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JOURNAL OF EURO ASIA TOURISM STUDIES

VOLUME I – December 2019
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Service Through Personal Encounters or Technology; the Preferences and Privacy Concerns of Generation Z

Introduction

The next generation to enter the work-force and become a spending costumer of accommodation establishments are Generation Z. They are famously high-tech, unique and prefer co-creation, therefore hotels all around the world will have to implement a different approach to make them into loyal, satisfied customers. (Williams and Page, 2011; Berkup, 2014; Fister-Gale, 2015; Turner, 2015) Personalization should give an advantage for hotels to reach this generation as it is essentially catering the service to the unique guest needs, (Crosby, 1979; Berry, Parasuraman, and Zeithaml, 1988; Grönroos 1984; Surprenant and Solomon,1987; Parasuraman et al. 1988; Kokko and Moilanen, 1997) however research shows that even though Generation Z values co – creation, (Williams and Page, 2011; Berkup, 2014; Fister-Gale, 2015; Turner, 2015; Sima, 2016; Fernandes and Radebe, 2018) they do not value personalization as much. (Smith, 2017, Matveeva and Krasnov, 2019)  There is plentiful research on the personalization preferences of Generation Z in online marketing (Smith, 2017; Matveeva and Krasnov, 2019) and in technological devices (Fernandes and Radebe, 2018) but there is a lack of understanding what would be Generation Zs’ attitude towards personalized service, such as hotel services, or face-to-face encounters. We have to separate Generation Zs’ attitude for personalization in technology and real life. This exploratory research is attempting to find if Generation Z values applications over face-to-face and other personal encounters; if they value personalization; how much trust they have towards the service provider and if they would be willing to pay for personalized services.

Personalization

Personalization is an existing concept both in high interaction relationships and in computer science. Giving “good service” has been the focus of all service organizations and is often translated as a more personalized service. It can refer to any behaviors occurring in the interaction intended to contribute to the individuation of the customer (Surprenant and Solomon, 1987); therefore, it is more effective in satisfying the needs of the customer. Because the consumer compares his or her expectations with the received service, quality service is more conforming to customer specifications than anything else. (Crosby, 1979; Grönroos 1984; Berry, Parasuraman, and Zeithaml, 1988; Parasuraman et al. 1988).

As individuals have different quality expectations, the service providers have to differentiate the provided service. What the consumer wants can differ between eye contact, a smile, friendly greeting to the want of customized basic service. Service individualization (or personalization) “in a broad sense (…) refers to any behavior occurring in the interaction intended to contribute to the individuation of customer”. (Surprenant and Solomon, 1987, p. 87) The benefits of personalized service can be a closer relationship with the consumer, which leads to increased loyalty and also motivates the employee, as they are benefiting from knowing the consumers instead of working with faceless individuals. (Kokko and Moilanen, 1997) However, personalization takes time as the service providers have to invest time in every customer, and it also means that the services are less standardized. There is a risk that the guest will become too demanding, and personalized services are more expensive than standard ones, there is the risk of high cost for the service provider. (Kokko and Moilanen, 1997)

Personalization and privacy

Firms use information about customers to improve their service quality and design by personalized offerings, for example, personalized ads which grab the attention of the customer. (Barwise and Strong, 2002) For it to be successful, firms must collect customer information; therefore, it can also cause customer privacy concerns. To be able to create a personalized service, the collection of personal customer information is inevitable (Andrade et al., 2002). If the customers feel that their data might be collected without their knowledge, it will create negative feelings towards the service they receive, possibly even diminishing the perceived benefits (Joinson and Paine, 2007). This dynamic creates the so-called “personalization-privacy paradox.”

