different OTAs. Instead, to answer the second and the third research questions, we ran an A/B test on the meta-search platform to compare the existing recommendation strategy of the meta-search platform with a local price-based re-rank algorithm. In both, the historical dataset and the A/B test, the company had no information about the anonymous users and their history of previous interactions with the site. Moreover, there was no explicit feedback (e.g., user ratings specific to properties), but we had to rely solely on implicit feedback [6], in our case user clicks.

#### 2 Data Analysis

In this section, we report the results of our data analysis to provide an answer to the first research question: "Do the variables (rank position, price, rating and number of reviews) influence user decision-making?". The dataset has been collected on a meta-search booking platform, where the roughly 130, 000 recommended lists and the associated user actions (click-throughs) of different anonymous user sessions were recorded, each consisting of 25 properties/recommended accommodations displayed on the same page.

The data has been collected on searches about 14 Italian cities on a metasearch booking platform in the period between 11/2021 and 4/2022. Moreover, in searches where users applied a filter criterion we could not unambiguously map the clicks to the specific search and therefore we removed all searches with any filter applied. In every recommended list, each property can be associated with a different OTA. As a result, each property can be presented with different OTAs and in each list multiple OTAs are present. Given the small number of users that look beyond the first page, we restricted the analysis to the first page with 25 ranked properties.

Figure 1 reports the CTR computed for each of the 25 positions in the recommended list. In this and all the following figures, the shaded area around the line represents the 99% confidence interval computed by the *Seaborn*3 python package using a bootstrapping technique. For the remainder of the paper, we will refer to the CTR distribution shown in Fig. 1 as the *a-priori CTR*. Instead, Figs. 2, 3 and 4, report the CTR distribution for values of price, rating and number of reviews that are higher than the 80% quantile and lower than the 20% quantile. For example, in Fig. 2, to obtain the CTR distribution for values higher than the 80% quantile, we took only the properties with a price higher than the 80% quantile into account within each recommended list, i.e. we removed all the properties with a price lower than the 80% quantile in each recommended list from the dataset. We chose this method to investigate the cheapest/best rated/most reviewed and most expensive/worst rated/least reviewed. We initially focused on the 10% and 90% quantiles, but due to the low number of samples we switched to 20% and 80% quantile in order to obtain statistically significant results.

3 https://seaborn.pydata.org/.

Fig. 1. CTR for each rank position in the recommendation list. The y-axis reports the CTR achieved in each rank position. Due to the large number of observations, the confidence interval is very small and difficult to see in the image.

*Influence of Rank Position.* It is well documented in the literature that the rank position affects user choice [4,9], and our analysis confirmed this assertion. From Fig. 1 it was clear that the rank position had a significant effect on CTR. In fact, the CTR for rank position k was always higher than the CTR for subsequent rank positions. For example, in our dataset, the CTR for the first rank position was around 5.75%, while the CTR for the last rank position was lower than 0.5%. This means that the probability of clicking the first rank position was about 12 times higher than clicking on rank position 25. However, while for the first 10 rank positions this was evident, the difference in CTR for the rank positions between 10 and 25 was close to zero. The answer to the research question: "To which extent does the rank position influence user decision-making?" is yes: the rank position clearly influenced user's choice.

*Influence of Price.* Figure 2 reports the results for price when considering only properties having a price above the 80% quantile and below the 20% quantile. For each rank position, the CTR was higher than the a-priori CTR if we considered properties with a price lower than the 20% quantile. This clearly means that lower prices positively influenced the users' propensity of clicking on a property and the opposite happened if we considered properties with a price higher than the 80% quantile: for higher prices the CTR was lower. The answer to the question: "To which extent does the price influence user decision-making?" is clearly yes. Price influenced the user decision-making both positively and negatively as also stated in [13,21].

*Influence of Rating.* Figure 3 reports the analysis for properties with a rating above the 80% quantile and below the 20% quantile. While the influence of price was statistically significant for every rank position, for rating this difference was only significant for properties with a rating below the 20% quantile and only for the first seven rank positions. We expected that a higher rating would lead

Fig. 2. CTR for each rank position in the recommendation list taking into account property's price.

to a higher CTR, while a lower rating would lead to a lower CTR. Instead, we observed the exact opposite phenomenon. Thus, the answer to the research question: "To which extent does the rating influence user decision-making?" is not so clear. While for rank position and price the effect was always relevant, for the rating it was more difficult to give a unique answer: the rating seemed to have a very weak influence or no influence at all. These results are in contrast with [5] and [2] that found that users were influenced by rating. However, these works ran user studies considering only rating and number of reviews, while for us price seemed to have a stronger influence that probably confounded the effect of rating.

Fig. 3. CTR for each rank position in the recommendation list taking into account property's rating.

*Influence of Number of Reviews.* Finally, we report in Fig. 4 the analysis for properties with a number of reviews above the 80% quantile and below the 20% quantile. In these cases, it was clear that the number of reviews did not influence the user's choice at all. These results are in contrast to other studies in the literature, such as [2], where users tend to trust the rating of the items only if they are made up on a sufficient number of reviews. To verify the existence of a sufficient number of reviews above which users trust the rating, we compared the distribution of the CTR for properties with above and below 35 reviews. We found that there was a positive influence for properties above the threshold confirming our claims. Given our data, the last research question: "To which extent does the number of reviews influence user decision-making?" has to be answered as follows: the number of reviews did not influence user decision-making when the number of the reviews exceeded a specific threshold.

Fig. 4. CTR for each rank position in the recommendation list taking into account the number of reviews of each property.

#### 3 Re-rank Algorithm

To answer the second research question, we implemented a simple and efficient algorithm to re-rank the top-25 list of offered properties as recommended by the current algorithm. Since the developed algorithm only re-ranks the top-25 items, it is ensured that all properties presented to users are of comparable quality with the baseline. To re-rank the properties, we computed a score and reordered the properties from highest to lowest score. The score, reported in Eq. 1, is composed by two logistic functions with two means:

$$y = \alpha \cdot \frac{1}{1 + e^{kx\_i - \mu\_1}} + \beta \cdot \frac{1}{1 + e^{kx\_i - \mu\_2}} \tag{1}$$

where α, β ∈ [0, 1] manage the weight of the two functions while α + β = 1, y represents the score, x*i* the price of the property i, and k controls the speed by which the function approaches the limits (i.e., 0 and 1). Finally, the two means, μ1 and μ2, represent respectively the mean price for the type of accommodation4

4 With *type of accommodation* we refer to the different type of properties, e.g., apartment, guest house, hotels with 3 stars, etc.

of the property i within the recommendation list and the median price of the properties within the recommended list (regardless of the type of accommodation). For μ2, we used the median instead of the mean to reduce the impact of outlier prices, for instance, the price of 5-stars hotels. μ1 allows us to account in a simple way the quality-price ratio, because a user may prefer to pay more for higher quality accommodations. While μ2 controls for the absolute price of the properties, because, as showed in Fig. 2, users tend to click on properties associated with a lower price. In the following experiments, we use α = β = 0.5. We select these values using the results from offline experiments on the dataset described in Sect. 2, because running multiples online experiments with different values of the two hyper-parameters was not possible.

#### 4 A/B Test Results

The A/B test was conducted on the company's website for 20 days (between June and July 2022), and, in the end, nearly 1 million searches were conducted by users worldwide.

Fig. 5. Example of the A/B test. On the left is the list of properties recommended by the Baseline policy, and on the right is the list reordered by the Re-rank policy. The achieved NDCG are 0*.*651 (Baseline policy) and 1*.*0 (Re-rank policy) even if the number of clicks are the same.

We compared the Baseline policy used by the company, a linear regression that takes several factors into account and where weights are manually chosen by experts, with the Re-rank policy described in Sect. 3. Figure 5 reports an example of the A/B test on a 5 items list while the results, in terms of CTR for each rank position, are reported in Fig. 6.

Figure 6 clearly depicts that, for the first position, the CTR achieved by the Re-rank policy was statistically significantly higher (more than 2%) than the Baseline policy. Instead, for all rank positions after the third, the Baseline policy achieved a slightly higher CTR, even if the difference was less than 0.5% and close to zero for bottom positions. The increase in the first position was expected, and the results confirmed our hypotheses. However, we also expected an improvement for more top ranked positions while from the third rank position we observed a decrease.

To further analyse the user click behaviour, given that we can not disclose the results in terms of conversion rates, we computed the CTR for search (SCTR). The SCTR is defined as the ratio between the number of clicked searches and the total number of searches: # of clicked searches # of searches . A search is clicked if at least one of the recommended item received a click. The Re-rank policy achieved a SCTR of 23.48%, while the Baseline policy achieved a slightly higher SCTR of 24.16%. The difference between the two policies was very small and showed that the increase in CTR in the first position for the Re-rank policy was compensated by the decrease for all the other positions. Finally, we also computed the *Normalized Discounted Cumulative Gain (NDCG)* on the clicks in order to assess if the Rerank policy improved the ranking quality w.r.t. the Baseline policy. The Baseline policy achieved a NDCG of 0.121 while the Re-rank policy improved the NDCG to 0.136. This further highlighted that the Re-rank policy was more effective in recommending more relevant properties in the top positions.

Fig. 6. Results from the A/B test in terms of CTR for each rank position. Due to the large number of observations, the confidence interval is very small and difficult to see in the image.

Given the results from the A/B test, the answer to the second research question, "Is a price-based re-rank of these offered properties sufficient to achieve a higher CTR?", is clearly yes if the main goal is to improve the CTR in the top position of the list. Despite the data analysis results, which showed that lower prices were a key factor to improve the CTR, a policy that re-rank items by price was only sufficient to improve the CTR w.r.t. the Baseline policy in the first position of the recommended list. However, since users usually pay more attention to the item in first position, this can be considered a good result even if the SCTR slightly decreased with the Re-rank policy.

#### 5 Influence of the OTA

To further study the differences in CTR and SCTR metrics between the two policies, we analysed additional variables with potential influence on user decisionmaking. While the variables analysed before, i.e. average rating, number of reviews and price, behave similarly as before, the OTA presented with each property emerged as one of the key factors in the A/B test. Furthermore, this variable is very important to the company's business, so we focused our attention on it.

Fig. 7. CTR comparison for each rank position between the *most common OTA* and the *other OTAs*. Due to the large number of observations, the confidence interval is very small and difficult to see in the image.

Figure 7 depicts the CTR at each rank position for the *most common OTA* and for all the *other OTAs*. Since we can not disclose the names of the OTAs, we only distinguish between the *most common OTA* and the *other OTAs*. The *most common OTA* always had a CTR that was significantly higher than the CTR of the *other OTAs*, at least for the first 15 positions, which means that users preferred this OTA to the others. One reason for this preference could be that the *most common OTA* might be more trusted by users.

This difference in CTR between OTA groups, joined with the number of recommendations for each OTA group at each rank position, reported in Fig. 8, could explain the difference in SCTR identified between the two policies. From Fig. 8a, we can see that the Re-rank policy recommended properties with the *most common OTA* less frequently at top-ranked positions while favouring more frequently *other OTAs*, Fig. 8b.

Thus, by favouring lower price offers the Re-rank policy pushed less wellknown OTAs to top-ranked positions and exposed them to higher levels of users' attention. Their lower likelihood of being clicked, however, seems to have neutralized the positive price effect and resulted in an overall decrease in the SCTR.

Fig. 8. Count of recommendations for the *most common OTA* and for the *other OTAs*.

