Dealing with KOMs and Badges: A Qualitative Study into the Use and Experience of Strava by Professional Cyclists

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Dealing with KOMs and Badges:

A Qualitative Study into the Use and Experience of Strava by

Professional Cyclists

Auke van de Hulst | Student ID: 10786880 Master in New Media and Digital Culture

Supervisor: Mr. D. Gauthier | Second Reader: Mw. dr. E.J.T. Weltevrede 23 June 2020

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1. Introduction

On the last Saturday of February, the professional cycling season starts according to real cycling fanatics. On this day the Flemish spring classic Omloop Het Nieuwsblad competition takes place. The participating riders have obviously been training very hard to appear as strong and fit as possible at the start of the new season. Many of the completed training sessions and performances of the competing athletes are available online. This is because many professional cyclists make use of Strava.

Strava is one of the many fitness apps that allow users to record, analyse and share their sporting performances using GPS tracking and advanced software. By using the app while exercising, data is generated that describes the effort that has been made. Strava ensures that this data can be viewed in an easy and clear way. In addition, the app uses many of the interaction-fostering features found in platforms like Facebook and Instagram: users can follow each other, analyse each other's workouts and compliment each other by giving kudos to one another. Because of that Strava lives at the intersection of social media and exercise. One of the most attractive features of the service is the use of ‘segments’. These user-generated sections of a cycling route are used to create a leaderboard of the fastest riders. Those with the fastest times are awarded with a digital crown and may call themselves King or Queen of the Mountain (KOM or QOM) for instance. With Strava, it is also possible to take on all kinds of long-term challenges for which digital badges can be earned. The app therefore aims to continue to challenge users and enrich the practice of various sports. Strava is particularly popular in the cycling community where it is used intensively by both professional and amateur athletes.

Before Omloop Het Nieuwsblad starts, it has become possible, thanks to Strava, to closely follow the various preparations of the athletes. The result of this is that exceptional performances in preparation no longer go unnoticed. For example, two days before the start of the race it was noted that cyclist Wout van Aert had set a new record on the Teide, a volcano on Tenerife (Ballinger, 2020). This was remarkable because he improved the old record that was set by four-time Tour de France champion Chris Froome, while Wout van Aert is not particularly known for his climbing abilities. Based on this impressive achievement shared on Strava, the Belgian cyclist was immediately seen as one of the big contenders for the victory of the first spring classic of the season.

This example shows that Strava has taken a role in professional cycling. A

considerable part of the professional athletes uses the app almost every day, which makes it possible to retrieve exactly when, where, and which sports performance was delivered. As the example shows, information made public through Strava does not go unnoticed. Journalists, fans, but also competitors can find out who is most likely in shape and which

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rider appears less prepared at the start of a race. By using Strava, valuable information thereby becomes available to the various actors involved in cycling. This could potentially affect how the sport is practiced. For example, riders who have done something exceptional during training will most likely be monitored a little more during a race.

Although Strava is used by professional cyclists, the app aims to allow all users of the service, professional or not, to achieve their sporting goals. The users of the app are therefore constantly encouraged to push their limits and adopt a healthier lifestyle. This characterises the so-called mobile health apps (mHealth apps) to which Strava belongs. In the past decade, the use of these types of apps has increased tremendously. In America alone, over 90 million people use an mHealth app on a daily basis (Liu, 2019). It is therefore not surprising that a great deal of scientific research has been carried out on the subject (Cederstrom & Spicer, 2015; Charitsis, 2019; French & Smith, 2013; Lupton, 2012; Lupton, 2013; Lupton, 2014; Millington, 2014; Van Dijck & Poell, 2016; Yoganathan & Kajanan, 2013), including research specifically focused on Strava (Barratt, 2017; Rivers, 2019; Smith & Treem, 2016; Smith, 2017; Whelan & Clohessy, 2019). The studies focused on Strava show that the use of the app influences real-world behaviors. Because digital prizes and badges can be won and users are challenged to keep improving themselves, there is always a type of competition when using the app. This may for example lead to different choices of route, longer rides and increased risk-taking during rides. Research into the app also shows that when uploading a ride on Strava, other users of the app who may come in contact with the upload are taken into account by the uploader. As a result, descriptions are added to rides to explain how certain achievements were reached.

