1 Introduction

Society is becoming increasingly technology-enabled. Smart cities are becoming a reality. Through the use of sensors, it is possible to develop mobile applications for human–computer interaction, which contributes to the connection between humans and society, even for those who have physical limitations, such as those associated with old age. With the increase in life expectancy, the aging population is growing in developed countries. The use of technologies by elderly users can contribute to their personal satisfaction by allowing communication with others and providing access to educational, entertainment, cultural and distracting resources that contribute to the well-being of the population in this age group.

The whole environment provided by information and communications technologies (ICT) has caused behavioral changes in users in general, and particularly in the elderly, who, despite having more difficulty learning and using technology [5, 45] than younger people, use this medium to communicate with family and friends as a way of keeping in touch with others while at the same time increasing their social inclusion and decreasing their loneliness [47].

The use of social networks can contribute to the diminishment of elderly isolation [19, 23], increase their cultural knowledge (which contributes to the increase of their personal valuation), provide entertainment as a way to pass the time, and at the same time contribute to an increase in their technological experience and the acquisition of digital skills. All of the above factors result in the enhancement of the quality of life and well-being of the elderly population.

The objective of the present study is to analyze the technological experience associated with the use of social networks by elderly users, taking into account the concepts of the experience economy of Pine and Gilmore [34]. The focus is based not from the point of view of the consumption of economic goods, but the consumption of information, to determine whether or not it contributes to the personal valuation of the elderly population. The study takes into account the dimensions associated with the economy of experience: evasion, education, entertainment and aesthetics.

This article is structured in four sections, in addition to the introduction and conclusions. The first section clarifies how social networks can contribute to the quality of life of elderly users and their well-being, followed by the concepts of the experience economy that are associated with technological experience. The second section presents the conceptual research model and the research hypotheses associated with the study. The third section defines the methodology used, which considers concepts of structural equation models. The fourth section analyzes and presents the results. In the final section, conclusions are drawn with implications, limitations, and future work.

2 Technological Experience for the Elderly

2.1 Contributions of Social Networks for Elderly Users

The SeniorNet website (www.seniornet.org), created in 1986 for adults over 55, receives more than 1 million visits per month. It was created with the mission to provide the elderly with education for, and access to, computer technologies, as a way to enhance their lives and enable them to share their knowledge and wisdom [42].

Internet access by this age group encourages the search for lifelong learning opportunities as the life expectancy of this population increases. For instance, in Portugal, according to the Pordata, the Aging Index was 125.8 in 2011 and 153.2 in 2017, which represents the number of older people per 100 young people [36]. As the older population seeks to stay active and find activities that improve their physical and emotional well-being, they have a tendency to adopt technology, especially the use of social networks. The perception of utility, security of use and frequency of internet use are the main motives that explain the adoption of social networks, as also reported in the work of Chakraborty, Vishik and Rao [8].

The potential of the internet to contribute to the well-being of the elderly population has led to the development of senior-friendly websites, created with more simplified designs to increase usability for older adults [2, 6, 12, 14].

The age at which a user is considered a senior or elderly has no consensus in the literature. Barnard, Bradley, Hodgson and Lloyd [3] find that “older adults” are an extremely diverse group—that “old age” may differ with context. For example, in the context of work, “old” refers to ages of 50 to 55, since this is the age range in which workers’ capacities tend to decrease [26, 46]. Sinclair and Grieve [43] also point out that there is no consensus on what age one considers a person as an “older adult”, but they say that the most referenced literature considers as elderly the persons whose age is greater than or equal to 55 years.

In older adults, women are more familiar with social networks and are more frequent users when compared to men [38, 44]. Gender, age and education are factors that have a significant impact on the use of social networks when compared to those of younger users.

The main reasons the elderly used social networks were [44]: (1) fun, which translates into feelings of pleasure [29]; (2) social inclusion, involving interacting with others, talking with family and socializing, thereby reducing feelings of loneliness, even for those with problems of physical mobility [14, 23, 30, 43]; and, (3) a sense of well-being, with a commensurate reduction in fear and anxiety, which is associated with the support and availability of care information for people who are physically or geographically isolated [28, 31,32,33].

