The Differential Effects of Touchpoint Functions in Airline Apps on Customer Engagement and Customer Retention for Different Traveler Personalities

The differential effects of touchpoint functions in airline apps on customer engagement and customer retention for different traveler personalities

Master Thesis 0

Student: Nadja Ella Hutton-Mills

Student No.: 11145781

Study: MSc in Business Administration- Marketing Track

1st Supervisor: Prof. Dr. Ed Peelen

University of Amsterdam

Date: 27.01.2017

Version: Final

Student: NE Hutton-Mills Student No.: 11145781

Statement of Originality This document is written by Student Nadja Ella Hutton-Mills who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and

its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

Student: NE Hutton-Mills Student No.: 11145781

Table of Contents Statement of Originality ...... 1 List of Figures ...... 4 Abstract ...... 5 1 Introduction ...... 6 1.1 Background ...... 6 1.2 Scientific Relevance ...... 7 1.3 Managerial Relevance ...... 8 1.4 Research Question ...... 8 2 Theoretical Framework ...... 9 2.1 Customer Relationship Management ...... 9 2.2 Mobile Customer Relationship Management (CRM) ...... 10 2.3 Mobile CRM Applications ...... 11 2.4 The airline industry ...... 11 2.5 Touchpoints in CRM ...... 12 2.6 Managerial Perspective of Touchpoints in Airline Apps ...... 13 2.7 Customer Engagement ...... 16 2.8 Customer Engagement and Customer Retention ...... 19 2.9 Traveler Personalities ...... 21 2.10 Conceptual Framework ...... 25 3 Data and Method ...... 27 3.1 Research Methodology ...... 27 3.2 Method Design and Collection Procedure ...... 27 3.2.1 Exploratory Research: Expert Interview ...... 27 3.2.2 Experimental Research: Vignette Study ...... 27 3.2.2.1 Pretest ...... 28 3.2.2.2 Experimental Survey ...... 29 3.2.2.3 Experimental Design ...... 30 3.3 Variable and Scale Development ...... 31 3.3.1 Traveler Personality ...... 31 3.3.2 Touchpoint Functionality ...... 31 3.3.3 Customer Engagement ...... 31 3.3.4 Customer Retention...... 32 4 Data Analysis ...... 33

Student: NE Hutton-Mills Student No.: 11145781

4.1 Sample and Between Group Homogeneity Analysis ...... 33 4.2 Homogeneity of Experimental Groups ...... 35 4.3 Reliability Analysis ...... 36 4.4 Descriptives and Correlation Matrix ...... 37 4.5 Hypothesis Testing ...... 43 5 Discussion ...... 57 5.1 Findings...... 57 5.1.1 Industry Developments and Trends ...... 57 5.1.2 Touchpoint Functionality ...... 58 5.1.3 Traveler Personality ...... 58 5.1.4 Touchpoint Functions, Customer Engagement and Customer Retention ...... 58 5.1.5 Hypothesis Testing ...... 59 5.1.6 The Research Question ...... 60 5.1.7 Context with Prior Research ...... 60 5.2 Limitations ...... 61 5.3 Future Implications ...... 62 5.2.1 Scientific Implications ...... 62 5.2.2 Managerial Implications ...... 63 Bibliography ...... 65 Appendix ...... 69 Appendix 1: Expert Interview with Fredrik Forstbach from Lufthansa Innovation Hub ...... 69 Appendix 2: Field Analysis of Touchpoints in Global Airline Apps ...... 76 Appendix 3: Pre-test Survey on Qualtrics ...... 93 Appendix 4: Pre-test Output on SPSS...... 102 Appendix 5: Experimental Survey ...... 104 5.1 High Functionality English ...... 104 5.2 Low Functionality English ...... 113 5.3 High Functionality German ...... 123 5.4 Low Functionality German ...... 133 Appendix 6: SPSS Data Output ...... 143

Student: NE Hutton-Mills Student No.: 11145781

List of Tables

Table 1: Categorization of Customer Engagement Foci by Vivek et al. (2012) (Vivek, Beatty, & Morgan, 2012)...... 17 Table 2: Pre-Test Results ...... 28 Table 3: Sample Descriptives of Experiment ...... 35 Table 4: Homogeneity of Experimental Treatment Groups ...... 36 Table 5: Scale Reliability Analysis for Customer Engagement and Customer Retention ...... 37 Table 6: Descriptive Analysis with Mean Scores, Frequencies and Standard Deviations ...... 41 Table 7: APA Style Spearman Correlation Matrix ...... 43 Table 8: Model summary of H1 Test ...... 44 Table 9: ANOVA Table of H1 Test ...... 44 Table 10: Coefficients Table for H1 Test ...... 45 Table 11: Descriptive Statistics of Non-Parametric Kruskal- Wallis Test ...... 45 Table 12: Non- Parametric Kruskal- Wallis Test ...... 46 Table 13: Pairwise Comparisons...... 47 Table 14: Descriptive Statistics for Customer Engagement (CEscaletot)...... 49 Table 15: Descriptive Statistics for Customer Retention (CRscaletot) ...... 51

List of Figures Figure 1: Experimental Design ...... 30 Figure 2: Plot Diagram for Customer Engagement per Touchpoint for each Traveler Personality ...... 49 Figure 3: Plot Diagram for Customer Retention per Touchpoint for each Traveler Personality ...... 52 Figure 4: Plot Diagram for Customer Engagement per Traveler Personality for each Touchpoint ...... 55 Figure 5: Plot Diagram for Customer Retention per Traveler Personality for each Touchpoint ...... 55

Student: NE Hutton-Mills Student No.: 11145781

Abstract This research has the purpose to shed more light into the field of customer engagement and retention research in the field of Customer Relationship Management. More specifically, it attempts to explore the potentials of touchpoints in mobile CRM apps and explain their effects on customer engagement and retention in the field of aviation. With different touchpoints in mobile CRM apps of airlines, the effects of touchpoints are assumed to be differential, especially under consideration of different traveler personalities.

The research consists of an exploratory research part, which provides insights into the current status quo of touchpoint development in mobile CRM airline apps and an explanatory research part, which tests the effects of those different touchpoints on engagement and retention among different traveler personalities. The research has been executed with an expert interview with Product Manager at the Lufthansa Innovation Hub in Berlin, a field analysis on current app development in the airline industry and an experiment conducted at Düsseldorf Airport in Germany.

The research affirms, that current touchpoint development in mobile CRM apps in the airline industry is moved by trends such as big data, digitalization, personalization and instant messaging. Touchpoints have many different functions and even incorporate other technologies to complement for better service offerings. Moreover the research revealed, that touchpoints do have the potential to evoke customer engagement and that they generally trigger even higher levels of customer retention. Moreover, research reveals that only a short term exposure to touchpoints in an airline app can affect the basic levels of engagement and that different touchpoints have different effects on among different traveler personalities. While value focused travelers generally show higher levels of customer engagement than other traveler personalities, efficiency travelers seem to show remarkably high levels of engagement and retention when being presented with additional help and contact touchpoints. Finally the research reveals, that touchpoints that provide customers with economic advantages, additional help for the journey, and travel related tips evoke the highest engagement levels, while personal information, and key travel information related touchpoints evoke the highest levels of retention. Ultimately, it was shown, that long term investments into mobile CRM touchpoint development are required to yield stronger engagement levels, but that touchpoints can already offer additional profits and competitive advantages in the short-run.

Student: NE Hutton-Mills Student No.: 11145781

1 Introduction

1.1 Background “In this light, relatively little is known about mobile customer relationship management and from marketing perspective we are still dealing with an extremely unacknowledged phenomenon.” (Sinisalo, Salo, Karjaluoto, & Leppäniemi, 2006, p. 1)

This statement was made by researcher Jaakko Sinisalo and his colleagues in their study called “Managing Customer Relationships through Mobile Medium- Underlying Issues and Opportunities”. The quote perfectly describes the current state of research done in the area of mobile CRM.

Mobile Customer Relationship Management (Mobile CRM), which has been named as one of the “5 Customer Experience Trends for 2016” by Forbes magazine, has emerged only recently and shown unique potential for building stronger, more interactive and more personal relationships with customers (Morgan, 2015). With mobile applications (apps) as part of mobile CRM being installed on customers’ phones, companies are now able to interact and engage with customers along their entire customer journey through touchpoints on the app faster, simpler, more personal and more responsive (Sinisalo, Salo, Karjaluoto, & Leppäniemi, 2006, p. 774).

The touchpoints used in the mobile applications are points, where customers can interact with firms and their products through different functions such as product information, special offers, discount pop-ups, store locators and feedback (Sinisalo, Salo, Karjaluoto, & Leppäniemi, 2006, p. 2). Not only have mobile CRM programs just started to flourish, but the functions of the mobile CRM touchpoints have also become more varied, thus now offering customers more ways to interact with brands. Also, the variety of the touchpoint functions have enabled companies to gather more specific data at every touchpoint along the entire customer journey in order to customize offers for the individual users (Edelman, 2010, p. 2) (Edelman, 2010, p. 4).

However, research so far has not identified the effects that the different touchpoint functions in mobile CRM apps have on customers or more specifically their levels of customer engagement and retention. Considering the fact that customers show different levels of influenceability in different touchpoints along the customer decision journey, there is also a need to research the differential effects that each touchpoint has on customer engagement (Edelman, 2010, p. 2) (Edelman, 2010, p. 4) Finding out about these effects on customer engagement and customer retention along the customer journey would give companies vital advantages when creating content and allocating resources to every touchpoint within the mobile app.

Student: NE Hutton-Mills Student No.: 11145781

Because mobile CRM programs are still in their infancy, many mobile CRM apps used by companies are not that sophisticated yet. Therefore, an industry is needed, that has higher levels of usage in the area of mobile CRM. For this research, the airline industry will be assessed as it has high share of usage of mobile CRM, it incorporates multiple touchpoints in its mobile technology to deliver its services and it shows customer satisfaction challenges faced in the area of mobile CRM (Budd & Vorley, 2013, p. 41) Furthermore, it is questionable whether the touchpoints in mobile CRM programs have the same effect on all customers, since customers have different traveler personalities and preferences (Arnold & Reynolds, 2003, p. 90) (Forstbach, 2016). Thus, different traveler personalities among customers have an antecedent effect to the main effect of touchpoint functions on customer engagement and customer retention.

This research firstly aims at identifying the different touchpoint functions found in mobile apps of mobile CRM programs in the airline industry. It aims at defining the status quo of touchpoints in mobile CRM apps at this point in time. Secondly, the research aims at contributing to mobile CRM effectiveness by researching the differential effects of each touchpoint along the customer journey in the mobile apps on customer engagement and customer retention among different traveler personalities. The methods of investigation applied to this research will be an exploratory expert interview, a field analysis of current airline apps, and an experiment executed at Düsseldorf Airport in Germany to ultimately research the potentials of touchpoints. Or as the statement made by the expert Fredrik Forstbach by the Lufthansa Innovation hub in Berlin says: “I think that any moment in the journey where the traveler actually needs personal contact or assistance is an opportunity for airlines. This means: If he just needs to drop his bag, check-in, get through security etc. he is not in need for any personal interaction. And in fact, research shows that most people would prefer a way of doing this without any need to stand in line or talk to somebody” (Forstbach, 2016). Fredrik Forstbach, Business Service Designer and Product Manager, Lufthansa Innovation Hub

1.2 Scientific Relevance Concerning the scientific relevance of this research project, this research aims at contributing to theory by providing exploratory insights into mobile CRM touchpoints and their connection to customer engagement and customer retention. More specifically, this research aims at exploring what this connection to customer engagement and customer retention looks like and how it can be influenced.

Student: NE Hutton-Mills Student No.: 11145781

Additionally, different customer typologies are explored and how they may influence the connection between CRM touchpoints and customer engagement and retention. Especially, the interplay between customer engagement and retention is interesting as customer engagement is often considered the foundation of customer retention, however retention may also occur without customer engagement being involved. Ultimately, the results gathered from this thesis study should provide scientific insights into how much potential impact touchpoints as personal interaction elements of CRM apps have on customers along their journey. The insights are particularly relevant since mobile CRM programs and the touchpoints within are highly personal and novel tools of interacting with different customer types and affecting their attitudes and behaviors. Based on that, the results gathered from this thesis study should also provide scientific insights into what extent and how customer engagement influences customer retention. Also, the research tests the suitability of scales for the different constructs will be done and how well they measure the different effects. Since the research on the constructs of traveler personality and customer engagement gains more scientific interest but is still in its infancy, it can be of value to assess how well these different scales involved measure the relationships.

1.3 Managerial Relevance In reference to the managerial relevance of this research project, this thesis study can contribute to managerial practice by firstly assessing the status quo of current mobile CRM app development in aviation and more specifically the touchpoints in the apps. Thus, current trends and developments in the industry will be assessed as well as industry leaders in terms of mobile CRM app development in aviation. Also, the different strategic approaches of different airlines in the market will be assessed and their foci on different touchpoints in their mobile CRM apps. Most importantly, the thesis study should provide useful insights into the impact that touchpoints in airline apps may have on consumers in terms of consumers’ engagement and retention. This impact will be assessed by also considering different customer typologies, and how their typologies may affect the impact of certain touchpoints. Ultimately, the thesis study should give useful advice on which content to create in which touchpoint along the customer journey for which type of airline customer. The latter contribution should help companies achieve refined touchpoint design and optimized customer engagement and retention ultimately leading to higher CRM effectiveness.

1.4 Research Question The main research question of this Master Thesis has been posed as the following:

What are the differential effects of touchpoints in mobile CRM apps in the airline industry on customer engagement and retention for different traveler personalities?

Student: NE Hutton-Mills Student No.: 11145781

2 Theoretical Framework

2.1 Customer Relationship Management Customer relationship management, also known as CRM, is “a comprehensive strategy and process of acquiring, retaining, and partnering with selective customers to create superior value for the company and the customer” (Parvatiyar & Sheth, 2001, p. 5). The term first emerged in the mid 1990’s within the information technology vendor community and was later connected to the academic community with the term “relationship marketing (Payne & Frow, 2005, p. 167)”. Academic literature mentions, that companies in the past were often ambiguous about what CRM exactly means and accounted multiple functions such as call center activities, direct mail or databases to CRM (Payne & Frow, 2005, p. 169). This shows that there are multiple activities included in the framework of CRM, and that companies are making use of a portfolio of various opportunities to engage with customers ranging from the point of initial advertising until the after- purchase phase of evaluation and feedback. The authors Reinartz, Krafft and Hoyer recognized the high variety of CRM activities as mentioned above and attempted to classify them into three major categories, namely: “(1) functional, (2) customer- facing, and (3) companywide” (Reinartz, Krafft, & Hoyer, 2004).

Since the rise of the internet and mobile technology, the mutual creation of superior value for the company, the customer and the CRM activities have become more varied, integrated and fast-paced. With the internet and mobile technology, companies were suddenly able to communicate with customers on more personalized levels and storing customer data from different channels in more integrative CRM systems (Nguyen & Mutum, 2012, p. 403). As Nguyen and Mutum (2012) state in their article, the technological advancements enabled CRM activities “being utilized to deal with customers individually, one customer at the time”, which once again stresses the increased level of customer centricity through technology (Nguyen & Mutum, 2012, p. 412).

The technology advancements named by Nguyen and Mutum (2012) also relate to the “electronic commerce environments” described in the article by Robert Davis, Margo Buchanan-Oliver, and Roderick Brodie (1999), which have been changing conventional marketing practices of building relationships with customers and which are said to be increasing exponentially (Davis, Buchanan- Oliver, & Brodie, 1999, p. 319). Considering early insights from studies by Frederick Reichheld, Darrell Rigby, Chris Dawson about customer loyalty and retention leading to increased firm revenues, the role of technology as a major contribution to customer centricity gained considerable interest (Rigby, Reichheld, & Dawson, 2003).

Student: NE Hutton-Mills Student No.: 11145781

Two important CRM activities, that have recently emerged as part of electronic commerce environments and in response to changing customer behaviors, are social and mobile technologies which Nguyen and Mutum rate as “increasingly effective ways for firms to interact with their customers” (Nguyen & Mutum, 2012, p. 412).

2.2 Mobile Customer Relationship Management (CRM) Mobile CRM, which is named one of the elements in the multichannel integration process in Payne and Frow’s Conceptual Framework for CRM Strategy (Payne & Frow, 2005), is about “communication, either one-way or interactive, which is related to sales, marketing, and customer service activities conducted through the mobile medium for the purpose of building and maintaining customer relationships between a company and its customers” (Sinisalo, Salo, Karjaluoto, & Leppäniemi, 2007, p. 773).

Unlike social CRM, mobile CRM is solely integrated into mobile devices which customers use frequently (Alahuhta , Helaakoski, & Smirnov, 2005) (Das, 2016) Because of that, it provides a unique additional value to firms and customers with its high mobility. Companies are now able to constantly reach their customers at any place and any time. Also, unlike PC’s, mobile devices are usually only used by one consumer, which makes the customer information gathered more accurate (Alahuhta , Helaakoski, & Smirnov, 2005) (Das, 2016).

Alahuhta et al. (2005) identify mobile CRM services as electronic services “on the move”, whose key drivers are ubiquity, reachability, security and convenience (Alahuhta , Helaakoski, & Smirnov, 2005). These authors once again stress the benefits of mobile CRM helping firms to strengthen the relationships with customers with less limitations of location or time and with increased ability to identify users and gather their data in comparison to other CRM activity channels. Sinisalo et al. (2007) mention that higher returns are earned when companies send personalized information via mobile CRM to a few customers than when they send impersonalized information via mobile CRM to many customers, thus once again outlining the profound benefits of building relationships with customers (Sinisalo , Salo , Karjaluoto, & Leppäniemi, 2007). The authors Lee and Jun add to this by highlighting the added value of two- way interaction between the firm and the customer, which is further facilitated through mobile. Thus, firms can not only personalize their communications more, but also receive direct feedback on their activities from customers in a more facilitated way (Lee & Jun, 2007, p. 798).

On the other hand, Sinisalo et al. importantly point out that due to the high degree of customization and ubiquity of mobile CRM and the high degree of intimacy of mobile devices, mobile CRM can also pose challenges to companies.

Student: NE Hutton-Mills Student No.: 11145781

This may happen if mobile CRM activities irritate customers or intrude their privacy (Sinisalo, Salo, Karjaluoto, & Leppäniemi, 2007, p. 774). As a result, if the exact effects and echelons of mobile CRM are not well understood by companies, they can actually do harm to a firm’s CRM.

2.3 Mobile CRM Applications Is mobile CRM about web browsers or mobile applications? According to an article by Forbes.com called “The Mobile Browser is Dead, Long Live the App” by Ewan Spence, users are increasingly turning away from mobile browsers and spend 86 percent of their mobile usage time on phones with apps (Spence, 2014). Additionally, Spence highlights that companies gain more control and transparency about who uses their apps and how than when offering mobile browsers to their customers (Spence, 2014). Another important aspect to mention is the high level of personal interaction of firms and customers via mobile apps, which does not exist when customers interact with companies via the regular internet browser (Das, Mobile CRM apps to grow by 500%, 2016). Thus for the scope of this discussion, mobile CRM is understood as all value creating mobile CRM activities via companies’ mobile apps.

