; this instrument adapts the one developed by Sweeney and Soutar (2001) applied over a chosen digital product. The selection of an appropriate product to properly evaluate the model was the next matter of importance. After the corresponding validation, data processing, and control of variables, we proposed to begin evaluating and perhaps answering the hypotheses with analysis of correlations between all the variables to observe the influence of the demographic variables: age, sex, income, and education level. We then proposed to proceed to an analysis of perceived value, and finally, use of PLS-SEM for evaluating the strength and vector direction of the relationships between the constructs. SEM methodologies are frequently used in marketing, because they facilitate the organization of hypotheses about the phenomenon studied and offer evidence of the quality and reliability of the measurements regarding the constructs studied (Babin et al., 2008). For the relationships of multifactorial abstract concepts such as perceived value and pricing, among others, partial least squares (PLS) is used to evaluate SEM models (Hair et al., 2021). Product Selection To select the product to be evaluated, various alternatives were considered, such as internet search engines, instant messaging services, and e-books, but it was necessary to choose a product while bearing in mind that the obtained results should be generalizable to most digital products—if not, the results would only allow discussion of a specific type of digital product. After a thorough and detailed review of the options, only a single digital product appeared able to provide all the required characteristics: WhatsApp, the mobile and web application, an instant messaging app and service available for free in all applications stores, meets all the requirements. It is worth mentioning that this product, initially, would be paid after a period of free use, which was communicated to users with a message on the first occasion of use; the free period, 365 days, was not effective in terms of the advised charge. The application was acquired in 2014 by Facebook (now called Meta), which produced an analogous version with additional functions for organizations, known as WhatsApp Business, which also included a paid version. WhatsApp was chosen because it is already available in the market for free and its use is sufficiently widespread, thus facilitating the acquisition of participants for the survey. It is, however, also a product with characteristics that are easily related to the factors of perceived value—that is, emotional value, social value, functional value associated with the price, and functional value associated with quality. Instruments The survey used were adapted from the instrument used by Valencia (2021) as well as the items established by Sweeney and Soutar (2001) with the corresponding translation [1] . The instruments have been previously validated in the studies mentioned, but two pilot tests and one validation were also carried out here, and thematic experts were consulted to confirm and revalidate the quality of the adaptation. Four items were also included to collect information for the pricing construct; the responses to these items were recorded on a 7-point Likert type scale. The part about the functional value associated with price from the Sweeney and Soutar (2001) scale was used as a basis but was modified to more specifically inquire about the perception of the price of a hypothetical paid version of WhatsApp. Survey Distribution and Sampling To facilitate distribution, the survey was reproduced on an ad hoc virtual platform available for free (i.e., Google Forms) which simplified its presentation and ensured it was available on any electronic device with Internet access. Initially, a cognitive validation was carried out with a pilot of 24 participants to verify that the items were comprehensible, and the response options did not represent a considerable cognitive load. This also helped to establish the minimum and recommended sample sizes to evaluate Model 1 (shown in Figure 1). The equation described by Westland (2010, 2012) was therefore applied with the recommended probability level of 0.05 and 0.8 as a statistical power level. In this model, we have 5 latent variables or constructs and 22 observable variables. From the preliminary analysis of the model, the minimum size of the anticipated effect was 0.172. The application of the equation showed that the minimum sample size for the model structure was 88 observations, but the minimum size recommended was 519 observations to detect the effect. A customized link for distribution was available to provide online access to the survey; this link was widely disseminated, along with a graphic piece that provided information about the study and invited voluntary participation. The link was distributed through social networks, instant messaging, emails, and in person. A total of 550 observations were obtained, of which 27 had to be discarded because they did not fulfill the participation criteria—that is, the discarded participants were under the age of 18, did not use the digital product or the responses were not coherent; then, 523 observations were able to use for the data analysis. Ethical Considerations Human Ethics declaration: This study involving human participants was conducted in accordance with the ethical principles of the Declaration of Helsinki. Ethics Approval declaration: Prior to data collection, approval was obtained from the Research Ethics Committee of the Faculty of Economic Sciences of the National University of Colombia - Bogotá Campus (Approval Date: December 14, 2022; Application Date: December 2, 2022, to March 18, 2023). Clinical trial number: not applicable. This study didn't evolve any kind of clinical trial or research. Consent to Participate declaration: Participants were informed that their participation would be voluntary, and they could withdraw from the study at any time without prejudice. To ensure informed consent, participants were provided with a consent form that detailed the study's purpose, the type of data to be collected, and the assurance of confidentiality and anonymity. Data collection was limited to a self-administered electronic survey, minimizing any potential risk to participants beyond that associated with typical internet use. The survey was designed to gather participants' perceptions without any manipulation, intervention, or influence on their behavior. Explicit informed consent was obtained from each participant prior to their inclusion in the study. [1] The survey was presented in Spanish, because the activity was carried out with Colombian participants. Results Several adjustments were made when processing the data to prepare it for subsequent analysis, which was supported in its entirety using R. The descriptive statistics are shown in Table 2 . Table 2 Descriptive Statistics of the Variables Variable n Median MAD Mean SD SE CI Age 523 22 5.93 27.1 11.8 0.514 1.01 MV1 523 7 0 6.53 1.01 0.044 0.087 MV2 523 7 0 6.46 0.937 0.041 0.08 MV3 523 7 0 6.48 1.13 0.049 0.97 MV4 523 7 0 6.33 1.20 0.053 0.103 FV1 523 6 1.48 6.15 1.02 0.045 0.088 FV2 523 7 0 6.30 0.948 0.041 0.081 FV3 523 7 0 6.35 0.885 0.039 0.076 FV4 523 4 1.48 4.17 1.63 0.071 0.14 FV5 523 6 1.48 6.14 0.984 0.043 0.085 EV1 523 7 0 6.16 1.16 0.051 0.1 EV2 523 6 1.48 5.65 1.49 0.065 0.128 EV3 523 4 1.48 4.06 1.67 0.073 0.144 EV4 523 4 1.48 4.50 1.61 0.07 0.138 EV5 523 4 1.48 4.18 1.69 0.074 0.145 SV1 523 5 1.48 4.9 1.82 0.08 0.156 SV2 523 4 1.48 3.84 1.80 0.079 0.155 SV3 523 4 1.48 3.91 1.82 0.08 0.156 SV4 523 4 2.96 3.65 1.93 0.084 0.166 Variable n Median MAD Mean SD SE CI P1 523 2 1.48 2.73 1.92 0.084 0.165 P2 523 3 2.96 3.35 1.94 0.085 0.167 P3 523 4 2.96 3.50 1.94 0.085 0.167 P4 523 4 2.96 3.92 2.12 0.093 0.182 Note. MV, functional perceived value because of the free cost; FV, functional perceived value because of quality; EV, emotional perceived value; SV, social perceived value; and P, price perceived value of the hypothetical paid version. Participants were aged between 18 and 74, with most between 20 and 25 years old, and the distribution according to sex was relatively even, but with a greater presence of the female respondents. At the income and educational levels, there was a predominance of the following responses: “I have no income” and “High school completed”. We can attribute this to the wide participation of young university students; however, there were also representative participations of all levels of education and income except the lowest educational level, which corresponded to not having finished high school. The next step involved verification of assumptions for subsequent tests, particularly the verification of the sample distributions corresponding to each variable. For this purpose, the Shapiro–Wilk test was applied (Pérez, 2000), and all variables were found to have non-parametric distributions. Examining the distributions as well as the variable values, it was found that free digital product customer perceived value was generally positive. The means, as well as the averages, of the variables associated with the perceived functional value, both monetary and with respect to the quality of the service/product, were high; for only one of the five corresponding items, was this not the case, but it was rated at intermediate value. Spearman correlation analysis was then performed for non-parametric samples (Pérez, 2000) between all variables. To facilitate the analysis, data were arranged in a matrix and show graphically in a heat map, which can be seen in Fig. 2, where the x represents uncorrelated pairs, and the size and color of the circles represent a statistically significant correlation in magnitude and vector direction; in other words, the largest circles represent a strong correlation, while the smallest ones a weak correlation, and the green color indicates positive correlation, while the red color indicates negative correlation. In Fig. 2, the internal correlations of the constructs are shown as green boxes, which indicates a strong positive correlation between the price-related variables. Age, income level, and educational level were positively correlated with each other, which denotes consistent coherence within the results. The variables SV1 to SV4 show positive correlations with pricing, which offers a first approximation to not reject H1 . H3 in this first approximation would, however, be rejected given that the observed correlations are negative, but in this case the results are clear for only two of the four variables of the construct. For H2 and H4 , an introductory observation cannot be made, because no evidence of correlation of the observable variables associated with the observed pricing variables was found, so these hypotheses must be evaluated in their entirety using PLS-SEM from the point of view of the relationships that the constructs have with pricing. As a final note of this correlation analysis, it is worth mentioning that the variable FV4 lacks observable correlations; even with the variables of its set of perceived functional value, it has weak correlations and does not correlate with other variables of perceived value, as is common among the rest of the variables of the same construct. FV4 only has a weak correlation with income level. This can be explained given that, according to the distribution graph of this variable, the responses are biased toward the central, neutral answer, which is not very informative, because it does not facilitate the inference of a population trend. This variable must therefore be observed with care and, if necessary, removed for other approaches, models, or subsequent analyses. PLS-SEM Analysis Table 3 illustrates the results associated with the validation process for Model 1 and shows: the indicators of external loads, highlighted with colors according to the construct; cross-loading indicators, which are not highlighted, and reliability indicators. With the evidence found and expressed in Table 3 , it is sufficient to establish the need for three adjustments before of the SEM analysis of Model 1—that is, MV2 and MV3 have external and cross loading indicators that suggest the need to remove these variables. These inconsistencies explain that the Cost-Free construct does not meet the criteria