In addition to the motives presented by Vošner et al. [44], social networks allow for the acquisition of new knowledge. Xiang and Gretzel [48] point out that these platforms serve as a means to learn about products, brands and services, since they include a large variety of information sources produced and shared by the users themselves, which increases credibility and trust in the shared content. In this context, the first hypothesis of this research is to validate whether the use of social networks is motivated by the personal valuation of the acquisition of knowledge and technological experience associated with their use.

Hypothesis #1(H1): Increase personal valuation through the acquisition of knowledge acquired through social networks?

For some older adults, social networks are their first technological experience of a social nature [13]. When they understand that social networks are easy to use and useful, they are more likely to use them [5], which helps prevent a more pronounced mental decline [23].

2.2 Technological Experience in the Use of Social Networks by the Elderly

As already mentioned, in recent years, participation in online communities has increased, including among the elderly [7, 45]. The technological experience associated with communication with distant relatives and friends, as well as the access information and knowledge available through clicks, has contributed to a lessening of the obstacles and difficulties of older adults in using these communities.

In general, users participate in a technological experience [7, 45] when they have developed a habit of utilization [16] and trustworthiness in the technology [15]; however, the elderly can experience a sensation of discomfort when new technologies are introduced, with new scenarios and new situations that emerge in a context in which they lack knowledge. Reticent elderly users quickly adopted emerging social networks as a way to communicate [16], however, because of their ease of use and its usefulness [5].

To reduce the discomfort associated with adopting a new technology, innovative design methods were necessary, taking into account the age group of older adults and their physical limitations: decreased vision, hearing loss, decreased physical mobility, cognitive and social impairment [2], as well as the user experience concepts, defined by ISO 9241-210 [25], which includes indications associated with the emotions, beliefs, preferences, perceptions, physical and psychological responses, user behaviors and achievements that occur before, during, and after use.

Although the technological experience associated with social networks is not analyzed from the economics of experience point of view [34], i.e., from the business point of view, these authors considered that consumers seek to be involved and absorbed in the experience [27, 41], and from the marketing point of view, the experience is consumer focused. In this context, it is possible to consider, in light of information consumption by the elderly user, whether the achievement is the perception of the value of a product or service, or merely the ability to be involved and absorbed in a memorable experience that contributes to well-being.

In light of the foregoing, we analyze the factors that influence elderly users in the use of social networks for information consumption, and identify the aspects that contribute to creating a memorable experience that influences how the elderly communicate, use time and consume information, as well increase their personal valorization [1].

Aluri [1] applied the economics of experience theory to the use of the Pokémon GO app to investigate the factors that influence travelers to use the application and its influence on the experience of each individual. On the other hand, Radder and Han [37] examined the experience of visiting a museum through the theory of the “experience economy”.

The experience economy by Pine and Gilmore [34] is a concept that unites the dimensions: educational, entertainment, aesthetics and evasion (see Fig. 1), on a scale from passive to active participation, and feelings of immersion to absorption.

Fig. 1.
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Source: Adapted from Pine and Gilmore, 1999, p. 30.

The experience economy.

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In terms of the use of social networks by the elderly, and taking into account the concepts of the experience economy, from an information-consumption perspective (in different formats: text, photographs, videos), the dimension of entertainment can be considered in the viewing of videos and photos, communication with others, the sharing of comments, and more. The educational dimension is revealed by observing searches for information regarding products, services and news. The dimension of evasion can be observed while the user is viewing photos and videos, which can permit the viewer to overcome the physical barriers where he or she is while allowing for the creation of other creative, mental spaces. The dimension of aesthetics can be measured in terms of the environment provoked by the interface, i.e., whether it is pleasant, beautiful or intuitive.

In this context, we intend to analyze the contribution of the four dimensions of the experience economy to the user experience in association with social networks and elderly users. For this analysis, the following four research hypotheses are considered:

  1. (1)

    Hypotheses #2 (H2): Does the technological experience contribute to the educational of the elderly user of social networks?