2.4 The airline industry However, considering that CRM differs among different industries and that many companies are just in the beginning phase of using mobile CRM, the insights for mobile CRM affecting customers could be very varied when being gathered per industry (Rivera & van der Meulen, 2013). Therefore, the choice of a relevant industry that engages in mobile CRM heavily is recommended.

One highly competitive industry that has a strong increase in customer numbers every year and that also represents one of the most important groups of mobile technology adopters is the airline industry (Budd & Vorley, 2013, p. 41). Many global airlines use mobile technology to inform customers about check- in times, ticketing and journey updates to interact with their customers and accompany them along their journey (Budd & Vorley, 2013, p. 41). Especially in the face of volatile fuel pricing, intense competition, security concerns and a focus on the environmental impact of air travel, airlines feel a stronger need to connect with their customers more directly as mentioned in an article by Ben Kepes on Forbes.com (Kepes, 2014).

In a study by Budd and Vorley (2013) mobile CRM apps used in the airline industry had high dissatisfaction scores with “45 percent of users being dissatisfied with the features and functionality offered with the airline apps” (Budd & Vorley, 2013, p. 42). However, the latter measure does not give any specific indications on which functions in the app caused dissatisfaction. Nonetheless, the study might indicate interesting challenges in the mobile CRM programs of airlines, where touchpoints might not be used effectively for fulfilling customers’ wishes.

Student: NE Hutton-Mills Student No.: 11145781

Therefore, the airline industry poses interesting implications on the topic as it has high share of usage of mobile CRM, it incorporates multiple touchpoints in its mobile technology to deliver its services, and it displays frictions in the areas of market developments and customer management.

2.5 Touchpoints in CRM As reported in academic literature, CRM has many different touchpoints with customers, which can be defined as “points of human, product, service, communication, spatial, and electronic interaction collectively constituting the interface between an enterprise and its customers over the course of customers’ experience cycles” (Dhebar, 2013, p. 200).

Sinisalo et al. mention CRM touchpoints such as internet, direct mail, sales call and mobile medium (Sinisalo, Salo, Karjaluoto, & Leppäniemi, 2006, p. 7). Furthermore, they mention that “communication through mobile can occur in the form of information, advertising, promotion, feedback, shopping, ordering, alerts, reminders, votes, competitions, lotteries to mention a few” (Sinisalo, Salo, Karjaluoto, & Leppäniemi, 2006, p. 2). However, the authors do not specify whether the communication forms above are used through mobile apps or through web browsers. Also, they do not mention the whole list of forms of communication.

In a research called “Branding in the Digital Age” by David C. Edelman, it is mentioned that touchpoints occur along the customer decision journey (CDJ), also referred to as the customer journey, and that this customer journey has several stages, namely: “consider, evaluate, buy, enjoy, advocate and bond” (Edelman, 2010, p. 3). The customer decision journey not only includes touchpoints before and during a purchase, but also after the purchase, where companies can interact and build relationships with their customers, whom might advocate the brand among peers and bond with it.

The author importantly mentions that there is a meaningful shift of consumers now engaging more with brands in the after-sale stages of a product purchase by reviewing the product purchased by sharing the experience online and using word-of-mouth online (Edelman, 2010, p. 4). Furthermore he mentions that the traditional funnel structure of selling concerned with the pre-purchase stages is outdated, as a deeper connection with a product develops after the purchase (Edelman, 2010, p. 3). This development increases the importance of touchpoints in the after-purchase stages and as stated in Edelman’s article, new media recently adapted to this by making the “evaluate and advocate stages more relevant” (Edelman, 2010, p. 4).

Student: NE Hutton-Mills Student No.: 11145781

Edelman (2001) interestingly highlights that the touchpoints have changed in number and nature and that customers show different levels of openness to influence in different touchpoints. Also, he found a mismatch between marketing allocations and the most influential touchpoints for customers (Edelman, 2010, p. 2) (Edelman, 2010, p. 4). Thus, leveraging touchpoints effectively is vital and requires thorough knowledge of their exact function and effects on customers.

Considering the unique customization and convenience of mobile CRM, the touchpoints in this novel CRM area could have different functions along the stages of the customer journey and also differential effects on customers. This would have important managerial implications on marketing budgeting and long-term effectiveness of a company’s CRM strategy.

2.6 Managerial Perspective of Touchpoints in Airline Apps As previously mentioned, scientific literature about touchpoints in mobile CRM apps is still in its infancy. Therefore, to gather further insights about touchpoints in mobile CRM apps in the airline industry, an in-depth expert interview was conducted with Business Service Designer and Product Manager Fredrik Forstbach from the Lufthansa Innovation Hub in Berlin. This was particularly beneficial, because the Lufthansa Innovation Hub is a well-connected leader in airline app development and digital innovation. The Lufthansa Innovation Hub was contacted via email and the Co-Founder of the company suggested a knowledgeable candidate suited for the interview. The interview was executed via phone and was executed in an unstructured and exploratory way. The aim of the interview was to increase the understanding of touchpoint functions in airline apps, the current trends and developments in the industry and how customers currently perceive and interact with touchpoints in the apps of different airlines. The findings of the interview are briefly discussed here.

According to Fredrik Forstbach, airline apps currently serve the function of facilitating the customer journey and even making it more exciting, more specifically:

„Airline apps currently serve the function of being at least efficient and reliable in leading the customer through his or her flight journey. They facilitate the journey and even make it more exciting. Also, they serve the function of giving quick and precise reactions and updates on the flight status. They should also have the function of offering the customer an attractive and easy-to-use interface” (Forstbach, 2016).

Moreover, the expert mentions that touchpoints in mobile CRM apps in the airline industry can be categorized into 7 main functions: (1)Personal and Account Information, (2)Booking and Flight Information, (3) At the Destination (Hotel, Travel Tips), (4) Special Offers, (5) Flight Inspiration, (6) Shopping and Entertainment, (7) Service and Feedback (Forstbach, 2016).

Student: NE Hutton-Mills Student No.: 11145781

In a study on the different touchpoint functions in mobile CRM apps executed by the Lufthansa Innovation Hub, it was found that not all airlines include all seven categories on their apps and that airlines put different weights on the touchpoints used in their apps (Forstbach, 2016). He stresses that especially the feedback touchpoints in airline apps have gained significant interest:

„Many airlines increasingly focus on the feedback touchpoints and invested in them for achieving closer interaction with their customers. Sri Lanka Airlines and Ryanair for example use a feedback pop-up in their apps, for the customer to directly give a short feedback about the flight once he landed” (Forstbach, 2016).

Besides trends and developments in the area such as instant messaging, travel updates and pop-ups in the app, Fredrik Forstbach also mentions developments related to data usages and customer centrification:

„There is of course the trend of location services, which can tailor the customer information even more and make apps more useful in giving customer journey updates and other interesting functions. On top of that, there is the trend of “Übersales”, which means that you bind the customer to the app by offering him or her everything through the app, so that the customer does not have to use the browser anymore. In that way, you can create deeper relationships between the customer and the company and find out more about them. Then you generally have the trend of automation and digitalization at the airport for the flight ticket, the check-in and the boarding passes, which can all be handled through the app now” (Forstbach, 2016).

Fredrik Forstbach interestingly mentions, that customers rather make use of the app after having purchased a flight and that airline app touchpoints can be used at any point of the customer journey, where a customer needs assistance by or interaction with the company. This may be during check-in and boarding times, during flight delays, and especially when the customer has landed at the destination and has to orient him or herself in the country:

“According to our data, people check in roughly 10 times more often (in absolute numbers) than they make bookings.(…) I think that any moment in the journey where the traveler actually wants personal contact is an opportunity for airlines. On the other hand any moment when the traveler needs help, like a change or cancellation of booking, or support when something goes wrong in the journey, as with delays, lost or delayed luggage, there is a big opportunity as a company to emerge with a better tie to the customer. The company can respond in a human and constructive way through the app, that does not make the traveler feel like an anonymous part in this huge operation that the airline business is. Another interesting moment is when the traveler arrives at the destination.

Student: NE Hutton-Mills Student No.: 11145781

This is one part that also causes high stress levels, since the traveler might be in a new country, with a new language, maybe tired and a bit helpless - and now needs to get to his hotel or other final destination. In this situation it seems to be extremely valuable for a company to assist the traveler with information- and possibly also with a channel for personal interaction, that gives the traveler the knowledge so he is covered and can explore the new country without fear” (Forstbach, 2016).

Finally, airlines seek to build the touchpoints in their app in a way that they have a high functionality. According to Fredrik Forstbach, a touchpoint shows high functionality if it helps the customer find what he is looking for in a fast and easy way:

“When touchpoints are reliable and efficient as mentioned before and when they can help the customer to quickly help him or her find what he is looking for in all relevant areas. This may be enabled with sorting criteria, quick and clear information and an easy interface of the selection options in the app. Its rather the absence of this level of information that will cause trouble and lower a touchpoints functionality” (Forstbach, 2016).

As general industry leaders, the expert considers the airlines RyanAir, EasyJet and AirBerlin , because of their “usability, features and overall look and feel” (Forstbach, 2016).

The complete interview results can be seen in Appendix 1 of this document.

The field analysis revealed that the vital touchpoint functions in airline apps are related to the flight booking, the check-in and boarding, any other flight related information and service and feedback (lufthansa.com) (klm.com, 2016) (ryanair.com, 2016). Often airlines also offer special offers, price alerts or tourist information on the app (qantas.com, 2016) (easyjet.com, 2016) (airberlin.com, 2016) (delta.com, 2016). However, touchpoint functions are often in line with the strategies that airlines pursue, thus Emirates airlines being more focused on lifestyle tourist information such as hotspots for dining and culture events at the destination and less focused on discount offers (emirates.com, 2016). The opposite is the case for AirBerlin, where a focus is set on special offers which can be searched within different categories (airberlin.com, 2016). The principle of “Übersales” as mentioned by Fredrik Forstbach was also found in the field analysis with Qantas Airlines for example enabling customers to book at the destination activities within the Qantas app (Forstbach, 2016) (qantas.com, 2016). Some special functions found in the field analysis were the incorporation of new technologies into the app such as live travel updates on the smart watch, passport scanners, special offer pop-ups for food and beverages at the airport or a travel concierge guiding the passenger through the airport (etihad.com, 2016) (emirates.com, 2016) (lufthansa.com). The full information of the touchpoint functions found through the field analysis can be found in Appendix 2 of this document.

Student: NE Hutton-Mills Student No.: 11145781

Scientific literature so far only addressed touchpoints along the customer journey in the broader sense and not in the specific mobile CRM context yet. And despite many sources elaborating on the IT- related challenges of mobile CRM, there is only little research on the psychological effects of mobile CRM on customers (Das, Mobile CRM apps to grow by 500%, 2016). Therefore, the question arises, what psychological effects mobile CRM touchpoints evoke in customers and how they could be measured to ultimately provide companies with profound customer insights and guidance on how to appeal to customers.

2.7 Customer Engagement One theoretical concept, which is frequently mentioned as representing a psychological response to CRM activities, is the concept of customer engagement. The term “customer engagement” (CE), which has only started to appear in the business world and in academic literature since the early 2000’s, is considered a strategic imperative for achieving increased firm performance and sales growth (Brodie, Hollebeek, Juric, & Ilic, 2011, p. 252). CE is defined as “the intensity of an individual’s participation and connection with an organization’s offering or organizational activities, which either the customer or the organization initiates and which may be manifested cognitively, affectively, behaviorally, or socially” (Vivek, Beatty, & Morgan, 2012, p. 133) The authors Vivek, Beatty and Morgan contribute greatly to the understanding of CE by mentioning that “the cognitive and affective elements of CE incorporate experiences and feelings of customers, and the behavioral and social elements of CE capture the participation by current and potential customers, both within and outside the exchange situations” (Vivek, Beatty, & Morgan, 2012, p. 133). Therefore, CE “involves the connection that individuals form with organizations based on their experiences with the offering and activities of the organization. Potential or current customers build experience-based relationships through intense participation with the brand by way of the unique experiences they have with the offering and activities of the organization” (Vivek, Beatty, & Morgan, 2012, p. 133).

Academic literature also refers to this concept as a psychological process that drives customer loyalty (Bowden, 2009), and that exceeds the concepts of “involvement” and “participation” by being “based on interactive and co- creative customer experiences with an engagement object” such as a firm or a brand (Brodie, Hollebeek, Juric, & Ilic, 2011, p. 264). Customer engagement “may be in the presence or absence of other consumers, may happen online and offline (Vivek, 2009) and may happen under high and low involvement (Vivek, Beatty, & Morgan, 2012, p. 137).

Vivek et al. capture the foci of the concept of CE in a fourfold table as it can be seen below in Table 1. According to the authors, CE can be provider initiated with offerings and activities geared towards the customer, and customer initiated with consumer offerings of a company’s products and services or

Student: NE Hutton-Mills Student No.: 11145781 with customer activities (Vivek, Beatty, & Morgan, 2012, p. 132). Thus engagement can be evoked in the classification of offerings and activities and by providers and customers, which may all be present in mobile CRM apps with companies for example posting events in the app or customers for example blogging about their travel on the app.

Table 1: Categorization of Customer Engagement Foci by Vivek et al. (2012) (Vivek, Beatty, & Morgan, 2012)

Besides creating competitive advantage for firms, customer engagement among customers may contribute to new product development and customer experience and value cocreation and was named a key research priority by the Marketing Science Institute in the year 2010 (Brodie, Hollebeek, Juric, & Ilic, 2011, p. 252).

In the literature field of social sciences, customer engagement builds on theories such as the self- schema theory (Markus, 1977) or the attachment theory, which both examine the drivers of how consumers start to relate their self-concept to a brand due to the brand supporting their identity and providing cognitive consistency with their self- schema (Ball & Tasaki , 1992). Thus, customer engagement connects with customers on a highly personal level. Higgins and Scholer add to this with their regulatory engagement theory by referring to engagement as a state of being occupied and fully absorbed by an engagement object (Higgins & Scholer, 2009, p. 100).

However, the conceptualization of customer engagement has not yet fully matured and it still remains a diffuse term in business as well as in the scientific world (Brodie, Hollebeek, Juric, & Ilic, 2011, pp. 252-253). An article by Adrian Swinscoe on Forbes.com called “What does customer engagement actually mean” adds to this by stating, that companies have actually not yet fully understood what customer engagement means and have not yet fully achieved to create deep customer engagement among their customers (Swinscoe, 2016).

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All in all, customer engagement is considered a multi-faceted customer behavior as the verb “to engage” with synonyms such as “to hold fast or to take part” implies (van Doorn, et al., 2010), which describes a deeper connection with customers and increased activity from customers. The concept of customer engagement and its facets show great potential for providing insights into how different touchpoint functions in mobile CRM apps affect customers along the customer journey. Furthermore, it has the potential to indicate in which touchpoints customers show more or less openness to influence as mentioned in Edelman 2001’s article. Also, it can indicate the interaction effects of touchpoints on each other, thus how a certain level of engagement in one touchpoint affects the level of engagement in another touchpoint. The latter indications could give companies specific guidance for which touchpoints to leverage in which part of the customer journey. But, in what way could customer engagement as a response to companies’ interactions in mobile CRM specifically be measured?

In their study called “Customer Engagement with Tourism Brands: Scale Development and Validation”, the authors called So, King and Sparks attempted to provide a rigorous measurement of the construct of customer engagement (So, King, & Sparks, 2014). The authors once again support previous statements by mentioning, that it is “a multidimensional concept subject to a context- and/or stakeholder-specific expression of relevant cognitive, emotional and/or behavioral dimensions” (So, King, & Sparks, 2014, p. 307). So et al. (2014) have come up with a scale of 25 items for the concept of customer engagement from the factors of: “enthusiasm”, “identification”, “attention”, “absorption”, and “interaction”, which can measure the differential effects of the different touchpoints on customers in mobile CRM. Besides all factors being found to be significant for measuring customer engagement, the authors recommend to put particular emphasis on the factors of attention and enthusiasm due to their high factor loadings (So, King, & Sparks, 2014, p. 304). The authors importantly mention, that the dimensions of “enthusiasm” and “attention” can be sparked through companies sending consumers newsletters, news or advertising, while the dimensions of “absorption”, “interaction” and “identification” are evoked in more engrossing ways (So, King, & Sparks, 2014, p. 307). While absorption is triggered by a company involving its customer with activities full of concentration and immersion, identification is triggered by companies offering memberships and clubs to customers and classifying them with other customers (So, King, & Sparks, 2014, p. 307). Finally, interaction is triggered with companies inviting customers to actively participate in activities instead of just being a passive receiver (So, King, & Sparks, 2014, p. 307).

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The latter insights show, that the dimensions “enthusiasm” and “attention” can already be triggered with basic and short-term CRM activities, while the other dimensions “absorption”, “interaction” and “identification” require more sophisticated and long-term activities.

The author called Shiri D. Vivek has come up with a similar CE scale consisting of 21 items as part of a three-dimensional scale with the dimensions of enthusiasm, conscious participation, and social (Vivek, 2009, p. 111). A highlight that he mentions is the importance of physical action as “an essential dimension of consumer engagement” (Vivek, 2009, p. 61). Vivek further explains that consumers physically participate in activities that engage them and that literature calls physical and social aspects of immediate environments antecedents of involvement (Vivek, 2009, p. 61). Despite Vivek putting much emphasis on physical action being an essential dimension of customer engagement, the author unfortunately does not present it as a separate item in his scale after all, which leads to the scale not adding significant value to be considered further.

All in all, the scale by So, King and Sparks can be used for measuring the effects of the individual touchpoint functions on customer engagement along the entire customer journey. But how does the concept of customer engagement evoked by touchpoint functions in mobile apps ultimately become profitable for firms?

2.8 Customer Engagement and Customer Retention In reference to the effects of customer engagement for firms, there are different effects of customer engagement mentioned in scientific literature. Connecting the dimensions of customer engagement to Edelman (2010)’s insights, customer engagement may evoke customers to further interact and bond with a company. Especially in the after-purchase phase, where customers increasingly bond with a company according to Edelman, customer engagement may evoke customers to advocate the company and its products through word of mouth and customer reviews (Edelman, 2010). Other research refers to firms having improved reputations as a result of increased customer engagement (Fombrun & Shanley, 1990), or customer engagement having an influence on customer equity through referrals and word-of- mouth affecting purchase behavior (Kumar, Aksoy, Donkers, Venkatesan, Wiesel, & Tillmanns, 2010).

The authors, Reinartz, and Krafft (2010) take an interesting approach in grasping customer engagement in a conceptual model comprising literature insights from different studies (Verhoef, Reinartz, & Krafft, 2010). In their study about the new perspective of customer engagement, the authors present that customer engagement ultimately increases customer retention with customers repeating purchases at a company (Verhoef, Reinartz, & Krafft, 2010, p. 249).