Lee and Cranage’s (2011) research shows that if privacy concerns are addressed, then the customer perceptions of personalization could be more positive. The study on the personalization of websites with high privacy assurance suggests that the presence of personalization leads to more positive perceptions about the usefulness of services and high levels of satisfaction. If the customer is given some privacy assurance, like open communication of the public data management practices, it can lead to fewer concerns about personal privacy. (Culnan and Amstron, 1999, Phelps et al. 2000, Lee and Cranage 2011) This kind of communication will create positive associations with the willingness to share personal information. However, customers will only use personalized service if they feel it will be useful. Trust in the company can help, as the company’s reputation will affect the customer’s willingness to share data. (Schonenbachler and Gordon, 2002) Chellappa and Sin (2005) however concluded that a consumer’s value on personalization would not depend on their privacy concerns but rather the consumer’s decisions to share information will depend on a “privacy calculus” an evaluation of the costs and benefits of sharing information, therefore, it is possible that the consumer finds the personalized service useful, but the advantages were smaller than, the disadvantage of sacrificing personal information. Also, more trust in the provider can still help to make the disadvantage smaller. The other factor that has to be considered is the level of understanding of the service user or in this case of the customer. Sundar and Marathe (2010) study shows that non-professional computer users preferred personalized websites while professionals had more concerns as they understood the underlying process of information collection; therefore, they preferred customization (where they can take an active role) over personalization.

Generation Z

The new generations have different needs when it comes to service or how they approach money, travel, and life. Generation Z is starting to grow into the age when they are capable of independently spending. They are continuously looking for novelty originality and experience. (Sima, 2016) They are individuals born after 1995 (Bassiouni and Hackley, 2014; Fister-Gale, 2015), and they are digital natives as they grow up with technological devices. (Berkup, 2014; Fister-Gale, 2015; Turner, 2015) In terms of characteristics, lifestyles, and attitudes, Generation Z individuals are the new conservatives embracing traditional beliefs, valuing the family unit, self-controlled, and more responsible. Even though they are accustomed to high-tech and multiple information sources, Generation Z values authenticity and “realness.” (Williams and Page, 2011) For this generation, the marketing of hotels must shift from telling the hotel story and start a conversation about the Z consumer’s story. The product has to have a social and environmental benefit too. They need to make the product better with their input, and they will prefer to have a beta product today with a better product tomorrow that they have co-created. (Berkup, 2014; Sima, 2016) Smart technologies influence Generation Z consumers’ experiences, and they use it to be able to make a more educated decision. (Priporas, Stylos, and Fotiadis, 2017; Salesforce, 2020). It might be the reason why Generation Z shows less interest toward personalization when it comes to marketing and mobile applications (Smith, 2017; Fernandes and Radebe, 2018).

Research Methodology

To catch the attention of a Generation who prefers to co-create (Berkup, 2014; Sima, 2016) and are technologically advanced (Berkup, 2014; Fister-Gale, 2015; Turner, 2015; Priporas, Stylos and Fotiadis, 2017,) personalization could be a great tool, however as Generation Zs are technological nomads they might react with higher distrust over their data being used, as they understand the underlying process of data collection (similar to professional users). (Sundar and Marathe, 2010)

Given the peculiarities of the personalization–privacy paradox and the lack of studies specifying this phenomenon on the different generations, we must proceed with caution before comparing Generation Z to any other Generations. Based on the literature we have formulated the following research questions in relation the behaviour of Generation Z. If Generation Z is looking for novelty originality and experience (Sima, 2016); do they value personalization? Furthermore, would they be willing to pay for such originality? Being digital natives who are familiar with technological devices (Berkup, 2014; Fister-Gale, 2015; Turner, 2015) are they valuing the use of an application over face to face (or other personal) encounters? As Sundar and Marathe (2010) study shows experts had more concerns regarding the privacy issues as they had more understanding of the processes. Generation Z grow up and have been using technological devices since an early age. Will they have similar concerns or, will they trust the service provider with their information?

It is wise, to begin with, exploratory studies that will produce an adequate volume of evidence. The research undertaken in this paper will concentrate specifically to bachelor students studying tourism and catering because they have more understanding of the hotel services and underlying procedures then other Generation Z members. The research questions therefore will be the following:

Q1: Do Generation Z tourism students prefer personalized services?

Q2: Do Generation Z tourism students prioritize devices over face-to-face encounters?

Q3: Do Generation Z tourism students have a high level of data privacy need?

Data Collection

The surveys were administered online. Our sample consists of bachelor students studying tourism and catering (who were born between 1995 – 2002) at the University of Pannonia. They have some basic knowledge of hospitality, so they are aware of the potential services and operations of hotels. We obtained a total of 80 valid responses, of which 64.2% (52) were female and 88.8% (71) has more than one social media platform.