The answer to the third research question, "Does the OTA associated with each offered property influence the CTR?", is yes. Although price and rank positions were identified as the most important features that influenced users' decision-making, there were also other factors, in our case the OTA, that could impact users' decision and thus overall performance metrics of a ranking policy. At the end, in our case, a price-based re-rank algorithm that also keep the balance for the OTA feature would probably have improved the baseline, whereas considering only the price was sufficient to achieve a marginal improvement.

### 6 Conclusions

In this paper, we studied how different variables affect the user click behaviour in online hotel search. Specifically, we took into account the following variables: (i) rank position, (ii) price, (iii) rating and (iv) number of reviews, and measured their influence by observing changes in the CTR distribution. We started by analysing a historical dataset collected in a *meta-search booking platform* in which as expected the rank position and the price heavily influenced the user's choice: a property in the top positions had a greater probability of receiving a click than a property in the last positions and a high price generally discouraged users.

On the other hand, differently from the previous literature, we found that the average rating had a weak influence on the user's choice. Probably, this influence was heavily confounded by the influence of the price because, as stated by [3], average ratings are influenced by price: products tend to have a higher rating when they have a higher price. Even regarding the number of reviews, we discovered that this variable did not have a significant effect on user's choice. This was because above a threshold, about 35 reviews in our case, the user trusted the average rating, and a larger number of reviews did not change the user's perception.

To further verify the influence of price, we ran an online A/B test on the company's website to compare a Baseline policy with a price-based re-rank policy that shuffles the top-25 offered properties in recommendation lists. The results showed that the re-rank policy improved the CTR for the first rank position, confirming that price was a key factor that influenced users click behaviour. Furthermore, we observed that also the OTA associated with a property, influenced the user decision. For example, in our case the *most common OTA* achieved a higher CTR for every rank position compared to the *other OTAs* and seems to be favoured more by users.

This work consequently highlights the many influence factors and biases on users decision-making in online travel search that are disregarded in most offline datasets by presenting the outcome of a price-based re-rank strategy.

#### References

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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## **Exploring Customer Experience with Service Robots in Hospitality and Tourism: Activity Theory Perspective**

Olena Ciftci(B) and Katerina Berezina

University of Mississippi, University, Oxford, MS 38677, USA<EMAIL_ADDRESS><EMAIL_ADDRESS>
**Abstract.** Addressing a call for theoretical development in human-robot interaction research, this study introduces activity theory to the field of service robots (SRs) in hospitality and tourism. Activity theory was used as the foundation for the conceptual analysis of in-depth interviews with hospitality customers. The results of content analysis of the interviews and future research directions are presented based on each of the service activity system's components: object (customer experience (CE) with SRs in a hospitality unit), subject (customers), technology (SRs), rules (implementation procedures of SRs), community (customers, other customers, and employees), division of labor (a division of service), outcome (satisfaction, overall experience with hospitality establishments, and behavioral intentions), and context. The study provides future research directions in using activity theory in studies on human-robot interaction and CE with SRs in hospitality and tourism. Robot developers and hospitality professionals can use the data analysis framework proposed in this study to evaluate CE with SRs.

**Keywords:** Service robot · Activity theory · Customer experience · Human-robot interaction

### **1 Introduction**

Research on service robots (SRs) in the hospitality and tourism industry has recently received significant attention. However, a need remains for further theoretical development. Jung and Hinds [1] call for creating more human-robot interaction (HRI) theories from the social sciences and interdisciplinary research combining robotics, design, and behavioral sciences. Most theories and conceptual models in HRI in service, hospitality, and tourism focus on robot design, features, functions, and performance when describing customer-robot interaction (e.g., the Service Robot Acceptance Model (sRAM) [2]) or a few aspects, like robot behavior, customer characteristics, and specific context of companies, that influence customer engagement and behavior [3, 4]. But those theories do not look at HRI in service as a whole system that changes during the service and over time. To close this gap, this study introduces activity theory [5–9] to the specific area of hospitality technology and hospitality robotics.

Activity theory has been frequently used in the fields of human-computer interaction (HCI) and technology design in diverse contexts [5] and service design [10–13]. For instance, activity theory has been deployed to study the design of interactional social robots [14], the concept of robot as a service in cloud computing [15], and the context of robot-assisted special education [16]. However, this is the first study to employ activity theory in the context of SRs in hospitality and tourism.

Activity is the central unit of analysis in activity theory [8]. Activity is described as "purposeful, social, mediated, multilevel, and developing interaction between actors ('subjects') and the objective world ('objects'). A central claim of the approach is that it is an activity that places the subject in objective reality and transforms the reality into a form of subjectivity" [5] (p. 609). The subject can be an individual or a group that participates in an activity and transform the object into outcomes using tools [17]. Both the object and tools can be tangible or intangible. In HCI, a tool is a computer or technology that mediates activity [8].

There are different interpretations and modifications of activity theory [5]. One of the most known and influential versions of activity theory in HCI research was created by Engeström [6, 7]. Engeström [6, 7] proposed activity theory for collective activities and a model for an activity system that includes the following components: subject, object, tools (mediators), rules, community, division of labor, and outcome. Components of activities are different in certain situations and specific contexts [6, 7], and they develop and transform over time [6, 8]. The analysis based on activity theory [6, 7] can help explore customer experience with SRs in the activity system of service in hospitality and tourism.

Customer experience (CE) with SRs is an important but understudied concept in studies about HRI in hospitality. There are many hospitality and tourism businesses that employ SRs. And with the current demographic trend of the aging population, robots will be employed in many services, including hospitality and tourism, and robot service experience will be a norm [18]. Enhancing CE can enhance service value and increase satisfaction, behavior intentions, and loyalty [19]. The service experience can also differentiate the business from its competitors [19].

Despite practical importance, academic literature still lacks in-depth knowledge about CE with SRs in hospitality and tourism. Only three studies explored CE with SRs based on content analysis of customers' online reviews on social media [20–22], and one study with a conceptual model of smart service experience in hospitality and tourism, including experience with SRs [23].

Thus, the aim of this research is: 1) to introduce activity theory to the field of SRs and demonstrate the application of this theory to CE with SRs in hospitality and tourism; and 2) to formulate future research directions for the investigation of HRI and CE with SRs in hospitality and tourism. This is the first study that introduces activity theory to HRI in the hospitality and tourism domain. It uses activity theory as a theoretical foundation for the conceptual analysis of in-depth interviews with hospitality customers from different countries and cultures that describe their experience with SRs in hospitality and tourism establishments. The analysis framework proposed in the study can be used by robot developers and hospitality professionals for the evaluation of CE with SRs. The study also proposes future research directions for using activity theory in research in the domain of HRI and CE with SRs in hospitality and tourism.

### **2 Methods**

#### **2.1 Interview Operationalization and Sample**

Thirteen in-depth interviews were conducted via Zoom with hospitality and tourism customers who had experience with service robots (SRs). To find qualified participants, an online survey was conducted on Prolific to identify those respondents who experienced SRs in hospitality and tourism. Their Prolific IDs were recorded in an Allowlist. Then, the invitation to participate in the interview was sent to qualified respondents from the Allowlist.

The identity of the interviewees was protected, only their demographic information and prolific IDs were recorded. The interviews were audio recorded and transcribed verbatim for further analysis. The interviews included questions about the customer experience (CE) with SRs, their satisfaction with their experience, service delivered by robots, and overall experience in hospitality and tourism establishments.

Residents of seven countries were interviewed. The sample had an almost equal representation of males and females. The average age of the interviewees was 29 years old. Please see the interviewees' profiles for more information.1

#### **2.2 Using Activity Theory for Interview Analysis**

Interviews were analyzed manually to identify patterns in data to develop themes and concepts that correspond to the study's goal. The data were analyzed using qualitative codes based on activity theory components in the activity system model by Engeström [6, 7]. Engeström's [6, 7] interpretation of activity theory is applicable to the analysis of CE with SRs in the hospitality and tourism environment because the focus of this theory is the activity system. The studies on CE design stated that CE forms an activity system or multiple activity systems [11, 13]. Engeström's [6, 7] activity theory interpretation was also a base for studies in designing robotic service systems [15], learning systems with robots [16], and other technologies [17, 24].

In the context of hospitality robotics and activity theory, the activity may be seen as a service delivery process by robots and human employees that form CE in hospitality and tourism settings. This study adopted the activity theory framework from [6, 7, 16] and adapted it to the study context (See Fig. 1). The elements of the activity system and codes for analysis in this study include subject, object, technology (tools), rules, community, division of labor, outcome, and context.

1 The table with the interviewees' profiles is available in the supplementary materials at https://drive.google.com/file/d/1WwyJrF0bYYeqJXrFWlC2-MFTkjV0Y-Sx/view?usp=sha ring or upon request.

**Fig. 1.** Activity theory model (adopted from Engeström [6, 7]) for analysis of customer experience with service robots in hospitality and tourism.

The codes for analysis of CE with SRs in hospitality and tourism are described below. *Object* includes CE with and an attitude towards SRs in a hospitality unit.

*Subject* refers to customers that received service from robots.

*Technology* refers to SRs that deliver service directly with or without the assistance of human employees or in combination with other systems. The robots may have different characteristics, roles, and tasks.

*Rules* include implementation procedures of SRs.

*Community* refers to customers, other customers, and employees that participate in service delivery or influence CE.

*Division of labor* refers to a division of service or, in other words, the separation of the tasks among customers, robots, and employees during service delivered by robots.

*Outcomes* of service delivery and experience with SRs are satisfaction, overall experience with hospitality establishments, and behavioral intentions.

*Context* is an important concept of activity theory that determines elements and the transformation of activity [7, 8, 25]. Depending on the context, the activity goals, tasks, rules, community, and objectives may change [7, 8, 25]. In this research, context relates to place (e.g., country, city), situation (e.g., a family reunion, friends gathering, travel), and hospitality setting, including different types of hotels, restaurants, cafes, bars, and airports.

The next section of this paper reports the analysis results of the interviews concerning each activity component.

#### **3 Results and Discussion**

#### **3.1 Object (Customer Experience with Service Robots in a Hospitality Unit)**

All interviewees stated that convenient, efficient, and quick service was the most important factor of service delivered by robots. Many interviewees also described their experiences as novel, interesting, fun, and entertaining. Interviewees evaluated their experience with service robots (SRs) based on the task they needed to complete, the context, and their prior perception of the robots' functions. They also described their experience with SRs as a part of the whole experience with hospitality service, in particular hospitality or tourism establishments. The interviewees' experience with SRs was affected by all other activity components in the activity system model (Fig. 1), such as subjects' characteristics, robots, community, rules (implementation procedures of SRs), division of labor (a division of service), context, as discussed in this section of the paper. Further, we discuss possible directions for research on customer experience (CE) and practical implications in relation to each activity system component.

#### **3.2 Subject (Customers of Hospitality Establishments)**

Most of the interviewees described themselves as individuals interested in new technology and robots. Such self-perception could be the reason that all interviewees evaluated their experience as satisfactory or exciting. Most interviewees chose to patronize hotels, restaurants, or a café because they wanted to try robot service. Only two interviewees did not know about SRs working in the hotels at the time they chose their accommodations. Interviewee 2 generally preferred to use self-service technology, so she found robot service very convenient and comfortable. Interviewees 2 and 8 explained that, as introverts, they enjoy robot service over human service.