corresponding to the reliability indicators. Likewise, FV4 should be removed because it presents inconsistency at the internal level of the construct, coupled with the observation mentioned in the correlation analysis. As a last adjustment, the variable EV1 was also removed; although its internal consistency indicator was not far from the threshold of 0.708, it did not have a considerable impact in explaining the variance of the construct, so its removal would not have a significant impact. Table 3 Measurement Model Assessment Results After the adjustments, the information was processed again for validation, which yielded the results summarized in Table 4 . All of the cross-loading indicators, highlighted with color, exceed the threshold, thus confirming the validity of all of the observed variables for SEM analysis. The Cronbach’s alpha, as well as the rhoC and rhoA for all constructs, is greater than 0.708, which also confirms the internal consistency of all of the constructs. The AVE indicators for all constructs exceed 0.5, which implies convergent validity. The cross-loading indicators as well as the HTMT are sufficiently below their corresponding thresholds to assume discriminant validity. Table 4 Adjusted Measurement Model Assessment Results Finally, a bootstrap was done with 10,000 resamples for the adjusted Model 1, because it was non-parametric. In general, the results (see Table 5 ) show figures about the corresponding thresholds. Possible collinearity problems were also verified, for which the analysis of variance in inflation factor (VIF) was carried out. No collinearity issues were found, as no value exceeded 0.4, which is below the threshold. After clarifying that the variables did not have collinearity issues, the relevance of the relationships in the model could be verified by analyzing the path coefficients (𝛽) that express the relationships between constructs, as well as the coefficient of determination (R 2 ), which can be found in Table 5 and are shown in Fig. 3. Table 5 Adjusted Measurement Model Assessment Results Pricing R 2 0.135 AdjR 2 0.128 Original Est. Bootstrap Mean Bootstrap SD T Stat. 5% CI 95% CI Cost Free ⇾ Pricing -0.319 -0.324 0.038 -8.422 -0.385 -0.260 Quality ⇾ Pricing 0.125 0.125 0.060 2.092 -0.004 0.201 Emotional ⇾ Pricing 0.006 0.021 0.047 0.133 -0.054 0.093 Social ⇾ Pricing 0.190 0.188 0.048 4.005 0.111 0.267 The magnitude of the relationship coefficients between the constructs, which vary between 0 and 1, indicate the strength of the relationship and suggest that a percentage variation in the endogenous construct—in this case, pricing—is estimated due to the change in the construct of origin—in this case, the factors of perceived value. The direction of the variation—that is, whether the percentage variation increases or decreases—is indicated with the vector operator prefixed to the value (Hair et al. 2021 ), so, for example: emotional value (𝛽 = 0.006) has a very weak positive relationship with pricing, while the value associated with being cost free (𝛽 = −0.319) has a relatively strong negative relationship with pricing. Both functional Value and social value have a positive and moderate relationship with pricing. R 2 is the explanatory power of the model (Shmueli & Koppius, 2011, cited in Hair et al., 2021 , p. 118), whose range of values is also between 0 and 1. In general, values of R 2 around 0.25 are considered weak, 0.50 moderate, and 0.75 substantial (Hair et al., 2011, cited in Hair et al., 2021 , p. 118). However, for this study, the value of R 2 = 0.135 was considered satisfactory, given that the main interest lies in observing the relationships between the constructs, rather than the explanatory power of the model as such. The technique to establish the predictive relevance of the SEM, recommended by Hair et al. ( 2021 , p. 119), involves comparing the errors of the square of the root-mean-square error (RMSE) of the predictive model, with the same indicator of a linear regression of the model (LM). The RMSE values for the model and the linear regression can be seen in Table 6 . The RMSE values for the model are lower for P1, P3, and P4 compared to the RMSE for the linear regression, so the model can thus be considered to have medium predictive power. Table 6 Adjusted Measurement Model Assessment Results RMSE P1 P2 P3 P4 PLS out-of-sample 1.789 1.884 1.882 2.095 LM out-of-sample 1.800 1.870 1.884 2.114 Evaluation of the Hypotheses H1: The perceived social value of the product is positively related to pricing. In the correlation analysis, positive correlation coefficients were identified between the observed variables of perceived social value and those of pricing. In the SEM analysis, a positive statistically significant relationship is evident for the constructs of perceived social value and pricing, with a path coefficient 𝛽 = 0.190. It can thus be inferred that there is indeed a positive relationship between the perceived social value and the pricing of the product, but there is no support to affirm that the relationship between the constructs is strong; it is more appropriate to understand this relationship as moderate. H2: The perceived emotional value of the product is positively related to pricing. In the correlation analysis, no statistically significant correlation coefficients were identified between the observed variables of perceived emotional value and those of pricing, nor was a relationship evident between the corresponding constructs in the SEM analysis, where the relationship observed was non-existent, with path coefficient 𝛽 = 0.006. There was also no notable difference after bootstrapping, which yielded ranges that do not allow the establishment of a relationship between these constructs. It can thus be inferred that no statistically relevant evidence of a relationship between emotional value and pricing of digital products was found. H3: The perceived value because of the product’s free cost is positively related to pricing. In the correlation analysis, negative correlation coefficients were identified between the observed variables of perceived value associated with the free product and those of pricing. In the SEM analysis, a statistically significant negative relationship is evident for the constructs of perceived value associated with free cost and pricing, with path coefficients 𝛽 = −0.319. It can thus be inferred that there is indeed a negative relationship between the value associated with the free cost and the pricing of the product. It should be noted that the observed variables MV2 and MV3 had to be discarded, because they were not sufficiently informative or were unreliable. However, this relationship can be understood as moderately strong and negative. H4: The perceived value because of the product’s quality is positively related to pricing. In the correlation analysis, no statistically significant correlation coefficients were identified between the observed variables of perceived functional value and those of pricing. In the SEM analysis, a positive relationship is evident for the constructs of functional value relative to perceived quality and pricing, with path coefficient 𝛽=0.125, along with mostly positive acceptable ranges to the bootstrapping results. It can thus be inferred that there is a statistically significant positive relationship, but with moderately low strength, between the functional value relative to the perceived quality and the pricing of the product. Discussion The existence of a free digital product, without advertising and well valued, motivates persistence, as discussed by Appel et al. ( 2020 ), which is a requirement to maintain a high valuation of the product. This persistence is noted here, in that practically none of the participants answered “No” to the question: Do you usually/daily use the WhatsApp application? The assessment of the application was achieved without obvious efforts to promote the psychological appropriation and/or existence suggested by authors such as Cauley ( 2018 ), Atasoy and Morewedage (2018), Kwon (2018), or Runge et al. ( 2022 ), although in this case this can be partially attributed to its personalization characteristics (Cauley, 2018 ). In any case, the suggestion of these authors makes sense mainly for digital products that have a current physical equivalent, because the digital and the physical product compete within the market (Kim et al., 2021; Taleizadeh et al., 2021 ); the digital product must therefore generate a perception of existence and psychological appropriation, in addition to having possibilities for customization, to survive and compete in the market with the physical equivalent. Regarding the constructs evaluated, it was found that the perceived value of a free digital product, as determined, with the scale developed by Sweeney and Soutar ( 2001 ), turned out to be a valid indicator, with statistically representative strength to predict the consumer’s perception of the product price for a paid version of the same digital product. To clarify this point, however, it is necessary to review each of the factors that constitute the perceived value according to the aforementioned scale in greater detail. First, the perceived social value of the free digital product evaluated has intermediate evaluations, which, for this scale, should be understood as indifference—in other words, it is a neither a good nor a bad evaluation. The relationship of this construct with pricing, which was established with a moderately low strength, indicates that a consumer knowing the perceived social value of the free version, can explain around 19% of variance in establishing the suggested price of the paid version. The findings for perceived social value indicate that even a digital product with important social implications, such as a social network or a messaging service, generates a perception of social value close to indifference for its consumers, even when it is available for free. This does not contradict the results of the studies by Hsieh and Tseng ( 2018 ), Ding and Li ( 2019 ), or Kim et al. ( 2011 ), because they focused directly on specifically defined social groups, whether in physical presence, or implicit virtual context associated with the digital product. Here only consumer’s perception was evaluated, excluding their social dynamics and constructs such as influence, happiness, or the sense of belonging of the individual in the social group. Second, the indicators of perceived emotional value are intermediate with a positive trend—that is, unlike those of perceived social value, they are positive, but still close to indifference. The emotional factor turns out not to have a considerable effect for the pricing of the paid version; in other words, users are not particularly affected on an emotional level by the product, in either its free or paid version, so it could be said that the use of the evaluated digital product does not have an impact on an emotional level, and in turn, the perceived emotional value is not informative in relation to the digital product. It is thus concluded that the emotional assessment of the product can be dispensed with in the pricing process. Third, the functional value received positive evaluations in terms of the free price and in relation to the quality of the product. It can thus be said that the consumers’ perceived value of the evaluated free digital product is mainly determined by the product’s functionality. Indeed, the functional value related to the free cost of the product is the factor that is most telling and informative for pricing, taking into account that it shows a path coefficient of 𝛽 = −0.319 in the PLS-SEM evaluation. This means that this sub-construct explains more than 30% variance in attitudes towards pricing, so this construct contributes more weight to pricing. The value perceived by characteristics related to the quality or in general to the functionality of the product explained about 13%, given the path coefficient of 𝛽=0.125. From the results of the evaluation of H4, it can be concluded that the relationship between the functional value associated with the free cost of the digital product evaluated and the pricing of the paid version was negative and of moderately high strength. This means that if the consumer of the digital product highly values the fact that it is cost free, he or she will be more likely