Scale development

The information was obtained through a questionnaire where the participants had to choose on a 5-point Likert scale. The scale went from “Strongly Disagree,” “Disagree,” “Neutral,” “Agree,” “Strongly Agree.” It was essential to use a 5-point Likert scale as the participants this way had a better understanding of the difference between the levels, as the participants were young adults and not professionals. We had 39 Likert items, from which 24 can be divided into 7 Likert scale categories.

We have identified the following categories (Appendix 1.);

  1. Technology Customizability,
  2. Provider to Customer Communication,
  3. Willingness to Share for Unique Service,
  4. Trust of Data Safety,
  5. Face-to-Face Encounter,
  6. Phone Calls,
  7. Application Use.

TC.’ Technology Customizability’ is based on a past study from Chellappa and Sin (2005), where they were researching Web personalization preferences. They have found that online vendors can improve in obtaining and using customer information if they build trust. Therefore, three of their six personalization indicators were in the survey as TC.’ Technology Customizability’. For CC.’ Provider to Customer Communication’ category, we used two out of five Nyheim et al. (2015) perceived personalization items. To keep the survey at a reasonable length, we concentrated on two of the items that are focused on the communication of the provider. In the case of TD.’ Trust of Data Safety, the Likert items were created based on the Lee and Cranage 2011 study (who adapted from Gefen, at all. (2003)).

The other Likert Items were produced and categorized to measure; how open Generation Z is to share their data (WS.’ Willingness to Share for Unique Service’), and what are their preferred communication channels (FE.’ Face-to-Face Encounter,’ PC.’ Phone Calls, ’AU. ‘Application Use’). Some of the Likert Items were not assigned to a Scale but were analyzed as ordinal values. (Appendix 1.)

When creating the scales due to the low number of items Cronbach’s Alpha cannot be used to measure internal consistency as a low value for alpha may come from the fact that there aren’t enough items on the scale. (Tavakol and Dennick 2011) Instead, first the total score of the scales were identified and coded into SPSS (PASW statistic 18.) and the items correlation were compared to the total. As Likert items are ordinal, Spearman rank correlation was used to determine the correlation between total and items. (Dimitrov, 2014; Trochim, 2020) Trochim (2020) suggest using correlation above 0.6 however in Spearman rank correlation coefficients of 0.50 and above represent a large association we will keep the items that are higher than 0.5.

Table 1: TC. Technology Customizability, the correlation to the Total TC.

TC1 – 0.438 will be left out of the scale
TC2 – 0.777 will be kept in the scale
TC3 – 0.813 will be kept in the scale

 

Table 2: CC. Provider to customer communication the correlation to the Total CC.

CC1 – 0. 824 will be kept in the scale
CC2 – 0.784. will be kept in the scale

 

Table 3: WS. Willingness to Share for Unique Service items correlation to the Total WS.

WS1 – 0.733 will be kept in the scale
WS2 – 0.711 will be kept in the scale
WS3 – 0.627 will be kept in the scale

 

Table 4: TD. Trust of Data Safety correlation to Total TD

TD1 – 0.627 will be kept in the scale
TD2 – 0,628 will be kept in the scale
TD3 – 0.486 will be left out of the scale

 

Item TD3. had to be revers coded first as it is a negative sentence. The reverse coded item was used to create Total TD. and the revers code was used for the correlation.

Table 5: FE. Face-to-Face Encounter items correlation to Total FE.

FE1 – 0.622 will be kept in the scale
FE2 – 0.703 will be kept in the scale
FE3 – 0.757 will be kept in the scale

 

Table 6: PC. Phone Call correlation to Total PC.

PC1 – 0.752 will be kept in the scale
PC2 – 0.678 will be kept in the scale
PC3 – 0.684 will be kept in the scale
PC4 – 0.560 will be kept in the scale
PC5 – 0.720 will be kept in the scale

 

Item PC5. also had to be revers coded first and the reverse coded item was used in the calculation of the correlation.

Table 7: AU. Application Use correlation to Total AU

AU1 – 0.711 will be kept in the scale
AU2 – 0.756 will be kept in the scale
AU3 – 0.536 will be kept in the scale
AU4 – 0.565 will be kept in the scale
AU5 – 0.709 will be kept in the scale

 

Item AU5. also had to be reverse coded before used in the correlation.