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More specifically, they line out that customer engagement first of all triggers activities such as customer- to- customer interactions and co-creation and ultimately leads to increased customer retention. (Hoyer, Rajesh , Dorotic, Krafft, & Singh, 2010) (Kumar, Aksoy, Donkers, Venkatesan, Wiesel, & Tillmanns, 2010). Customer retention, which can be defined as “the probability of a customer repurchasing from a firm after an initial purchase” (Gupta, et al., 2006), is an important measure indicating the effectiveness of a CRM strategy (Gustafsson, Johnson , & Roos, 2005). The authors called Reichheld and Schefter have found that a 5% increase of customer retention may lead to 25%-95% of firm profitability increases (Reichheld & Schefter, 2000, p. 106).

In two articles on Forbes.com, it is interestingly mentioned, that customer retention is greatly affected by personalization strategies and that it has gained more significance in recent years due to consumers having “shorter attention spans and an increased excess of options” (Jao, 2015) (Jao, forbes.com, 2014). These statements can be linked back to the earlier mentioned research findings by Sinisalo et al. (2007) and Alahuhta et al. (2005) in a way that the personalization of touchpoints in mobile CRM apps is indeed beneficial for customer retention and that retention is especially important nowadays that customers interact with mobile CRM touchpoints on their mobile phones “on the move” and with an excess of other information that may interfere (Helaskowski, Alahuhta, & Smirnov, 2005, p. 3).

The latter insights have significant implication for this research context. Applied to the context of mobile CRM apps, the different touchpoint functions should have differential effects on the construct of customer engagement, which will ultimately affect customer retention. Thus the first hypothesis is posed as: H1: Customer engagement has a direct positive relationship with customer retention.

In reference to the measurement of customer retention, the authors called Kassim and Souiden developed a scale for customer retention measured by a likert scale. More specifically they probed respondents on sub- items of intention of recommending the service to others, intention of continuing using the service, and intention of increasing the usage of the service (Kassim & Souiden, 2007, p. 221). All in all, the measurement scale by Kassim and Souiden (2007) is suitable for this research context for the measurement of customer retention in response to changed customer engagement levels through touchpoint functions in airline apps. But will all consumers be affected by touchpoints in the same way? Are there no other human factors that might influence the effect of touchpoints on customer engagement and retention?

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2.9 Traveler Personalities Considering that this research context deals with touchpoints in mobile airline apps, it is noticeable that customers will interact with these touchpoints on a very personal level. Therefore, other human factors influencing the effect of touchpoints on customer engagement in the airline app are quite probable. But what kind of human factors would that be and how would they differ among customers, or even groups of customers? Several travel magazines and the ABTA Travel Trends Report 2014 mention traveler personality as a factor to influence company interactions with customers and that traveler personality influences a customers’ expectations of the desired experience they want to receive when purchasing a product or service (Kresge, 2015) (ABTA.com, 2014) (airtreks.com, 2016). However, these sources are not scientifically validated. Regrettably, there is also no validated scientific literature available on travel personality types, which is suitable for this discussion.

One suitable and frequently mentioned human factor variable in shopping behavior and experience literature is the concept of “shopping motivation”, which is about the “underlying reasons for why people shop and start interacting in a shopping environment” (Arnold & Reynolds, 2003, p. 77). These underlying reasons may be functional or non-functional (Tauber, 1972, p. 46). The authors Westbrook and Black extend the latter thought with their research claiming, that shopping behavior may arise for three reasons: “(1) to acquire the products for which needs are experienced, (2) to acquire the desired product and to provide satisfaction for various additional non-product- related needs, (3) in service of needs unrelated to the acquisition of the product” (Westbrook & Black, 1985, p. 79). As a result, shopping motivation is not only related to the ultimate purchase of a product but also to other need fulfillments in the shopping environment such as the gathering of information, inspiration or seeking interaction. The different motivations can “magnify the experience in the mind of the shopper” and can have the effect of “making in-store evaluations and affective responses to the elements in the shopping environment more intense, either positive or negative” (Arnold & Reynolds, 2003, p. 90). The identification of shopping motivations has significant advantages as companies can tailor products and services more adequately and interact with customer in a more compelling way (Arnold & Reynolds, 2003, p. 90).

Applied to this research context, the latter insights have interesting implications for this research: When customers with different shopping motivations use mobile CRM apps for purchasing a flight, gathering information, seeking customer service or getting inspired, their different shopping motivations could interfere with the effects the different touchpoints used in the mobile CRM apps on

Student: NE Hutton-Mills Student No.: 11145781 their levels of customer engagement. They can influence the effectiveness of the touchpoints in a positive or negative way. As a result, the variable of shopping motivation acts as an antecedent to mobile airline app touchpoints ultimately affecting levels of customer engagement. However, before researching the interference of shopping motivations, the different motivation types should be defined. The authors called Kiseol Yang and Hye-Young Kim identified different shopping motivations among customers in the context of mobile shopping in their article “Mobile Shopping Motivation: an application of multiple discriminant analysis (Yang & Kim, 2012). They assessed how shopping motivations and demographic characteristics differ among mobile shoppers and non-mobile shoppers by executing a study including eight different shopping motivations, namely: (1) idea shopping, (2) efficiency shopping, (3) adventure shopping, (4) gratification shopping, (5) role shopping, (6) achievement shopping, (7) social shopping, (8) value shopping (Yang & Kim, 2012, p. 784) While customers using mobile with an idea shopping motivation aim at obtaining information about the product, the price and the latest product-related trends and news, efficiency shopping motivated customers favor general efficiency enhancing functions and location- based functions in mobile shopping to quickly find products, information and purchase related locations. While customers with an adventure shopping motivations like to try new functions and features of shopping services that entertain them, customers with a gratification shopping motivation favor a shopping experience that provides them with good feelings and special treats to reduce tensions. Furthermore, customers using mobile with the role shopping motivation focus on finding the right product or gift for their family, and achievement shopping motivated customers easy access and exploration of the functions to fulfill the shopping task with a couple of touches. Lastly, socially motivated customers focus on social interaction, reference group affiliation and communicating with others and value shopping motivated customers focus on finding special discount and bargain information in to seek inexpensive shopping opportunities (Yang & Kim, 2012, pp. 778-780).

Fredrik Forstbach from the Lufthansa Innovation importantly mentions that the shopping motivations outlined by Yang and Kim (2012), can also be translated into traveler personalities in the airline industry (Forstbach, 2016). Thus, there are traveler personalities with the same motivations such as efficiency shopping or value shopping, that can in this case be called the efficiency traveler or the value traveler. However, Forstbach limits the traveler personalities to the following types: the efficiency traveler, the gratification traveler, the social traveler and the value traveler. This is due to the fact, that through research done at the Lufthansa Innovation Hub, the expert observed motivations such as efficiency and gratification, but also strong social motivations among travelers and value motivations.

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In the interview this was expressed with “Bleisure” travelers, whom incorporate efficiency motivations for their business travel but also gratification motives for enjoying culture and activities during the free time of their business trip (Forstbach, 2016). The social motivations were mentioned in the context of consumers whom according to the expert like to gather and share travel information in their social circles (Forstbach, 2016). Also the expert mentions value motivations with many consumers trying to get the best deal when they have carefully decided for the destination (Forstbach, 2016). The expert considered these motivations as the most prevalent ones (Forstbach, 2016). Thus for the scope of this research, the shopping motivations are translated into traveler personalities and the latter traveler personalities mentioned are considered. Therefore the efficiency traveler personality is a traveler that favors efficiency enhancing functions and information that help him or her to quickly find products or locations (Yang & Kim, 2012, pp. 778-780) (Forstbach, 2016). while the gratification traveler personality like to treat him or herself with special gifts and good feelings along the journey (Yang & Kim, 2012, pp. 778-780) (Forstbach, 2016). And while the value traveler personality focusses on special discount and bargain information for lower priced purchases, the social traveler personality focus on social interaction, group communication and affiliation (Yang & Kim, 2012, pp. 778-780) (Forstbach, 2016).

Especially in personalized CRM app environments, the different traveler personalities of customers can have a significant preliminary effect on the effectiveness of the touchpoints on customer engagement and ultimately customer retention. Thus, for the design of effective mobile CRM apps and their corresponding touchpoints, the effect should be taken into consideration as an antecedent variable.

Touchpoint functions categorized under account information and check-in and boarding provide customers with vital information about the journey. Touchpoints such as check-in, flight status and passport scan automate and simplify the customer journey (Forstbach, 2016) (lufthansa.com). Customers with the efficiency shopping traveler personality favor efficiency enhancing functions to quickly help them achieve their shopping goal with a couple of touches, which would intensify the effects of the information touchpoints on the customer engagement of customers with this traveler personality (Yang & Kim, 2012, p. 778) (Forstbach, 2016). Therefore, the second hypothesis will be posed as: H2: Touchpoint functions categorized as “Account Information” and “Check-in& Boarding” have the strongest direct positive relationship with customer engagement for the Efficiency Traveler Personality.

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Touchpoint functions categorized under shopping and entertainment provide customers with retail shopping options and media entertain along the customer journey (Forstbach, 2016) (lufthansa.com) (klm.com, 2016). Customers with the adventure and gratification shopping traveler personalities seek special touchpoint functions that entertain them and give them good feelings or special treats to evoke feelings of pleasure and well-being, which would intensify the effects of the shopping and entertainment touchpoints on the customer engagement of those customers (Yang & Kim, 2012, p. 778). Therefore, it can be hypothesized: H3: Touchpoint functions categorized as “At the Destination Options” and “Shopping and Entertainment” have the strongest direct positive relationship with customer engagement for the Gratification Traveler Personality.

Touchpoint functions categorized as “Service and Feedback” provide customers with options to get into contact with the company to seek help, assistance or give feedback (Forstbach, 2016) (klm.com, 2016). Customers with the social shopping traveler personality seek human interactions and social affiliation throughout their journey, which would intensify the effects of the service and feedback touchpoints on the customer engagement of those customers (Yang & Kim, 2012, p. 778). Therefore, it can be hypothesized: H4: Touchpoint functions categorized as „Service& Feedback“ have the strongest direct positive relationship with customer engagement among the Social Traveler Personality.

Touchpoint functions categorized as “Special Offers” provide customers with special deals and discount prices for products (Forstbach, 2016) (airberlin.com, 2016) (easyjet.com, 2016). Customers with the value shopping traveler personality seek special bargain information and inexpensive purchases, which would intensify the effects of the special offers touchpoints on the customer engagement of those customers. However, since the benefits, that customers with the value shopping traveler personality derive from the touchpoints are mainly transactional, one might hypothesize, that only customer retention is affected, thus skipping customer engagement (Yang & Kim, 2012, p. 778) (Verhoef, Reinartz, & Krafft, 2010, p. 249) Therefore, it can be hypothesized: H5: Touchpoint functions categorized as „Special Offers“ have no direct relationship with customer engagement among the Value Traveler Type but have a positive relationship with customer lifetime value.

The hypotheses posed are summarized in the following conceptual framework.

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2.10 Conceptual Framework Variables: Traveler Personality (Antecedent Variable):

. Efficiency shopping . Gratification shopping . Social shopping . Value shopping

Mobile CRM Touchpoint Functions (Independent Variable):

. Account Information . Check-in& Booking . At the Destination Options . Special Offers . Shopping& Entertainment . Service& Feedback

Customer Engagement (Dependent Variable):

. Enthusiasm . Identification . Attention . Absorption . Interaction

Customer Retention (Dependent Variable):

. Continuation (Intention of continuing to use the service) . Frequency (Intention of increasing the usage of the service) . Recommendation (Intention of recommending the service to others)

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Conceptual Model

Mobile CRM Touchpoint Functions (IV): Traveler A) Account Personality Information (Antecedent) : Customer B) Check-in& H2 + Customer H1 1) Efficiency Retention Boarding Engagement H3 + (DV) C) At the Destination 2) Gratification (DV) + Options H4 + D) Special Offers 3) Value H5 + E) Shopping& 4) Social Entertainment F) Service& Feedback

Hypotheses

H1: Customer engagement has a direct positive relationship with customer retention.

H2: Touchpoint functions categorized as „Account Information“ and „Check-in& Boarding“ have the strongest direct positive relationship with customer engagement for the Efficiency Traveler Personality.

H3: Touchpoint functions categorized as „At the Destination Options“ and „Shopping& Entertainment“ have the strongest direct positive relationship with customer engagement for the Gratification Traveler Personality.

H4: Touchpoint functions categorized as „Service& Feedback“ have the strongest direct positive relationship with customer engagement among the Social Traveler Personality.

H5: Touchpoint functions categorized as „Special Offers“ have no direct relationship with customer engagement among the Value Traveler Personality but have a positive relationship with customer retention.

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3 Data and Method

3.1 Research Methodology In order to test the hypotheses posed, a cross-sectional mixed method research design was adopted. The mixed method design contained an exploratory part with a semi-structured in-depth expert interview and a field analysis on the one hand and an explanatory part with an experimental Vignette study distributed in the form of a survey on the other hand.

The in-depth interview and the field analysis were conducted in order to gain deeper insights into the field of touchpoints in airline apps, which is still in its infancy in the business and scientific world. The experimental Vignette study was conducted for the purpose of researching possible causation between the airline touchpoint functionality and customer engagement under consideration of different traveler personalities. Thus the mixed method design was chosen for firstly collecting state of the art insights on airline app touchpoints and then testing how these touchpoints affect customer engagement and retention.

3.2 Method Design and Collection Procedure

3.2.1 Exploratory Research: Expert Interview For the semi-structured in- depth interview, an interview script was written that would probe and extend the insights gathered through existing literature on airline app touchpoints. Since, the field of airline app touchpoints is still in its infancy, the interview was semi-structured in nature in order to enable opportunities for additional information on the matter.

The in-depth expert interview was conducted via phone with Fredrik Forstbach, Business Service Designer and Product Manager at the Lufthansa Innovation Hub in Berlin. The Lufthansa Innovation Hub is dedicated to developing state of the art digital solutions for the airline industry in corporation with innovation leaders from different industries. The Lufthansa Innovation Hub also developed the Lufthansa App, which is currently used by the airline. Therefore the company was approached.

3.2.2 Experimental Research: Vignette Study For the experimental Vignette study, a realistic airline app containing the most critical touchpoints along the customer journey was to be built. App pictures have been created displaying the different touchpoint functions along the customer journey. For implementing the experimental treatments of high and low functionality of the airline touchpoints, two different versions of pictures were created. In that way the causation between airline app touchpoint functionality and customer engagement could be tested. While one version of the touchpoint was very helpful for the respondents, the other version was not very helpful. Subsequently customer engagement and retention among respondents were measured for each version.

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3.2.2.1 Pretest Before implementing these two treatments of high and low functionality, a pre-test with n=50 was conducted in order to test whether high and low functionality was perceived accordingly by consumers. The pre-test was designed and distributed via Qualtrics displaying app pictures for each airline touchpoint function, which can be seen in Appendix 3 of this document. For the pre-test a convenience sampling was chosen and a total of n=50 respondents filled in the pretest. Respondents were presented the high and low functionality versions of the touchpoints and were asked to rate, which version was more helpful, the “right” or the “left” version. After having conducted the pre-test, the codes of “right” and “left” were recoded into the scores of “10” meaning high functionality and “0” meaning low functionality. Subsequently, mean scores were computed for each touchpoint in order to analyze, whether most respondents recognized the high functionality version of the two. The mean scores had to be equal to or above 5 in order to show that the majority of respondents recognized the functionality of the touchpoints correctly. The results of the pre-test are displayed in table…below:

N=50 Special Flight Account Shopping& Check-in At the Service& Offers Inspiration Information Entertainment & Destinatio Feedback Boarding n Options Mean 7,6 5,6 8,8 8,4 4,8 7,4 6,4 Score of Touchpoi nt Function ality Table 2: Pre-Test Results

In reference to the results of the pre-test, it can be said that the touchpoint functions of “Flight Inspiration”, “Service and Feedback” and “Check-in and Boarding” had lower scores of perceived recognition of the high and low functionality with mean scores lower than 7.0. Especially the mean score of the touchpoint function of “Check-in and Boarding” was below the score of 5 and thus not satisfactory. In order to test for significance of these findings, a One Sample T-Test was performed with a test value of 8.0 in order to test for significant deviations in the mean scores. The test revealed that the deviations in mean scores from the test value of 8.0 were significant for the touchpoint functions of “Flight Inspiration”, “Service and Feedback” and “Check-in and Boarding” with p- values= 0.001 for Flight Inspiration, 0.000 for “Check-in& Boarding”, and 0.024 for Service& Feedback.

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Upon feedback from respondents, the low perceived helpfulness of the touchpoint function categorized as “Check-in& Boarding” was due to the lack of visibility of the elements displayed in the pictures. As a consequence, the visibility of the airline app pictures was modified in a way that the picture was made more visible and the messages written were made bigger. The touchpoint function of “flight inspiration” was excluded for subsequent analyses as it did not yield a high mean score of perceived functionality and as it is not part of any hypothesis (Forstbach, 2016).

3.2.2.2 Experimental Survey According to the literature by Aguinis and Bradley, Vignette studies are created as very realistic scenarios (Aguinis & Bradley, 2014, p. 352). In that way the testing of dependent variables such as “intentions attitudes and behaviors is improved” (Aguinis & Bradley, 2014, p. 352). Therefore, a Vignette study was chosen as the appropriate way of testing the hypotheses.

For the execution of the Vignette study, Düsseldorf Airport was approached for the possibility of conducting the research at the facilities of the airport, where many passengers are starting their business or holiday journeys. For the research at the airport, the two questionnaire versions were available in English and German. Questionnaires were distributed and collected at the airport with an iPad or via QR codes for passengers on the go. The questionnaires were distributed in the waiting areas near the arrivals and departures at the airport.

In reference to the sample, there were no limitations in the characteristics of the sample with respondents from all genders, ages and occupations being approached.

The experimental survey was designed on Qualtrics also incorporating the adjustments from the pretest. For the two experimental treatments, two versions of questionnaires were created. At first, both survey questionnaires routed through demographic questions and the traveler personality categorization question. Then two different routings were taken with one version showing the airline app touchpoints with high functionality and the other version showing the touchpoints with low functionality. Respondents were then asked in both versions to observe the different touchpoints functions and select the two functions that were most interesting and relevant to them. Next, the two functions that were selected by the respondent were displayed in a close up and for each function the respondents were asked to answer questions related to their customer engagement. Finally the last question in both versions asked about the customer retention of respondents. The survey questionnaire structure can be seen in Figure 1 below and screenshots of the experimental survey are displayed in Appendix 5 of this document:

3.2.2.3 Experimental Design

Figure 1: Experimental Design

3.3 Variable and Scale Development In order to measure the effects of the different parts of the conceptual model, measurement scales from different scientific literature were used.

3.3.1 Traveler Personality In reference to the variable of traveler personality, the measurement scale by Yang and Kim(2012) was used to a partial extent (Yang & Kim, 2012, p. 778). Due to the fact that these scales are originally used for shopping motivations, they have been transformed into traveler personalities after approval by the expert Mr. Fredrik Forstbach (Forstbach, 2016). The scale contained different items for the different motivations, which were rated on a 5-point- likert scale. The dimensions are namely: the “Efficiency Traveler Personality”, the “Gratification Traveler Personality”, the “Social Traveler Personality” and the “Value Traveler Personality”. However, for shortening the time of the experiment, the scale was in fact transformed into a categorization question, where respondents could choose the personality type that they most identified themselves with. Thus the construct of traveler personality was not assessed as a scale in during the research, but as a categorical question.