to reject the paid version, so the price would have to be relatively lower for this consumer to pay for the corresponding paid version. This result also implies, however, that if the consumer rating based on the product being free is low or negative, then the consumer, who is already a frequent user of the product, will be more receptive to a paid version; the suggested price could thus be higher, and the user would still be willing to purchase it. Here is where we can see the consequence of having a free version of the product available, but also, why is so relevant, for the pricing of the paid version, to know and understand the costumer’s perceived value over the free version. At this point it is necessary to highlight that H3 was established to evaluate the relationship between the perceived value associated with the free digital product and the pricing of the paid version of the same product; this was structured with the same tendency as hypotheses H1, H2, and H4, because it referred to the constituent factors of perceived value, such that the four hypotheses respond to a single tendency of perceived value in general. This structure for the evaluation agrees with the findings of the literature review (Chuang, 2020 ; Kouhia, 2017 ; Sweney & Swoutar, 2001; Valencia, 2021 ). The conclusion derived from the evaluation of H3 and from the analysis of the quantitative results explains, in practical terms, why H3 is ultimately rejected. It also makes sense—even prior to the evaluation—and implies a contradiction in proposing a positive relationship in the formulation of H3, such that it is appropriate to evaluate the perceived value factors differentially according to the object and objectives of the study. This contrasts with Kouhia ( 2017 ), who, although using a qualitative method, treated the meaning of the factors indistinctly, and with Valencia ( 2021 ), who directly evaluated all of the factors of the construct with the same tendency precisely because they are part of the same construct. Valencia ( 2021 ) showed statistically significant evidence for the relationship between perceived value and loyalty. Chuang ( 2020 ) related loyalty, in terms of continuity and consistency in the use of the product, with a greater possibility of acquiring sub-products, as well as an inverse relationship with satisfaction, which implies that the greater satisfaction with the digital product, the lower the purchase intention. The results of this study are consistent with those findings, as the participants denoted loyalty in terms of constancy and continuity in the use of the product, as well as the product’s high perceived value, which denotes a margin of dissatisfaction framed by the perceived functional value. From the consumer perspective, this dissatisfaction can generate the intention to purchase the paid version, because if the application were absolutely satisfactory, with a functional value absolutely placed at the maximum margin of the range, nothing would justify a paid version. Likewise, if the product—of hypothetical mandatory and/or unavoidable use—were highly unsatisfactory, or if the functional value were at the lower end of the range, then the intention to acquire a paid version is likely to be higher, within reason (Appel et al., 2020 ). It could also be said that this applies to virtual products as a subset of digital products, as mentioned by Hamari and Keronen ( 2017 ) in highly contextualized markets, which exhibit similar behaviors to digital markets in general (Hsieh & Tseng, 2018 ; Runge et al. 2022 ). The results presented for the construct of perceived value, as a whole and from each of its factors, suggest that it would be possible to set a potential price for a digital product that is distributed for free based on the operationalization of the scale indicators. A basic reference would also be required to allow this abstract information about valuation to be translated into monetary terms, because the scale itself does not inquire about the numerical value of the price, but rather about the valuation perceived by the consumer. Contributions to Market and General Management The results related to age, income, and education levels are likely to be useful for the segmentation and definition of the target audience when a free digital product is going to be launched on the market. These results will make it possible to better locate the segment toward which marketing efforts and resources should be directed, and to position the product in order to follow the procedure recommended by the subsequent placement of a paid version. Based on the findings of this study, it is clear why organizations that produce digital goods and services can obtain an income through marketing models that involve the free distribution of their products, which is very relevant for digital product markets. There was already evidence of this (Deng et al., 2020 ), but it was limited to observing the methods and factors that can generate benefits despite the product being free, while here a cause is offered for the phenomenon, where the perceived value of the free version can even help to predict the reception of the paid version. The findings associated with the perceived value suggest that the reasons for purchasing later paid versions are related to the value that the person attributes to the free digital product. A higher rating may therefore result in a higher potential price for the paid version. Organizations should thus try to include and transfer the greatest possible value in their free digital products, to raise the user’s evaluation and increase the possibility of acquiring subsequent paid versions. When an organization is engaged in pricing, it will be valuable to include information related to the consumer’s perspective and value assigned to the free product and what they could give to the paid version. This recommendation focuses on organizations that produce digital goods and services in response to the difficulties they may have in establishing the price of their products (Appel et al., 2020 ; Laatikainen & Ojala, 2019 ; Lambrecht et al., 2014). Limitations and Further Research There is no technique that makes it possible to obtain a potential price directly from the perceived value construct. The price must therefore be found using another construct as a reference, for which there is an established technique to find the price—such as willingness to pay, because the price can be found using Van Westendorp’s price sensitivity meter. The analysis developed in this study establishes the foundations to facilitate a subsequent study to develop a pricing process based on the operationalization of the indicators of perceived value, provided that the aforementioned drawbacks can be overcome. It can be addressed either the use of the price taken from another perspective than the one presented here, or an alternative reference can be found that allows a change of proportion from the relative magnitudes of the perceived value indicators toward a concrete monetary value. Finally, it is important to mention that there is quite few knowledge in the field related to digital markets, which also offer multiple research opportunities. It would therefore be valuable to replicate this study with other digital products and samples from more regionally, socially, and culturally diverse populations. There is much more to study and research in this area that would be valuable both for the study of organizations and their management, as well as for other fields, particularly the social, human, and information sciences. Declarations Acknowledgments Funding Declaration: This research received no specific grant from any funding agency, commercial or not-for-profit. Funding Declaration: This research received no specific grant from any funding agency, commercial or not-for-profit. Author Contribution The three authors participated in the design and development of the research, as well as in the writing and correction of the manuscript. Data Availability Data supporting the research results is held by the lead author in an authorized institutional repository. 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Markets of all kinds are created and stimulated around many digital products, and it is worth mentioning that most products could have a digital equivalent.\u003c/p\u003e \u003cp\u003eOrganizations\u0026mdash;even the most traditional ones\u0026mdash;find themselves having to produce digital goods and services, and those firms that produce physical products are being forced to provide a digital marketing service to market their physical products. It has also been observed that, in general, organizations have difficulty in choosing the model for making their digital products profitable (Lambrecht et al., 2014; Masuda \u0026amp; Whang, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sai, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Van Angeren et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLambrecht et al. (2014) reviewed the documentation of the income generation models of companies offering digital goods or services and found that the most frequently used models include a free version of their products. Marketing models in which a product is offered totally or partially for free then lead consumers subsequently to pay for that product or spend money on additional content or updated, subsequent, or serialized versions.\u003c/p\u003e \u003cp\u003eDeng et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found that the existence of a free version has a strong positive correlation with an increase in consumption and sales of a subsequent paid version. As possible explanations for this result, they mention that there were changes to the free version when the paid version was issued, such as an update, design changes, or the announcement of the paid version; developers commonly advertised their free version by indirectly generating advertising for the paid version; and downloads of the free version caused the paid version to appear highlighted on the product acquisition service platform, among others.\u003c/p\u003e \u003cp\u003eLaatikainen and Ojala (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) examined the real problems faced by companies that develop and sell digital products, the most frequent being pricing. The importance of appropriate pricing lies in the fact that price is one of the most important factors that customers consider when purchasing goods (Albari \u0026amp; Safitri, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Cauley, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Hinterhuber \u0026amp; Liozu, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kotler et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kotler \u0026amp; Keller, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Laatikainen \u0026amp; Ojala, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lambrecht et al., 2014; Runge et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sai, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Solomon, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Taleizadeh et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For this reason, most companies tend to use strategies based on value to establish the prices of their digital products (Taleizadeh et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), including a self-assumed added value, the subjective value of the consumer, and value based on underlying technological characteristics (Laatikainen \u0026amp; Ojala, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), among others.\u003c/p\u003e \u003cp\u003eInnovation in pricing can be a significant, but frequently unexplored, source of competitive advantage (Hinterhuber \u0026amp; Liozu, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Also, the issue about the digital products pricing, has been highlighted, evidenced, and reviewed (Laatikainen and Ojala, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lambrecht et al., 2014), showing that traditional pricing process have not been fully functional in address it, nor have any solutions been offered. While some authors focus on why consumer buy paid versions or sub-products in freemium strategies (Chuang, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Deng et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hamari \u0026amp; Keronen, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e); and other ones just try to understand the consumer perspective (Arunrangsiwed, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Kouhia, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kwon 2020); we try to functionally use the valuable information, we can get from the already widely spread freemium strategies, to propose a path to address the problem of pricing digital products, whether sub-products or paid versions.