After leaving TC1. and TD3. out, average inter-item correlation was used to analyze the internal consistency reliability. This shows if the individual questions give consistent, appropriate results. The ideal range of average inter-item correlation is 0.15 to 0.50. If it is smaller than the items do not measure the same construct. More than 0.50, would mean that the items are repetitive. (Trochim, 2020)

As the table shows, all the average inter-item correlation falls within the range therefore the Likert scales will be accepted. (Table 8.)

Table 8: Inner-Item Correlation

Mean Minimum Maximum Range Maximum / Minimum Variance N of Items
TC. ,403 ,403 ,403 ,000 1,000 ,000 2
CC. ,311 ,311 ,311 ,000 1,000 ,000 2
WS. ,219 ,124 ,272 ,148 2,199 ,005 3
TD. ,215 ,215 ,215 ,000 1,000 ,000 2
FE. ,242 ,161 ,336 ,175 2,084 ,006 3
PC. ,327 ,196 ,512 ,317 2,616 ,011 5
AU. ,282 ,086 ,490 ,404 5,696 ,019 5

Analyses

After finalizing the scales, we performed Spearman’s rank correlation to identify relationship between the scales and the items. For this we computed the total for each scale. (Appendix 3.) We have found a significant positive relationship between (TC.) technology customizability and (CC.) provider to customer communication 0.227 on 0.05 level of significance, and (TC.) technology customizability and (OSM.) customers with only one Social Media platform had a significant negative relationship of – 0.271 on 0.05 level of significance. We have also found (CC.) provider to customer communication has a positive correlation on a 0.01 level to (WS.) willingness to share for service 0.656, (FE.),  face-to-face encounter 0.416, (TD3.) consumer that trust that their data will be treated with confidence 0.341 and  (PPS.) those who would be willing to pay more to get a personalized service 0.292. We have also found positive relationship on 0.05 significance between (WS.) willingness to share (information) for personalized service and (FE.)  face-to-face encounter 0.269, (PE.) personal encounter on the phone 0.229 and (PPS.) those who would be willing to pay more to get a personalized service 0.253 while we have found a positive correlation on 0.01 level  between WS. and (TD3.)  consumer that trust that their data will be treated with confidence 0.363.

Trust towards the service provider (TD.) has a 0.01 positive correlation with (PPI.) consumers who don’t like to provide information 0.496 and a 0.05 significance level negative correlation to (PUI.) consumers who do not mind when hotels use information they have shared on social media -0.285. Consumers who prefer (FE.) face-to-face encounters had a negative correlation on the 0.01 level towards application use (AU.) – 0.341 and with AOF. (If there is an App, I prefer to use it over going personally) – 0.314 while E. also had a positive correlation on 0.05 significance with TD3. (I trust my data will be treated with confidence) 0.282 and (PPS.) those who would be willing to pay more to get a personalized service 0.256.

Have one social media account (OSM.) positively 0.222 correlated on a 0.05 significance level with phone call preference (Total F.). The scale measuring the preference of application use (AU.) had a positive correlation on a 0.01 significance with TC1. (Value of webpages that can be personalized) 0.325, AOF. (Using application over face to face encounter) 0.496 and AOP. (preference of application usage over phone calls) 0.573 and a negative correlation towards M. having only one social media account (– 0.314).

There is a negative correlation on the 0.05 significance between (PPI.) the phrase ‘I do not like it when I have to provide my personal information’ and (SLI.) ‘I share a lot of information about myself on the social media interface’ – 0.224 and (PUI.) ‘I do not mind if the provider uses that information’ – 0242. Finally, we have found a positive correlation on the 0.01 significance level between AOF. and AOP. that was 0.707.

Interpretation

To be able to interpret our findings we looked at respondents that agreed to individual items, and compared them with the correlations we have discovered.