  2. (2)

    Hypotheses #3 (H3): Does the technological experience contribute to the evasion of the elderly user of social networks?

  3. (3)

    Hypotheses #4 (H4): Does the technological experience contribute to the entertainment of the elderly user of social networks?

  4. (4)

    Hypotheses #5 (H5): Does the technological experience contribute to the aesthetics of the elderly user of social networks?

All four dimensions contribute to the overall experience [1]. The overall experience is associated with the motivation that prompted the use of social networks: entertainment, social inclusion, well-being and education. It also contributes to the personal valuation of the elderly user, as presented in H1.

So far, and to our knowledge, no research has considered education as a reason why older adults use social networks, nor has there been an analysis of social networks on the basis of the concept of the experience economy from the point of view of information consumption or an educational perspective.

In this context, and in the present study, taking into account information consumption and consumer involvement, we intend to investigate the impact that the four dimensions of technological experience (according to the concepts of Pine and Gilmore [34]) has on older adults, as well as identify how the technological experience of using social networks contributes to an increase in the personal valuation and quality of life of elderly users.

3 Conceptual Research Model and Hypotheses

As the aim of the study is to analyze the contribution of social networks to the technological experience (user experience) of older adults, the concepts associated with the experience economy were considered.

3.1 Research Hypothesis

In order to investigate the objectives presented and the hypotheses formulated, a set of questions was considered to assess the experience associated with the use of social networks, taking into account the profile of the respondent.

A set of questions was considered for each dimension associated with the concept of the experience economy.

In the educational dimension, the use of social networks can contribute to: (1) becoming more cultured; (2) stimulating the curiosity to learn about new subjects; (3) learning new experiences; and, (4) increasing skills.

In the aesthetic dimension, the use of social networks can contribute to: (1) a harmonious experience; (2) a very attractive experience; (3) a carefully crafted interface design; and, (4) an appealing interface design.

In the entertainment dimension, the use of social networks can contribute to: (1) a captivating experience; (2) help passing the time; (3) enjoying the fun publications of friends; and (4) the discovery that friends’ publications are interesting.

In the evasion dimension, the use of social networks can contribute to: (1) the feeling that the user is playing a different role when he or she uses social networks; (2) the feeling that the user lives in a different place; (3) complete distraction; and, (4) a way to help forget the daily routine. To investigate these hypotheses, a research model was developed to evaluate the elderly–user’s experience with the use of the social networks, called the Social Networks User Experience Model (SNUX), as presented in Fig. 2.

Fig. 2.
figure 2

Source: Author’s elaboration.

Research model.

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3.2 Research Model

The SNUX model evaluates the dimensions of the technological experience associated with social networks (Social Networks Technological Experience (SNTE)), where the concepts of the experience economy were considered in the context of the consumption of information and its contribution to added personal value to the elderly user. Such was measured by the variable, add value to the user (AVU), which contributes to the well-being of the users that belong to these age groups.

4 Methodology

After the identification of the research hypotheses, the literature review and delimitation of the research problem, the proposed study methodology was based on the following steps: (1) construction of the survey; (2) data collection; (3) selection and codification of data; (4) selection of methods and techniques of data analysis; and, (5) analysis of the results.

Through the elaboration of the survey that was released in December 2017, of 60 survey responses, 58 valid responses were obtained.

The base sample was social network users over 55, using the probabilistic method of “convenience sampling”, in which the sample is selected based on the availability and accessibility of the elements of the population. After data collection, responses were codified to make possible the analysis of the data by descriptive statistics to characterize the sample, and the structural equation model was applied to evaluate the SNUX research model. The interpretation of results was elaborated and the conclusions presented, along with the limitations and future work.

5 Results

5.1 Characterization of the Sample

Of the 58 respondents, the sample characterization is defined by a majority of women (55.2%) and possession of a university degree (32.8%). The respondents was divided in two groups: one group was aged greater than or equal to 60 (55.2%), and the second group was between 55 and 59 years of age (44.8%). In terms of occupation, the majority was retired (48.3%), followed by those employed on behalf of others (25.9%), self-employed (19.0%) and unemployed (6.9%). In terms of education level, the majority was graduated (32.8%), followed by those with a high–school diploma (29.3%) and PhD degree (17.2%). The majority of the respondents prefer to use computers (43.1%), followed by smartphones (36.2%) and tablets (19.0%).