\u003c/p\u003e \u003cp\u003eIn a prototypical demand curve for any product, demand increases when the price decreases, so a price equal to zero would, in turn, result in demand tending to infinity. This, in practice, is not true with products that are of infinite supply, such as digital products, which tend to lose value quickly for consumers, so the simple offer of a free digital product does not guarantee its consumption. This research aims to serve as a foundational exploration, examining how consumer perception of the free version can contribute to pricing for digital products within a freemium business model. Integral to this endeavor is the comprehension of consumers' perceived value of digital goods, especially those distributed at no cost.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eOnline firms have difficulty in choosing a model to obtain income, and the question frequently boils down to whether or not to use advertising, keeping in mind that this way to obtain income is most frequently combined with other possible models. Under the possibility of selling content directly, companies usually decide to sell a subscription to access content, because it is difficult to control the resale and piracy of content sold directly (Lambrecht et al., 2014).\u003c/p\u003e\n\u003cp\u003eThere are other alternative ways to obtain income, such as the resale of consumer information, which suggest that, in models that include advertising, this advertising can be understood as a resale of the consumer\u0026rsquo;s eyes to advertisers (Lambrecht et al., 2014; Van Angeren et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). It has also been found that using several approaches to income generation with the same product is advantageous, but also makes it possible to take action to adjust the parameters of the different versions of the product over its useful life in a dynamic market (Appel et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eAppel et al. (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) presented a way to characterize the influence of digital products on the consumer using four concepts: \u003cem\u003eSampling\u0026mdash;\u003c/em\u003ethere is uncertainty about the consumer remaining unsure of the idiosyncratic utility they can expect from product use; \u003cem\u003eAdvertising\u0026mdash;\u003c/em\u003ethe free version of the product includes advertising, and the intensity of advertising decreases the utility for the consumer; \u003cem\u003ePrice\u0026mdash;\u003c/em\u003econsumers can choose to eliminate the hassle of ads, for a price; and \u003cem\u003eSatiety\u0026mdash;\u003c/em\u003edigital products tend to draw people out more quickly compared to traditional products, which reduces their life cycle. Motivating persistence in the use of the product has also been found to be valuable for the producer, regardless of whether advertising is used in the generation model, because the effectiveness and persistence of advertisements can, in turn, increase the optimal price and advertising revenue in each scenario. In the payment-based scenario, advertising increases uncertainty, so the user is willing to pay more for the version without advertising. Likewise, in the model with ads, there is a decrease in uncertainty and the user will generate more advertising revenue (Appel et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003cp\u003eDigital products\u003c/p\u003e\n \u003cp\u003eCauley (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) proposed that producers and marketers should focus on clarifying the existence and potential appropriation for the consumer of digital products, because the power of personalization in digital goods is key to their massification. More and better ways must be found to make the consumer perceive ownership over the given good, because whether these goods reach more people are dependent on this (Kwon, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). To this end, the attractiveness of digital products for consumers must be maintained, and their customizable nature must be supported and enriched to guarantee that the consumer perceives some type of real belonging to the purchased product (Catapano et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Cauley, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kwon, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Runge et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eYu-Wei Chuang (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) related perceived value to satisfaction and clarified that a very satisfactory digital product still will not be very profitable; products must therefore satisfy, but leave a room for the consumer, seeking greater satisfaction, to make purchases. Chuang showed that the satisfaction offered by the product is not what affects purchase intention most, but rather loyalty, continuity, and consistency of participation in the associated virtual environment activate purchase intention. This importance of loyalty to the virtual environment was empirically tested and suggested an essential mediator between satisfaction and purchase intention.\u003c/p\u003e\n \u003cp\u003eDigital goods are less valued than their physical equivalents, perhaps because a different psychological sense of ownership of the products is generated in the consumer (Atasoy \u0026amp; Morewedge, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Catapano et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kwon, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). This sense would be a consequence of the possibility of manipulating and having physical control over the products. People create a property relationship with the goods they acquire, and this property relationship gives the goods they acquire greater perceived value due to positive self-perception (Atasoy \u0026amp; Morewedge, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe nature of digital products affects their consumption, which is why they are copied and pirated more than physical goods (Taleizadeh et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Because digital goods are basically information, their consumption could be influenced by, for example, the way in which that information is presented, whether on a large screen of a desktop computer or on the small screen of a mobile phone, and its use could modify its consumption (Catapano et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lambrecht et al., 2014; Van Angeren et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Piracy has a significant impact on retail prices and order amounts. Piracy in the supply chain leads to lower retail prices, fewer total orders, and reduced profit margins for all members of the supply chain. When the traditional channel and the digital channel grow in the market, however, and each one has a market share, competition is created between the two channels (Taleizadeh et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThere are several forms of digital products, including virtual goods, which are highly contextualized goods associated with a specific virtual environment (Hamari \u0026amp; Keronen, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e), such as items to decorate the user\u0026rsquo;s profile on a social network or accessories purchased within a video game. In general, such products only have meaning, usefulness, and usability exclusively in their context; their existence is apparent and equally linked to the existence of their virtual environment. These goods have no use or function outside the virtual context for which they were created, and one might think that their consumption is mainly hedonic, but there are complete, complex, and well-defined markets in which virtual goods are traded (Hsieh \u0026amp; Tseng, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Runge et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), and content creators, distributors, commercial mediators, consumers of different levels of purchasing power, and varied interests participate in those markets (Hamari \u0026amp; Keronen, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eDigital consumption\u003c/h3\u003e\n\u003cp\u003eThe meta-analysis carried out by Hamari and Keronen (\u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) revealed that the reasons why people buy virtual goods are closely related to the platform on which they are sold. They found that there is a strong positive correlation between the attitude toward virtual goods and purchase intention. They also found that most of the factors that were significant predictors of purchasing behavior are directly related to the aspects and design of the platform, beyond general attitudes toward the virtual goods themselves. The consumer\u0026rsquo;s attitude toward virtual worlds and virtuality itself is also an important factor shaping purchase intention for virtual goods.\u003c/p\u003e\n\u003cp\u003eThe differences between the free and paid versions that are most valued by consumers are, in order: the offer of more content, followed by alternative modes or themes, then other additional functions, the possibility for social interactions, and finally, the removal of app advertising and better technical or post-acquisition support. It has also been found that paid products are judged more harshly in user ratings than those that are free (Deng et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eConsumers\u0026rsquo; social groups effectively influence the purchase intention of digital products, regardless of whether the social circle involves other consumers of the same type of product who congregated in the same virtual context or if the social circle is external to the virtual environment. In either case, there is a considerable influence on purchase intention (Ding \u0026amp; Li, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hsieh \u0026amp; Tseng, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kim et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). There is, however, a mediating factor: the happiness in the social group studied, something that is relatively linked to the feeling of belonging to a social group, as well as the level of satisfaction and coverage of the social needs that the group brings to the individual environment (Hsieh \u0026amp; Tseng, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003ePerceived Value\u003c/h3\u003e\n\u003cp\u003eThe concept of value is fundamental in marketing and has been heavily researched (Kotler et al., \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e; Kotler \u0026amp; Armstrong, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Zeithaml, \u003cspan class=\"CitationRef\"\u003e1988\u003c/span\u003e). Perceived value is a key determinant of purchase intention (Chuang, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Hannu Kouhia (\u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) studied the perceived value of digital goods; she mentions it as one of the main notions for understanding consumer thinking about marketing in general and digital products, and that there are different approaches to this concept (e.g., Babin et al., 1994; Mattsson, \u003cspan class=\"CitationRef\"\u003e1991\u003c/span\u003e; Monroe \u003cspan class=\"CitationRef\"\u003e1991\u003c/span\u003e; Sheth et al., \u003cspan class=\"CitationRef\"\u003e1991\u003c/span\u003e; Sweeney \u0026amp; Soutar, \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e, cited in Kouhia, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eZeithaml (\u003cspan class=\"CitationRef\"\u003e1988\u003c/span\u003e, p. 14) defines perceived value as: \u0026ldquo;the consumer\u0026rsquo;s overall assessment of the utility of a product based on perceptions of what is received and what is given\u0026rdquo; and points out the