Generation Z and personalized services

When we talk about (TC.) technology customizability preferences the respondents agreed at its importance, 58.8% strongly agreed or agreed (Appendix 4.), and they were valuing (TC2.) personalized websites (41.3% strongly agreed 36.3% agreed) more than (TC3.) personalized services based on voluntarily given information (where only 26.3% Strongly and 31.3 Agreed). They also gave high value to web pages that are personalized for the device they use (TC 1.) as 56.4% strongly agreed and 37.5% agreed on its value. Based on the results it seems that personalization of technical devices is important, however they were neutral towards personalized communication between them and the hotel (CC.) and about sharing information for a personalized service (WS.)

We found (Appendix 3.) that there is a small association between (TC.) technology customizability and (PC.) personalized communication preference (0.227). Therefore, those who like the personalized communication from the service provider will be more likely preferring customized technology. However, those who have only one social media platform (M.) were less likely to prefer (Total TC.) customizability of technology (-0.271). This means that the more they use social media, the more of an impact personalized websites and apps will have on them. Although respondents were neutral, we found that (CC.) respondents with personalized communication preference have a medium correlation with the preference of face-to-face encounter (0.416) and are likely to trust the service provider with their data (0.341). This can mean that they prefer the personalized communication in person. For service providers to be able to give personalized advertising will need data from the guest. Those who prefer personalized advertisement are the ones who are also trusting the service provider with their data. We also found that (WS.) respondents who are more likely to share information about themselves for a personalized service, not only prefer (CC.) personalized communication (0.656), but also have a small correlation towards (FE.) personal encounters (0.269) and (PC.) phone calls (0.229). These items are focusing on human encounters, having more emphasis on communicating with a person. Those participants who trust more in the service provider will have more trust with their data. We have also found a medium correlation between those who are willing to share information for a personalized service (WS.) and their trust in that their data will be treated with confidence 0,363 (TD3.).

Based on this we can say that personalization has some importance to Generation Z but they give small to moderate focus to it at the moment. Those who preferred the personalized communication and are willing to share personal information where preferring more the personal encounters (phone calls etc.) trusting more that the service provider will that their data will be treated safely. This opens new questions for the research. Do they feel more assurance because they talked with a person instead of just using a “faceless” device and they do not trust applications with handling personal data as much as humans, or do they find human interactions more personal and unique?

Generation Z and data privacy

Respondents shown (Appendix 4.) high concerns about the safety of their data, 55% strongly agreed (26.3% agreed) that the service provider only needs to know about what is most necessary about the guest (TD1.) and 31.3% strongly agreed (28.7% agreed) that they are afraid that their personal information will be misused (TD2.). However, if they trust that their data will be treated with confidence (TD3.) the answers were more between agreeing and neutral. The same can be said about their attitude (Appendix 4.) (PPI.) when they must provide personal information. When it comes to information that they have shared on their social media platforms they were strongly disagreeing 45% (and disagreeing 32.5%) for the service provider to use that information (PUI.)

In line with these results we found (Appendix. 3.) a medium association between trust of data safety, (TD.) (I.) the fact that they don’t like to share their information (0.496) and a negative association about (PUI.) using information they have shared on social media (-0.285).

We can conclude that Generation Z (tourism students) have high privacy concerns, but when they trust that the hotel will handle their personal information with care, they are willing to provide personal information. However, they don’t like the idea of their information being mined from social media. Control over their personal data is important.

Generation Z prioritize and devices.

When looking at (Appendix 3.) the preferences between face-to-face encounter (FE.), phone calls (AU.) and application usage (AU.) they were neutral about face-to-face encounters and phone calls, but they were positive (agreeing) with usage of applications. 70% strongly agreed and agreed that if there is an application, they would use it over personal encounters (AOF.) and 70% agreed that they would prefer to use it over speaking on the phone. (AOP.)

To support this (Appendix 3.), we found that the preference of face-to-face encounters (FE.) have medium negative association with (AU.) application usage (-0.314) and with application use over face to face encounters (AOF. -0.341). So the more someone will prefer face-to-face encounter the less likely they would like to use an App, however interestingly, those who prefer face-to-face encounters had (TD3.) trust in the hotel treating the data in confidence (0.282) and they are also willing to share information for a personalized the service (0.269).