Previous experience with SRs influences customer perception and experience with SRs. Customers who experienced robot service multiple times evaluate and describe their current experience in comparison with previous experiences. When interviewees experienced robot service for the first time, they used a comparison with their image of SRs that was formed based on their imagination and other sources (e.g., movies, articles, books).

Demographic characteristics of customers also influence their perception of SRs and their experience with the service. When explaining their experience with SRs, females described their feelings and emotions, while males focused more on the functionality and utilitarian characteristics of the robots. Thus, researchers should consider using demographic characteristics, psychological traits, interest in technology, and previous experience with SRs as moderators in robot hospitality research. Likewise, industry practitioners should study their customer base carefully, understand their experiences with and preferences toward robots, and educate customers about the role of robotics in the hospitality industry (e.g., why robots are used and their impacts on employment).

#### **3.3 Technology (Service Robots)**

The interviewees had experience with different artificial intelligence-powered SRs: the robots had different characteristics, roles, and tasks. When interviewees pictured their experience, they described robots' physical appearance, moves, motions, and voice. Some interviewees compared the SRs with movie or cartoon characters (e.g., a "robot from Star Wars") or living creatures, like animals (pets).

The interface and functional features of SRs also were central factors of CE in all interviews. Interviewees 3, 8, and 11 noted that the robot service is uninformed and lacks customization and personalization. The interface also is a factor that can affect CE because, generally, interviewees want to interact with robots. Interviewee 8 was not impressed with the robot waiter's interface: all interactions with the robot were performed via "the monitor with a touchpad." Interviewee 8 suggested it would be better if the robot had a voice recognition system so that he could communicate with the robot by voice. Thus, robot developers and hospitality managers should consider that customers expect SRs employed in the hospitality industry to have a higher level of interaction and communication abilities.

The research showed that robot design is also important for customer attitude towards robot service failures [26]. There are already many studies on SRs' anthropomorphism effect on customers (users) behavioral intentions and HRI [27–29]. However, more empirical, experimental, and qualitative studies are needed to explore robots' physical and functional features on CE and satisfaction with robot service in hospitality.

#### **3.4 Community**

Community includes customers themself, other customers, and employees that participate in service delivery or influence CE. Positive attitudes and good service of human employees that assist robots or work with robots on service delivery can affect CE with robots and overall experience. Also, interviewees indicated that they enjoyed watching the interaction of other customers, their family members, or friends with SRs. Interviews 5 and 11 confirm that SRs are an attraction for children [22], and thus, they can be a motivation for families with children to patronize the hospitality business with robotic employees. Therefore, practitioners also may consider employing robots with kid-friendly interfaces and designs if their customers are families with children and it aligns with the concept and goals of the business. Furthermore, it opens one more research problem in identifying elements of the children-friendly design of robots and their interaction interface.

#### **3.5 Rules (Implementation Procedures of Service Robots)**

Customers who repeatedly patronize the same hospitality business with SRs may notice changes in the robot operations and evaluate the effectiveness of the changes. Interviewees 1 and 2 used food delivery robots multiple times on the university campus. They described some modifications in functionality and service delivery of the robots over time and evaluated the effectiveness of the changes when describing their experience with robots.

While customers may not have an idea about the robot's implementation rules, they may notice some of the failures in the robot's implementation. For example, Interviewee 11 also described a situation when a robot runner in a restaurant dropped a plate that was being delivered to another customer because it moved" wobbly." The customers may also notice when the service takes longer than expected. Thus, to ensure service quality and a positive CE, managers should encourage customers to report any robot failure or unsatisfactory service. For example, they can employ a satisfaction survey displayed on the robot's screen or in their mobile app.

Similarly, Interviewee 1 explained why the food delivery robot hit her on a crosswalk: "Maybe they [robots] got … a better GPS [navigation], but, you know, it's not that precise." It shows that customers take robot failures easy. Many customers accept the fact that SRs are new technology and it is not perfect yet, and "any technology may fail or brake." Lv et al. [26] came to a similar conclusion: customers tolerate service failures by robots with cute designs. However, more attention from research is needed in the exploration of customer understanding of SR implementation and attitude towards SR failures.

#### **3.6 Division of Labor (Division of Service)**

Some hospitality businesses organize the division of labor in a way when SRs support services delivered by human employees. Some interviewees find self-service more interesting in this service arrangement. For example, Interviewee 11 liked taking their plates from a robot runner instead of a waiter doing it for them in a restaurant. In this situation, there is an opportunity for experience co-creation that should be organized by management to support a more memorable experience at their establishment. However, more research is needed on self-service and experience co-creation with SRs in hospitality and tourism.

Many interviewees associated unsatisfactory experiences with robot malfunctions. Thus, when SRs work independently, customers appreciate it when human employees are present and support the service delivered by robots or can quickly come and fix the failure of the robot service. Otherwise, the robots' errors or malfunctions may affect the overall experience with hospitality service. Choi et al. [27] also found that customers are more satisfied with service recovery when human employees apologize for nonhumanoid robot failure. Similarly, Wang et al. [30] results show that a human employee's apology for a service failure increases the intention to revisit a hotel while a robot's apology has no effect; however, reactions to human employees vs robot employees' apologies are different for younger and older travelers. Thus, there is a need for studies on robot service failure and the role of human employees in robot service failure prevention and resolution.

#### **3.7 Outcomes**

Outcomes of service delivery and experience with SRs are satisfaction, overall experience with hospitality establishment, and behavioral intentions. Interestingly, half of the interviewees said that robot service did not affect their overall service. It is one more piece of evidence that customers evaluate service experience in hospitality as a whole. A positive experience with SRs does not guarantee a positive or memorable experience with a hospitality business overall. Almost half of the interviewees also said they chose a hospitality establishment to experience robot service at first, but they may not patronize the restaurant or hotel in the future. So, SRs may be an attraction or motivation to choose a service provider, but they do not guarantee repeat business. Thus, there is a need to investigate factors of CE in the hospitality business with robot employees that lead to an overall memorable experience, satisfaction with service, repeat patronage, and positive word of mouth.

#### **3.8 Context**

The subjects' objectives of activity when interacting with SRs may differ depending on the context (e.g., place, situation, and hospitality settings). Some interviewees would like to have more social interaction in hospitality settings, which was not possible with robots that they encountered, and human service was not available. But some interviewees did not want to interact with human employees for different reasons. Interviewees 2 and 3 found robot service and distance from human employees as a precaution against COVID-19 spread. This result supports the findings by Kim et al. [31] that the COVID-19 pandemic contributed to customers' more positive attitude to robot-staffed hotels versus only human-staffed hotels. Interviewee 2 was very tired after a long trip and was happy to receive an item from a robot butler in her hotel room and not have to talk to a human employee. Interviewee 2 stayed in a midscale hotel at that time. However, she would prefer more social interaction with employees in a resort or upscale hotel. Similarly, other interviewees also pointed out that they would prefer to use SRs in quick-service or chain restaurants with known menus, where little customization and personalization is offered. But they would choose a human employee service in a fine dining independent restaurant.

The interface of some robots was described as primitive but sufficient for the service tasks and roles that they performed, for example, robots used for delivery and luggage storage. However, the limited interface and lack of social interaction were identified as a problem for more complex services like providing information to travelers at airports. Thus, CE with SRs and robot acceptance factors should be investigated and compared for different contexts in hospitality and tourism. Given the interview results, engineers should cooperate with hospitality practitioners to create or customize robots according to the concept of the business where their robots will be employed and the specific task they are assigned.

### **4 General Directions on Using Activity Theory for Future Research**

The results of this study showed that customers evaluate service robot (SR) performance and their experience with the robots based on robot design, context, specific task and environment, support from human employees, and specific customer needs and goals of the service. Thus, future empirical and experimental research should examine customer experience (CE), attitudes, satisfaction, and behavioral intentions with robots with different designs in different contexts. For example, future research should investigate what design elements of SRs contribute to positive CE, satisfaction, and behavioral intentions in different types of restaurants or hotels; what SR experience factors are important for customers of different cultures to form positive overall experiences and satisfaction with hospitality establishments.

Furthermore, activity theory can be used to build research on experience co-creation by customers, SRs, and employees. The results of the interview analysis in this study show that hospitality patrons want to interact with SRs during the service; they also desire interaction with human employees during or instead of robot-delivered service in different situations. In addition, in the systematic application of activity theory for HCI, Bødker [32] emphasized the important phenomenon for the analysis and design of technology that people act through technology rather than interact with it. Thus, empirical research is needed to investigate motivators and factors of experience co-creation with SRs in hospitality and tourism.

Following the results of this study and calls for future research from other studies, there is a need to investigate employees and SRs working in teams to create a better CE [1]. Future research may use activity theory to study human employee roles, tasks, and operations when they work with SRs to create memorable CEs in hospitality and tourism. In activity theory, it is also important to study how the employees' roles, tasks, and operations transform when they work with SRs.

There are different modifications of activity theory and models that are based on this theory [5], including cultural-historical activity theory (CHAT) [33], human activity modeling (HAM) [10, 12] that can be used in research about HRI and CE with SRs in hospitality and tourism. Many studies call for supplementing activity theory with other theories to create more specific technology [5]. Thus, there is a need for creating new theoretical models that are based on activity theory and other theories that fit the context of CE and experience co-creation with SRs in hospitality and tourism.

The development of systems as a result of an activity is one of the central parts of activity theory [5, 8, 9]. Development can be both an object of research and research methodology in studies that are based on activity theory [9]. The activity theory's principle of development allows for conducting a thorough analysis of complex phenomena [8]. It may include methods like field experiments and observations [8], "the formative experiment which combines active participation with monitoring of the developmental changes of the study participants" [9] (p. 159), and ethnographic methods [9, 33]. However, most HCI research, including studies on HRI, is based on traditional laboratory experiments [5]. Thus, research in the domain of SRs in hospitality and tourism should use more methods to capture the development of service systems that include SRs.

#### **5 Limitations**

The study employed in-depth interviews that allowed a conceptual understanding of customer experience (CE) with service robots (SRs) and an explanation of the application of activity theory in the research that studies this phenomenon. However, this method has a limitation of generalization of findings. While the interviewees experienced different types of SRs in various hospitality and tourism settings in different countries, there are still many more types of robots that the interviewees did not experience. So, more qualitative research on CE with SRs is encouraged. All interviewees identified themselves as interested in technology and excited about meeting robots. However, other customers are less interested in technology and robots. Thus, further research is needed to understand their experience with SRs in hospitality and tourism.

### **6 Significance of the Study**

This study introduces activity theory to the domain of hospitality robotics research. It applies this theory to understand user experience with hospitality robots and forwards direction for further research that stands from this theory. The framework proposed in this study can be used by robot developers and hospitality professionals in the evaluation of service and customer experience with service robots. This study design also contributes to building the generalizable theory of HRI based on a study of different types of robots with different tasks in different contexts [1].