subjective and individual nature of perceived value, which varies among consumers. Consumers may evaluate the same products in different ways on different occasions. On the other hand, Kotler and Armstrong (\u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e) noted that customers do not objectively or accurately judge the costs of products and services, and they establish perceived value as the \u0026ldquo;customer\u0026rsquo;s evaluation of the difference between all benefits and all the costs of a market offer, in relation to competing offers\u0026rdquo; (p. 13). Perceived value is presented in different ways in the literature: as a unidimensional (Agarwal \u0026amp; Teas 2002; Brady \u0026amp; Robertson 1999; Chang \u0026amp; Wildt 1994; Dodds 1991; Hartline \u0026amp; Jones 1996; Kerin et al., 1992; Sweeney et al., 1999, cited in Kouhia \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) or a multidimensional construct, with different attributes, variations, and sub-dimensions that form a holistic representation of a complex phenomenon (Kouhia, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e), such as those seen in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003ePerceived Value Constructs\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAuthor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eDimensions\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHartman (\u003cspan class=\"CitationRef\"\u003e1967\u003c/span\u003e, \u003cstrong\u003e1973)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eIntrinsic value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eExtrinsic value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMattsson (\u003cspan class=\"CitationRef\"\u003e1991\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmotional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePractical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLogical\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWohlfeil and Whelan (\u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eUtilitarian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eEconomic hedonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSheth et al. (\u003cspan class=\"CitationRef\"\u003e1991\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmotional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFunctional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocial\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonroe (\u003cspan class=\"CitationRef\"\u003e1991\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003ePerceived benefit / Perceived sacrifice (Ratio)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSweeney and Soutar (\u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmotional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFunctional by price\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFunctional by performance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eMeasuring value through factors or components does not necessarily show how these goods are valued, nor is the characterization of each of these factors necessarily well established (Kim et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). So, to test the usefulness of the perceived value construct in the pricing of a paid version of a cost-free digital product, the scale developed by Sweeney and Soutar (\u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e; Chuang, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kouhia, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Valencia, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) was used, because it is sufficiently rigorous and robust (Gil \u0026amp; Gonzales, 2008).\u003c/p\u003e\n\u003cp\u003eOf the four factors that constitute perceived value based on constructs, it could be thought that each one, as a contributor to the perception of value by the consumer, could equally contribute to the pricing process from the consumer perspective. To evaluate the perceived value, must evaluate the social perceived value of the free digital product, as well as its possible relation with pricing, while bearing in mind that social influence can have a positive impact in increasing the price of the paid version. The first hypothesis of this paper is thus:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eH1: The perceived social value of the product is positively related to pricing.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe emotional perceived value, \u0026ldquo;derived from the feelings or affective states that a product generates\u0026rdquo; (Sweeney \u0026amp; Soutar, \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e, p. 211) could have a positive relation with pricing, so the second hypothesis is:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eH2: The perceived emotional value of the product is positively related to pricing.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBoth functional sub-dimensions could have a positive relation to pricing, because they must be understood, along with the other dimensions, as an increasingly substantial parts of the perceived value. The perceived functional value is related to the price and, in general, to the costs associated with the product, which yield the third hypothesis:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eH3: The perceived value connected to the product\u0026rsquo;s free cost is positively related to pricing.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe perceived functional value related to the quality, performance, and usability of the product must be taken as being positively related alongside the other dimensions, which pleads to the fourth and final hypothesis of this study:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eH4: The perceived value connected to the product\u0026rsquo;s quality is positively related to pricing.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIt is worth clarifying that pricing here does not refer to the entire pricing process for a new product, where there are factors from various states of the organization to consider. The approach taken here to pricing is in terms of the consumer\u0026rsquo;s perspective and what this information can contribute to the pricing process.\u003c/p\u003e\n\u003cp\u003eTo evaluate the hypotheses, we proposed an analysis using SEM to establish the relationships between the constructs of perceived value and pricing, using the PLS-SEM technique to analyze the model shown in Fig. 1.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003eAn abductive approach was applied in which hypothesis ware established based on a review of the literature on digital products, given their particular characteristics and how to approach the pricing of paid versions of products previously distributed for free, with the information that these insights can provide about the consumer.\u003c/p\u003e\n\u003cp\u003eDesign\u003c/p\u003e\n\u003cp\u003eIt is necessary to determine the perceived value of a free digital product, so a survey instrument was established as a data collection strategy; this instrument adapts the one developed by Sweeney and Soutar (2001) applied over a chosen digital product. The selection of an appropriate product to properly evaluate the model was the next matter of importance. After the corresponding validation, data processing, and control of variables, we proposed to begin evaluating and perhaps answering the hypotheses with analysis of correlations between all the variables to observe the influence of the demographic variables: age, sex, income, and education level. We then proposed to proceed to an analysis of perceived value, and finally, use of PLS-SEM for evaluating the strength and vector direction of the relationships between the constructs. SEM methodologies are frequently used in marketing, because they facilitate the organization of hypotheses about the phenomenon studied and offer evidence of the quality and reliability of the measurements regarding the constructs studied (Babin et al., 2008). For the relationships of multifactorial abstract concepts such as perceived value and pricing, among others, partial least squares (PLS) is used to evaluate SEM models (Hair et al., 2021).\u003c/p\u003e\n\u003cp\u003eProduct Selection\u003c/p\u003e\n\u003cp\u003eTo select the product to be evaluated, various alternatives were considered, such as internet search engines, instant messaging services, and e-books, but it was necessary to choose a product while bearing in mind that the obtained results should be generalizable to most digital products\u0026mdash;if not, the results would only allow discussion of a specific type of digital product. After a thorough and detailed review of the options, only a single digital product appeared able to provide all the required characteristics: WhatsApp, the mobile and web application, an instant messaging app and service available for free in all applications stores, meets all the requirements. It is worth mentioning that this product, initially, would be paid after a period of free use, which was communicated to users with a message on the first occasion of use; the free period, 365 days, was not effective in terms of the advised charge. The application was acquired in 2014 by Facebook (now called Meta), which produced an analogous version with additional functions for organizations, known as WhatsApp Business, which also included a paid version. WhatsApp was chosen because it is already available in the market for free and its use is sufficiently widespread, thus facilitating the acquisition of participants for the survey. It is, however, also a product with characteristics that are easily related to the factors of perceived value\u0026mdash;that is, emotional value, social value, functional value associated with the price, and functional value associated with quality.\u003c/p\u003e\n\u003cp\u003eInstruments\u003c/p\u003e\n\u003cp\u003eThe survey used were adapted from the instrument used by Valencia (2021) as well as the items established by Sweeney and Soutar (2001) with the corresponding translation\u003csup\u003e\u003csup\u003e[1]\u003c/sup\u003e\u003c/sup\u003e. The instruments have been previously validated in the studies mentioned, but two pilot tests and one validation were also carried out here, and thematic experts were consulted to confirm and revalidate the quality of the adaptation. Four items were also included to collect information for the pricing construct; the responses to these items were recorded on a 7-point Likert type scale. The part about the functional value associated with price from the Sweeney and Soutar (2001) scale was used as a basis but was modified to more specifically inquire about the perception of the price of a hypothetical paid version of WhatsApp.\u003c/p\u003e\n\u003cp\u003eSurvey Distribution and Sampling\u003c/p\u003e\n\u003cp\u003eTo facilitate distribution, the survey was reproduced on an ad hoc virtual platform available for free (i.e., Google Forms) which simplified its presentation and ensured it was available on any electronic device with Internet access.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInitially, a cognitive validation was carried out with a pilot of 24 participants to verify that the items were comprehensible, and the response options did not represent a considerable cognitive load. This also helped to establish the minimum and recommended sample sizes to evaluate Model 1 (shown in Figure 1). The equation described by Westland (2010, 2012) was therefore applied with the recommended probability level of 0.05 and 0.8 as a statistical power level. In this model, we have 5 latent variables or constructs and 22 observable variables. From the preliminary analysis of the model, the minimum size of the anticipated effect was 0.172. The application of the equation showed that the minimum sample size for the model structure was 88 observations, but the minimum size recommended was 519 observations to detect the effect.