The example given above and existing research show that the use of Strava affects the sport and the way people ride their bicycles. What is lacking, however, is research specifically aimed at professional cyclists who use the app. Existing research has so far always focused on recreational cyclists. This is unfortunate because it turns out that Strava has taken a prominent position in professional cycling as more and more athletes use the service. Research into how this specific group of users uses the app is therefore very relevant because potentially different results may emerge compared to recreational cyclists. This is because professional cyclists ride their bike with a different ideology and are much more dependent on performance than recreational cyclists.

In addition, research into the use and experience of Strava by professional cyclists is pressing because existing research shows that the sports landscape is developing due to the arrival of new media (Filo, Lock & Karg, 2015; Frederick et al., 2014; Hambrick, Frederick & Sanderson, 2013; Pegoraro, 2010; Pronschinske, Groza & Walker, 2012; Sanderson, 2009). Traditional media, for example, have a decreasingly prominent position because the advent of social media allows athletes to decide for themselves when to bring

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out news and thus communicate with their fans in their own way. Strava is an excellent example of a new media that has taken up a prominent position in the sports landscape. For all these reasons, I believe it is valuable to conduct research that focuses specifically on the app as used by professional cyclists. This could provide new insights into how Strava influences cycling and sport in general. All in all, this thesis will seek to answer the following research question:

- What is the influence of Strava on professional cycling?

In order to gain insight into the answer to this question, the following sub-questions will be answered as well:

- How are professional cyclists influenced by Strava’s main gamification features? - How do professional cyclists present themselves on Strava?

- How are professional cyclists influenced by the knowledge that can be gained through Strava?

In order to answer these questions, a qualitative study will take place in which eight semi-structured interviews will be conducted with professional cyclists who make frequent use of Strava. Based on the answers given by the participants, in combination with existing research, it will be possible to answer the main research question this thesis poses.

To guide this study, this thesis is structured in the following way: The first part will consist of a theoretical framework where the most important topics regarding mHealth apps will be introduced. The focus will be on datafication, self-tracking, influential design features, surveillance and self-presentation. This chapter will also concentrate on research specifically focused on the influence of new media in sports and research specifically focused on Strava. The second part will describe in detail the ways in which the qualitative research was

conducted. After the case studies have been presented, there will be three thematic chapters in which the results will be discussed. In these three chapters the sub-questions mentioned above will be addressed and answered. The thesis will conclude with a

discussion in which the main research question will be answered and recommendations for further research will be made.

2. Theoretical Framework

A healthy lifestyle with enough exercise is something many people try to achieve. In order to reach this goal effectively and to keep track of progress towards it, all kinds of mHealth apps

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are frequently used that assist, motivate and inspire users in their journey. Strava belongs to this category of apps. As a result of the increasing use of mHealth apps, research into these apps has been conducted from various scientific approaches. As was posed in the

introduction, this thesis focuses specifically on the use of Strava by professional cyclists. For that reason, it is important to review relevant research regarding mHealth apps as to identify what scientific debates around mHealth apps consist of and what to pay attention to.

Research on new media in the sports world will also be discussed. This will make clear what shifts have taken place in the sports landscape due to the advent of new digital possibilities and what role new media seem to play in this. Finally, scientific research that specifically focuses on Strava will also be looked into. In this way it will become clear which knowledge about the app and its use has already been gained and to what aspects, when conducting research, attention must be paid to obtain interesting insights. All this together provides a good scientific basis for conducting the research.

2.1 Datafication and Self-tracking

mHealth apps promise to advance the health, fitness and physical or mental well-being of users. To achieve this, personalized user data is needed from which information can be extracted. The various apps in combination with the various wearables that are used to generate and collect this data, make it possible to map all kinds of personal health developments. In most mHealth apps, the collected data is automatically aggregated, analysed and processed in order to inform a variety of services (Van Dijck and Poell, 2016, p. 2). As a result, the user of mHealth apps is ultimately confronted with his own

achievements and health depending on the service used.