### **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

## **How Do Affect the Infusion of Smart Technology and Mindfulness of Tourism SMEs on Competitiveness?**

Estrella Diaz1(B) , Águeda Esteban2 , Rocío Carranza3 , Carlos Sánchez-Camacho3 , and David Martín-Consuegra1

1 University of Castilla-La Mancha, Ronda de Toledo, 13071 Ciudad Real, Spain<EMAIL_ADDRESS>
2 University of Castilla-La Mancha, San Pedro Mártir, 7, 45002 Toledo, Spain

3 Universidad International de La Rioja, Av. de La Paz, 137, 26006 Logroño, Spain

**Abstract.** The purpose of this research is to analyse the effect of the infusion of smart technologies and the mindfulness of tourism small and medium-sized organisations (SMEs) in the use of smart technologies on several aspects related to the companies and their employees. Specifically, its effect on service advantage, value co-creation, employee job satisfaction, employees' perception of service cannibalisation and competitiveness of tourism companies is analyzed. The total sample obtained was 877 employees/managers of tourism SMEs located in Europe and the United States. The model proposed in the study was estimated with PLS-SEM, a variance-based structural equation modeling statistical technique. The results of the study offer important implications and recommendations to researchers in the tourism field, tourism organisations and destinations on the importance of using smart technologies.

**Keywords:** Infusion · Mindfulness · Smart technologies

### **1 Introduction and Research Aim**

Smart technologies and their use by tourism small and medium-sized organisations (SMEs) are modifying the tasks performed by employees that are in contact with consumers/tourists [1, 2]. Technology infusion towards smart technologies is defined by [3] as the incorporation of smart technological elements by service organisations with the aim of modifying the customer experience by employees in direct contact with consumers/tourists. This area is of great relevance in companies and facilitates service encounters between people, from people to technology and between different technologies with each other [4].

On the other hand, organisational mindfulness towards digital transformation is of great importance in the incorporation of smart technologies, as it would help to proactively manage digital technologies and reduce the possibility of rigidity [5]. Organisational mindfulness is defined as the degree to which a company grasps differential details about emerging threats and builds a capacity to act quickly in response to these elements [6]. The presence of organisational mindfulness increases the likelihood of digital transformation decisions by firms and enables more effective deployment of their resources to better implement smart technologies.

Although the key role of technologies has been increasingly recognised in the literature, the impact of infusion of smart technologies and mindfulness in the use of smart technologies on value creation, performance and competitiveness within tourism SMEs has been scarcely analyzed. In this sense, the main aim of this research is to examine the effect of the infusion of smart technologies and the mindfulness of tourism SMEs in the use of smart technologies on a series of aspects linked to the companies and their employees. Specifically, its effect on service advantage, value co-creation, employee job satisfaction, employee perception of service cannibalisation and competitiveness of tourism companies is analyzed.

### **2 Background and/or Conceptual Model**

In the realm of digital transformation and the smart environment, infusion is defined as the extent to which consumer-facing employees use smart technology to its full extent to enhance their productivity [7]. Specifically, analyzing studies by [8], these authors define employee-applied technology infusion as any combination of hardware, software, information, and/or networks that support the co-creation of value between a service provider and the customer. Currently, smart employee-applied technology functions primarily as a resource that facilitates value creation in the exchange process between a customer and a service provider [9]. For example, Artificial Intelligence (AI) is increasingly reshaping service by performing various tasks, constituting an important source of innovation [10].

Another element linked to the use of technologies that has great influence is organisational mindfulness [11]. This concept represents the activities of actively searching for digital transformation opportunities, anticipating and evaluating business transformation, as well as providing alternatives for decision making. Key aspects of this variable include: anticipating digital technological change through the use of market intelligence [12], the company's strategic plan for technological change, choosing platforms (including hardware, network and software standards) that can accommodate technological change, and informing management of the most appropriate option before making a strategic technological transformation change decision [13].

The inclusion of smart technologies and their association to tourism refers to a platform where tourism organisations (managers and employees) and consumers/tourists use technologies to reinvent and strengthen their roles in the shared service economy and improve the quality of consumer/tourist experiences. The new smart tourism paradigm is centered on real-time consumer and value co-creation. Through interactions and assistance from the service provider, value is created [14]. Therefore, this study suggests that technology infusion will have a direct and positive effect on service advantage (H1), value co-creation (H2), employee job satisfaction (H3), and organisational competitiveness (H5). Likewise, increased awareness on the part of tourism organisations toward the use of smart technologies can have a direct positive effect on service advantage (H6), value co-creation (H7), employee job satisfaction (H8) and organisational competitiveness (H10).

However, several studies suggest that the use of ICT and smart technologies are raising doubts about the future of face-to-face employees, who perceive that their sales, market shares and roles are decreasing in favor of online channels [15]. In this sense, one of the consequences of technological infusion and the use of smart technologies in the tourism context is the perception of service cannibalisation (H4). Similarly, greater awareness on the part of tourism organisations toward the use of smart technologies could generate in employees who are in contact with consumers/tourists a greater insecurity in their role within the company, generating a perception of service cannibalisation (H9). In addition, some studies have determined the relationship between organisational awareness towards technology with greater innovation in the market and better performance of operations [16]. Thus, these variables (service advantage, value co-creation, employee job satisfaction and perception of service cannibalisation) can affect the competitiveness of tourism companies (H11, H12, H13 and H14). The following Fig. 1 gives a summary of all hypotheses.

**Fig. 1.** Theoretical model

### **3 Methodology**

The sample of the present study was obtained through quantitative primary research aimed at managers and employees of small and medium-sized tourism companies (hotels, travel agencies, etc.) located in Europe (nEU=454) and the United States (nUSA=423). Data were collected using a structured questionnaire consisting of closed-ended questions measured on 5-point Likert-type scales. To determine the relationships included in the proposed model, the PLS-SEM technique was applied using SmartPLS statistical software.

#### **4 Results and/or Discussion and Contributions**

The aim of this study is the creation of a model to analyse the effects that the infusion of smart technologies and the awareness of the organisation toward the use of these technologies have on some variables linked to tourism SMEs. From the context of Europe, researchers found that smart technology infusion appears to be positive and significant on service advantage, value co-creation, employee job satisfaction, employee perception of service cannibalisation, and competitiveness. Similarly, the mindfulness toward digital transformation has a significant influence on service advantage, value co-creation, employee job satisfaction, employee perception of service cannibalisation, and competitiveness. Finally, service advantage, value co-creation, employee job satisfaction, and employee perception of service cannibalisation have a significant influence on competitiveness. When this model is examined for the United States, smart technology infusion appears to be positive and significant on service advantage, value co-creation, employee perception of service cannibalisation, and competitiveness. However, the relationship between smart technology infusion, and employee job satisfaction is not supported.

On the other hand, the mindfulness toward digital transformation has a significant influence on service advantage, value co-creation, employee job satisfaction, employee perception of service cannibalisation, and competitiveness. Finally, service advantage, employee job satisfaction, and employee perception of service cannibalisation have a significant influence on competitiveness. Nevertheless, the relationship between value co-creation and competitiveness is not supported in the model. When analysing the moderating effect of the context variable, the findings indicate that there were no significant differences between the American and European tourism SMEs regarding the consequences of smart technology infusion and mindfulness toward digital transformation, only in the relationship between value co-creation and competitiveness amongst both groups.

The results of this study demonstrate the impact that the use of smart technologies to their full potential and the organisations' mindset and/or awareness toward the use of technology have on service advantage, value co-creation, employee satisfaction and competitiveness. However, the use of smart technologies in tourism organisations can create more negative effects such as the perception of service cannibalisation among employees. Ultimately, this study helps to explain how smart technologies create different effects on employees in tourism organisations (hotels, tourist attractions and travel agencies), enabling the development of more tailored services to meet real consumer demands.

In addition to the contributions to academic research in this area, this study highlights several interesting implications for practitioners. This research advances the analysis of technological infusion, organisational awareness of smart technologies and their consequences for organisations. Smart technologies and connected objects are set to change service as we know it, and to have a significant impact on the other players in the service pyramid: consumers/tourists, employees, the organisation and the tourism destinations where these organisations are located. This study would confirm that the business value of technology is not only a tool to support the operational process, but also functions as part of the business for various business capabilities.

### **References**

1. De Keyser, A., Köcher, S., Alkire, L., Verbeeck, C., Kandampully, J.: Frontline service technology infusion: conceptual archetypes and future research directions. J. Serv. Manag. **30**(1), 156–183 (2019)

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

## **User Experience and Spatial Presence in a Walk-In Augmented Reality 3D Model for Tourists**

Timo Partala(B) , Mikko Kosonen, Tuomas Hellsten, and Henri Riissanen

South-Eastern Finland University of Applied Sciences, Patteristonkatu 3, 50100 Mikkeli, Finland {timo.partala,mikko.kosonen,tuomas.hellsten<EMAIL_ADDRESS>
**Abstract.** In the context of vulnerable tourist attractions, it is often the case that tourists cannot be allowed the enter all the premises of the attraction, which potentially has a negative impact on the visitors' experience. Virtual reality technologies are available for presenting the attraction for tourists in these cases, but the use of augmented reality (AR) also seems to have a role in certain use cases. The current paper presents our first experiment of using a walk-in augmented reality model to present an attraction to visitors near the original attraction. The tourist attraction visualized using AR was Mannerheim's Saloon Car dating back to World War II. Nine participants used a tablet-based AR application to examine a realistically sized three-dimensional (3D) model of the saloon car in the close vicinity of the actual tourist attraction and filled in a questionnaire, which probed their user experience, spatial situation model, and spatial presence when using the system. These early results suggested that viewing a 3D model of the attraction using tablet-based AR provided a positive user experience for the participants and that the tablet-based AR implementation was successful in supporting the participants' formation of a spatial situation model of the saloon car. The participants also gave relatively high ratings for spatial presence while viewing the 3D model using tablet-based AR.

**Keywords:** User experience · Spatial presence · Augmented reality · Tourism · Mannerheim's saloon car

### **1 Introduction**

Augmented reality (AR) has long been a promising technology for tourism applications. While early systems were mostly different kinds of AR tourist guides with points of interest [1], the latest advancements in AR technology have made the development of more advanced AR applications feasible [2]. One interesting direction is related to using large 3D models such as models of entire buildings and letting the user walk freely inside the model and examine it using AR. Many important tourist attractions have already been modeled in 3D, and a model is thus readily available. This kind of feature is already available, for example, in certain real estate AR applications (e.g. [3]), and interiors of ancient buildings can be examined in some archaeological tours with AR glasses (e.g. [4]). However, this approach is still less often studied in the field of tourism. Naturally, virtual reality technologies could also be used for visualizing interiors, however, it may be difficult to provide virtual reality equipment for tourists, for example, when the attraction is located outdoors.

The current paper describes our first experiment with an AR application, which allows its users to walk freely inside a 3D model of a tourist attraction and to view the model by using tablet-based AR. A user evaluation was arranged with nine participants. The aim of the evaluation was to understand, whether the current approach of using AR technology in tourism can result in positive user experiences. In addition, the goal was to study, whether the users can successfully form a spatial situation model based on viewing the model. Finally, we were also interested in whether the users experience spatial presence while viewing the 3D model with tablet-based AR.

#### **2 AR Implementation of Mannerheim's Saloon Car**

Mannerheim's Saloon Car is a historical train carriage that was used by field marshal and president of Finland Carl Gustaf Mannerheim during World War II, when it housed important historical events. The saloon car is nowadays a tourist attraction located next to the railway station in Mikkeli, Finland. Because of the vulnerability of the attraction, the interior of the carriage can be visited only once a year on a festival day. A detailed 3D model of the saloon car was created based on lessons learned from previous work [5, 6] regarding the salience of visual cues in 3D models. The textures were closely modeled based on photos, and the model included furniture as well as several detailed objects including newspapers, maps, decorations, and Mannerheim's personal items.