\u003c/p\u003e\n\u003cp\u003eA customized link for distribution was available to provide online access to the survey; this link was widely disseminated, along with a graphic piece that provided information about the study and invited voluntary participation. The link was distributed through social networks, instant messaging, emails, and in person. A total of 550 observations were obtained, of which 27 had to be discarded because they did not fulfill the participation criteria\u0026mdash;that is, the discarded participants were under the age of 18, did not use the digital product or the responses were not coherent; then, 523 observations were able to use for the data analysis.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthical Considerations\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics declaration:\u003c/strong\u003e This study involving human participants was conducted in accordance with the ethical principles of the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval declaration:\u003c/strong\u003e Prior to data collection, approval was obtained from the Research Ethics Committee of the Faculty of Economic Sciences of the National University of Colombia - Bogot\u0026aacute; Campus (Approval Date: December 14, 2022; Application Date: December 2, 2022, to March 18, 2023). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e not applicable. This study didn\u0026apos;t evolve any kind of clinical trial or research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate declaration:\u003c/strong\u003e Participants were informed that their participation would be voluntary, and they could withdraw from the study at any time without prejudice. To ensure informed consent, participants were provided with a consent form that detailed the study\u0026apos;s purpose, the type of data to be collected, and the assurance of confidentiality and anonymity. Data collection was limited to a self-administered electronic survey, minimizing any potential risk to participants beyond that associated with typical internet use. The survey was designed to gather participants\u0026apos; perceptions without any manipulation, intervention, or influence on their behavior. \u003cu\u003eExplicit informed consent was obtained from each participant prior to their inclusion in the study.\u003c/u\u003e\u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u003csup\u003e[1]\u003c/sup\u003e\u003c/sup\u003e The survey was presented in Spanish, because the activity was carried out with Colombian participants.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eSeveral adjustments were made when processing the data to prepare it for subsequent analysis, which was supported in its entirety using R. The descriptive statistics are shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eDescriptive Statistics of the Variables\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMAD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMV1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMV2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMV3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMV4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFV1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFV2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFV3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFV4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFV5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEV1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEV2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEV3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEV4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEV5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSV1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSV2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSV3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSV4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003cem\u003eNote.\u003c/em\u003e MV, functional perceived value because of the free cost; FV, functional perceived value because of quality; EV, emotional perceived value; SV, social perceived value; and P, price perceived value of the hypothetical paid version.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eParticipants were aged between 18 and 74, with most between 20 and 25 years old, and the distribution according to sex was relatively even, but with a greater presence of the female respondents. At the income and educational levels, there was a predominance of the following responses: \u0026ldquo;I have no income\u0026rdquo; and \u0026ldquo;High school completed\u0026rdquo;. We can attribute this to the wide participation of young university students; however, there were also representative participations of all levels of education and income except the lowest educational level, which corresponded to not having finished high school.\u003c/p\u003e\n\u003cp\u003eThe next step involved verification of assumptions for subsequent tests, particularly the verification of the sample distributions corresponding to each variable. For this purpose, the Shapiro\u0026ndash;Wilk test was applied (P\u0026eacute;rez, 2000), and all variables were found to have non-parametric distributions. Examining the distributions as well as the variable values, it was found that free digital product customer perceived value was generally positive. The means, as well as the averages, of the variables associated with the perceived functional value, both monetary and with respect to the quality of the service/product, were high; for only one of the five corresponding items, was this not the case, but it was rated at intermediate value.\u003c/p\u003e\n\u003cp\u003eSpearman correlation analysis was then performed for non-parametric samples (P\u0026eacute;rez, 2000) between all variables. To facilitate the analysis, data were arranged in a matrix and show graphically in a heat map, which can be seen in Fig. 2, where the \u003cem\u003ex\u003c/em\u003e represents uncorrelated pairs, and the size and color of the circles represent a statistically significant correlation in magnitude and vector direction; in other words, the largest circles represent a strong correlation, while the smallest ones a weak correlation, and the green color indicates positive correlation, while the red color indicates negative correlation.\u003c/p\u003e\n\u003cp\u003eIn Fig.\u0026nbsp;2, the internal correlations of the constructs are shown as green boxes, which indicates a strong positive correlation between the price-related variables. Age, income level, and educational level were positively correlated with each other, which denotes consistent coherence within the results.\u003c/p\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003cp\u003eThe variables SV1 to SV4 show positive correlations with pricing, which offers a first approximation to not reject \u003cem\u003eH1\u003c/em\u003e. \u003cem\u003eH3\u003c/em\u003e in this first approximation would, however, be rejected given that the observed correlations are negative, but in this case the results are clear for only two of the four variables of the construct. For \u003cem\u003eH2\u003c/em\u003e and \u003cem\u003eH4\u003c/em\u003e, an introductory observation cannot be made, because no evidence of correlation of the observable variables associated with the observed pricing variables was found, so these hypotheses must be evaluated in their entirety using PLS-SEM from the point of view of the relationships that the constructs have with pricing.\u003c/p\u003e\n \u003cp\u003eAs a final note of this correlation analysis, it is worth mentioning that the variable FV4 lacks observable correlations; even with the variables of its set of perceived functional value, it has weak correlations and does not correlate with other variables of perceived value, as is common among the rest of the variables of the same construct. FV4 only has a weak correlation with income level. This can be explained given that, according to the distribution graph of this variable, the responses are biased toward the central, neutral answer, which is not very informative, because it does not facilitate the inference of a population trend. This variable must therefore be observed with care and, if necessary, removed for other approaches, models, or subsequent analyses.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003ePLS-SEM Analysis\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the results associated with the validation process for Model 1 and shows: the indicators of external loads, highlighted with colors according to the construct; cross-loading indicators, which are not highlighted, and reliability indicators. With the evidence found and expressed in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, it is sufficient to establish the need for three adjustments before of the SEM analysis of Model 1\u0026mdash;that is, MV2 and MV3 have external and cross loading indicators that suggest the need to remove these variables. These inconsistencies explain that the Cost-Free construct does not meet the criteria corresponding to the reliability indicators. Likewise, FV4 should be removed because it presents inconsistency at the internal level of the construct, coupled with the observation mentioned in the correlation analysis. As a last adjustment, the variable EV1 was also removed; although its internal consistency indicator was not far from the threshold of 0.708, it did not have a considerable impact in explaining the variance of the construct, so its removal would not have a significant impact.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003e\u003cem\u003eMeasurement Model Assessment Results\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eAfter the adjustments, the information was processed again for validation, which yielded the results summarized in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. All of the cross-loading indicators, highlighted with color, exceed the threshold, thus confirming the validity of all of the observed variables for SEM analysis. The Cronbach\u0026rsquo;s alpha, as well as the rhoC and rhoA for all constructs, is greater than 0.708, which also confirms the internal consistency of all of the constructs. The AVE indicators for all constructs exceed 0.5, which implies convergent validity. The cross-loading indicators as well as the HTMT are sufficiently below their corresponding thresholds to assume discriminant validity.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003e\u003cem\u003eAdjusted Measurement Model Assessment Results\u003c/em\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u003cimg 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\"\u003e\u003c/div\u003e\n \u003cp\u003eFinally, a bootstrap was done with 10,000 resamples for the adjusted Model 1, because it was non-parametric. In general, the results (see Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e) show figures about the corresponding thresholds. Possible collinearity problems were also verified, for which the analysis of variance in inflation factor (VIF) was carried out. No collinearity issues were found, as no value exceeded 0.4, which is below the threshold. After clarifying that the variables did not have collinearity issues, the relevance of the relationships in the model could be verified by analyzing the path coefficients (𝛽) that express the relationships between constructs, as well as the coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e), which can be found in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and are shown in Fig. 3.\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eAdjusted Measurement Model Assessment Results\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePricing\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOriginal Est.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBootstrap Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBootstrap SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT Stat.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCost Free ⇾ Pricing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.260\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQuality ⇾ Pricing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmotional ⇾ Pricing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocial ⇾ Pricing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eThe magnitude of the relationship coefficients between the constructs, which vary between 0 and 1, indicate the strength of the relationship and suggest that a percentage variation in the endogenous construct\u0026mdash;in this case, pricing\u0026mdash;is estimated due to the change in the construct of origin\u0026mdash;in this case, the factors of perceived value. The direction of the variation\u0026mdash;that is, whether the percentage variation increases or decreases\u0026mdash;is indicated with the vector operator prefixed to the value (Hair et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), so, for example: emotional value (𝛽 = 0.006) has a very weak positive relationship with pricing, while the value associated with being cost free (𝛽 = \u0026minus;0.319) has a relatively strong negative relationship with pricing. Both functional Value and social value have a positive and moderate relationship with pricing.