3.3.2 Touchpoint Functionality The touchpoint functions as displayed in the pictures, have been created in accordance with insights from scientific literature by Sinisalo et. al. (2006) and Edelman(2010) (Sinisalo, Salo, Karjaluoto, & Leppäniemi, 2006, p. 7) (Edelman, 2010, p. 4), insights from the expert interview with Fredrik Forstbach (Forstbach, 2016) and insights from the field analysis of current airline apps in the market in Appendix 2 of this document. All in all, six touchpoint functions were identified, namely: “Account Information”, “Check-in Boarding”, “Special Offers”, “Shopping Entertainment”, “At the Destination Options”, “Service Feedback”. In accordance with the results of the pre-test, the different functions have been created as either very helpful, displaying high functionality, or less helpful, thus displaying low functionality. The functionality levels according to Fredrik Forstbach have been defined as leading the customer to his goal in the fastest and most direct way (Forstbach, 2016). The function 31 called “Flight Inspiration” was originally displayed in the Pretest, but was excluded from the experiment for the purpose of shortening the length of the experimental survey and because it was not part of the hypotheses.

3.3.3 Customer Engagement

Customer Engagement (Choice) This variable was extracted from the measurement scale by So, King and Sparks (So, King, & Sparks, 2014, p. 314). The scale dimensions used were: “Enthusiasm”, “Identification”, “Attention”, “Absorption”, and “Interaction”. The original scale included different items per dimension, rated by respondents on a 5-point-likert scale.

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However, for the purpose of shortening the experimental survey, this scale was also partly used for the categorical question of letting respondents choose two out of the total six touchpoint functions.

This was done for the purpose of testing respondents on the customer engagement dimensions of “Enthusiasm”, and “Attention” by asking them to choose the two functions that are most interesting and relevant to them. Therefore this variable was a categorical variable with the aim of measuring preference frequencies of respondents, that would indicate their enthusiasm and attention for the touchpoint functions they choose.

Customer Engagement Scale The variable customer engagement was implemented with the measurement scale by So, King and Sparks (So, King, & Sparks, 2014, p. 314). The scale dimensions used were: “Enthusiasm”, “Identification”, “Attention”, “Absorption”, and “Interaction”. The original scale included different items per dimension, rated by respondents on a 5-point-likert scale. Due to the considerable length of this experiment, the item list was shortened to one item per dimension resulting in 5 items with high factor loadings in the original scale being explored in the question.

3.3.4 Customer Retention

Customer Retention (Scale) The variable of “customer retention” was assessed with a measurement scale by Kassim& Souiden, which included three dimensions to be assessed on a 5-point- likert scale (Kassim & Souiden, 2007, p. 221). The dimensions used were namely: “Intention of continuing using the service”, “Intention of increasing the usage of the service”, and “Intention of recommending the service to others” with one item per dimension leading to three probing questions.

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4 Data Analysis This chapter provides insights into the data gathered during the experiment at the airport. The chapter will serve as the foundation for later conclusions.

4.1 Sample and Between Group Homogeneity Analysis The experimental research at Düsseldorf Airport started on Thursday 27th November 2016 and was closed on Friday 9th December 2016. The experimental survey questionnaires were distributed in the departure and arrival areas of the airport via QR codes for passengers on the go, or filled in on an iPad provided by the student. The experimental survey questionnaires containing the high and low functionality treatments in German and English language are displayed in Appendix 5 of this document.

All four questionnaires provided a total of 338 responses. The response numbers for the German surveys were higher, which is due to the fact, that the experiment was executed at a German airport. Nonetheless, there was still a considerable number of English speakers at the airport, whom filled in the English survey questionnaires. The German and English questionnaires have been merged together for each functionality treatment for the data analysis. Out of the total of 338 responses, there were several missing responses in the data set for the different questions about age, gender, occupation and traveler personality. While the gender and occupation variables had 6 missing responses and age had 7 missing responses, the variable of traveler personality had 35 missing responses. This is quite a high number of missing responses, which may have been due to an error in the Qualtrics questionnaires, which have been spread via the QR codes.

Coming to the description of the sample, it can be said, that there was a roughly equal number of men and women in both the low and high functionality treatments. While in the low functionality group there were 48.2% men and 51.8% women, there were slightly more men (51.2%) in the high functionality group than women (48.8%). In reference to the age ranges, it can be said that in both treatment groups, more than 50% of the respondents were in the age range of 25-34 years, followed by the second biggest age range, the 15-24 year-olds (14.7% and 16.1%). The next biggest age range represented in the sample are the ranges of 35-44 years and 45-59 years. The least respondents were between 60-69 years old and 70 years and older. Concerning the occupations of the respondents in the sample, most respondents, which is about 40% in each treatment are professionals. Next, there are 28.7% and 26.8% of students in the high and low treatment groups, which is followed by about one fourth of respondents being young professionals. The minority of respondents were pupils, retired or had other occupations.

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In reference to the traveler personalities of the respondents in the sample, it is noteworthy that in both treatment groups, there were about 46% respondents identifying themselves with the gratification traveler personality. This was followed by the efficiency traveler personality being chosen by 29.8% of the respondents in the high functionality treatment and 28.3% in the low functionality treatment group. Next, the value traveler personality was chosen by 17.9% of respondents in the high treatment group and 20.4% in the low treatment group. The least respondents identified themselves with the social traveler personality.

All in all, it can be said, that the sample contains an equal number of men and women, out of which most are in their middle ages and employed for several years, newly employed or still in their studies. Finally, most respondents have the gratification and efficiency traveler personalities, which shows that they either like to treat themselves to something special or like to travel in a time and resource efficient way. A comprised table with the most important data findings can be seen in the following table 3:

Sample Descriptives Group 1: High Functionality Group 2: Low Functionality Sample Size n 151 152 Age Range 15-24 years 14.7% 16.1% 25-34 years 57.7% 55.4% 35-44 years 13.5% 11.9% 45-59 years 12.3% 13.1% 60-69 years 1.2% 3.0% 70 years and 0.6% 0.6% older Gender Male 51.2% 48.2% Female 48.8% 51.8% Occupation Pupil 0.6% 1.2% Student 28.7% 26.8% Young 24.4% 25.0% Professional Professional 38.4% 41.7% Retired 6.7% 3.6% Other 1.2% 1.8% Traveler Efficiency 29.8% 28.3%

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Personality Gratification 46.4% 46.1% Value 17.9% 20.4% Social 6.0% 5.3% Table 3: Sample Descriptives of Experiment 4.2 Homogeneity of Experimental Groups Furthermore, the homogeneity of the sample has been assessed in form of Chi-square tests and Kruskal Wallis tests in order to control for certain variables not having an effect on the dependent variable. Although respondents have been allocated to the experimental groups in a randomized manner, the homogeneity tests detect possible differences between the distributions in the high and low treatment groups. After all, an experimental setting should provide a certain level of homogeneity of group members, to create the right conditions for the manipulation execution (Field, 2013). The homogeneity test was executed for the variables of age, gender, occupation and traveler personality.

To begin with the variable of gender, a Pearson Chi-square test was executed. The test reported a Pearson Chi-square reported a p- value of 0.584, which validates that there are no significant differences between the gender distributions of the high and low functionality treatment groups. The same was done for the variable of age, however the Chi square test reported that more than 33.3% of the cells had an expected count less than 5, which does not fit the requirements of a Pearson Chi- square test. Thus for the variable of age, a Kruskal Wallis test was executed, which reported a p- value of 0.916. This p-value shows that there are no significant differences between the age distributions of the high and low treatment groups, which is also supported by equal median scores(2.00) of the variable of age. The variable of occupation also turned out to not fit the requirements of a Pearson Chi-square test with 33.3% of cells having an expected count less than 5. Therefore, a Kruskal Wallis test was also conducted for the occupation distribution of the two treatment groups. The result of the test reported a p-value of 0.977, which signals that there are no significant differences between the occupation distributions of the two groups. This is also supported by comparing medians between the two groups for the variable of occupation, which led to the result of equal medians of 3.00 in both groups. Finally, the variable of traveler personality fit the prerequisites of a Pearson Chi-square test again and the test was executed. The reported p- value of the test was 0.945, which implies that there are no significant differences in the traveler personality distributions between the high and low functionality treatment groups.

All in all, for the distributions of age, gender, occupation and traveler personality, the tests have shown that there are no differences between the two experimental groups of high and low functionality, which could affect the dependent variable.

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Therefore the homogeneity of the two treatment groups is accepted. The p-values of the Pearson Chi- square test and the Kruskal Wallis test have been consolidated in Table 4 below:

Homogeneity Test Tests (Parametric/ Homogeneity Nonparametric) Pearson Chi- Kruskal square Wallis Age 0.916 accepted Gender 0.584 accepted Occupation 0.977 accepted Traveler Personality 0.945 accepted Table 4: Homogeneity of Experimental Treatment Groups 4.3 Reliability Analysis In order to assess whether the scales used are sufficiently consistent, a reliability analysis has been conducted. This was done, by assessing the Cronbach Alpha’s of the scale variables of Customer Engagement and Customer Retention.

Firstly, the reliability analysis of the variable of “Customer Engagement(Scale)” was made. Due to the convoluted survey design of respondents being able to choose two out of the total six functions that appear most interesting and relevant to them, there was the condition of systematic missing data in the data set. The experimental survey questionnaires were designed this way to get more information on customer engagement but with less required time than if all functions were rated by respondents. This led to a staggering 69% of missing data for the variable of customer engagement. The condition was handled by separating the two customer engagement ratings that each respondent gave on the two functions of his choice and transforming them into two new, separate cases. Thus, each rating of a functions became one separate case, which led to the sample size to a new n=686. However, the sample characteristics stayed the same. Therefore, unlike the prior analyses, the subsequent statistical tests will work with the new sample size of n=686.

Once the case separation was successfully conducted, the Cronbach’s Alpha could be drawn. The score of the Cronbach’ s Alpha for “Customer Engagement(Scale)” could was 0.818, which is very satisfactory.

Secondly, the reliability analysis of the variable of “Customer Retention” with a total of 3 items was made. Here, a normal research design was used with respondents in both treatment groups answering all items, and thus no multiple imputation method was required. The Cronbach’s Alphas for the variable of “Customer Retention” was 0.897 and was also very satisfactory.

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To sum the reliability analysis up, it can be concluded that both scales had good scores of reliability. The scored of both analyses can be seen in table 5 below:

Scales No. of items α n (valid) Scale Reliability Customer Engagement 5 0.818 627 accepted (CEscaletot) Customer Retention 3 0.897 632 accepted (CRscaletot) Table 5: Scale Reliability Analysis for Customer Engagement and Customer Retention 4.4 Descriptives and Correlation Matrix Next, an analysis containing descriptives and a correlation matrix has been conducted in order to gather first overall insights on the data results. The insights can provide basic information on general preferences of respondents and overall differences between the experimental groups. The frequencies table below contains the mean scores and standard deviations for the constructs of “Traveler Personality”, “Customer Engagement(Scale)”, and “Customer Retention”. Though the mean scores of the variable of “Traveler Personality” have already been analyzed before, it is helpful to display them once again in one table together with the mean score and frequencies of the constructs of “Customer Engagement” and “Customer Retention”. For the construct of “Customer Engagement(Choice)”, the frequencies have been calculated, because due to survey design this construct was solely coded with the number “1”, and thus the variation cannot be observed through mean scores. Respondents had to observe the different touchpoint functions and were then asked to choose two touchpoint functions that were the most interesting and relevant to them. There were also missing cases or more than two cases per respondents, where respondents mistakenly chose only one function or more than two functions.

In reference to the construct of “Customer Engagement(Choice)”, there were some differences observed in reference to the touchpoint functions that respondents in the experimental groups found most relevant and interesting. First of all, the function called “Check-in& Boarding” was chosen the most by respondents in both experimental groups with 42.2% in the high functionality treatment group and 40.6% in the low functionality treatment group. This links back to the insights from the interview with the expert Fredrik Forstbach, who stated that customers generally favor information in the most critical parts of their customer journey, which also includes the check-in and boarding of a journey (Forstbach, 2016). The percentage shares show that respondents’ preference for receiving check-in and boarding information were equally high. Next, the touchpoint function called “At the

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Destination Options” and “Special Offers” were also frequently chosen. The touchpoint function of “Service& Feedback” was the least frequently chosen.

However, the high functionality treatment group prefers “At the Destination Option” slightly more to “Special Offers”, while the reverse is true for the low functionality treatment group, where respondents prefer “Special Offers” slightly more over “At the Destination Options”.

This finding may be due to the fact, that the touchpoint function pictures for special offers looked more attractive and interesting than at the destination options to respondents in the low functionality group. Furthermore, the high functionality treatment group choose the “Account Information” option slightly more than the low functionality treatment group. The touchpoint functions called “Shopping& Entertainment” and “Service& Feedback” were least chosen as interesting and relevant by respondents in both treatment groups.

Coming to the observations of the construct of “Customer Engagement(Scale)” overall, there are some interesting findings to be reported. One of the findings is the fact that overall the High Functionality treatments evoked higher mean scores of “Customer Engagement(5-point likert scale)”, than the Low Functionality treatments. This shows, that the differences in perceived functionality of the app touchpoints were indeed effective. While respondents as part of the High Functionality treatment showed an average customer engagement level of 3.24, which is coded as “neutral”, respondents as part of the Low Functionality treatment showed an average customer engagement of about 2.48, which is coded as “disagree”. The Customer engagement score for the High Functionality group is not very high, which could be due to respondents only seeing the app for the first time and in form of app pictures and not as a real app. This might have drawn down the items of interaction and identification as part of the “Customer Engagement(Scale)” construct, thus leading to medium scores.

Taking the latter thought into consideration, the mean scores per item of the variable of customer engagement per treatment have also been analyzed with some noteworthy results. It can be said, that between the five items of the customer engagement scale there are considerable differences. While for the items “Enthusiasm” and “Attention” the highest mean scores of 3.99 and 3.70 in the high functionality treatment, and 3.09 and 2.83 in the low functionality treatment, the other items “Identification, “Absorption”, and “Interaction” had considerably lower scores. The differences between the scale items of each treatment group have been tested for their significance by performing an ANOVA and all differences between the high and low treatments have been marked as significant with p-values lower than p=0.05. Moreover, while in the high functionality treatment the item “Interaction” had the lowest score, in the low functionality treatment the item “Identification” had the lowest score.

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These per item mean score findings indicate that in both treatments respondents were more enthusiastic and attentive to the app functions than they were interactive, absorbed or identifying themselves with the function.

Also the app functions in the high functionality treatment evoked interaction the least among respondents while the low functionality treatment app functions evoked identification the least among respondents. All in all, these findings highlight the fact that the items of customer engagement called enthusiasm and attention could successfully be evoked in the short time frame of the experiment. However the engagement items such as identification or interaction could not be evoked in both treatments, which shows that triggering these items might require more long term app exposure.

In reference to the construct of “Customer Retention(Scale)” overall, similar observations between the high and low treatments can be made in relation to the overall “Customer Engagement(Scale)”overall observations. The “High Functionality” treatments display higher scores than the “Low Functionality” treatments. Here, the high functionality treatment group shows retention scores of 3.72 and the low functionality treatment group shows retention scores of 2.80. What is remarkable, is the fact that, compared to the construct of “Customer Engagement(Scale)”, the construct of “Customer Retention(Scale)” has higher scores. This shows, that in all treatments, customer retention among respondents was higher than customer engagement thus respondents would use the app again despite not being too engaged with it.

In reference to the latter, it is also of interest to research the individual mean score of the three items of customer retention. Here, it can be observed that in the high functionality treatment the items “Continuation” and “Recommendation” have the highest score of 3.80, while “Frequency” has a lower score of 3.55. In the low functionality treatment, the item “Continuation” has the highest score with 3.11 and the other two items have lower scores. The latter findings reveal that respondents in both treatments on mostly would continue to use the app, however not necessarily use it more frequently. Also in the high functionality treatment, respondents would recommend the app more than they feel the necessity to increase the usage frequency. This was different in the low functionality case, where respondents neither would recommend it that much nor increase usage frequency. Also in this case, the differences between the scale items of customer retention in each treatment group have been tested for their significance by performing an ANOVA and all differences between the high and low treatments have been marked as significant with p-values lower than p=0.05. The corresponding partial eta’s squared were of 0.137 for the item of continuation, 0.203 for the item of frequency and 0.296 for the item of recommendation, which all signal a high effect size of the differences.

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All in all, it can be concluded that respondents found the touchpoint functions of check-in and boarding, at the destination options and special offers most relevant and interesting for their journey. Finally the high treatment group had higher engagement and retention scores than the low treatment group, with retention scores overall being higher than engagement scores for both groups.

Moreover, respondents mostly felt enthusiastic and attentive to the app functions and would mostly continue to use the apps. The findings have been consolidated in table 6 below:

Experimental Group 1 Group 2 Survey Construct High Functionality Low Functionality Mean Std. Dev N Mean Std. Dev N

Traveler 2.01 0.843 308 2.03 0.843 306 Personality

Customer Choice Frequencies Choice Frequencies Engagement (Category) n=303 n=330

A Account Information: A Account Information: 4.5% 3.6% B Check-in& Boarding: B Check-in& Boarding: 42.2% 40.6% C Special Offers: C Special Offers: 18.2% 21.5% D Shopping& Entertainment: D Shopping& Entertainment: 7.9% 7.6% E At the Destination Options: E At the Destination Options: 19.8% 18.8% F Service& Feedback: F Service& Feedback: 4.3% 4.2%

Total: Total: 100% 100% Customer 3.24 0.775 309 2.48 0.621 326 Engagement (Scale) Overall

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Customer 3.99 0.888 305 3.09 0.826 326 Engagement Item: Enthusiasm

Customer 2.83 1.097 309 2.15 0.897 325 Engagement Item: Identification Customer 3.70 1.002 308 2.83 0.895 326 Engagement Item: Attention

Customer 2.90 1.098 308 2.17 0.870 325 Engagement Item: Absorption

Customer 2.76 1.152 309 2.19 0.944 326 Engagement Item: Interaction

Customer 3.72 0.886 310 2.80 0.686 324 Retention(Scale) Overall

Customer 3.80 0.977 310 3.11 0.765 324 Retention Item: Continuation Customer 3.55 0.996 308 2.64 0.800 324 Retention Item: Frequency Customer 3.80 0.950 310 2.65 0.820 324 Retention Item: Recommendation Table 6: Descriptive Analysis with Mean Scores, Frequencies and Standard Deviations

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As a next step, the correlations between the control variables and scale items have been assessed through an APA style Inter- item Correlation Matrix. Beforehand, scale means have been computed for the variables “Customer Engagement(Scale)”and “Customer Retention(Scale)”. Also, scatter plots have been drafted before the correlation matrix for possible linear relationships between variables.