The augmented reality application for the saloon car was created using the Tarina augmented reality platform [7] developed by CTRL Reality. Using the platform, the 3D model of the saloon car was anchored to a location right next to the real saloon car (Fig. 1). Interaction was quite simple: the user saw the real size 3D model by looking through the tablet into the direction of the location, where the model was anchored. The model remained stationary, and the user could walk freely inside the 3D model, also through walls and furniture. The user could scan the 3D model by moving the tablet in his or her hands, or by moving or rotating her/himself, while looking through the tablet.

#### **3 Methods**

#### **3.1 Participants**

Nine Finnish participants (six males and three females; mean age 34.3 years, range 22–61 years) participated in the user evaluation. They rated their previous experience of using information technology as relatively high (mean 3.8 on a 1–5 scale), but their previous experience of AR applications as low (mean 2.0 on a 1–5 scale). They also rated both their interest in AR applications (mean 4.0 on a 1–5 scale) and their interest in historical tourist attractions (mean 4.2 on a 1–5 scale) as high. The participants received a small merchandise as a reward for their participation.

**Fig. 1.** AR implementation of Mannerheim's Saloon Car displayed next to the real saloon car (left). Screenshot from actual user evaluation (right).

#### **3.2 Procedure, Materials, and Equipment**

The participants were recruited near the real saloon car at the Mikkeli railway station with the goal of carrying out the evaluation with participants who represent the target group of the study (tourists, who are interested in the saloon car). Indeed, a clear majority of the participants showed spontaneous interest towards the saloon car before they were recruited. The rest were asked whether they were interested. In all the evaluations, three researchers participated in the implementation of the study: one was recruiting and instructing participants, one taking care of the AR application and technology, and one taking care of the post-test questionnaire. All the evaluations were carried out on a single sunny, hot, and slightly windy summer day. Most of the time, the railway station area was nearly empty, but some of the evaluations were witnessed by other persons.

The participants' task was to examine the 3D model of the saloon car using the tablet-based AR system. The participants were told that this can be achieved by looking through and moving the tablet and walking inside the model. The participants started using the system, and the test ended when the participant had walked through the whole carriage at least once and examined its objects using the AR system.

In the post-test questionnaire, a short version of Hassenzahl's AttrakDiff2 user experience questionnaire exactly as used in [8] was used for evaluating user experience using eight scales. The four-item scale from the MEC-SPQ spatial presence questionnaire [9] was used to study the formation of a spatial situation model and the eight-scale Spatial Presence Experience Scale SPES [10] was used for studying spatial presence. The scales were translated literally from English to Finnish so that the AR saloon car was defined as the object of evaluation.

A Samsung S7 tablet with 6 Gb of RAM memory and an 11- display with a resolution of 1600 × 2560 pixels was used by the participants to view Mannerheim's saloon car in AR. The system was able to run the AR application and display the 3D model smoothly. The participants used another tablet to respond to the post-test questionnaire.

#### **3.3 Data Analysis**

Due to the distribution-free nature of the data, Friedman's rank tests were used to compare the ratings of the different user experience and spatial experience dimensions for significant differences, and Wilcoxon's matched pairs signed ranks tests were used in pairwise comparisons. Internal reliabilities were studied using Cronbach's Alphas.

#### **4 Results**

Mean ratings and standard errors of the means for the user experience dimensions and the spatial experience dimensions are presented in Table 1 below. Larger scores indicate better user experience or spatial experience.

**Table 1.** Mean ratings and standard errors of the means for the different user experience and spatial experience dimensions

The statistical analysis showed that there were no statistically significant differences between the different user experience dimensions. In contrast, there were significant differences between the three ratings related to spatial experience χF 2 (3) = 6.4, p *<* .05. Spatial situation model received higher ratings than both spatial presence: self-location Z = 2.1, p *<* .05 and spatial presence: possible actions Z = 2.2, p *<* .05. Internal reliabilities were on an acceptable level for all the components: spatial situation model α = .77; spatial presence: self-location α = .95; spatial presence: possible actions α = .90.

### **5 Discussion**

The results of this first study indicated positive user experience for the tablet-based AR implementation of Mannerheim's Saloon Car on all main dimensions of the AttrakDiff2 scale: pragmatic quality, hedonic quality (identification and stimulation), and attractiveness. In addition, the results indicated that the participants could form a precise mental spatial situation model of the saloon car (the floor plan and the sizes of the different rooms etc.). They also experienced relatively high spatial presence, even though they just used a tablet to view the 3D model instead of immersive technologies. It should also be noted that the tablet-based system was used on a sunny day, and the participants had low previous experience of AR systems, but the results were still good.

Some initial challenges related to the usage of the system were identified based on on-site observations, screen recordings of the tests, and the participants' post-test comments. A few users became briefly disoriented during the use of the system, which was mostly related to walking through inside or outside walls of the carriage instead of using doors and corridors. Two users started walking backwards, which also caused some disorientation. One participant also accidentally blocked the camera of the tablet briefly with his finger. However, the most important activities such as walking along the main corridor of the carriage and looking into the different rooms of the carriage could be performed well. Suggestions for improvement included more objects and interactivity with objects. Interactivity is an important challenge for the future, as current AR platforms do not do an especially good job in providing support for interactivity within 3D models in a tablet-based AR application.

Overall, the results suggest that the current approach, in which the users can walk inside the 3D model of a tourist attraction using a tablet-based AR application, can evoke a good user experience and is a noteworthy option to consider, when tourists cannot be let to enter the attraction itself. The AR approach could be especially useful in situations, in which virtual reality experiences cannot be effectively provided for the users, such as in the context of different unstaffed attractions located outdoors. Clearly, however, the findings from this experiment need to be confirmed by further studies.

**Acknowledgements.** This research was partially funded by Regional Council of South Savo from European Regional Development Fund (REACT-EU funding, project A78200). The authors would like to thank all the test participants, everybody who participated in the development of the 3D model of the Mannerheim's Saloon Car, and people at CTRL Reality for providing the Tarina platform used in the current study.

### **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

## **Spatial Spillover Effects of the Digital Economy on Tourism Demand: Evidence from China**

Zhen Liu1,2(B) and Yoo Ri Kim2

1 East China Normal University, Shanghai, China 2 University of Surrey, Guildford, Surrey, UK<EMAIL_ADDRESS>
**Abstract.** Despite widespread concerns about the effect of the digital economy on productivity, few studies have examined the relationship between the digital economy and tourism demand. Based on 285 China's prefectural-level city statistical data and big data from 2011 to 2019, a comprehensive index of the digital economy is developed and a spatial econometric model is employed to investigate such relationship. The result shows that the growth of the digital economy has positive spatial spillover effects on tourism demand, contributing to extending the impact of the digital economy on tourism demand from the spatial perspective and providing insights for policymakers on regional cooperation in the digitalisation context.

**Keywords:** Digital economy · Spatial spillover effects · Tourism demand

### **1 Introduction**

The growing importance of tourism to the economic system has caused obvious interest in identifying the fundamental factors that influence tourism demand. As different theories have been introduced, the significance of variables has been examined. Nevertheless, little light has been shed on the effect of the digital economy despite its potential to generate more than \$300 billion in value for the tourism industry. Tourism may benefit more than any other industry from the digital economy due to the extremely high information asymmetry and transaction costs that have hindered the growth of tourism demand [2]. Recent empirical research has indicated that improved local digital capabilities have contributed to inbound tourism [1]. However, past studies have neglected externalities of the digital economy and its spatial effects [14]. The digital economy can reduce transaction costs and facilitate interaction between tourists and destinations, resulting in its impact not only being limited to the local area [9]. This research, therefore, examines the spatial spillover effects of the digital economy on tourism demand utilising panel data from China's prefectural-level cities between 2011 and 2019. Considering multiple data sources, a comprehensive index is constructed to measure the development of the digital economy at the city level. The spatial Durbin model is used to capture both the direct and spatial spillover (indirect) effects of the digital economy on tourism demand, thus increasing the understanding of the spatio-temporal relationship between the digital economy and tourism demand and enabling policymakers to adopt effective destination management strategies in the digitalisation context.

© The Author(s) 2023

B. Ferrer-Rosell et al. (Eds.): ENTER 2023, SPBE, pp. 88–93, 2023. https://doi.org/10.1007/978-3-031-25752-0\_9

#### **2 Literature Review**

The digital economy refers to a new economic form that creates value via digital technology and infrastructure, digital industry, and engagement in utilising and creating digital services [6]. Due to the externality of the digital economy, it can have effects on both local and neighbouring regions as the digital economy effectively connects market participants across regions, hence generating spatial spillover effects [12]. On the one hand, the digital economy can have negative spatial spillover effects as it can reduce the transmission costs of explicit knowledge, resulting in an increasing demand for tacit knowledge and offline services [3]. On the other hand, the digital economy can have positive spatial spillover effects as it promotes user connectedness and becomes a feasible substitute for physical space [16]. In this case, the digital economy can better facilitate the matching of supply and demand across spatial regions [11].

According to existing studies, tourism demand is impacted by factors such as price and income [8], but spatial and temporal constraints experienced by tourists are ignored. As a result of the widespread adoption of digital technology, the digital economy has completely enhanced the interaction between tourists and destinations [15]. At the micro and tourist levels, previous research has highlighted the role of the digital economy in easing information asymmetry and promoting transactions [7]. As for destinations, providers' increasing reliance on ICT to build a "virtual link" with visitors can enable them to elevate the accuracy of tourism demand forecasting and rapidly supply products that meet tourists' needs, thus enhancing the attractiveness of destinations [5]. Moreover, the digital economy has blurred the boundaries between tourists and destinations; tourists become co-creators in the design of services, hence decreasing the cost of personalised services and enriching the tourism experience [4].

However, previous micro-level research employing qualitative analysis and questionnaires have argued that different aspects of the digital economy help reduce tourism uncertainty, hence influencing tourists' behavioral intentions. Direct evidence of the digital economy's effect on actual tourism demand at the macro level is still lacking. Recent research has examined the effect of the digital economy on local tourism demand at the national level [1]. Yet, few studies investigated the impact of the digital economy on tourism demand at a regional level. Given time and distance limits, inter-regional tourism is likely to be the most popular option for tourists, especially for a country with a wide territory like China, where inter-regional tourists accounted for 98% of total tourists in 2019 [10]. Moreover, due to the externalities of the digital economy, it becomes essential to examine the spatial effects of the digital economy on tourism demand. However, it is still unclear whether the digital economy has significant spillover effects on tourism demand across space and time and this study aims to fill this knowledge gap.

### **3 Methodology and Data**

Considering the existence of spatial spillover effects, this research explores the relationship between the digital economy and tourism demand using the spatial Durbin model (SDM):

$$\ln Tourism\_{il} = \rho\_1 \sum\_{j=1}^{285} \alpha\_{lj} \ln Tourism\_{il} + \beta\_1 \ln Dig\_{il}$$

$$+ \theta\_1 \sum\_{j=1}^{285} \alpha\_{lj} \ln Dig\_{il} + \beta\_2 X\_{il} + \theta\_2 \sum\_{j=1}^{285} \alpha\_{lj} X\_{il} + \delta\_{ij} + \varepsilon\_{il}$$

*Tourismit* is the tourism demand; ρ is the coefficient of the spatial lag of tourism demand; ω*ij* is the spatial matrix; *Digit* is the level of the digital economy; *Xit* is other control variables that may affect tourism demand; β*i* is the estimated coefficient of the explanatory variable; θ*i* is the estimated coefficient of the corresponding spatial lag; ω*ij* is the spatial weight matrix that represents the spatial relationship between different cities – queen contiguity-based and distance-based spatial weights matrices.