\u003c/p\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e is the explanatory power of the model (Shmueli \u0026amp; Koppius, 2011, cited in Hair et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e, p. 118), whose range of values is also between 0 and 1. In general, values of R\u003csup\u003e2\u003c/sup\u003e around 0.25 are considered weak, 0.50 moderate, and 0.75 substantial (Hair et al., 2011, cited in Hair et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e, p. 118). However, for this study, the value of R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.135 was considered satisfactory, given that the main interest lies in observing the relationships between the constructs, rather than the explanatory power of the model as such.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003cp\u003eThe technique to establish the predictive relevance of the SEM, recommended by Hair et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e, p. 119), involves comparing the errors of the square of the root-mean-square error (RMSE) of the predictive model, with the same indicator of a linear regression of the model (LM). The RMSE values for the model and the linear regression can be seen in Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. The RMSE values for the model are lower for P1, P3, and P4 compared to the RMSE for the linear regression, so the model can thus be considered to have medium predictive power.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eAdjusted Measurement Model Assessment Results\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRMSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP4\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePLS out-of-sample\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLM out-of-sample\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.114\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\n \u003ch2\u003eEvaluation of the Hypotheses\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eH1: The perceived social value of the product is positively related to pricing.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn the correlation analysis, positive correlation coefficients were identified between the observed variables of perceived social value and those of pricing. In the SEM analysis, a positive statistically significant relationship is evident for the constructs of perceived social value and pricing, with a path coefficient 𝛽 = 0.190. It can thus be inferred that there is indeed a positive relationship between the perceived social value and the pricing of the product, but there is no support to affirm that the relationship between the constructs is strong; it is more appropriate to understand this relationship as moderate.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eH2: The perceived emotional value of the product is positively related to pricing.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn the correlation analysis, no statistically significant correlation coefficients were identified between the observed variables of perceived emotional value and those of pricing, nor was a relationship evident between the corresponding constructs in the SEM analysis, where the relationship observed was non-existent, with path coefficient 𝛽 = 0.006. There was also no notable difference after bootstrapping, which yielded ranges that do not allow the establishment of a relationship between these constructs. It can thus be inferred that no statistically relevant evidence of a relationship between emotional value and pricing of digital products was found.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eH3: The perceived value because of the product\u0026rsquo;s free cost is positively related to pricing.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn the correlation analysis, negative correlation coefficients were identified between the observed variables of perceived value associated with the free product and those of pricing. In the SEM analysis, a statistically significant negative relationship is evident for the constructs of perceived value associated with free cost and pricing, with path coefficients 𝛽 = \u0026minus;0.319. It can thus be inferred that there is indeed a negative relationship between the value associated with the free cost and the pricing of the product. It should be noted that the observed variables MV2 and MV3 had to be discarded, because they were not sufficiently informative or were unreliable. However, this relationship can be understood as moderately strong and negative.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eH4: The perceived value because of the product\u0026rsquo;s quality is positively related to pricing.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn the correlation analysis, no statistically significant correlation coefficients were identified between the observed variables of perceived functional value and those of pricing. In the SEM analysis, a positive relationship is evident for the constructs of functional value relative to perceived quality and pricing, with path coefficient 𝛽=0.125, along with mostly positive acceptable ranges to the bootstrapping results. It can thus be inferred that there is a statistically significant positive relationship, but with moderately low strength, between the functional value relative to the perceived quality and the pricing of the product.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe existence of a free digital product, without advertising and well valued, motivates persistence, as discussed by Appel et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which is a requirement to maintain a high valuation of the product. This persistence is noted here, in that practically none of the participants answered \u0026ldquo;No\u0026rdquo; to the question: Do you usually/daily use the WhatsApp application? The assessment of the application was achieved without obvious efforts to promote the psychological appropriation and/or existence suggested by authors such as Cauley (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), Atasoy and Morewedage (2018), Kwon (2018), or Runge et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), although in this case this can be partially attributed to its personalization characteristics (Cauley, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In any case, the suggestion of these authors makes sense mainly for digital products that have a current physical equivalent, because the digital and the physical product compete within the market (Kim et al., 2021; Taleizadeh et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); the digital product must therefore generate a perception of existence and psychological appropriation, in addition to having possibilities for customization, to survive and compete in the market with the physical equivalent.\u003c/p\u003e \u003cp\u003eRegarding the constructs evaluated, it was found that the perceived value of a free digital product, as determined, with the scale developed by Sweeney and Soutar (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), turned out to be a valid indicator, with statistically representative strength to predict the consumer\u0026rsquo;s perception of the product price for a paid version of the same digital product. To clarify this point, however, it is necessary to review each of the factors that constitute the perceived value according to the aforementioned scale in greater detail.\u003c/p\u003e \u003cp\u003eFirst, the perceived social value of the free digital product evaluated has intermediate evaluations, which, for this scale, should be understood as indifference\u0026mdash;in other words, it is a neither a good nor a bad evaluation. The relationship of this construct with pricing, which was established with a moderately low strength, indicates that a consumer knowing the perceived social value of the free version, can explain around 19% of variance in establishing the suggested price of the paid version.\u003c/p\u003e \u003cp\u003eThe findings for perceived social value indicate that even a digital product with important social implications, such as a social network or a messaging service, generates a perception of social value close to indifference for its consumers, even when it is available for free. This does not contradict the results of the studies by Hsieh and Tseng (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), Ding and Li (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), or Kim et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), because they focused directly on specifically defined social groups, whether in physical presence, or implicit virtual context associated with the digital product. Here only consumer\u0026rsquo;s perception was evaluated, excluding their social dynamics and constructs such as influence, happiness, or the sense of belonging of the individual in the social group.\u003c/p\u003e \u003cp\u003eSecond, the indicators of perceived emotional value are intermediate with a positive trend\u0026mdash;that is, unlike those of perceived social value, they are positive, but still close to indifference. The emotional factor turns out not to have a considerable effect for the pricing of the paid version; in other words, users are not particularly affected on an emotional level by the product, in either its free or paid version, so it could be said that the use of the evaluated digital product does not have an impact on an emotional level, and in turn, the perceived emotional value is not informative in relation to the digital product. It is thus concluded that the emotional assessment of the product can be dispensed with in the pricing process.\u003c/p\u003e \u003cp\u003eThird, the functional value received positive evaluations in terms of the free price and in relation to the quality of the product. It can thus be said that the consumers\u0026rsquo; perceived value of the evaluated free digital product is mainly determined by the product\u0026rsquo;s functionality. Indeed, the functional value related to the free cost of the product is the factor that is most telling and informative for pricing, taking into account that it shows a path coefficient of \u0026#120573; = \u0026minus;0.319 in the PLS-SEM evaluation. This means that this sub-construct explains more than 30% variance in attitudes towards pricing, so this construct contributes more weight to pricing. The value perceived by characteristics related to the quality or in general to the functionality of the product explained about 13%, given the path coefficient of \u0026#120573;=0.125.\u003c/p\u003e \u003cp\u003eFrom the results of the evaluation of H4, it can be concluded that the relationship between the functional value associated with the free cost of the digital product evaluated and the pricing of the paid version was negative and of moderately high strength. This means that if the consumer of the digital product highly values the fact that it is cost free, he or she will be more likely to reject the paid version, so the price would have to be relatively lower for this consumer to pay for the corresponding paid version. This result also implies, however, that if the consumer rating based on the product being free is low or negative, then the consumer, who is already a frequent user of the product, will be more receptive to a paid version; the suggested price could thus be higher, and the user would still be willing to purchase it. Here is where we can see the consequence of having a free version of the product available, but also, why is so relevant, for the pricing of the paid version, to know and understand the costumer\u0026rsquo;s perceived value over the free version.