Linear positive relationships could be detected in two cases between the variables of age and occupation and customer engagement and customer retention, which are displayed in Appendix 6 of this document. Due to most variables having a polytomous nature and not being normally distributed, the Spearman Inter-Item Correlation was used as the appropriate analysis. An APA Style Spearman Correlation Matrix was created and it displays some striking results.

First of all, there is a significant positive correlation between the variables of “Age” and “Occupation”. With the correlation score being 0.620, this can be considered a strong correlation. This correlation can be explained in a way that the higher the age of the respondents, the higher is also their occupation. Another significant positive correlation can be found between the variables of “Customer Engagement (Scale)” and “Customer Retention (Scale)”. The correlation score is 0.658, which reveals the level of correlation is quite high. This correlation reveals, that the higher the level of a respondent’s engagement, the higher is also his or her retention. Considering the fact, that the previously discussed overall mean scores of retention are higher than the ones of engagement, this correlation proves that the two variables are still inter- connected. Moreover, there is a significant positive correlation between the variables of “Touchpoint Functionality” and “Customer Engagement(Scale)” with a medium strength score of 0.472. This correlation can be explained in a way that the higher the functionality level of the app function, the more engagement the respondents show. This once again demonstrates the effects of the experimental treatments, which aimed at evoking higher scores of engagement with more functionality in the app functions. Nonetheless, the correlation is not very strong. A similar finding can be made with the significant positive correlation between the variables of “Touchpoint Functionality” and “Customer Retention”, where a strong correlation score of 0.520 is prevalent. This shows, that the higher the functionality of the app functions show, the higher was customer retention among respondents. However in this case it can be seen that the correlation with retention is higher than the one with engagement.

All in all the Spearman APA Correlation Matrix shows, that older respondents usually have higher ranked occupations in the experiment and that higher scores of customer engagement also lead to higher scores of customer retention. Finally, the correlation matrix also revealed, that high functionality in app functions led to higher scores of customer engagement and customer retention. The latter findings are displayed in table 7 below:

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Inter- Item APA Correlation Matrix

Correlations Age Gender Occupatio Touchpoint Customer Custome n Functionalit Engagemen r y t Retentio n Spearma Age 1 n's rho 671 Gender 0.04 1 0.303 671 674 Occupation .646** 0.07 1 0 0.069 671 674 674 Touchpoint -0.001 -0.039 -0.001 1 Functionalit y 0.984 0.317 0.988 671 674 674 686 Customer 0.013 -0.044 -0.003 .472** 1 Engagement 0.744 0.266 0.931 0 630 633 633 635 635 Customer -0.005 -0.054 -0.021 .520** .657** 1 Retention 0.902 0.176 0.596 0 0 629 632 632 634 629 634 Table 7: APA Style Spearman Correlation Matrix

**. Correlation is significant at the 0.01 level (2-tailed).

4.5 Hypothesis Testing In this section, the hypotheses that have been posed will be tested with different statistical analyses. While the most important findings will be discussed in the following section, the full SPSS output of the tests executed can be found in Appendix 6 of this document. Before testing the hypotheses, some preliminary analyses have been conducted in order to test for the possible differences in perceptions helpfulness of the high and low functionality treatments.

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Hypothesis 1(H1): Customer engagement has a direct positive relationship with customer retention. For testing the first hypothesis, a linear regression was performed containing the variables “Customer Engagement(Scale)” as independent variable and “Customer Retention(Scale)” as dependent variable and controlling for the variables “Age”, “Gender”, and “Occupation”. An overall effect was tested which did not require a differentiation between the experimental groups.

After conducting the linear regression, the Adjusted R square had a value of 0.430, which accounts of 43% of variance in the dependent variable “Customer Retention(Scale)”being caused by the model. Moreover, the F-value reported in the model is 118.588 and the model is significant with a p- value lower than 0.05, which makes the relationship between “Customer Engagement(Scale)”and “Customer Retention(Scale)”significant. In addition to that, the Beta value of “Customer Engagement(Scale)” is 0.659, which accounts for a 0.659 increase in retention if engagement is increased by 1. As a result, it can be concluded, that H1 is supported. Model Summary Model R R Square Std. Error of Change Statistics the Estimate R F df1 df2 Sig. F Square Change Change Change 1 .659a .434 .68596 .434 118.588 4 619 .000 Table 8: Model summary of H1 Test a. Predictors: (Constant), CEscaletot, Occupation, Gender, Age

ANOVA Model Sum of df Mean F Sig. Squares Square 1 Regression 223.206 4 55.801 118.588 .000b Residual 291.269 619 .471 Total 514.474 623 Table 9: ANOVA Table of H1 Test a. Dependent Variable: CRscaletot b. Predictors: (Constant), CEscaletot, Occupation, Gender, Age

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Coefficients

Model Unstandardized Standardized t Sig. Coefficients Coefficients B Std. Beta Error 1 (Constant) 1.116 .159 7.015 .000 Age -.023 .036 -.025 -.645 .519 Gender -.010 .055 -.005 -.177 .859 Occupation .017 .036 .018 .469 .639 CEscaletot .759 .035 .659 21.725 .000 Table 10: Coefficients Table for H1 Test a. Dependent Variable: CRscaletot

For the testing of the hypotheses H2, H3, and H4, a Univariate ANOVA was performed, which was accompanied by a Tukey’s Post- Hoc Test and Plot Diagrams to display the relationships between the variables. In this test, the variable “Customer Engagement(Scale)”was the dependent variable and the variables “Traveler Personality” and “Customer Engagement(Choice)” were the independent variables. The findings revealed some very interesting findings.

First of all, the Levene’s Test had a p-value of 0.030, which reveals that there is a significant difference between the error variance of customer engagement among the different groups of traveler personalities. Next, a non-parametric Kruskal- Wallis Test was performed to test for possible differences in customer engagement levels for the categorical variable of “Traveler Personality”. The Kruskal- Wallis test reported a p-value of p=0.047, which exhibits that there is a small significance of differences between the traveler personalities for the variable customer engagement.

Descriptive Statistics

Variable N Mean Std. Minimum Maximum Deviation CEscaletot 635 2.8487 .79442 1.00 5.00

Traveler Personality 614 2.02 .842 1 4

Table 11: Descriptive Statistics of Non-Parametric Kruskal- Wallis Test

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Test Statistics

Test Statistics CEscaletot

Chi-Square 7.957 df 3 Asymp. Sig. .047

Table 12: Non- Parametric Kruskal- Wallis Test a. Kruskal Wallis Test b. Grouping Variable: Traveler Personality

Next, the Pairwise Comparisons table was drafted and it revealed, that there are significant differences between the traveler personalities coded as 1 and 2, and the personalities coded as 2 and 3. Thus, there are significant differences between the “Efficiency Traveler Personality” and the “Gratification Traveler Personality” with a p-value of 0.012. Also, there are significant differences between the “Gratification Traveler Personality” and the “Value Traveler Personality” with a reported p-value of 0.044. Generally speaking, the “Efficiency Traveler Personality” and the “Value Traveler Personality” showed the highest overall levels of customer engagement with scores of 3.02 and 2.95, while the “Gratification Traveler Personality” and the “Social Traveler Personality” showed slightly lower overall levels of customer engagement with scores of 2.71 and 2.73.

Pairwise Comparisons

Dependent Variable: CEscaletot

Traveler Personality Mean Difference (I- Std. Error Sig.b J) 1 2 .305* .120 .012 3 .068 .134 .613 4 .283 .247 .253 2 1 -.305* .120 .012 3 -.237* .117 .044 4 -.022 .238 .927 3 1 -.068 .134 .613 2 .237* .117 .044 4 .215 .245 .381

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4 1 -.283 .247 .253 2 .022 .238 .927 3 -.215 .245 .381 Table 13: Pairwise Comparisons

Based on estimated marginal means *. The mean difference is significant at the .05 level. b. Adjustment for multiple comparisons: Least Significant Difference (equivalent to no adjustments).

Coming to the hypothesis testing, the results of the different customer engagement mean scores between the different traveler personalities per touchpoint are taken into consideration.

Hypothesis 2(H2): The touchpoint functions categorized as „Account Information“ and „Check- in& Boarding“ have the strongest direct positive relationship with customer engagement for the “Efficiency Traveler Personality”.

In reference to the mean scores of “Customer Engagement(Scale)” for “Traveler Personality” per “Customer Engagement(Choice)”, it can be said that the touchpoint function categorized as “Account Information” indeed had the strongest direct positive relationship with customer engagement for the “Efficiency Traveler Personality”. With a mean customer engagement score of 2.95, respondents with the “Efficiency Traveler Personality” had the highest engagement. However, the touchpoint function categorized as “Check-in& Boarding” had the strongest direct positive relationship with customer engagement for the “Value Traveler Personality” with a mean customer engagement score of 3.04.

Therefore, H2 can only be partially supported.

Hypothesis 3(H3): The touchpoint functions categorized as „At the Destination Options“ and „Shopping& Entertainment“ have the strongest direct positive relationship with customer engagement for the “Gratification Traveler Personality”. This hypothesis was tested the same way as H2 by looking at the mean customer engagement scores for each traveler personality in each touchpoint function. Here it can be said, that the touchpoint function categorized as “At the Destination Options” has the strongest direct positive relationship with customer engagement for the “Social Traveler Personality” with a mean customer engagement score of 3.07. The touchpoint function categorized as “Shopping& Entertainment” has the strongest direct positive relationship with customer engagement for the “Social Traveler Personality” with a mean customer engagement score of 3.07.

Therefore, H3 can be rejected.

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Hypothesis 4(H4): The touchpoint function categorized as „Service& Feedback“ has the strongest direct positive relationship with customer engagement among the “Social Traveler Personality”. Also hypothesis 4 is tested with the mean scores of customer engagement for the different traveler personalities in the different touchpoint functions. The touchpoint function categorized as “Service& Feedback” has the strongest direct positive relationship with customer engagement among the “Efficiency Traveler Personality”.

Therefore, H4 can be rejected.

The relevant results of the customer engagement mean scores per traveler personality and touchpoint function are displayed in table 13 below:

Descriptive Statistics

Dependent Variable: CEscaletot

Traveler Personality Mean Std. Deviation

1 A: Account Information 2.9500 .87989 B: Check-in& Boarding 2.8910 .73398 C: Special Offers 3.0118 .69091 D: Shopping& Entertainment 2.8800 .76942 E: At the Destination Options 2.5700 .89506 F: Service& Feedback 3.8000 .46904 Total 2.8609 .80469 2 A: Account Information 2.6706 .76138 B: Check-in& Boarding 2.8780 .73190 C: Special Offers 2.9276 .69874 D: Shopping& Entertainment 2.7177 .87508 E: At the Destination Options 2.7149 .91505 F: Service& Feedback 2.3667 .58538 Total 2.8071 .78780 3 A: Account Information 2.8000 .83495 B: Check-in& Boarding 3.0390 .69314 C: Special Offers 3.2771 .92731 D: Shopping& Entertainment 2.8750 .59462 E: At the Destination Options 3.0308 .81994 F: Service& Feedback 2.6750 1.05255 Total 3.0575 .82001 4 A: Account Information 2.4000 .28284 B: Check-in& Boarding 3.0250 .57908 C: Special Offers 2.9143 .19518 D: Shopping& Entertainment 2.8000 . E: At the Destination Options 3.0667 .61101

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F: Service& Feedback 2.2000 . Total 2.9267 .50510 Table 14: Descriptive Statistics for Customer Engagement (CEscaletot)

In reference to the latter findings, a plot diagram has also been drafted, which is displayed in figure 2 below. The diagram once again shows that there are similar tendencies of customer engagement among the different traveler personalities in the different touchpoint functions, with customer engagement generally being lower for the “Account Information” touchpoint, then increasing for the “Check-in& Boarding” touchpoint function, further increasing for the “Special Offers” touchpoint function and decreasing for the “Shopping and Entertainment” touchpoint function. Customer engagement levels then generally increase again for the “At the Destination Options" touchpoint function and finally decrease for the “Service and Feedback” touchpoint function. Among all traveler personality types, the “Efficiency Traveler Personality” does not show quite the similar customer engagement levels as the other traveler personalities. Even for the last touchpoint of “Service& Feedback”, the Efficiency Traveler Personality has an outlier value of 3.8 of customer engagement, which is surprising. All in all, it can be concluded that the touchpoints called “Check- in& Boarding”, “Special Offers, and “At the Destination Options” created the highest levels of customer engagement, which links back to the finding that these three touchpoint functions were also most frequently chosen.

Figure 2: Plot Diagram for Customer Engagement per Touchpoint for each Traveler Personality

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The last hypothesis H5 was tested with a Multivariate Analysis of Variance / MANOVA with two dependent variables: “Customer Engagement(Scale)” and “Customer Retention(Scale)” and the independent variables “Traveler Personality” and “Customer Engagement(Choice)”. There was no Post Hoc test performed this time, as the fundamental MANOVA analyses give sufficient indication for testing the hypothesis.

In reference to the Levene’s test, it can be stated that with a p-value of 0.009, which reveals that there is a significant difference between the error variance of customer retention among the different groups of traveler personalities.

When assessing the Tests of Between-Subjects Effects, the p-value for traveler personality for customer retention is 0.743 and thus not significant. The table for the Descriptives for Traveler Personality and Customer Engagement Choice reveals the most important findings displaying the mean scores for customer retention per traveler personality for the different touchpoint functions. In terms of general observations, the “Social Traveler Personality” had the highest level of customer retention in the touchpoint functions of “Account Information” and “Check-in& Boarding”. For the touchpoint function categorized as “Special Offers”, the “Value Traveler Personality” had the highest level of customer retention. For the touchpoint function categorized as “Shopping and Entertainment”, the “Value Traveler Personality” displays the highest score of customer retention. For the touchpoint function categorized as “At the Destination Options”, the “Social Traveler Personality” shows the highest levels of customer retention and finally for the touchpoint function of “Service& Feedback” the “Efficiency Traveler Personality” has the highest score of customer retention.

Hypothesis 5(H5): Touchpoint functions categorized as „Special Offers“ have no direct relationship with customer engagement among the “Value Traveler Personality” but have a positive relationship with Customer Retention. Coming to the testing of the hypothesis, the touchpoint function categorized as “Special Offers” in fact has a direct relationship with customer engagement among the “Value Traveler Personality”, and also has a positive relationship with customer retention. In fact, the “Value Traveler Personality” shows the highest level of customer retention in the touchpoint of “Special Offers”.

Thus, H5 is only partially accepted.

The results of the mean customer retention scores per traveler personality and touchpoint function are displayed in table 14 below:

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Dependent Variable: CRscaletot

Traveler Personality Mean Std. Deviation

1 A: Account Information 3.4912 .65138 B: Check-in& Boarding 3.3213 .98697 C: Special Offers 3.2745 .70941 D: Shopping& Entertainment 2.7333 .72265 E: At the Destination Options 3.1167 1.09532 F: Service& Feedback 4.0833 .56928 Total 3.2877 .95259 2 A: Account Information 3.3137 1.05060 B: Check-in& Boarding 3.3767 .84574 C: Special Offers 3.1565 .76407 D: Shopping& Entertainment 3.0056 1.00619 E: At the Destination Options 3.1101 .99508

F: Service& Feedback 3.2778 .64693 Total 3.2274 .89805 3 A: Account Information 3.4583 .50198 B: Check-in& Boarding 3.4065 .84167 C: Special Offers 3.4857 .95433 D: Shopping& Entertainment 3.3750 .60257 E: At the Destination Options 3.2564 .80684 F: Service& Feedback 3.0000 .95950 Total 3.3864 .84347 4 A: Account Information 3.5000 .70711 B: Check-in& Boarding 3.5778 .62319 C: Special Offers 3.1111 .62063 D: Shopping& Entertainment 3.0000 E: At the Destination Options 3.4444 .38490 F: Service& Feedback 3.0000 Total 3.4167 .59230 Table 15: Descriptive Statistics for Customer Retention (CRscaletot)

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All of the above discussed findings are displayed in the plot diagram in figure 3 below. As the diagram shows, there is a lot of variance in the customer retention levels among the different traveler personalities for the different touchpoint functions with hardly any general tendencies to be detected.

When analyzing the data from the perspective of customer levels in each touchpoint function, it can be observed that the customer engagement levels for the touchpoint function of “Account Information” are rather similar and that they generally decrease for the touchpoint functions of “Check-in& Boarding”, “Special Offers”, and “Shopping& Entertainment”. The touchpoint function of “At the Destination Options” in comparison to the latter has higher scores. However, there are still differences to be observed between the variables such as customer retention being the considerably higher for the “Value Traveler Personality” in the touchpoint function of “Special Offers” than for the other traveler personalities.

Moreover, the “Efficiency Traveler Personality” has a considerably low score of customer retention in the touchpoint function of “Shopping& Entertainment” in comparison to the other traveler personalities, and having an extremely high score for the touchpoint function of “Service& Feedback” than the other traveler personalities. Linking this back to the customer engagement scores, there is a similarity of the “Efficiency Traveler Personality” having much higher scores of engagement and retention for the “Service& Feedback” touchpoint functions. All in all, the customer retention scores indicate mixed tendencies among the traveler personalities for the different touchpoint functions.

Figure 3: Plot Diagram for Customer Retention per Touchpoint for each Traveler Personality

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When illuminating the data from the perspective of the individual customer engagement and retention levels evoked through the touchpoints for each traveler personality, the following observations can be made: The Efficiency Traveler Personality showed the highest levels of customer engagement for the touchpoint of “Service& Feedback” followed by the touchpoint “Special Offers”. The touchpoint of “At the Destination Options” has the lowest levels of customer engagement among the “Efficiency Traveler Personality”. For the Gratification Traveler Personality, different findings have emerged from the analysis. The touchpoint functions with the highest levels of customer engagement were the “Special Offers” function and the “Check-in& Boarding” function. The touchpoint of “Service and Feedback” received the lowest level of customer engagement from the Gratification Traveler Personality. Coming to the Value Traveler Personality it could be observed that this respondent group had the highest level of customer engagement for the “Special Offers” touchpoint function. The touchpoint functions with lower customer engagement for this personality are the ones categorized as “Shopping& Entertainment”, “Account Information”, and “Service& Feedback”. The Social Traveler Personality had the highest reported customer engagement level for the touchpoints of “At the Destination Options” and “Check-in& Boarding”. The lowest levels of customer engagement for this traveler personality are reported for the touchpoints categorized as “Account Information” and “Service and Feedback”. For the customer retention scales per traveler personality, only the highest scores will be highlighted. While the “Efficiency Traveler Personality” showed the highest level of retention for the “Service& Feedback” touchpoint with a huge distance in customer retention levels from the other touchpoints, the “Gratification Traveler Personality” showed the highest levels of customer engagement for the touchpoint functions of “Check-in& Boarding”, “Account Information”, and “Service& Feedback”. And while the “Value Traveler Personality” had the highest levels of retention for the touchpoint functions of “Special Offers” and “Account Information”, the “Social Traveler Personality” had the highest levels of customer retention for the touchpoints of “Check-in& Boarding” and “Account Information”.