The independent variable is the digital economy. It is difficult to accurately capture the digital economy using a single indicator as it is a complex system. In this paper, we evaluate the level of the digital economy by developing a comprehensive index. With the availability of big data, we not only incorporate key indicators from previous research [1, 14, 16] but also introduce new indicators. The comprehensive index system includes three dimensions. The indicators and corresponding data sources are presented in Table 1. Indicators of the digital economy Considering these indicators are highly correlated, the principal component analysis (PCA) method is employed to transform multiple indicators into a comprehensive index by dimension reduction. Bartlett's sphericity and KMO tests indicated that the above indicators are statistically appropriate for PCA. This study identified three components based on the cumulative variance contribution of 80% and generated the index using the factor scores and weights.

**Table 1.** Indicators of the digital economy

The control variables include: house prices and GDP per capita are added to illustrate the effect of the local price level and income on tourism demand; road density and the high-speed rail reflect the level of local traffic condition; the proportion of tertiary industry to GDP as a measurement of localisation economics; and 5A top-grade tourist attractions authorised by the Ministry of Culture and Tourism of China. Data of these variables was gathered from China City Statistical Yearbook, CEIC database, China Real Estate Statistical Yearbook, and China's HSR network map.

### **4 Results**

Various tests are run to determine which type of econometric model is appropriate for this study. The (Robust) LM Spatial error and Spatial lag statistics under various matrices were statistically significant at the 1% level. Both the Wald and Lratio tests were also significant at the 1% level, confirming that the spatial Durbin model was suitable for this study. Model 1 shows the results of the fixed effect model (Table 2). The coefficient of the digital economy is 0.5803 and statistically significant at the 1% level, showing that the digital economy has a significant positive effect on the rise of local tourism demand. The findings of the spatial Durbin model based on the queen contiguity-based spatial matrix (Model 2) and geographic distance matrix (Model 3) show that the digital economy has a positive spatial spillover effect that can increase both local and neighbouring tourism demand. This implies that the digital economy can contribution to inter-regional tourism. In addition, the coefficient of the spatial lag of tourism demand (Spatial rho) is significantly positive at the 1% level, indicating the existence of the spatial spillover effect of tourism demand. This implies that an increase in tourism demand in one city leads to an increase in demand in neighbouring cities. Thus, findings infer the importance of spatial effects of both tourism demand and the digital economy. Spatial spillover effects can also be observed in the control variables.

**Table 2.** SDM coefficient estimations

**Table 2.** (*continued*)

\* *p* < 0.10, \*\* *p* < 0.05, \*\*\* *p* < 0.001

### **5 Conclusion**

According to the results of the spatial panel Durbin model, the digital economy has the positive spatial spillover effects on tourism demand. The result remains robust under different matrices, demonstrating that the digital economy can strengthen the interaction of tourism between cities and significantly contribute to inter-regional tourism. To the best of the authors' knowledge, this study is the first to analyse the spatial spillover effects of the digital economy on tourism demand. Given the significance of the spatial spillover effects, tourism researchers are challenged to examine effects of the digital economy from the inter-regional perspective; otherwise, it could lead to estimation bias. Capturing and understanding spatial spillover effects of the digital economy on tourism demand can enable industry practitioners and destination management organisations to depend on digital technology to exchange market information, jointly build a crossregional tourism collaboration system and achieve the connectivity benefits of the digital economy.

### **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

## **Mobile Eye-Tracking as a Research Method to Explore the D/Deaf Experience at Arts and Cultural Venues**

Vanessa Cumper(B) , Caroline Scarles, Hongbo (Daisy) Liu, and Albert Kimbu

University of Surrey, Guildford GU2 7XH, England<EMAIL_ADDRESS>
**Abstract.** D/deaf activists have consistently lamented their exclusion from the decision-making process by service providers. Accessibility is only effective when designed with contributions from those affected by the perceived or known barrier. This paper redresses the historic absence of the D/deaf paradigm, and recenters the focus to the individual's perspective of accessibility requirements by developing a conceptual framework, constructed through the review of empirical and theoretical literature. The conceptual dimensions presented are from the D/deaf person's perspective as valued through shared power and ownership. The aim of this conceptual paper is to explore how D/deaf-centric research can be applied and qualitatively measured through the combination of self-report, observation and Mobile eye tracking (MET).

**Keywords:** D/deaf access · Mobile eye-tracking · Arts and culture

### **1 Introduction**

People who are D/deaf continue to be a marginalised group. The accepted definition for D/deaf where the use of capital 'D' is used to describe someone who identifies as culturally Deaf, their first language is signed, not spoken, lowercase 'd' identifies everyone else who is living with a hearing impairment [1]. Despite the recognised opportunity to improve competitiveness and economic value of creating an accessible and inclusive tourism industry, accessibility remains a low priority [2–4]. The latest estimates [5] state that 1 in 6 people worldwide, have some form of hearing loss [6]. Previous research has focused on assumed communication barriers [7], without consulting their 'dehumanised subjects' [1], rendering D/deaf perceptions inconsequential, [8] this conceptual paper argues, there are substantive reasons to co-create solutions. Research conducted within an authentic setting of an art gallery presents an opportunity to fully understand how people who are D/deaf can participate in leisure, recreation, and cultural life [1]. D/deaf epistemology should be central to any research which pertains to an accessibility solution. A D/deaf-centric investigation challenges societal barriers, rendering D/deaf disabled or D/deaf problematic irrelevant, to develop an egalitarian relationship [1, 9].

This paper argues the advantage of the application of qualitative methodology of self-report in conjunction with Mobile Eye-Tracking (MET) data, accredits the D/deaf

© The Author(s) 2023

B. Ferrer-Rosell et al. (Eds.): ENTER 2023, SPBE, pp. 94–98, 2023. https://doi.org/10.1007/978-3-031-25752-0\_10

perspective centrally within the research, empowering reciprocal communication. The Tobii 2 mobile eye-tracking (MET) hardware, video-records the temporal and spatial eye-movements, through the integrated forward and rear facing cameras. The collected data identifies points of interest, through the recognition of objects, location, and duration of the gaze. The concept of power and control is assigned to the wearer of MET, as data is gathered through their gaze, and instant playback enables accurate self-report dialogue of experience from their perspective [10]. Previous research which has relied on quantitative eye-tracking measurements [10, 11] is deficient in meaning without the clarity of the D/deaf perspective. Eye-tracking data collection is becoming more ubiquitous in the marketing, gaming, and medical industries. However, it has been neglected in the tourism industry in relation to a co-design approach to provide D/deaf accessible experiences. Building on review of existing literature, a conceptual framework of D/deaf centralism is constructed, with important future research directions indicated.

#### **2 Deaf Experience**

People who are D/deaf have been categorised by society as a non-contributing group according to D/deaf activists [1, 9, 12–15] asserting, society lacks the comprehension of D/deaf as a unique linguistic group. The frequency of stigmatization occurs within the audition and oral social parameters thus, obstructing access. Intensified by traditional exhibition curation expressly reliant on the principles of artistic merit, chronology, and taxonomy, in preference to the centrality of accessibility in the visitor experience [16]. The corresponding societal bias dictates the body as disabled, contrary to the preference of the body as different [9, 14]. Recognising D/deaf body as different legitimately implies how people who are D/deaf experience the world, and the indifference society has to their diversity, unique communication, and situatedness [1, 9, 14, 16]. Fixations on individual words and difficulty articulating their experience regarding known and perceived barriers, is by definition a barrier for people who are D/deaf. The importance of eliciting personal biographies from participants corroborates the complex influences of the various dimensions of identity, understanding and recognition[16]. Previous research has indicated ethnographic film making, observations and interviews are effective methods of data collection of D/deaf narratives [9, 14, 16]. Although these methods have been favored in social anthropology, psychology, and other disciplines the barriers discussed earlier remain unsurmountable, utilising traditional qualitative methods[9, 13, 15, 16]. Although previous research papers have discussed the overarching principle of visual acuity of people who are D/deaf, there is a deficit of comparative studies of gaze patterns or areas of interest for this group within tourism [17].

#### **3 MET as Method**

Recent research has indicated people who are D/deaf exhibit remarkably different gaze patterns in comparison to hearing people [17]. Through the deployment of MET the researcher can observe participants' decoding and comprehension, to formulate strategies to reduce perceived barriers, in accessing and engaging in tourism experiences[18]. The eye tracking software, iMotions in this instance, generates pictorial evidence of gaze patterns and heatmaps. Gaze patterns consist of saccades and fixations. Fixations are the momentary pauses on an area that either consciously or unconsciously are found interesting [19, 20]. The saccades are the rapid eye movements between each fixation lasting a few seconds. Specific dependent variables of data will be collected with analysis of the correlations between fixations, matched by the analytical software to the photographs of the visual stimuli [20]. A group of fixations collectively create an area of interest (AOI). Participant interviews will reveal their conscious and unconscious AOI [18, 21, 22]. The salience of an AOI is calculated by the algorithm of the analytical software, iMotions, based on the duration before fixation on a AOI and the commonality or proportion of participants who fixate on an AOI. If the first fixation to AOI is brief, the salience is more relative in terms of participant interest and engagement [18]. Capturing meaningful qualitative data through MET provide clear indicators of participant engagement, relevant narratives and motivations with art and culture and the actualisation of intangible and tangible barriers are realized [19, 20], therefore, providing data to construct an impactful accessible solution [12, 18, 21, 22].

### **4 Conceptual Framework**

The review of empirical literature has highlighted the absence of the D/deaf narrative when designing accessible experiences[2–4, 8, 16]. The traditional museum curation of object assemblage disregards the visitor perspective, incidentally, facilitating an inaccessible exhibition [23]. However, the proposed conceptual framework propounds a D/deafcentric (Fig. 1) co-design paradigm [24]. Direct dialogue, moderates' opportunities to transform inaccessible experiences, into accessible experiences for a diverse population [12, 16]. The proposed experimental design places the participant in an authentic gallery environment with genuine artefacts for the purpose of the replication of natural behaviour [23, 24]. Meaning is applied through the careful consideration of observations in conjunction with MET, preceding and informing the semi-structured interviews. The qualitative instruments provide a unique opportunity to gain valuable insight of D/deaf situatedness[9, 14, 16]. Consequently, transforming D/deaf disabled into, valued D/deaf different, re-dressing the historical imbalance of power and autonomy[1].

**Fig. 1.** Conceptual framework of factors positively affecting D/deaf-centric design.

### **5 Discussion**

Although communication between D/deaf people and venues is a complex construct with indistinct variables, these can be overcome through the adoption of D/deaf body difference [9, 14]. This paper has argued the application of MET with semi-structured interviews and self-report, the person who is D/deaf is afforded the authority to codesign solutions to perceived and known barriers to participation in leisure, recreation, and cultural life [16]. The limitation of the conceptual framework is the propensity of singular MET observation, attributable to the immense volume of high-quality data manufactured through the data gathering process. For example, over 50,000 frames of data from studies lasting approximately 3 min, rendering this method prohibitive in some circumstances [10, 18–20]. Further prohibitions are the cost of hardware, software and licenses required, although these may diminish through higher demand, ubiquitous use, and technological innovation. However, the utilisation of MET in a qualitative field study can yield the D/deaf-centric perspective to perceived and known barriers, to design innovative solutions. Moving beyond the scope of this paper to conduct empirical research with diverse D/deaf communities framed within contemporary arts and cultural practice.