Ninety-three percent of the elderly users interviewed had a Facebook profile, followed by 67.0% on YouTube, and a very close 64.0% on Google +. Snapchat and Twitter are the networks that had the lowest value of 7.0%, probably because they did not arouse the interest of this age group.

In response to the question “What kind of content do you share on social networks?”, our analysis concluded that the most “often” shared content was images (36.2%), followed by texts (24.1%) and movies (13.8%). The answer “sometimes” was identical for all three of these types of information (39.7%).

In response to the question “What are the reasons why you use social networks?”, among those who used social networks “many times”, most respondents (55.2%) replied “to communicate with their friends”, followed by “find information” (39.7%) and “entertainment” (36.2%). What aroused less interest for this group was the “discovery of new trends”.

In the group that “sometimes” used social networks, 39.7% responded that they use it to “find information”, followed by 34.5% who used it for the “discovery of new trends”. In this group, “entertainment” was the least valued use, with 24.1% of the answers.

5.2 Social Networks User Experience Model (SNUX) Model

Following the selection and codification of data, methods and techniques were identified to analyze the data. The structural equation model (SEM) was considered since it is the model indicated to overcome the need to measure multidimensional and not-directly-observable concepts, also called constructs or latent variables [4, 24]. According to the work of Gefen, Straub and Boudreau [18], the SEM “has become the rigueur in validating instruments and testing linkages between constructs”.

The SEM considered is based on variance-based SEM or partial least squares path SEM [39], which permits the construction of the model in an exploratory phase, with a little portion of the sample that can be without normal distribution [9, 40].

Figure 3 shows the Social Networks User Experience Model, and Table 1 provides an overview of all variables, including the exogenous variables: education, entertainment, aesthetics and evasion; and endogenous variables: SNUX and AVU.

Fig. 3.
figure 3

The social networks user experience model (SNUX).

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Table 1. List of the latent variables and their constructs.
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5.3 Model Estimation

After the definition of the variables and constructs of the SEM model, the next step is the model estimation to the structural equation modeling coefficients [35]. This study used SmartPLS 2.0 software, and the results are presented in Fig. 4.

Fig. 4.
figure 4

Estimation of the SNUX model.

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5.4 Evaluation of the Measurement Model

It is necessary to analyze the adjustment quality of the model through three steps: (1) evaluation of the measurement model to guarantee the convergent validity; (2) observation of internal consistency values; and (3) discrimination quality assessment [40].

The evaluation of the measurement model to guarantee the convergent validity is evaluated by observation of the average variance extracted (AVE) (Fornel and Larcker [17, 21]), where all the AVE values should be more than 0.5, which is a condition to guarantee that the model converges to a satisfactory result.

Observation of internal consistency values takes into consideration the values the Cronbach’s alpha (CA) and composite reliability (CR), expressed by the rho of Dillon-Goldstein, which makes it possible to ascertain whether the sample is free of biases and whether, on the whole, it is reliable. The values of CA should be higher than 0.6, and values of 0.7 are considered adequate. Values of CR should be higher than 0.7, and values of 0.9 are considered satisfactory [11, 40].

Table 2 presents the estimated values of the adjustment quality of the SNUX model. By using the AVE values, it is possible to conclude that the SNUX model will converge to a satisfactory result, once all the values are higher than 0.5 [21].

Table 2. Values of the adjustment quality of the SNUX model.
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Table 2 also permits analysis of the internal consistency of the model through the values of CA and CR. CA values higher than 0.6 and 0.7 and CR values higher than 0.7 and 0.9 indicate that the model is satisfactory.

In the third step, the discriminant validity assessment permits investigation of the independence between latent variables and other variables. This analysis can be done by observing cross loading, which should be the indicators with the highest factor loadings in their respective latent variables, when compared with the observed variables [10], or by the criterion of Fornell and Larcker [21], which compares the square roots of the AVE values of each latent variable with the Pearson’s correlations between the latent variables. The square roots of AVEs should be larger than correlations between those of latent variables.