Those respondents who preferred phone calls (PC.) we found more likely to have (OSM.) only one social media account (0.222) and those respondents who preferred personalized websites (TC1.) have also preferred (AU.) applications used 0.325. We found medium and strong correlation towards AOF. (0.496) and AOP. (0.573) and also between AOF. and AOP. (0.707) This shows that respondents prefer to use an application over face-to-face encounters and phone calls.

As there was a negative medium association between having only one social media account (OSM.) and Application usage (AU. -0.314) we can conclude that those who only have one social media account are less likely to prefer applications and will want personal encounters, however the majority of the respondents prefers applications over other forms of communication.

Generation Z and the willingness to pay for personalization

Respondents were neutral regarding their willingness to pay, 30% agreeing 33,8% being neutral and 36.3% disagreeing, but we found small association between the willingness to pay (PPS.) and in the trust that the data is treated with confidence (0.292), those who willing to share information for personalized service (0.253) and those who preferred face to face encounters (0.256). This means when guest feel that their data is safely handled, they will be willing to pay for unique services. Also, when they value personalized services enough to willingly share personal information about themselves they will also be willing to pay for the individualized service.

Conclusion

Generation Z customers value co-created products and services (Williams and Page, 2011; Berkup, 2014; Fister-Gale, 2015; Turner, 2015; Sima, 2016; Fernandes and Radebe, 2018) but they show moderate interest in personalization. Our research which included 81 Hungarian student from Generation Z confirms that personalization is important for Generation Z but in specific circumstances. What this research wanted to explore is how this generation seeking unique experience would weigh the importance of personalization in relation to sharing personal data or paying more for the unique services. We were also interested if they would prefer face-to-face encounters and traditional channels to communicate with the service provider.

Personalization is a good way for service providers to make something unique and catch the attention of the customers. As Generation Z likes “realness” and uniqueness (William and Page, 2011), personalization could be valuable, but it requires high amounts of information. Information about the guest needs, wants and likes that can be only provided by the guest themselves either directly by telling the hotel or indirectly by sharing it on social media. As the research has shown, when they trust that their data will be safe they are willing to share such information, but as Generation Z guests are digital nomads (Berkup, 2014; Fister-Gale, 2015; Turner, 2015), they have a higher understanding of the underlying constructs and functions of technology therefore they are also having high awareness of information they share. The research shows that they were more trusting of their information when they preferred personal encounters. We found that Generation Z has some interest in personalization, and they are not refusing the idea to pay for it (only 36.3% disagreeing) however they did not show an outstanding interest towards personalized service. Generation Zs are mostly young adults and students therefore they might not have the chance to experience hotel service and high-quality service. This could influence their willingness to pay for special service. We have found however, clear signs of data awareness. Perhaps high data protection measurements would encourage Generation Z to share more information about themselves and use more personalized services.

We also found proof that using an application is preferred over phone calls, and more likely to be used over face-to-face encounter. However, when it comes to personalization, those who preferred it would also use face-to-face encounter. In future research we must elaborate more on which platforms consumers are willing to use. The question we have to analyze in the future is if they prefer face-to-face encounters because they prefer personalization and they find it more personal and safer or there is another underlying reason for the connection. They might prefer to use applications overall but not to share their information through it. Still, for hotels which are concentrating on the younger generation, using apps, and selling their services through apps can be beneficial as we have concluded this generation would rather use the apps than engage in personal interactions. Giving them the opportunity to use technology for the hotel services can give them more satisfaction in the hotel experience as they were clearly preferring application usage over personal encounters. With high data assurance (or transparent communication), a hotel can even provide personalized services. The research must be repeated with a second group to see if we can get similar results of preferences to identify if there is also causation not just correlation.

 

Appendix

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Cite this article

Hiezl K., Gyurácz-Németh P. (2020) Service Through Personal Encounters or Technology; the Preferences and Privacy Concerns of Generation Z. EATSJ - Euro-Asia Tourism Studies Journal, Vol.1, Issue: December 2020 pp. 76-92.

Received: 29 July 2020 | Accepted: 6 December 2020 | Published online: 16 December 2020
Volume: 1 | Issue: December 2020 |

Authors


KH

Kitti Hiezl (Corresponding author)
University of Pannonia, Hungary


PG

Petra Gyurácz-Németh (Corresponding author)
University of Pannonia, Hungary