When looking at the data from the perspective of overall customer engagement levels per traveler personality, the “Value Traveler Personality” showed higher customer engagement levels than the other traveler personalities, while the others showed roughly equal levels of customer engagement. For the levels of customer retention there are rather mixed tendencies of the traveler personalities to be observed.

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When observing the data from the perspective of the highest engagement evoking touchpoints, all traveler personalities showed high customer engagement levels for the touchpoint categorized as “Special Offers” with levels ranging from about 2.7 until 3.6. Moreover, the touchpoint functions categorized as “Check-in& Boarding”, “Shopping& Entertainment”, “Account Information”, and “At the Destination Options” seem to be the core of the touchpoint functions, with rather similar customer engagement levels between 2.5 and 3.0. The touchpoint that received the evoked the lowest customer engagement levels was the touchpoint of “Service& Feedback”, with the only exception for the “Efficiency Traveler Personality”. For the customer retention levels, it can be concluded that the touchpoints categorized as “Account Information” and “Check-in& Boarding” evoked the highest levels of customer retention across the traveler personality groups with values around 2.7-3.0, with the exception of the “Efficiency Traveler Personality”. The other touchpoints categorized as “At the Destination Options” and “Special Offers” had moderate levels of customer retention from about 3.1 until 3.6. The touchpoints that evoked lower levels of customer retention across all groups are the ones categorized as “Shopping& Entertainment” and “Service& Feedback”.

To come to a conclusion, it can be summed up that touchpoints related to special deals or offers evoked higher levels of engagement and touchpoints about receiving service& feedback evoke lower levels of engagement. The touchpoints that have to do with check-in, boarding, at the destination options and shopping and entertainment seemingly evoke medium levels of engagement. Concerning retention, it can be concluded that touchpoints that deal with personal account information and check-in and boarding evoke the highest levels of retention. All of the latter observations are once again displayed in the plot diagrams below:

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Figure 4: Plot Diagram for Customer Engagement per Traveler Personality for each Touchpoint

Figure 5: Plot Diagram for Customer Retention per Traveler Personality for each Touchpoint

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The hypotheses, their variables, the related statistical tests and the final conclusion whether the hypothesis is supported have been consolidated in the Hypothesis Testing Summary in table 15 below:

Hypothesis Testing Summary Hypothesis IV DV Statistical Support of Test Hypothesis H1: Customer engagement has a direct CE CR Linear Supported positive relationship with customer (Scale) (Scale) Regression retention. H2: Touchpoint functions categorized as Traveller CE Factorial Partially „Account Information“ and „Check-in& Personality, (Scale) Design Supported Boarding“ have the strongest direct positive Customer Univariate relationship with customer engagement for Engagement ANOVA, the efficiency traveler personality. Choice Kruskal Wallis H3: Touchpoint functions categorized as Traveler CE Factorial Rejected „At the Destination Options“ and Personality, (Scale) Design „Shopping& Entertainment“ have the Customer Univariate strongest direct positive relationship with Engagement ANOVA, customer engagement for the gratification Choice Kruskal traveler personality. Wallis H4: Touchpoint function categorized as Traveler CE Factorial Rejected „Service& Feedback“ has the strongest Personality, (Scale) Design direct positive relationship with customer Customer Univariate engagement among the social traveler Engagement ANOVA, personality. Choice Kruskal Wallis H5: Touchpoint functions categorized as Traveler CE and Factorial Partially „Special Offers“ have no direct relationship Personality, CR Design Supported with customer engagement among the value Customer (Scale) MANOVA traveler type but have a positive relationship Engagement with Customer Retention Choice

Table 15: Summary of Hypothesis Testing

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5 Discussion This thesis study had the aim of exploring what touchpoints currently exist in mobile CRM apps in the airline industry. Furthermore, this thesis study had the aim to assess the relationships between airline touchpoints and customer engagement and retention among different traveler personalities. The research contained two main parts, namely an exploratory research part with an expert interview with an employee from the Lufthansa Innovation Hub and a field analysis for exploring the current touchpoints used in the airline industry. The second part contained explanatory research by conducting an experiment at Düsseldorf Airport in Germany for assessing the relationship between touchpoints and the constructs of customer engagement and customer retention.

The corresponding research question has been posed as: “What are the differential effects of touchpoints in mobile CRM apps in the airline industry on customer engagement and retention under consideration of different traveler personalities?”

5.1 Findings

5.1.1 Industry Developments and Trends There were several important insights gathered from the exploratory research part. The status quo of touchpoints in mobile CRM apps at this point in time, mobile CRM app development is moved by trends of personalization, digitalization, big data, instant messaging and location services. Airlines increasingly incorporate these trends into their mobile CRM app development in order to facilitate and customize their product and service offerings. Moreover, the merging of technologies is also used by airlines such as connecting the mobile CRM app with smart watches of customers, smart digital payment systems and also smart digital passport scanners. Moreover the trend of instant messaging and alerts is on the rise with customers receiving more and more real time updates for their journey, sometimes even with the possibility of live guidance through the journey by a concierge as it was found in the field analysis (etihad.com, 2016) (klm.com, 2016), leading to more interaction in mobile CRM. In terms of touchpoint functions used by airlines, touchpoints related to essential customer journey steps such as the booking, the check-in and the boarding are present the most in current airline apps. Coming to the most common touchpoints in airline apps as learnt in the expert interview and the field study, the six most common touchpoints can be categorized as: “Account Information”, “Check-in& Boarding”, “Special Offers”, “Shopping& Entertainment”, “At the Destination Options”, and “Service& Feedback”. The field analysis revealed that the basic touchpoint functions in every mobile CRM app serve Booking, check-in, boarding, information and account functions. The more advanced apps then incorporate other functions such as flight inspiration, games or shopping and the technologies that have been mentioned before. Also, most airlines incorporate the touchpoint functions in their apps in line with their strategies, of being a low-cost carrier or a luxury airline.

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5.1.2 Touchpoint Functionality High functionality touchpoint functions as presented by the expert Fredrik Forstbach, are the ones that help the customer reach his goal in the fastest and most direct way. High functionality touchpoints are characterized by their high level of helpfulness and richness of relevant information for the customer journey.

5.1.3 Traveler Personality In relation to the construct of Traveler Personality, several personality types have been identified by academic research, however they were defined as shopping motivations. In line with statements by the expert Fredrik Forstbach, the most common traveler personalities identified are the “Efficiency Traveler Personality”, the “Gratification Traveler Personality”, the “Value Traveler Personality”, and the “Social Traveler Personality”. As indicated by the expert Fredrik Forstbach, the traveler personality of a customer might change along the journey, however every customer has a prevalent traveler personality.

5.1.4 Touchpoint Functions, Customer Engagement and Customer Retention In the following section, the most important findings of the explanatory research part will be assessed. The experiment that was executed at Düsseldorf airport first of all revealed, that respondents found the touchpoints categorized as “Check-in& Boarding”, “Special Offers”, and “At the Destination Options” the most interesting and relevant. Furthermore, they indeed felt higher engagement and retention in the high functionality treatment than the low functionality treatment. Also it was striking, that customer retention levels were generally higher than customer engagement levels in both treatments. Respondents would be willing to use the app again, more frequently or recommend it without being too engaged with it. Also, within the customer engagement scale, the items of enthusiasm and attention had the highest values while the other items, namely “identification”, “absorption”, and “interaction” had lower values, which once again stresses that consumers rather displayed “short-term” levels of engagement but lacked “long-term” levels of engagement. This observation was made in both functionality treatments. The finding might have to do with the way the app functions were presented to consumers, however this will be further discussed in the limitations of this document. Within the customer retention scale, the item of “continuation” was higher scores than the items of “frequency” and “recommendation”. Thus, for the most part, respondents would indeed continue to use the app but not necessarily increase its use or recommend it to their peers. However in the high functionality treatment, respondents showed higher willingness to recommend the app.

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5.1.5 Hypothesis Testing Concerning the hypothesis testing done the following insights should be mentioned. In reference to H1, it was found that customer engagement does indeed have a positive relationship with customer retention. Therefore, if a customer displayed a certain level of engagement that would also lead to a certain level of retention. Coming to H2, H3, and H4 only a limited number of assumptions could be supported. While touchpoints categorized as “Account Information” indeed had the strongest positive relationship with customer engagement for the “Efficiency Traveler Personality”, the touchpoint categorized as “Check-in& Boarding” did not. This reveals that customers who like to travel in an efficient way are highly engaged with personal information however they do not have the highest level of engagement when it comes to the check-in and boarding related touchpoints. For H3, it was found that the touchpoint functions categorized as “At the Destination Options” and “Shopping& Entertainment” had the strongest positive relationship with customer engagement for the “Social Traveler Personality”. This shows, that the customers who favor travelling for making social contact are very engaged with options such as activities at the destination and entertainment and shopping along the journey. For H4 it was found, that the touchpoint function categorized as “Service& Feedback” had a remarkably high score of customer engagement and highest one for the “Efficiency Traveler Personality”. This reveals that customers who favor travelling in an efficient way may feel very engaged with touchpoints that offer them additional help and service along their journey. For H5, it was found that the touchpoint categorized as “Special Offers” against the odds did have a positive relationship with customer engagement for the “Value Traveler Personality”, but also had a positive relationship with customer retention. This findings reveals, that price-conscious and bargain focused travelers indeed engage in returning to use the app, but can also feel engaged with it.

Lastly coming to the more overall findings, the experiment revealed, that the touchpoint of “Special Offers” generally evokes the highest levels of customer engagement among all traveler personalities, while the opposite is true for the touchpoint of “Service& Feedback” with the exception of the “Efficiency Traveler Personality”. The other touchpoint functions had moderate levels of customer engagement and retention. The “Value Traveler Personality” overall had the highest levels of customer engagement and retention among all traveler personalities. Finally it can be concluded that against expectations, the traveler personalities displayed different preferences than expected.

When reflecting on the conceptual framework that has been priorly built, it can be said that the basic effect of touchpoints on engagement and retention was indeed found. But the priorly hypothesized different levels of engagement for the different traveler personalities could not be proven in the data analysis.

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5.1.6 The Research Question Coming to the answering of the research question. First of all, it was proven that touchpoints in airline apps and their different levels of functionality do have an effect on customer engagement and customer retention. Therefore, they have the potential to affect and influence consumers’ thoughts, feelings, attitudes and even behavior with an airline. This fact in itself is powerful. The differential effects of touchpoints in airline apps are that touchpoints can lead to retention and repurchasing behaviors among consumers to higher extent than to feelings of engagement. Also, after short use, touchpoints in mobile CRM apps can already affect consumers attention and enthusiasm for the app or the airline. An important finding is that the touchpoints related to giving consumers economic advantages, helping them further along the journey or offering additional travel related information and entertainment evoked the highest levels of engagement. This was revealed with the touchpoints categorized as “Special Offers”, Check-in& Boarding”, and “At the Destination Options” being among the touchpoints with the highest customer engagement levels across all traveler personality groups. Not only do consumers choose those touchpoints the most frequently but they also show higher levels of engagement for them. Nonetheless the differential effects of the touchpoints on customer engagement could not be further categorized per traveler personality.

5.1.7 Context with Prior Research It can be further noticed that the research findings corroborate prior research of traveler personalities or shopping motivations by demonstrating that certain personality traits do not necessarily predict certain preferences in the context of airline app touchpoints. However, it should be considered here, that the scale of traveler personality was transformed from the scale of shopping motivations by Yang and Kim et al. in accordance with the opinion by the expert Fredrik Forstbach (Yang & Kim, 2012) (Forstbach, 2016)Furthermore, the research findings corroborate with the self-schema theory and the attachment theory by Markus(1977) and Ball and Tasaki(1992), which are about consumers “relating their self-concept and schema to a brand” (Markus, 1977) (Ball & Tasaki , 1992). The research findings do not show obvious signs of this with rather lower scores for the customer engagement dimension of “identification”. However, the theories might apply after longer use of the app.

In reference to the research by Edelman(2010) about an increase in touchpoint variety to customize the journey of customers and gather more data, the variable “Customer Engagement(Choice)” in this research has shown, that it offers more insights into which touchpoints a customer prefers to see (Edelman, 2010, p. 4). This can have valuable implications for customizing the app for the customer and building a deeper engagement. Also in reference with Edelman’s research, mobile CRM nowadays offers customers to interact in more different ways (Edelman, 2010, p. 4). The insights gathered in the field analysis are in line with this, because they show that airlines nowadays even interact with their customers through smart watches or a live chat with a concierge to guide the

Student: NE Hutton-Mills Student No.: 11145781 customer along the journey (emirates.com, 2016) (etihad.com, 2016). Also, it he research field analysis of current mobile CRM apps has shown that after- purchase touchpoints are incorporated in apps, as it was mentioned in Edelman’s research (Edelman, 2010, p. 4). This fact was also mentioned by Fredrik Forstbach with more airlines increasingly focusing on the feedback touchpoints in apps (Forstbach, 2016). The research insights by Lee and Jun(2007) about two- way interaction between the firm and the customer (Lee & Jun, 2007, p. 798), is reflected in the insights by the expert Fredrik Forstbach, who mentioned the trend of direct messaging in mobile CRM apps.

The findings however lend support to the several theories mentioned in the theoretical framework. First of all, the research has proven that as previously mentioned by…, the creation of deep customer engagement is hard to achieve for companies and brands. Respondents in the research did not show the deeper engagement signs of identifying with the app function or being fully absorbed. The research once again proves that the creation of deep customer engagement is a long term investment that companies have to be willing to make. Secondly, customer engagement has the potential to lead to competitive advantages for companies as priorly mentioned by Brodie et. al. (Brodie, Hollebeek, Juric, & Ilic, 2011, p. 252) and it has a positive relationship with customer retention. Thus, engagement generally leads to customers continuing to use the app, service or product. The third insight is quite remarkable as the data analysis found the same results. Several articles state that retention is greatly affected by personalization strategies and that it has become a bigger concern in recent years with consumers having shorter attention spans (Jao, 2015) (Jao, forbes.com, 2014). The findings of this research lend support to the latter with the highest retention levels being reported for the touchpoint functions categorized as “Account Information” and “Check-in and Boarding”, which are rather personal touchpoints.

Finally, the multidimensional scales for customer engagement and customer retention provided good measurements for the effects of touchpoints on the respondents.

5.2 Limitations Throughout the research, there were also a three limitations identified, which should be considered for future research on touchpoints in mobile CRM apps for different customer typologies.

Firstly, there were app function related limitations identified. The app functions that were presented to the respondents were only pictures and not a real app, that respondents could interact with. Thus, they could only observe the functions, which may have affected the customer engagement scores and led to lower interaction, absorption and identification scores as observed in the survey data. Additionally, respondents only saw the app for the first time and as mentioned before, customer engagement is a long-term process that evolves over time, but respondents did not get the chance to interact with the

Student: NE Hutton-Mills Student No.: 11145781 app functions for a longer period of time. This may have also affected the results of customer engagement levels being rather moderate for the high functionality treatment.

Secondly there might have been experiment related limitations with respondent bias such as careless responses or self-reporting issues. Also there might very well be country-related differences between consumers’ preferences for certain touchpoints. Through observation and dialogue with respondents after the experiment it was found that German respondents had higher preferences for efficiency related touchpoints, while international respondents such as French respondents mentioned that they favored social interaction elements in the app. Thus, this calls for caution when wanting to generalize the findings on the international level and might be an interesting research field for the future.

The third limitation of this research is a scale related limitation. The scale of traveler personality, which was transformed from a shopping motivation scale in accordance with the expert Fredrik Forstbach and then implemented as a categorical question in the experimental survey. In fact, the validity of the construct of traveler personality is questionable for the scope of this research as it was only presented with one statement per personality type. This was done due to time constraints of the experimental survey, however it may have led to drawbacks on validity, that in fact, consumers had to categorize their traveler personality based on one statement. As a result, there may have been occurrences of mis- categorizations of respondents which in turn may be reflected in the data with mixed findings for the hypotheses. For future research, a refined scale for traveler personalities should be found, which currently does not exist yet.

5.3 Future Implications The research findings can also provide some implications for theory and practice, which will be outlined in the following paragraph.

5.2.1 Scientific Implications This thesis study sought to provide additional insights into the field of the constructs of customer engagement and retention in the context of the novel field of touchpoint functions in mobile CRM apps. With CRM programs moving more and more towards mobile, current theoretical insights on engagement and retention are challenged, because mobile adds a new very personal component to CRM. It was the task to assess the effects of very personal touchpoints in a mobile app on engagement and retention, to shed light on whether they evoke different levels of engagement.

The research results imply, that prior insights on customer engagement research could be supported.

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Customer engagement remains one of the most complex multidimensional constructs in CRM, which in itself is somehow split into more short-term evoked dimensions such as attention and enthusiasm and dimensions such as identification, absorption and interaction, which are probably rather evoked on the long-term. While the short-term dimensions of engagement can already be triggered through personal interaction with a customer, the deeper customer engagement dimensions seem to require more research into what exactly triggers them.

Moreover the interplay between engagement and retention as presented in this research provided the implication that the two constructs are well connected in a positive linear relationship. This shows once again, that whatever construct is triggered, it will affect the other to an extent.

The research on touchpoints implies that CRM research develops more and more small but powerful elements within CRM programs that can have big impact on the constructs of customer engagement and retention. With touchpoint research still being a research field in its infancy it already now shows, that the little elements along a customer’s journey may influence his or her attitudes, feelings, wishes and even behavior.

Finally, the research on traveler personalities implies that there is a strong need for further research into the field of traveler typologies of customers. With the fact that there is no scientifically well founded research on traveler typologies, there should be future research done in this field.

5.2.2 Managerial Implications In reference to the managerial implications for companies and managers it can be said, that touchpoints in mobile CRM apps in general are important elements for interacting with customers. In the airline business, they are not only very personalized and interactive, but they also incorporate other technologies, which increasingly improve the service offering for customers.

Moreover, this research gives the implication, that touchpoints have strong potential to boost sales by increasing retention levels of customers, especially when comprising higher levels of personalization. In combination with big data and personalization technologies this is an effective research finding to be applied by companies for their mobile CRM programs. Furthermore, after only a small time of app exposure, customer engagement levels can already rise in terms of enthusiasm and attention, which implies that companies can indeed achieve even higher levels of long term engagement if they can make customers use their app for a longer period of time. Also companies should consider binding their customers with personalized special offers, which according to this study lead to the highest levels of customer engagement across all customer groups.

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Due to the fact, that the basic level of customer retention involved the customer to be willing to continue using an app, but not necessarily to use it more frequently or recommend it to others, managers can use this insight to invest in longer term engagement and retention programs, where they bind customers on the basic retention level by offering more special deals and activities over time and along the customer journey until customers are also willing to use the app more frequently and recommend it. In terms of traveler personalities, companies should be attentive to the fact, that it seems to be easier to evoke customer engagement among price- conscious customers than efficiency or gratification focused customers and that efficiency focused customers seem to show remarkably high levels of engagement and retention when being presented with service& feedback touchpoints. Therefore, airlines can leverage these insights and allocate their resources to the right touchpoints for the different traveler personalities. However more research should be invested in the field of customer typologies.