\u003c/p\u003e \u003cp\u003eAt this point it is necessary to highlight that H3 was established to evaluate the relationship between the perceived value associated with the free digital product and the pricing of the paid version of the same product; this was structured with the same tendency as hypotheses H1, H2, and H4, because it referred to the constituent factors of perceived value, such that the four hypotheses respond to a single tendency of perceived value in general. This structure for the evaluation agrees with the findings of the literature review (Chuang, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kouhia, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sweney \u0026amp; Swoutar, 2001; Valencia, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe conclusion derived from the evaluation of H3 and from the analysis of the quantitative results explains, in practical terms, why H3 is ultimately rejected. It also makes sense\u0026mdash;even prior to the evaluation\u0026mdash;and implies a contradiction in proposing a positive relationship in the formulation of H3, such that it is appropriate to evaluate the perceived value factors differentially according to the object and objectives of the study. This contrasts with Kouhia (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), who, although using a qualitative method, treated the meaning of the factors indistinctly, and with Valencia (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who directly evaluated all of the factors of the construct with the same tendency precisely because they are part of the same construct.\u003c/p\u003e \u003cp\u003eValencia (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) showed statistically significant evidence for the relationship between perceived value and loyalty. Chuang (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) related loyalty, in terms of continuity and consistency in the use of the product, with a greater possibility of acquiring sub-products, as well as an inverse relationship with satisfaction, which implies that the greater satisfaction with the digital product, the lower the purchase intention. The results of this study are consistent with those findings, as the participants denoted loyalty in terms of constancy and continuity in the use of the product, as well as the product\u0026rsquo;s high perceived value, which denotes a margin of dissatisfaction framed by the perceived functional value.\u003c/p\u003e \u003cp\u003eFrom the consumer perspective, this dissatisfaction can generate the intention to purchase the paid version, because if the application were absolutely satisfactory, with a functional value absolutely placed at the maximum margin of the range, nothing would justify a paid version. Likewise, if the product\u0026mdash;of hypothetical mandatory and/or unavoidable use\u0026mdash;were highly unsatisfactory, or if the functional value were at the lower end of the range, then the intention to acquire a paid version is likely to be higher, within reason (Appel et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It could also be said that this applies to virtual products as a subset of digital products, as mentioned by Hamari and Keronen (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) in highly contextualized markets, which exhibit similar behaviors to digital markets in general (Hsieh \u0026amp; Tseng, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Runge et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results presented for the construct of perceived value, as a whole and from each of its factors, suggest that it would be possible to set a potential price for a digital product that is distributed for free based on the operationalization of the scale indicators. A basic reference would also be required to allow this abstract information about valuation to be translated into monetary terms, because the scale itself does not inquire about the numerical value of the price, but rather about the valuation perceived by the consumer.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eContributions to Market and General Management\u003c/h2\u003e \u003cp\u003eThe results related to age, income, and education levels are likely to be useful for the segmentation and definition of the target audience when a free digital product is going to be launched on the market. These results will make it possible to better locate the segment toward which marketing efforts and resources should be directed, and to position the product in order to follow the procedure recommended by the subsequent placement of a paid version.\u003c/p\u003e \u003cp\u003eBased on the findings of this study, it is clear why organizations that produce digital goods and services can obtain an income through marketing models that involve the free distribution of their products, which is very relevant for digital product markets. There was already evidence of this (Deng et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), but it was limited to observing the methods and factors that can generate benefits despite the product being free, while here a cause is offered for the phenomenon, where the perceived value of the free version can even help to predict the reception of the paid version.\u003c/p\u003e \u003cp\u003eThe findings associated with the perceived value suggest that the reasons for purchasing later paid versions are related to the value that the person attributes to the free digital product. A higher rating may therefore result in a higher potential price for the paid version. Organizations should thus try to include and transfer the greatest possible value in their free digital products, to raise the user\u0026rsquo;s evaluation and increase the possibility of acquiring subsequent paid versions. When an organization is engaged in pricing, it will be valuable to include information related to the consumer\u0026rsquo;s perspective and value assigned to the free product and what they could give to the paid version. This recommendation focuses on organizations that produce digital goods and services in response to the difficulties they may have in establishing the price of their products (Appel et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Laatikainen \u0026amp; Ojala, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lambrecht et al., 2014).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and Further Research\u003c/h2\u003e \u003cp\u003eThere is no technique that makes it possible to obtain a potential price directly from the perceived value construct. The price must therefore be found using another construct as a reference, for which there is an established technique to find the price\u0026mdash;such as willingness to pay, because the price can be found using Van Westendorp\u0026rsquo;s price sensitivity meter.\u003c/p\u003e \u003cp\u003eThe analysis developed in this study establishes the foundations to facilitate a subsequent study to develop a pricing process based on the operationalization of the indicators of perceived value, provided that the aforementioned drawbacks can be overcome. It can be addressed either the use of the price taken from another perspective than the one presented here, or an alternative reference can be found that allows a change of proportion from the relative magnitudes of the perceived value indicators toward a concrete monetary value.\u003c/p\u003e \u003cp\u003eFinally, it is important to mention that there is quite few knowledge in the field related to digital markets, which also offer multiple research opportunities. It would therefore be valuable to replicate this study with other digital products and samples from more regionally, socially, and culturally diverse populations. There is much more to study and research in this area that would be valuable both for the study of organizations and their management, as well as for other fields, particularly the social, human, and information sciences.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding Declaration: This research received no specific grant from any funding agency, commercial or not-for-profit.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eDeclaration: This research received no specific grant from any funding agency, commercial or not-for-profit.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe three authors participated in the design and development of the research, as well as in the writing and correction of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData supporting the research results is held by the lead author in an authorized institutional repository. This information can be accessed upon request to the author via email.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eArunrangsiwed, P. (2021). Meta-analysis of perceived product value. Humanities, Arts and Social Sciences Studies 21(1): 11-21.\u003c/li\u003e\n \u003cli\u003eAppel, G., Libai, B., Muller, E., \u0026amp; Shachar, R. (2020). On the monetization of mobile apps. International Journal of Research in Marketing, 37(1), 93\u0026ndash;107. https://doi.org/10.1016/j.ijresmar.2019.07.007.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAtasoy, O., \u0026amp; Morewedge, C. K. (2018). Digital goods are valued less than physical goods. Journal of Consumer Research, 44(6), 1343-1357. https://doi.org/10.1093/jcr/ucx102.\u003c/li\u003e\n \u003cli\u003eAlbari \u0026amp; Safitri, I. (2018). The Influence of Product Price on Consumers\u0026rsquo; Purchasing Decisions. 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Optimal distinctiveness across revenue models: Performance effects of differentiation of paid and free products in a mobile app market. \u003cem\u003eStrategic Management Journal\u003c/em\u003e, \u003cem\u003e43\u003c/em\u003e(10), 2066\u0026ndash;2100. https://doi.org/10.1002/smj.3394\u003c/li\u003e\n \u003cli\u003eZeithaml, V. (1988). Consumer Perceptions of Price, Quality and Value: A Means-End Model and Synthesis of Evidence. Journal of Marketing. 52. 2-22. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"digital products, perceived value, pricing, PLS-SEM, digital consumer behavior, WhatsApp, freemium","lastPublishedDoi":"10.21203/rs.3.rs-6523766/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6523766/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe pre-existence free version of a digital product increases the profit potential of a paid version, that\u0026rsquo;s why freemium model is widely spread, but organizations still have difficulty establishing the price of their digital products. This paper proposes an adjustment to pricing processes of the paid version of a digital product, with the contribution of consumer perception through the collection and understanding of the perceived value of the free version. An instrument was adapted from a multiple factor construct of perceived value to investigate over a hypothetical paid version of WhatsApp. A total of 523 observations were obtained, which were subjected to correlation analysis and partial least square structured equation modeling to identify the contribution of perceived value to pricing. This made it possible to establish that consumers perceive WhatsApp has good value, and the perceived value has predictive strength for the consumer\u0026rsquo;s perception of the paid version; but the factors of perceived value have different impacts and informative levels, so each one will have different relevance to the pricing. By elucidating these dynamics, our findings may help managers gain essential insights for making informed pricing decisions and encourage them to innovate in pricing as a significant competitive advantage.\u003c/p\u003e","manuscriptTitle":"Perceived value and price of digital products:The contribution of consumer perception to pricing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-14 10:30:40","doi":"10.21203/rs.3.rs-6523766/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"964d5857-d9ae-4cfe-8a98-cd249a002255","owner":[],"postedDate":"May 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-29T02:08:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-14 10:30:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6523766","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6523766","identity":"rs-6523766","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}