In the first process, and taking into consideration the values presented in Table 3, the cross loadings of the observed variables in one latent variable is always higher than the cross loadings of the observed variables in another latent variable; these are not the variables that help to measure, which shows that the model has discriminant validity, in accordance with the work of Chin [10].

Table 3. Values of cross loadings of the observed variables in the latent variables
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In the second process, and taking into consideration the values presented in Table 4, the variance identified in the AVE must exceed the variance that the overserved variables share with other latent variables of the model. In practice, discriminant validity exists when the squared root of the AVE of each construct is greater than the correlation values between the latent variables and the observed variables [35, 40]. Table 4 shows that the SNUX model has discriminant validity, as confirmed by the first process, once the values of the square roots of the AVE values, presented in the main diagonal, are higher than the correlation between the latent variables, in accordance with the work of Fornell and Larcker [21].

Table 4. Values of the correlations between the latent variables and the square roots of the AVE values (on the main diagonal)
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After guaranteeing discriminant validity in the evaluation of the measurement model (meaning that the adjustments in the measurement model have ended), the next step is the evaluation of the structural model.

5.5 Evaluation of the Structural Model

In the evaluation of the structural model, the first step is the evaluation of the Pearson’s correlations coefficient (R2) of the endogenous latent variables. In this study there are two endogenous latent variables: AVU with an R2 = 0.4959 and SNTE with a R2 = 0.5086, as presented in Table 2. These values are moderate and represent the portion of the variance of the endogenous variables that is explained by the structural model [9, 35].

Another aspect to analyze is the model’s capacity to predict, which requires the calculation of the Stone-Geisser indicator (Q2) and the Cohen indicator (f2) associated to the effect size [9, 20].

The Stone-Geisser indicator (Q2) evaluates how close the model is to what was expected; the Cohen indicator (f2) evaluates how useful each construct is for the model, as presented in Table 5. The Q2 associated with the exogenous latent variables presents a value higher than zero, which means that both variables have predictive power, and the structural model has predictive relevance. The f2 indicators associated with all the latent variables are higher than 0.35, which shows how useful each construct is for the model, which, in this case, all have a high impact on the structural model, as presented in Table 5.

Table 5. Indicators values of the predictive validity and effect size.
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The structural model analyses ends with the individual analysis of the coefficients of the respective model (path coefficients) [21, 35], where it is necessary to analyze the sign, the value and the statistical significance, which should be more than 1.96 (bilateral and with a 5% significance level).

Analysis of Fig. 5 shows that only the relationship between aesthetic and SNTE has a lower t-test value (lower than 1.96), which implies the acceptation of the H0 (null hypothesis), meaning that the structural coefficient is equal to zero, with a 5% significance level. This permits the conclusion that there is no empirical evidence to support the structural relationship between aesthetics and SNTE.

Fig. 5.
figure 5

SNUX model with the t-test values obtained by SmartPLS bootstrapping.

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The model can also be analyzed by its direct, indirect and total effects. Table 6 presents the direct effect, which indicates, by the t-test value of 0.0476, that the H0 should be accepted and the direct coefficient should be zero. This means that the aesthetic dimension does not have an effect on the social network technological experience. Also, the evasion dimension decreases on the social networks once the sign is negative. However, as the SNTE increases the AVU, it can be concluded that the utilization of social networks contributes added value to the elderly user (personal value). The entertainment dimension is the concept associated with the experience economy [34], which contributes to an increase in the SNTE, as it has a higher value among the four dimensions of the experience economy.

Table 6. Direct effects in the structural relationships between the latent variables
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In the SNUX model, there are no indirect effects; it is only necessary to analyze the total effects, presented in Table 7. In direct effects, the t-test value associated with the relationship between aesthetic and SNTE is lower than 1.96, which implies that the H0 should be accepted and the direct coefficient should be zero, such as in the relationship between aesthetic and AVU. The total effect between evasion and SNTE, and evasion and AVU contributes to a decrease in the SNTE and AVU.