### **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

## **The Effect of Purchasing Power Parity (PPP) and Cryptocurrency Use on Changes in the Transaction Utility of International Tourists**

Ikram Nur Muharam1,2(B) and Iis Tussyadiah1

1 School of Hospitality and Tourism Management, University of Surrey, Guildford, UK<EMAIL_ADDRESS>2 Department of Economics, Universitas Padjadjaran, Bandung, Indonesia

**Abstract.** This study investigates how exposure to local prices changes the transaction utility of international tourists, and the role of purchasing power parity (PPP) and the use of cryptocurrency in these changes. Findings indicate that tourists' transaction utility did not vary all that much when they visited a country with comparable PPP to their own. Meanwhile, when traveling to countries with a lower PPP, tourists enjoy a heightened transaction utility. Furthermore, using Bitcoin results in greater transaction utility than using fiat currency.

**Keywords:** Transaction utility · Purchasing power parity · Travellers behaviour · Cryptocurrency · International tourism · Bitcoin

### **1 Introduction**

Transaction utility represents our perceived value of getting a good deal [12]. This topic needs to be better understood and requires more investigation [3]. However, this topic was rarely touched, notably in the tourism and hospitality literature. This study thus seeks to extend past research on the topic by investigating transaction utility in the context of international tourism. It takes a novel approach by evaluating the changes in tourists' transaction utility before and after exposure to local prices, exploring how the changes were affected by the destination's purchasing power parity (PPP), and measuring the effect of Bitcoin usage in hypothetical scenarios.

As everyone tends to something familiar [15], it was anticipated that tourists' perceptions of the prices of goods and services in an unfamiliar international destination would initially be based on the prices of goods and services in their home country. The expected scenarios were that tourists overestimate prices initially (the transaction utility is high) when visiting countries with lower PPP than their home country; then, it decreases as they become familiar with the destination (i.e., its currency and prices). In a different scenario, when tourists visit countries with PPP comparable to their home country, the transaction utility probably will not change much.

#### **2 Literature Review**

#### **2.1 Transaction Utility**

Transaction utility is the difference between the reference and actual prices [12]. It is a significant predictor of perceived service value, and omitting it from a model will significantly diminish its explanatory power [5]. While perceived value emphasises acquisition utility (residual pleasure) and transaction utility [12], the former better portrays post-consumption circumstances; therefore, this study focuses on the latter, which can describe pre-consumption conditions that match the context of this study.

The reference price is a person's perception of a fair price before being exposed to new information. In this study, the international travellers' reference prices are the prices of similar goods and services in their own country. A higher reference price than the actual price indicates a good deal, as the actual price is cheaper than expected. If the reference price is lower than the actual price, the transaction utility is negative, suggesting the actual price is more expensive than expected. Investigating how the transaction utility changes when international tourists are exposed to local prices will supplement existing research on transaction utility in tourism and hospitality, which has primarily focused on the context of hotels [5, 7] and restaurants [6, 8].

#### **2.2 Purchasing Power Parity (PPP) and Type of Currency**

An awareness of PPP and its related aspects will benefit stakeholders in tourism and hospitality. [4] used a price variable based on PPP to show that domestic tourism in less elastic conditions than outbound tourism can be improved with a pricing strategy, notably during a low season, or by enhancing innovation. [1] found that significant exchange rate volatility could diminish tourism inflows in the long run, probably because international tourists will have difficulty estimating prices in such a destination. Also, understanding this issue will help policymakers utilise tourism to balance PPP [14]. This study itself will add extant literature on PPP in tourism and hospitality by assessing its influence on changes in the transaction utility of international travellers.

Furthermore, as tourism and hospitality stakeholders must also stay up to date with the latest trends, this study explored the use of Bitcoin, measuring its impact on the changes in transaction utility. Many tourism vendors have begun accepting cryptocurrency [13], and some nations have even begun regulating its use [11].

#### **3 Methods**

This study uses a 2 × 2 experimental design with treatments of a country's purchasing power parity (lower or similar) and currency type (fiat or cryptocurrency) (See Table 1).

Participants were recruited through Amazon's Mechanical Turk (MTurk), the most often utilised online data collection method [10]. They were US citizens selected using a convenience sampling method. This study anticipated 120 participants (30 for each scenario). More survey slots (150) were opened to accommodate unusable responses, as suggested by [2]. Two participants did not complete the survey, and 23 responses were

**Table 1.** Experimental conditions

unreasonable and eliminated (the price reference was 100% greater or smaller than the actual price). The total participant used in the analysis was 125.

At the beginning of the experiment, participants were asked to state their perceived standard (fair) prices for budget and luxury hotel rooms (in USD). Participants were then randomly allocated to 1 of 4 scenarios and showed some tourism products/services in local currency (a currency converter was provided). After being exposed to local prices, participants were given photographs of a budget hotel and a luxury hotel (without name and brand) and asked to estimate the price of a standard room in those hotels.

Non-parametric approaches were used since not all subgroups (experimental conditions) satisfied normality and homogeneity assumptions. Non-parametric alternatives of ANOVA, Wilcoxon paired test and Mann-Whitney test [9] were used to compare transaction utility before and after exposure to local prices and to compare experimental conditions.

#### **4 Results and Discussion**

#### **4.1 Changes in Transaction Utility**

The results presented in Table 2 confirm the initial assumption that tourists visiting destinations in countries with lower purchasing power parity (PPP) initially overestimate prices. This is especially evident for budget hotels, where transaction utility before being exposed to local prices reaches 6 times the actual price. Even after seeing local prices, transaction utility remains positive (*>*1), which explains that the actual price was a good deal. Same goes for luxury hotels, but with a lower scale of change. Otherwise, in both budget and luxury hotels, the Wilcoxon paired test shows a significant difference in transaction utility before and after exposure to local prices.

Meanwhile, for visitors visiting countries with comparable PPP, in the case of budget hotels, the tests demonstrate no significant difference in transaction utility before and after exposure to local prices. In the case of luxury hotels, the Wilcoxon test yielded significant results, and the Mean value shows that transaction utility turned negative (*<*1). This means that tourists perceive that luxury hotel pricey after being familiar with the local prices.

The Mean value after exposure to local prices was higher in the cryptocurrency subgroup than the fiat-currency subgroup. This could be a sort of risk mitigation from tourists due to the high volatility of cryptocurrencies, in which when using cryptocurrencies, it is sensible to budget higher.

**Table 2.** Wilcoxon paired test results

#### **4.2 The Effect of Purchasing Power Parity and Type of Currency**

The Mann-Whitney test using the difference in transaction utility before and after being exposed to local prices reveals that variations in the transaction utility of budget hotels were strongly affected by purchasing power parity (Asymp. Sig. [2-tailed] = *<*0.001). However, the difference was not statistically significant for luxury hotels (Asymp. Sig. [2-tailed] = 0.2497).

The use of fiat currency or cryptocurrency had no significant effect on changes in transaction utility in both budget and luxury hotels (Asymp. Sig. [2-tailed] = 0.3232 and 0.4587, respectively).

### **5 Implication and Future Research**

This study adds to the existing literature on international tourism by explaining changes in transaction utility before and after exposure to local prices in the destination country, as well as providing an overview of the effect of purchasing power parity (PPP) and the use of cryptocurrency in these changes.

In practice, this study provides information that stakeholders can utilise to examine target markets and pricing strategies. For example, destination management organisations (DMOs) and budget hotels can consider targeting international tourists from countries with higher PPP; the high transaction utility before being exposed to local rates implies a big gap that providers can exploit to sell at higher prices, and at the same time, the transaction utility that remains high after exposure to local prices also benefits these tourists. The results may also encourage early adopters of cryptocurrency in tourism and hospitality because it has been discovered that utilising cryptocurrency results in tourists having a higher fair pricing estimate than when using fiat currency.

Future research can include the intention to purchase the deal or assess postconsumption factors such as acquisition utility, perceived quality, or deal evaluation to obtain a complete picture of the effect of PPP and the use of cryptocurrency. A labbased experiment employing biometric devices will also be highly intriguing because it will demonstrate whether the provided settings (e.g., the use of Bitcoin) elicit distinct emotional reactions.

### **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

## **Daily Deals Usage in the Pandemic Context of the Slovenian Hotels: Technology Adoption Perspective**

Miha Bratec1 , Petra Zabukovec Baruca1(B) , and Katarzyna Minor2

1 Faculty of Tourism Studies—Turistica, University of Primorska, Portorož, Slovenia<EMAIL_ADDRESS>2 Tourism, Hospitality and Events Department, Cardiff Metropolitan University, Wales, UK<EMAIL_ADDRESS>
**Abstract.** This paper analyses the usage of Daily Deals (DDs) in the COVID-19 pandemic context. A constructivist approach and grounded theory methodology are applied to study the managerial DDs usage in Slovenian hotels. The results show a predominance of organisational and external environmental factors, such as government regulation, linking structures, and industry characteristics, on the usage of DDs. These are shown in a conceptual DDs usage model. The study contributes to the hotel digital marketing literature by merging the two leading technology adoption frameworks to explain the formation of attitude and usage of DDs in a specific pandemic context.

**Keywords:** Daily deals · Flash sales · Coupon marketing · Technology adoption · TAO · UTAUT · COVID-19

### **1 Introduction and Study Rationale**

In traditional coupon marketing, discounts and offers are used to attract new customers. More recently, online coupons have proliferated. Despite this, they have received limited attention in the literature [1]. Daily Deal websites (DDs), also known as Flash sales, are coupon-based online marketplaces combining benefits for merchants with discounts for customers. They are typically characterised by advance-purchase conditions, limited purchasing time (7–14 days usually) and a discount (between 20 and 50% of the regular price) [2]. Budler et al. [3] distinguish between generic DDs with local reach (Groupon), which mostly rely on coupon redemption, and niche DDs, which have real-time availability features and capture mainly international markets (Secret Escapes).

Few studies with a customer-centric focus have addressed DDs, yet their managerial usage perspective (e.g. [4]) is lacking. Further, the research so far has failed to answer why hotels adopt and more importantly use DDs. Considering the arguably positive relationship between DDs and the crisis, this research aims to explore the hotel industry's use of DDs through the pandemic, made even more peculiar due to governmental intervention in Slovenia, where staycation vouchers were issued to all its residents (200 EUR in 2020, and 100 EUR in 2021), to stimulate domestic travel demand [5]. Meanwhile, most hotels faced reduced marketing budgets, a lack of staff and an inability to rely on established foreign tour operator cooperation. This paper aims to inductively analyse the antecedents of DDs usage amidst the pandemic, present an abduction-based conceptual model rooted in technology adoption literature and discuss its explanatory power.

### **2 Technology Adoption Frameworks**

Technology adoption has been a common focus of Information Systems (IS) research. The tourism field mostly relies on Davis' [6] dated Technology Acceptance Model (TAM). The more recent IS models look at adoption from the individual's cognitive or broader organisational perspective. The influential Unified Theory of Acceptance and Use of Technology (UTUAT) is used to describe user intentions towards using technologies and usage behaviour via four key variables, which are performance expectancy, effort expectancy, social influence, and facilitating conditions [7] proving itself useful to explain the adoption of various e-commerce phenomena [8]. Yet, the framework does not address the conditions that influence behavioural intention and use significantly [9]. Therefore, to encompass the pandemic-affected business context, Tornatzky and Fleisher's [10] Technology-Organisation-Environment Framework (TOE), a classic organisational framework offers itself as a more suitable choice [11]. It includes three contexts, namely: technology development [12]; organisational configuration [13]; and industry environment [14]. The TOE has been tested in e-commerce and has earned substantial validations as well as the widespread impact of government involvement (staycation vouchers) that fueled hotel marketing in Slovenia during the pandemic [15].