When taking a broader perspective and relating the findings of this research about apps to the context of other media and channels, touchpoints on websites or social media networks for example could also have the potentials to evoke different levels of engagement and retention. Similarly to touchpoints found in CRM app, touchpoint functions on websites or social media could trigger engagement or retention by creating more personalized and interactive websites. Furthermore, companies could create higher levels of retention among customers by offering special deals and different events and activities on the website with the outlook to create higher levels of engagement for the long term. Also, companies could evoke higher levels of enthusiasm and attention among customers by putting touchpoints functions giving customers special advantages, important information and entertainment in the foreground. However, companies should engage in such activities while not forgetting to create the websites and social media feeds in a more personalized way for customers, since this was the case for the app.

The overall implications for practice are that in line with prior research, the achievement of deep customer engagement has once again proven to be a long-term investment for companies, that is developed over time through intimate and personalized relationships with customers. However, by only investing into small levels of personalization and some activities on the app, that help customers and give them advantages, companies can achieve retention and engagement. After all companies should find their way into customers’ hearts of engagement by increasing their retention in the first place, which will ultimately also increase engagement on the long run.

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Appendix

Appendix 1: Expert Interview with Fredrik Forstbach from Lufthansa Innovation Hub

Thesis Topic: The differential effects of mobile CRM app touchpoint functions on customer engagement and customer retention among different traveler personalities

Interviewee / Expert: Fredrik Forstbach, Business Service Designer and Product Manager at Lufthansa Innovation Hub

Interviewer: Nadja Ella Hutton-Mills, Master Student at the University of Amsterdam

Interview Type: Semi-Structured Expert Interview Date: 22.09.2016

Nadja: Good afternoon Fredrik. Thank you so much for finding time to participate in this expert interview.

Fredrik: Hello Nadja, yes that it no problem. I am happy to answer your questions.

Nadja: That is great. So you are a consultant for software development at the Lufthansa Innovation Hub in Berlin?

Fredrik: Yes, that is correct. I am currently dealing with the functions that we develop for the airline apps.

Nadja: Okay, nice. Fredrik, in general what kind of functions do airline apps generally serve on the market?

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Fredrik: Airline apps currently serve the function of being at least efficient and reliable in leading the customer through his or her flight journey. They facilitate the journey and even make it more exciting. Also, they serve the function of giving quick and precise reactions and updates on the flight status. They should also have the function of offering the customer an attractive and easy-to-use interface.

Nadja: Okay that is interesting. And what kind of touchpoints are there currently in airline apps?

Fredrik: Well first of all there are touchpoints that deal with flight booking, check-in and boarding, and of course also finding the gate and so on. Then you have a variety of entertainment touchpoints offered where you can for example read magazines, watch videos or play different games. SAS Scandinavian airlines for example offers a variety of entertainment touchpoints in their app. There are also touchpoints for special offers and flight deals and some where the airline inspires you for travel locations.

Nadja: Are the special offers touchpoints and the inspiration touchpoints also sometimes connected?

Fredrik: Yes, that may also happen in order to connect the content back to the product. Moreover, there are touchpoints, that incorporate interfaces with hotel, taxi and restaurant offers for the customer once he arrives at his destination. However in my opinion, the value that the airline derives from that is limited and generally touchpoints are rather oriented around the product.

Nadja: I understand. After all it is still about the product.

Fredrik: Indeed. Of course, you also have the account information touchpoints, which in Lufthansa’s case are also connected to the Miles& More account, where you can redeem your collected miles. And last but not least you have a contact button, where the customer can seek help or give feedback. Some airlines even offer messenger services in their app, where the customer can instant message with the airline contact agents. Many airlines increasingly focus on the feedback touchpoints and invested in them for achieving closer interaction with their customers. Sri Lanka Airlines and Ryanair for example use a feedback pop-up in their apps, for the customer to directly give a short feedback about the flight once he landed.

Nadja: Okay, interesting that airlines increasingly invest in feedback touchpoints to interact more closely with the customer.

Fredrik: Yes, indeed.

Nadja: Which touchpoints in airline apps are new on the market and what are the current trends and newcomers among touchpoints on the market?

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Fredrik: Well, there is a lot going on in the market at the moment. First of all, the touchpoints related to contact are changing by becoming more like messengers and text chats. This development contributes to more facilitation when it comes to customer interaction with the brand, with more updates and pop-ups being sent. There are even developments of the contact touchpoints incorporating WhatsApp chat functions. This instant chat feature can be found in the Lufthansa Service Center App FAQ for example, where you can instantly start chatting with our contact agents. New touchpoints are also the ones where customers are inspired for travel destinations, by airlines incorporating the topic of flying into other topics such as events and experiences. Another interesting trend on the rise is the function of face identification and navigation via the app. Concerning other current trends, there is a lot of debate about data use for the different touchpoints of course. Each touchpoint can collect so many different types of data from customers and the handling and use of that data is currently one of the bigger topics in airline app development. In combination with the latter, there is of course the trend of location services, which can tailor the customer information even more and make apps more useful in giving customer journey updates and other interesting functions. On top of that, there is the trend of “Übersales”, which means that you bind the customer to the app by offering him or her everything through the app, so that the customer does not have to use the browser anymore. In that way, you can create deeper relationships between the customer and the company and find out more about them. Then you generally have the trend of automation and digitalization at the airport for the flight ticket, the check-in and the boarding passes, which can all be handled through the app now. Customers generally enjoy higher levels of automation on their journey.

Nadja: Oh the last aspects that you mentioned related to customer preferences are interesting. Can you elaborate a bit further on that? What customer preferences could you observe so far in airline app touchpoints?

Fredrik: Sure, so customers prefer their journeys to be automated in order for the flow of the journey to become more seamless and with less interruptions.

Nadja: Makes sense. And when do touchpoints have a high level of functionality?

Fredrik: When they are reliable and efficient as mentioned before and when they can help the customer to quickly help him or her find what he is looking for in all relevant areas. This may be enabled with sorting criteria, quick and clear information and an easy interface of the selection options in the app.

And which touchpoints according to you have the biggest effect on the variable customer engagement?

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Fredrik: In my opinion, the touchpoints, that offer the customer direct contact with the company have a big effect on customer engagement. In the airline app, those could be the contact touchpoints for when the customer for example has problems with the check-in, if he wants to change is booking or if he loses his luggage. In this situation, the airline not only interacts with the customer through the contact touchpoint, but also helps him in a very critical situation of his journey. I think in general, that an airline can earn a lot of points on customer engagement when helping a customer in a critical, stressful or unpleasant situation.

Nadja: Did you also research other factors that could moderate the effect of touchpoints on customers? And if so, what were the results?

Fredrik: Not much. But unforeseen unpleasant events can generally moderate the effects of touchpoints on the customer.

Nadja: To what degree do airline apps and their touchpoints support the business of airlines and how?

Fredrik: Airline apps are geared towards increasing the number of touchpoints with the customer, in terms of customer loyalty. So after a user has used an app for check- in and boarding, he could next time book directly via the airline’s own app - either directly researching on the supplier app/website, or coming from an OTA website. The most important use case for the airline app is the flight booking. Potentially the app can also become relevant in booking ancillary services after the flight booking is complete, from flight upgrades, and entertainment to local activities.

Nadja: Okay, interesting. And are there any measurements of CRM effectiveness among different CRM channels : website versus email versus newsletters versus airline app?

Fredrik: I do not have any data related to this.

Nadja: Out of all airlines operating on the market, how many percent of those airlines currently have apps to support their business?

Fredrik: I unfortunately also do not have any data on this. But I am sure, that you can find out about that with some research in the app stores.

Nadja: How is the airline app usage in % divided among business travelers and leisure travelers (or others)? And have you observed any significant shifts in this area?

Fredrik: I would say that 70% are business, 30% leisure travelers that use the LH app, which is a guesstimate based on our experience. I cannot say anything about a shift.

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Nadja: Okay. And do customers usually use the airline app before or after purchasing a flight? And have you observed any significant shifts in this area?

Fredrik: Usually after the purchase. According to our data, people check in roughly 10 times more often (in absolute numbers) than they make bookings.

Nadja: Are there any innovation leaders in the airline app market? And if so, what makes them special? Which special touchpoints do they use in their apps?

Fredrik: For the general flight booking app, I would say Easyjet, Ryanair and AirBerlin in that order due to their usability, features and overall look and feel.

Nadja: Okay, that is interesting considering their similar market positionings of these airlines. And which moments of the customer journey are critical, where an airline can assist and interact with the customer for building a stronger relationship?

Fredrik: I think that any moment in the journey where the traveler actually needs personal contact or assistance is an opportunity for airlines. This means: If he just needs to drop his bag, check-in, get through security etc. he is not in need for any personal interaction. And in fact, research shows that most people would prefer a way of doing this without any need to stand in line or talk to somebody.

On the other hand any moment when the traveler needs help, like a change or cancellation of booking, or support when something goes wrong in the journey, as with delays, lost or delayed luggage, there is a big opportunity as a company to emerge with a better tie to the customer. The company can respond in a human and constructive way, that does not make the traveler feel like an anonymous part in this huge operation that the airline business is.

Nadja: Very interesting.

Fredrik: As we also know that roughly 60% of travelers can be categorized as planners that value it highly when all goes according to plan. This means on the other hand, that these travelers do not appreciate any unforeseen events and will endure stress when something does not go as planned. Therefore it seems like a very good idea to keep the traveler in a very close loop in the time when the traveler leaves home until he boards the plane. Any information he needs should be made available through the airlines.

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Another interesting moment is when the traveler arrives at the destination. This is one part that also causes high stress levels, since the traveler might be in a new country, with a new language, maybe tired and a bit helpless - and now needs to get to his hotel or other final destination. In this situation it seems to be extremely valuable for a company to assist the traveler with information - and possibly also with a channel for personal interaction, that gives the traveler the knowledge that he is covered and can explore the new country without fear.

Nadja: Okay, very insightful. I have another question. Below you can see shopping motivations identified in the literature review of my thesis. Can these shopping motivations also be translated into travel personalities? If yes, can all of them be translated or only some? Which ones are most common among airline customers?

Shopping motivations . idea shopping ( for inspiration) . efficiency shopping (for lean travelling without big interruptions) . gratification shopping (for comfort, entertainment and luxury) . role shopping ( shopping for others) . achievement shopping (shopping for treating oneself and feeling well) . social shopping (seeking interaction and social contact while shopping) . value shopping (seeking cheap deals and special offers)

Fredrik: I would say that there are definitely airline customers with the efficiency-, the value-, the gratification- and the social shopping motivations. So you can indeed transform these shopping motivations into traveler types. However, it is important to distinguish between business and leisure travelers here and also between ages. We observe more and more so called "Bleisure" Travelers, who are younger business travelers that are very free and independent in travelling, because they do not have a family yet. These younger business travelers are also interested in culture and exploring during their business trips. Another interesting insight, that we gathered is the fact millennials actually do not like taking risks in travel selection and they often seek information and tips from their social networks (friends, family and travel blogs), which is an important aspect for this age group. In that case, social shopping is a relevant motivation here. Another important thought here is that I believe that the shopping motives or traveler types are actually connected to each other along the customer journey of one single traveler. So, in the beginning, a traveler may have the idea shopping motivation to get inspired for travel destination. Millenials for example get inspired via friends and acquaintances. Then the customer transforms over into the value shopping motivation of finding good flight deals. Next,

Student: NE Hutton-Mills Student No.: 11145781 the booking process should be easy, idealistically mobile and available to accomplish by the traveler him or herself. Last but not least, the travelers will want to share nice and authentic pictures and stories of the trip with their peers. So all in all you may transform the shopping motivations into traveler types and a single traveler may incorporate several travel personalities. However, an airline customer will most probably have one prevalent traveler personality, which he displays and the most common traveler personalities are the value shopper, the efficiency shopper, the gratification shopper and the social shopper.

Nadja: Great information Fredrik! Thank you. Please take a look at the conceptual model of this Master thesis. Do you think that the most effective touchpoints for each traveler personality will affect customer engagement and ultimately customer retention? Or do you think that some may also skip customer engagement and directly affect customer retention?

Fredrik: My thinking goes in the following way: H4 > H3 > H2. I think that the information in the different touchpoints is very important but more or less expected from the companies. Its rather the absence of this level of information that will cause trouble and lower a touchpoints functionality. And on the other end, I think that social travelers that get a more social experience will value this very highly, and this will make them more likely to consider the same company again. Always this depends a lot on how these groups are defined, and on the experiences they receive. Concerning the skipping effect in H5, I am not familiar enough with the exact definition of engagement and retention to be able to say if you could achieve retention without engagement. But you could hypothesize that a certain type of traveler, would be more inclined towards using the same operator, when the last experience was good enough without seeing the need to further optimize.

Nadja: Okay, understood. It believe, we have come to the end of this interview. I thank you so much for taking the time in answering my questions, Fredrik. It was a pleasure to learn about the latest developments in the industry and I will certainly provide you with the results of my Master thesis.

Fredrik: Thank you for giving me this opportunity. It was a pleasure for me too and I am curious to receive the results of your experiment.

Appendix 2: Field Analysis of Touchpoints in Global Airline Apps Research Method: The data in this list has been gathered through a combination of live app use and information gathered on the internet. Not all apps allowed full access to the touchpoints without having booked a flight, which affected the research results in a way that there might be touchpoints missing. However, main functions and general trends among touchpoints in airline apps could be gathered.

Airline Touchpoint Functions Unique Touchpoint function Airline Positioning Associative Source Touchpoints and Focus Network Complementary Services Lufthans Personal Data and Account - SmartBag Booking and Flights Best prices, Travel Inspiration, http://www.luftha nsa.com/de/de/Luf a Information: Check-in Information, Flight and Premium High quality Travel, thansa-App-alle- Personal Data, Baggage(Baggage - Credit card Travel Inspiration, Comfort, Quality Funktionen

Receipts, Baggage Policy), scan Service, feedback and Messages, All boarding passes, - Passport Other Messages, My Bookings, Credit scanner card scan

Booking and Flights Information: Book flights, My bookings, Check- in, Flight status, Route map, Experience Lufthansa, Scan Passport, Lounge Finder, Live Travel Updates about Check-in,

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Departure gate, Boarding time, baggage belt and more

Special Offers: Flight offers, Top Offers

Flight/ Travel Inspiration: Plan& Explore, (Route Map, Travel guides with City Map, City Guide, Best price search, top 10 sights…),

At the destination (hotel, travel tips): Hotel

Shopping and Entertainment: Miles& More, Social Media, Inspiration& Games, Lufthansa eJournals

Service, Feedback and Other: Info& Service(News, at the airport,

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on board, Mobile FAQs, Contact Lufthansa), Feedback(Feedback to Lufthansa, Your opinion about our app, Rate App), More (Lufthansa Partner, Lufthansa group Airlines, Data protection policy, Imprint, Recommend this app, Rate this app)

Emirates Personal Data and Account - Life Travel Booking and Flight High quality, Extensive, https://www.emira Information: Updates Information, At the Entertainment, Comfort, Travel tes.com/english/fl My account, My statement, My through destination, Shopping Inspiration, Luxury and Premium ying/emirates-app/ personal details, My contact details, Apple Watch and Entertainment Travel, A wholesome travel My preferences, My friends& - Credit card experience family, My app settings, About scan skywards miles, About tier miles, - Passport Credit card scan scanner

Booking and Flights Information: My trips, Book a flight, Upcoming Flights, Check-in, Handle bookings, Scan Passport, My boarding pass,

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Flight status, My In-flight Services, Flying with Emirates, Meal preference and seat choice, Live Travel Updates about check-in, departure gate, boarding time, baggage belt and more (also via Apple Watch)

Special Offers: Multiple fare comparison options, Upgrade choices

Flight/ Travel Inspiration: Explore Destinations, Route Map

At the destination (hotel, travel tips): My Car service, My Hotel& Car rental, Flightinerary (Travel Tips, Dining Options, What to watch, Listening List), Chauffeur Drive

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Shopping and Entertainment: Inflight-Chat and Games, TripClips (Explore my library preview my trip clip, edit my timeline), Flightinerary (Travel Tips, Dining Options, What to watch, Listening List)

Service, Feedback and Other: Contact us, Local Emirates App feedback

Etihad Personal Data and Account - Life Travel Booking and Flight Luxury, high quality Travel, http://www.etihad. com/en- Information: Updates Information, Shopping Maximum Comfort, Innovation us/experience- My account, Information, My trips, through and Entertainment, (Flying Reimagined) etihad/mobile- app/ Credit card scan Apple Watch Special offers

- Credit card scan Booking and Flights Information: - Passport Book flights, Check-in, Flight scanner alerts, Boarding pass, Choose a - Concierge seat, Scan Passport, Flight status, Live Feature

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Add booking, Upcoming trips, Past for Check-in trips, Manage my bookings, Flight guidance schedules, The Etihad - Special food experience(Our partners, and beverage Destinations, Baggage Information, offers at bars Our Offices, Our Home Abi Dhabi, and On Board, lounges, Etihad TV restaurants Commercial), Airport Map Navigation, Live Travel Updates about check-in, departure gate, boarding time, baggage belt and more (also via Apple Watch)

Special Offers: Multiple fare comparison options, Upgrade choices, Special food and beverage offers (at Bars and Restaurants)

Flight/ Travel Inspiration:

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At the destination (hotel, travel tips):

Shopping and Entertainment: Special food and beverage offers (at Bars and Restaurants), Your Rewards (Partners, Promotions, Program, Account) Service, Feedback and Other: Contact KLM Personal Data and Account - Travel Booking and Flight Customer centricity, https://www.klm.c om/travel/nl_en/pl Information: Inspiration Information, Flight Entertainment, Travel inspiration, an_and_book/mob My profile, Settings, Previous after Inspiration, Shopping innovation, Flight offers ile_services/the_kl m_app_for_smart bookings, Save credit card details, categories and Entertainment, phone_tablet_and (adventure, Service, Feedback and _smartwatch/inde x.htm Booking and Flights Information: beach etc.) Other

My trips, Book a flight, My - Complement booking, Flight information, Flight ary Apps: status, Change booking, Change KLM seats, View details, More comfort, Houses, Check-in, Manage my booking, KLM Media, Add baggage, Live Travel Updates Aviation

Student: NE Hutton-Mills Student No.: 11145781 about check-in, departure gate, Empire boarding time, baggage belt and Game, KLM more (also via Apple and Android Movies and Watch) More, Jets - Papercraft Air-O-Batics Special Offers: - Life Travel Special Offers, Deals on KLM.com Updates through Flight/ Travel Inspiration: Apple Watch Inspiration(Amsterdam to beach, active& outdoor, romance, history, skiing…), At the destination (hotel, travel tips):

Shopping and Entertainment: Join Flying Blue, Complementary Apps: KLM Houses, KLM Media, Aviation Empire Game, KLM Movies and More, Jets - Papercraft Air-O-Batics

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Service, Feedback and Other: Contact and Feedback(Contact KLM, App feedback, Give a ompiment, Rate the app), Settings (KLM news and updates, Country of residence, About)

Air Personal Data and Account - Special Booking and Flight Special offers, travel inspiration, https://www.airbe rlin.com/de/site/la Berlin Information: offers after Information, Special convenience, Broad Route ndingpages/eservi Topbonus Login, Account month, price, Offers, At the network ces.php?cat=2