Table 7. Total effects in the structural relationships between the latent variables
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With respect to the total effect of AVU, Table 7 shows that the SNTE has the most impact on the AVU (0.7042), followed by entertainment (0.3573) and education (0.3002). Taking into consideration the SNTE variable, the dimension that has the most impact is entertainment (0.5074), followed by education (0.4263).

There are studies that still consider the goodness of fit (GoF) to assess the quality of the overall reflective model as a whole, but Henseler and Sarstedt [22] have shown that it has no power to disrupt valid models or invalid models, so this indicator was not used.

6 Conclusions

More and more users are turning to social networks to communicate with friends and family. At the same time, companies have changed the way they communicate about products by building these networks into their business strategies. The elderly population has more difficulties using these platforms, as most of them have no technology literacy. While this means that it is necessary for them to learn how to use social networks, once they realize that it is easy and simple their resistance begins to decrease. Consequently, they realize that use of social networks contributes to their well-being and quality of life, since it can be used for entertainment, to pass time, communicate with family and friends, and learn new things.

It is relevant to analyze elderly users’ perceptions of the technological experience of using social networks and to study how social networks contribute to a sense of well-being and personal valuation by reducing feelings of exclusion and loneliness while opening up the possibility of acquiring new knowledge. This study investigated this issue by considering the concepts associated with the experience economy [34]; not in an asset acquisition perspective, but in an information acquisition perspective that added value to the users, which led to the development of the research model called Social Networks User Experience (SNUX), which was analyzed through a structural equations system.

The analysis of the research data allowed for the development of a model of the involvement of elderly users in social networks, which helps us understand the technological experience associated with the adoption and use of social networks, as well as develop strategies that should be considered for decreasing social isolation and increasing well-being and quality of life through communication, entertainment and the acquisition of knowledge.

This study’s interviews revealed that Facebook is the social network where elderly users have the greatest presence (93.0%), followed by YouTube (67.0%) and Google + (64.0%). The content they prefer to share on social networks is images (36.2%), followed by texts (24.1%). Their stated reasons for using social networks are: “communicate with their friends” (55.2%), followed by “find information” (39.7%) and “entertainment” (36.2%), with the remainder choosing “discovery of new trends”.

In terms of the technological experience associated with the concepts of the experience economy [34] and the use of social networks by elderly people, this research model found no empirical evidence to support a structural relationship between the aesthetic dimension and the SNTE. Another conclusion, achieved with the SNUX model, is that the evasion dimension of the social networks contributes to a decrease in the SNTE. On the other hand, the educational and entertainment dimensions contribute to the SNTE, and the effect of the SNTE in elderly people contributes to an increase in added value to these users, and consequently their well-being.

With the SNUX model, it is possible to conclude that with respect to the SNTE, entertainment and education contribute to added personal value for elderly users. Taking into consideration the research hypotheses, H1 was proven to show that the use of social networks increases personal valuation through the acquisition of knowledge. H2 and H4 were proven to show that the technological experience contributes to the education and entertainment of the elderly user of social networks. However, H3 and H5 were rejected based on the responses of the interviewed, which showed that the technological experience does not contribute to the evasion and aesthetic dimension of the elderly user of social networks.

The overall experience is associated with the motivation to use social networks in terms of: entertainment, social inclusion, well-being and education, as well as the contributions made to the personal valuation of the elderly user. However, the SNUX model permits the conclusion that the entertainment and education dimensions have more impact on the technological experience associated with this age group. The results validate that the educational dimension is a main driver for the use of social networks.

The limitations with the present study include the fact that the members of the age group that use social networks have a secondary or higher education qualification level, which leaves out the elderly with less literacy and who do not use social networks, or even realize its potentialities. Also, the sample size of 58 responses is a small number to represent the entire elderly population, which is increasing in number.

In terms of future work, the analysis of the relationship between the reasons and the dimensions of the technological experience provided by social networks will be considered for different ages groups (for example, users aged from 60 to 90), and for different, less developed countries, where persons who are 70 to 80 years old are very common. These results will then be compared with the present work, in the context of the concepts of the experience economy.