### **3 Methodology**

Using the constructivist paradigm and Charmaz and Thornberg's [16] version of grounded theory, this research allows for the exploration of literature pre- and post-data collection. The questions for the focus group were designed to be broad and exploratory in nature. 4 senior S&M managers representing 4 medium to large hotel companies, reached a total sample of over 25% of Slovenian hotel room capacity. Their attitudes toward DDs and the actual use of DDs during the pandemic were examined.

We analysed the data using thematic analysis [17] by manually and inductively coding first- and second-order codes. These were manually aggregated into thirdorder themes and further abductively fit into the structure of the two above-presented technology adoption frameworks.

### **4 Preliminary Findings and Conceptual Model Proposal**

We present our findings in a conceptual model (Fig. 1) explaining the use of DDs. We fit the third-order constructs arising from data coding into the three main contexts of the TOE framework. Moreover, UTAUT's Performance Expectancy and Effort Expectancy were placed in TOE's Technological and Organisational contexts. The particularities in the usage of DDs are indicated by the following key TOE contexts:

– In the technological context, the characteristics of DDs as a distribution channel for discounted sales conflict with the goal of the Slovenian hotel industry to maintain rate parity across all e-distribution channels. This reduces the importance of any type of price promotion.

– The organisational context links structures at formal and informal levels concerning the work process. This takes the form of staff shortages in marketing and operations departments, which diminished the attractiveness of DDs usage. Further, it was argued that DDs: (a) increased operational tasks that require manual work; (b) brought undesirable, 'inferior' discount consumer profiles; (c) caused communication problems linked to the questionable reputation and image of hotels featured on DDs, especially the generic ones operating on the domestic market.

– Within the environmental context government regulation with intervention in the form of lockdowns in 2020 and 2021 and staycation vouchers influenced marketing. With the "government-sponsored" vouchers, there was enough domestic demand to fill the capacity at rack rates. Industry characteristics and market structure related to the regulation of terms and conditions adopted by the DDs (who for the pandemic period adopted flexible booking conditions) increased the risk for hotels that were used to working with DDs on non-refundable terms. Lastly, we identified issues with inadequate technological support infrastructure related to dynamic pricing. In addition, we identified the lack of integration of DDs with the channel manager and CRS systems used in Slovenian hotels.

**Fig. 1.** Conceptual model of pandemic DD usage based on merging TOE & UTAUT;

Legend: TC…Tech. Context; OC…Org. Context; EC…Env. Context – TOE structural dimensions; PE…Performance Expectancy; EE…Effort Expectancy – UTAUT constructs.

### **5 Discussion and Future Research Directions**

Even though DDs originated in the previous recession and were designed to stimulate demand and advertise hotels to new audiences, our results exposed their certain weaknesses for usage in the current pandemic context. Particularly in times of staff shortages, the UTAUT construct of effort expectation seems problematic. Furthermore, the Performance Expectancy UTAUT construct, which is part of the Technological Context of our conceptual model, raises questions, i.e. Slovenian hoteliers were arguably focused on attracting guests from Slovenia and could therefore only rely on generic DDs. However, these offer no instant booking feature, resulting in additional work for understaffed reservation departments, as well as reducing the brand image of featured hotels. Surprisingly, our analysis identified only two of the four core UTAUT constructs. This is because Social Impact and Facilitating Conditions did not emerge from the data analysis. Therefore, this will require further exploration.

When used in the context of a pandemic, DDs require a more holistic approach than psychological-factors-based models that do not take the external business environment into consideration. The organisational TOE framework thus proved to have stronger explanatory power for our analysis. We further confirm the claims of Andersen and Henriksen [15], noting that Organisational & Environmental contexts have a more direct influence on the usage of certain technology. Data analysis indicates their direct influence on usage, while the technological context appears to only affect managerial attitudes toward DDs. As depicted by one of our respondents "actually I find DDs extremely useful, but the volume of domestic demand made us capable of filling our rooms directly at rates well above the usual pre-pandemic ADR level". Consequently, even if managers develop positive attitudes toward DDs, the manual work (Organisational context) and the focus on domestic markets due to governmental intervention (Environmental context) will hinder their use. Therefore, they both have a more direct and significant influence on usage intention than the characteristics and Performance Expectancy of DDs (Technical context), as shown in Fig. 1, thereby forming a valid contribution to theory.

Using the TOE framework while integrating UTAUT constructs allowed us to properly explain the (lack of) DDs adoption in Slovenia amidst the pandemic. This clearly indicates the potential of IS research frameworks (and their combinations) - often absent from the theoretically weak hotel marketing research, to explain the usage of e-marketing channels and solutions in hospitality and tourism. Hence, this affirmation forms our main contribution to theory. In terms of limitations, we recognise our proposed model is very context-, time- and place-specific and needs further refinement and empirical verification. We aim to collect additional data in the future to reach data saturation and cover at least 50% of the Slovenian hotel population as part of a longitudinal study that takes into account post-pandemic recovery conditions as well as the emerging economic crisis. We will further refine the data analysis with the use of computer-assisted tools and the application of AI-based text analysis methods to explore potentially hidden links between the contexts, the attitude and the actual usage intention.

Last but not least, practitioners will benefit from learning of critical factors that DDs and their supporting technology infrastructure providers need to overcome in order to strengthen their role in the e-distribution mix post-pandemic.

### **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

## **Social Media and User Generated Content**

## **Factors Influencing Users' Content Sharing Intention in Travel-Related Consumer Generated Media**

Jiao Li1 , Kaige Zhu1 , and Juhyeok Jang2(B)

1 Graduate School of International Media, Communication, and Tourism Studies, Hokkaido University, Sapporo, Japan 2 Research Faculty of Media and Communication, Hokkaido University, Sapporo, Japan<EMAIL_ADDRESS>
**Abstract.** Travel-related consumer generated media (CGM) plays an increasingly important role in travelers' decision-making process. Strenuous effort has been dedicated to explore CGM's impact on users' travel behaviors. However, little is known about the motivations that drive users to post information on travelrelated CGM. By combining the technology acceptance model (TAM), theory of planned behavior (TPB), and uses and gratifications theory (U&G), this study aims to delve into such motivational factors. The results revealed that users' perceived ease of use when posting content significantly affects perceived usefulness. Additionally, users' gratification derived from entertainment, information sharing, and rewards could positively influence their attitudes toward content sharing. Moreover, attitude, subjective norms, perceived behavioral control, and perceived usefulness jointly determine users' intention to use travel-related CGM for content sharing. However, the impacts of perceived usefulness, perceived ease of use, status-seeking, socializing, and passing time on their attitudes toward content sharing were insignificant. This study tests the possibility of combining the TAM, TPB, and U&G to expand their application in the field of travel-related CGM, and provides suggestions for travel-related CGM managers to improve their services.

**Keywords:** Travel-related CGM · Content sharing · U&G · TAM · TPB

### **1 Introduction**

With the development of information technology, travel-related consumer generated media (CGM)—platforms for users to share information through voluntary participation [1]—have become increasingly significant in the travel planning and decision-making process [2, 3]. The number of related studies is growing in parallel with the popularity of travel-related CGM, as represented by Tripadvisor. Examples include the perceived impact of CGM on the decision-making process of travelers [4], users' intention to continue using CGM [5], methods to reduce the switching intention of Generation Z users [6], and credibility measures of CGM [7].

However, very few studies have explored content creation. Yoo and Gretzel [2] and Gretzel and Yoo [8] investigated the relationships between the personal characteristics (e.g. age and gender) of the creator, their personality, and content creation, noting that the content was mainly provided by a few users. Yoo and Gretzel [3] explored users' motivations for commenting online. User-provided content is the basis of travel-related CGM [1], because millions would not visit Tripadvisor without the tips, recommendations, and advice from fellow travelers [9]. Moreover, as the impact of COVID-19 has subsided after two years, the number of planned trips in 2022 is expected to exceed that of actual trips in 2019 [10], and therefore, the use of travel-related CGM may significantly increase. Additionally, reviews of travel-related CGM, particularly long-form reviews, are helpful to potential travelers [11] to ascertain the destination's COVID-19-related safety, security, and cleanliness standards [12]. Therefore, it is necessary to determine the factors that influence users' intentions to use travel-related CGM to create and share content.

### **2 Theoretical Background and Research Hypotheses**

### **2.1 Content Sharing**

In this study, content sharing refers to the behavior of users posting word-of-mouth, reviews, and tips, based on their travel experiences and knowledge rather than the behavior of forwarding others' content. Several studies have explored the motivations for content sharing. Yoo and Gretzel [3] examined whether helping service providers, concern for other consumers, the need for enjoyment/positive self-improvement, and venting negative feelings were motivations for users to post online travel reviews. However, the four aforementioned factors do not adequately cover all motivations; additional factors may also impact content sharing intention. Shao [13] suggested that users continue to use CGM because of its utility. Gagné [14] confirmed that ease of sharing may influence people's intention to share. Therefore, the technology acceptance model (TAM) was chosen, which considers the relationships between perceived usefulness and ease of use of information technology and continued use [15]. Additionally, Park and Lee [16] argued that motivations for content sharing were divided into personal and social purposes. The uses and gratifications theory (U&G) describes the reasons why individuals use media from the perspective of gratification or psychological needs [17]. The theory of planned behavior (TPB) considers the influence of social and psychological factors on consumer behavior [18]. Both theories include factors related to personal and social purposes. Therefore, we constructed a research model using three theories; the possibility of their combination has been tested by a few studies [19, 20]. The combination of these three theories provides a more comprehensive understanding of the determinants of content sharing intention in travel-related CGM and how these factors influence users' sharing intentions.

#### **2.2 Technology Acceptance Model (TAM)**

The TAM was proposed by Davis [15] based on the theory of reasoned action (TRA). Its purpose is to analyze the motivations of users of information systems to use new technologies; it has five components: perceived usefulness, perceived ease of use, attitude, behavioral intention, and system use. Perceived usefulness refers to the extent to which individuals believe their performance will improve by using a particular system [15]. Perceived ease of use implies the extent to which individuals believe they can effortlessly use a particular system [15]. Attitude is influenced by perceived usefulness and ease of use. The intention to use is influenced by perceived usefulness and attitude, and perceived ease of use also directly influences perceived usefulness. The TAM views information systems such as the Internet as a tool for improving user performance [21]. Fari [22] noted that despite the complexity of information sharing and knowledge sharing activities and processes, the TAM is a strong guide for knowledge sharing research. Using the TAM, this study intended to determine whether travel-related CGM would facilitate the sharing of travel-related information by users and increase their efficiency. Therefore, we propose the following hypotheses:

*H1: Perceived usefulness positively influences users' intention to share travel content. H2: Perceived usefulness positively influences users' attitude. H3: Perceived ease of use positively influences users' attitude. H4: Perceived ease of use positively influences users' perceived usefulness.*

#### **2.3 Uses and Gratifications Theory (U&G)**