Information temperature destination (hotel, travel Booking and Flights Information: and category tips) Book flight, Boarding pass, - Push Security Note, Check-in, My notifications flights, Arrivals& departures, Seat for special choice, My flights, Push settings for offers flight and service information (also - Weather via Apple Watch) information Special Offers: and Special offers In categories: month, information price, temperature, category, Push about time to

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settings for special offers get to Flight/ Travel Inspiration: destination - Life Travel At the destination (hotel, travel Updates tips): through Weather information at destination, Apple Watch calculate time to get to destination

Shopping and Entertainment: Topbonus Information Service, Feedback and Other: Feedback, General Terms and Conditions of Carriage, Imprint

Ryanair Personal Data and Account - Hotels and Booking and Flight Cheap flight fares, special offers, https://www.ryana ir.com/de/de/nutzl Information: car hire Information, Special simple flight management iche-infos/service- Log in information Offers, At the center/haufige- fragen/Mobile- Booking and Flights Information: at the destination (hotel, travel bordkarten Search for flights, Plan Trip, destination tips) Manage booking, Check-in, Flight Info, Boarding Pass, Seat choice,

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Timetables, Boarding Pass, Flight Schedule Special Offers: Search for flights with lowest fares

Flight/ Travel Inspiration:

At the destination (hotel, travel tips): Hotels, Car hire

Shopping and Entertainment:

Service, Feedback and Other: News, Terms and Conditions, Contact Ryanair Easyjet Personal Data and Account - Traveler city Booking and Flight Cheap flights, Special inclusive http://www.easyje t.com/de/mobile Information: guides as Information, Special holiday deals, Holiday packages,

Login, My flights Youtube Offers, At the Variety of complementary Booking and Flights Information: videos destination (hotel, travel services around the trip, Fun and Book flight, My bookings, - Special deals tips), Shopping and easy, Luggage, Speedy boarding, Check- for entertainment

Student: NE Hutton-Mills Student No.: 11145781 in, Flight status, My flights, accommodati Boarding passes, on, car Special Offers: rental, travel Special deals for accommodation insurance and parking, rental Flight/ Travel Inspiration: Easyjet traveler city guides on Youtube

At the destination (hotel, travel tips): Book hotel, Book rental car, Book Travel insurance,

Shopping and Entertainment: Easyjet Social Media Integration, Bistro& Boutique Magazine, Service, Feedback and Other: Help& assistance(FAQs, Travel hrlp, Help with other services, special help, latest news), Mobile terms and policy, use policies,

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Delta Personal Data and Account - Destination Booking and Flight Customer centricity, Travel http://de.delta.com Airlines Information: World map Information, Special Inspiration, Entertainment, /content/www/en_ Log-in, Redeem Miles, My trips for Offers, Flight/ Travel Experience, Innovation US/mobile.html Booking and Flights Information: Inspiration Inspiration, Shopping Book a trip,, My trips, Boarding via and Entertainment pass, Flight status, Delta Skymiles categories status, Find my trip, Visit a Delta - Social media Sky club, Menu options and integration to amenities, Upgrades, Push settings pull in for flight and service information, content, parking (also via Apple Watch) images and places from Special Offers: friends Explore deals, Fare specials, - Glass bottom Beverage vouchers jet - CSR news Flight/ Travel Inspiration: - Delta Destination World map for Innovation Inspiration via categories (Fare Class specials, Foodie, Romantic - Life Travel getaways, On a budget, Top Updates beaches, Family favorites, Luxury, through

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The classics, Shopping, Eco- Apple Watch friendly), Explore deals, Travel guide (Basic info, Must sees, Insider tips, Dining, Shopping, Nightlife, Get in the mood)

At the destination (hotel, travel tips): Weather at destination

Shopping and Entertainment: Delta Sky Miles, Social media integration to pull in content, images and places from friends, Delta studio, Sky Magazine, Glass bottom jet, CSR news, Delta Innovation Class

Service, Feedback and Other: Need help, App tour, Visit Delta.com View Privacy policy, Qantas Personal Data and Account - Book Booking and Flight Special deals, local deals, quality http://www.qantas .com/travel/airline

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Information: activities at Information, Special s/mobile/global/en

Log in, Frequent flyer, Qantas destination Offers, Shopping and points balance, - Set fare alert Entertainment Booking and Flights Information: Add trip, Book flights, Trips, Boarding Pass, Check-in, Flight status, Live Travel Updates about Check-in, Departure gate, Boarding time, baggage belt and more

Special Offers: Current offers, Set fare alert

Flight/ Travel Inspiration:

At the destination (hotel, travel tips): Book hotel, Book car, Book transfers, Book activities, Book travel insurance,

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Shopping and Entertainment: Press Reader Magazines, Complementary apps: Qantas Entertainment, Qantas Magazine

Service, Feedback and Other: Contact us, Send app feedback, Terms of use, Privacy and security Turkish Personal Data and Account Booking and Flight Unlocking amazing travel http://www.turkis hairlines.com/en- Airlines Information: Information, Special destinations, In-flight food, int/travel- My trips, Miles& smiles login, Offers quality information/turkis h-airlines-mobile- Settings, applications-are- Booking and Flights Information: at-your-service- with-an- Book a flight, Mobile ticket, innovative-design Baggage, Reservations, Timetable, Check-in, Pay& fly, My trips, Flight status, Special Offers: Special offers after price Flight/ Travel Inspiration: Special offers At the destination (hotel, travel

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Shopping and Entertainment:

Service, Feedback and Other:

Appendix 3: Pre-test Survey on Qualtrics The following screenshots display the pre-test that has been executed for exploring consumers’ perceptions of high and low functionality in airline apps.

Screenshot 1: Introduction to the Pre-test

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Screenshot 2: Touchpoint Function: Special Offers

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Screenshot 3: Touchpoint Function: Flight Inspiration

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Screenshot 4: Touchpoint Function: Account Information

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Screenshot 5: Touchpoint Function: Shopping& Entertainment

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Screenshot 6: Touchpoint Function: Check-in& Boarding

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Screenshot 7: Touchpoint Function: At the Destination Options

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Screenshot 8: Touchpoint Function: Service& Feedback

Screenshot 9: Demographic Questions

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Screenshot 10: Outro of Pre-test

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Appendix 4: Pre-test Output on SPSS Screenshot 1: Average Mean Scores for Touchpoint Ratings of Helpfulness

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Screenshot 2: Frequency Tables for touchpoint ratings of helpfulness

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Appendix 5: Experimental Survey

5.1 High Functionality English Screenshot 1: Introduction to Pretest and Demographic Questions

Screenshot 2: Introduction to Pretest and Demographic Questions

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Screenshot 3: Customer Engagement Choice

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Screenshot 4: Function A: Account Information and Customer Engagement Scale

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Screenshot 5: Function B: Check-in& Boarding and Customer Engagement Scale

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Screenshot 6: Function C: Special Offers and Customer Engagement Scale

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Screenshot 7: Function D: Shopping and Entertainment and Customer Engagement Scale

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Screenshot 8: Function D: At the Destination Options and Customer Engagement Scale

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Screenshot 9: Function D: Service& Feedback and Customer Engagement Scale

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Screenshot 10: Customer Retention Scale

Screenshot 11: Outro

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5.2 Low Functionality English Screenshot 1: Introduction to Pretest and Demographic Questions

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Screenshot 2: Introduction to Pretest and Demographic Questions

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Screenshot 3: Customer Engagement Choice

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Screenshot 4: Function A: Account Information and Customer Engagement Scale

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Screenshot 5: Function B: Check-in& Boarding and Customer Engagement Scale

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Screenshot 6: Function C: Special Offers and Customer Engagement Scale

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Screenshot 7: Function D: Shopping& Entertainment and Customer Engagement Scale

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Screenshot 8: Function D: At the Destination Options and Customer Engagement Scale

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Screenshot 9: Function D: Service& Feedback and Customer Engagement Scale

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Screenshot 10: Customer Retention Scale

Screenshot 11: Outro

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5.3 High Functionality German Screenshot 1: Introduction to Pretest and Demographic Questions

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Screenshot 2: Introduction to the Experiment and Demographic Questions

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Screenshot 3: Customer Engagement Choice

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Screenshot 4: Function A: Account Information and Customer Engagement Scale

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Screenshot 5: Function B: Check-in& Boarding and Customer Engagement Scale

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Screenshot 6: Function C: Special Offers and Customer Engagement Scale

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Screenshot 7: Function D: Shopping& Entertainment and Customer Engagement Scale

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Screenshot 8: Function D: At the Destination Options and Customer Engagement Scale

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Screenshot 9: Function D: Service& Feedback and Customer Engagement Scale

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Screenshot 10: Customer Retention Scale

Screenshot 11: Outro

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5.4 Low Functionality German Screenshot 1: Introduction to Pretest and Demographic Questions

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Screenshot 2: Introduction to Pretest and Demographic Questions

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Screenshot 3: Customer Engagement Choice

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Screenshot 4: Function A: Account Information and Customer Engagement Scale

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Screenshot 5: Function B: Check-in& Boarding and Customer Engagement Scale

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Screenshot 6: Function C: Special Offers and Customer Engagement Scale

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Screenshot 7: Function D: Shopping& Entertainment and Customer Engagement Scale

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Screenshot 8: Function D: At the Destination Options and Customer Engagement Scale

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Screenshot 9: Function D: Service& Feedback and Customer Engagement Scale

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Screenshot 10: Customer Retention Scale

Screenshot 11: Outro

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Appendix 6: SPSS Data Output

List of Output Tables

Table 1: Cross tabulation for Age per Functionality

Table 2: Cross tabulation for Gender per Functionality

Table 3: Cross tabulation for Occupation per Functionality

Table 4: Cross tabulation for Traveler Personality per Functionality

Table 5: Kruskal Wallis Test for Age with Median Score

Table 6: Pearson Chi-Square Test for Gender

Table 7: Kruskal Wallis Test for Occupation with Median Score

Table 8: Pearson Chi-Square Test for traveler Personality

Table 9: Cronbach’s Alpha for Customer Engagement (Scale)

Table 10: Cronbach’s Alpha for Customer Retention (Scale)

Table 11: Compared Means for Descriptive Analysis

Table 12: Significance test for within group differences for Customer Engagement Scale Items

Table 13: Spearman Correlation Matrix for Control Variables and Scales

Table 14: Scatter Plot for the Variables of Age and Occupation

Table 15: Scatter Plot for the Variables of “Customer Engagement(Scale)” and “Customer Retention (Scale)”

Table 16: N for Linear Regression for Testing Hypothesis 1

Table 17: Model Summary for Linear Regression for Testing Hypothesis 1

Table 18: ANOVA Table for Linear Regression for Testing Hypothesis 1

Table 19: Coefficients for Linear Regression for Testing Hypothesis 1

Table 20: N for Univariate Analysis of Variance for Testing for Testing H2, H3, and H4

Table 21: Levene’s Test of Equality of Error Variances for Univariate Analysis of Variance for Testing H2, H3, and H4

Table 22: Kruskal- Wallis Test for Variances in CEscaletot between different Traveler Personalities for Testing H2, H3, and H4

Table 23: Tukey’s Post Hoc Test for Univariate Analysis of Variance for Testing H2, H3, and H4

Table 24: Plot 1 for Univariate Analysis of Variance for Testing H2, H3, and H4

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Table 25: Plot 2 for Univariate Analysis of Variance for Testing H2, H3, and H4

Table 26: Between- Subject Factors for Univariate Analysis of Variance for Testing H2, H3, and H4

Table 27: Descriptive Statistics for Univariate Analysis of Variance for Testing H2, H3, and H4

Table 28: Levene’s Test for Univariate Analysis of Variance for Testing H2, H3, and H4

Table 29: Tests of Between- Subjects Effects for Univariate Analysis of Variance for Testing H2, H3, and H4

Table 30: Estimates for Traveler Personality for Univariate Analysis of Variance for Testing H2, H3, and H4

Table 31: Pairwise Comparisons for Traveler Personality for Univariate Analysis of Variance for Testing H2, H3, and H4

Table 32: Estimates for Customer Engagement (Choice) for Univariate Analysis of Variance for Testing H2, H3, and H4

Table 33: Pairwise Comparisons for Customer Engagement (Choice) for Univariate Analysis of Variance for Testing H2, H3, and H4

Table 34: Post Hoc Test Multiple Comparisons for Traveler Personality for Univariate Analysis of Variance for Testing H2, H3, and H4

Table 35: Post Hoc Test Homogeneous Subsets for Traveler Personality for Univariate Analysis of Variance

Table 36: Post Hoc Test Multiple Comparisons for Customer Engagement (Choice) for Univariate Analysis of Variance for Testing H2, H3, and H4

Table 37: Post Hoc Test Homogeneous Subsets for Customer Engagement (Choice) for Univariate Analysis of Variance for Testing H2, H3, and H4

Table 38: Overall Plot Diagram for Univariate Analysis of Variance for Testing H2, H3, and H4

Table 39: Overall Plot Diagram for Univariate Analysis of Variance for Testing H2, H3, and H4

Table 40: Between-Subjects Factors for Multivariate Analysis of Variance for Testing H5

Table 41: Descriptive Statistics for Multivariate Analysis of Variance for Testing H5

Table 42: Box’s Test of Equality of Covariance Matrices for Multivariate Analysis of Variance for Testing H5

Table 43: Multivariate Tests for Multivariate Analysis of Variance for Testing H5

Table 44: Levene’s Test of Equality of Error Variances for Multivariate Analysis of Variance for Testing H5

Table 45: Tests of Between- Subjects Effects for Multivariate Analysis of Variance for Testing H5

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Table 46: Grand Mean of Customer Engagement and Customer Retention for Multivariate Analysis of Variance for Testing H5

Table 47: Mean scores for Traveler Personality for Multivariate Analysis of Variance for Testing H5

Table 48: Mean Score for Customer Engagement(Choice) for Multivariate Analysis of Variance for Testing H5

Table 49: Descriptives for Traveler Personality and Customer Engagement(Choice) for Multivariate Analysis of Variance for Testing H5

Table 50: Plot Diagrams for Customer Retention for Multivariate Analysis of Variance for Testing H5

Table 51: Plot Diagrams for Customer Retention for Multivariate Analysis of Variance for Testing H5

Table 1: Cross tabulation for Age per Functionality

Table 2: Cross tabulation for Gender per Functionality

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Table 3: Cross tabulation for Occupation per Functionality

Table 4: Cross tabulation for Traveler Personality per Functionality

Table 5: Kruskal Wallis Test for Age with Median Score

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Table 6: Pearson Chi-Square Test for Gender

Table 7: Kruskal Wallis Test for Occupation with Median Score

Table 8: Pearson Chi-Square Test for traveler Personality

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Table 9: Cronbach’s Alpha for Customer Engagement (Scale)

Table 10: Cronbach’s Alpha for Customer Retention (Scale)

Table 11: Compared Means for Descriptive Analysis

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Table 12: Significance test for within group differences for Customer Engagement Scale Items

Table 13: Significance test for within group differences for Customer Retention Scale Items

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Table 14: Spearman Correlation Matrix for Control Variables and Scales

Table 15: Scatter Plot for the Variables of Age and Occupation

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Table 16: Scatter Plot for the Variables of “Customer Engagement(Scale)” and “Customer Retention (Scale)”

Table 17: N for Linear Regression for Testing Hypothesis 1

Table 18: Model Summary for Linear Regression for Testing Hypothesis 1

Table 19: ANOVA Table for Linear Regression for Testing Hypothesis 1

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Table 20: Coefficients for Linear Regression for Testing Hypothesis 1

Table 21: N for Univariate Analysis of Variance for Testing for Testing H2, H3, and H4

Table 22: Levene’s Test of Equality of Error Variances for Univariate Analysis of Variance for Testing H2, H3, and H4

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Table 23: Kruskal- Wallis Test with Descriptives and Ranks for Variances in CEscaletot between different Traveler Personalities for Testing H2, H3, and H4

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Table 24: Tukey’s Post Hoc Test for Univariate Analysis of Variance for Testing H2, H3, and H4

Table 25: Plot 1 for Univariate Analysis of Variance for Testing H2, H3, and H4

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Table 26: Plot 2 for Univariate Analysis of Variance for Testing H2, H3, and H4

Table 27: Between- Subject Factors for Univariate Analysis of Variance for Testing H2, H3, and H4

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Table 28: Descriptive Statistics for Univariate Analysis of Variance for Testing H2, H3, and H4

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Table 29: Levene’s Test for Univariate Analysis of Variance for Testing H2, H3, and H4

Table 30: Tests of Between- Subjects Effects for Univariate Analysis of Variance for Testing H2, H3, and H4

Table 31: Estimates for Traveler Personality for Univariate Analysis of Variance for Testing H2, H3, and H4

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Table 32: Pairwise Comparisons for Traveler Personality for Univariate Analysis of Variance for Testing H2, H3, and H4

Table 33: Estimates for Customer Engagement (Choice) for Univariate Analysis of Variance for Testing H2, H3, and H4

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Table 34: Pairwise Comparisons for Customer Engagement (Choice) for Univariate Analysis of Variance for Testing H2, H3, and H4

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Table 35: Post Hoc Test Multiple Comparisons for Traveler Personality for Univariate Analysis of Variance for Testing H2, H3, and H4

Table 36: Post Hoc Test Homogeneous Subsets for Traveler Personality for Univariate Analysis of Variance

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Table 37: Post Hoc Test Multiple Comparisons for Customer Engagement (Choice) for Univariate Analysis of Variance for Testing H2, H3, and H4

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Table 38: Post Hoc Test Homogeneous Subsets for Customer Engagement (Choice) for Univariate Analysis of Variance for Testing H2, H3, and H4

Table 39: Overall Plot Diagram for Univariate Analysis of Variance for Testing H2, H3, and H4

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Table 40: Overall Plot Diagram for Univariate Analysis of Variance for Testing H2, H3, and H4

Table 41: Between-Subjects Factors for Multivariate Analysis of Variance for Testing H5

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Table 42: Descriptive Statistics for Multivariate Analysis of Variance for Testing H5

To be continued

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Table 43: Box’s Test of Equality of Covariance Matrices for Multivariate Analysis of Variance for Testing H5

Table 44: Multivariate Tests for Multivariate Analysis of Variance for Testing H5

Table 45: Levene’s Test of Equality of Error Variances for Multivariate Analysis of Variance for Testing H5

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Table 46: Tests of Between- Subjects Effects for Multivariate Analysis of Variance for Testing H5

Table 47: Grand Mean of Customer Engagement and Customer Retention for Multivariate Analysis of Variance for Testing H5

Table 48: Mean scores for Traveler Personality for Multivariate Analysis of Variance for Testing H5

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Table 49: Mean Score for Customer Engagement(Choice) for Multivariate Analysis of Variance for Testing H5

Table 50: Descriptives for Traveler Personality and Customer Engagement(Choice) for Multivariate Analysis of Variance for Testing H5

To be continued

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Table 51: Plot Diagrams for Customer Retention for Multivariate Analysis of Variance for Testing H5

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