When using mHealth apps, actions and processes are thus transformed into (online) quantified data from which information can be extracted. This is also known as datafication. In the world of mHealth apps, datafication specifically means that every aspect of one's physical or mental well-being is translated into data and can subsequently be transformed into new kinds of value (Van Dijck and Poell, 2016, p. 3). Since there is a very wide variety of mHealth apps, all kinds of data can be collected. The data can therefore have different characteristics. It can be private and personal, but also public and collective. The way in which data is generated also varies. Measurements are often taken automatically, such as measuring the heart rate via a smartwatch. Sometimes the user is required to enter data manually, such as his body weight or the number of calories he has consumed during the day.

In addition to datafication, self-tracking also takes place when using mHealth apps. Self-tracking is known as tracking and monitoring one's health and wellbeing using various

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digital technologies. This practice has increased over the past decade (Lupton, 2013, p. 394). The reason for this can be explained by the arrival of all kinds of apps and

technological devices (wearables and otherwise) that have made self-tracking a lot more accessible, leading to the rise of the so-called Quantified Self movement. This movement believes that integrating technology into everyday life to discover certain patterns through self-tracking is beneficial (Millington, 2014, p. 480). By using the technological possibilities, self-tracking can provide insight into all kinds of processes that are taking place. Based on this information, adjustments can then be made to achieve the matching intended result. This not only relates to the health and well-being of a person, but is also applied to many other areas of everyday life, such as personal finances.

Self-tracking and datafication take place on a very large scale because there is a certain ideology about data attached to it. This ideology is known as “dataism”. Van Dijck (2014) states that dataism implies a belief in the objectivity of quantification (p. 202), that is, an ideology that states that the more data is available, the closer to the truth one can be. This is because data is assumed to be objective by nature. Big data is therefore regarded as the holy grail of behavioral knowledge within various fields of science. The supporters of dataism argue that data collection happens outside a preset framework with the result that large amounts of data are central to studies because they are considered as objective (Van Dijck, 2014, p. 202) Dataism and the Quantified Self, as movement and ideologies, have given data a prominent place in many aspects of daily life, including in the world of health and well-being, resulting in the development of a wide variety of mHealth apps.

However, there is much criticism of these ideologies and the use of data. boyd and Crawford (2012) argue that data cannot be viewed as objective (p. 9). They argue that data is generated in and by an online environment and services that are anything but objective. As a result, objective data can never be produced in the first place. After all, choices are always necessarily made about what should and should not be collected in terms of data. Van Dijck (2014) adds that data is the result of careful interpretation and intervention in the imaging process and must therefore be approached as multi-interpretable texts, and consequently, data can have different meanings (p. 202).

Due to technological development, datafication and self-tracking have taken a prominent position in the world of fitness, health and wellbeing. However, scientific research shows that it is important to take a critical look at the data generated by self-tracking by the various mHealth apps. When conducting research into the use of Strava by professional cyclists, it is therefore interesting to investigate how the athletes deal with the datafication and self-tracking that occurs when using the app and what value they, themselves, attach to the generated data.

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2.2 Influential Design Features

In addition to scientific research that focused on generating and using data, research has also been conducted into how mHealth apps are designed to continuously challenge users and attract new ones. The design of the apps and the way they work is crucial to help users live a healthier lifestyle. Without an attractive design and accessible operation, the service would most likely not work in a very effective way and would not be used much. In addition, it is also important for app developers that users continue to use their service and continue to generate data. Decline in the use of the app could put pressure on the survival of the service, especially considering that here is a lot of competition in the mHealth apps space.

So-called influential design features are therefore crucial for mHealth apps. Yoganathan and Kajanan (2013) indicate that these features are there to allow users to achieve their fitness goal and to enhance their experience (p. 3). The success and

effectiveness of an app therefore largely depends on these features. Two influential design features are crucial in the world of mHealth apps: these are the application of gamification and the facilitation of social influence. Both can be found in the design of Strava.

2.2.1 Gamification

An influential design feature that is used in most mHealth apps is gamification. Gamification can be defined as using game elements and techniques in non-gaming contexts (Deterding et al., 2011, p. 10). When applying gamification, certain game features are used to achieve better outcomes and to keep the user attracted to the service. Research by Malone (1980) shows that games provide a feeling of “fun” because they create fantasies, provoke curiosity and create challenges. This makes realizing a healthier lifestyle a lot more attractive and fun. Wu et al. (2015) add that the key function of a game is to create challenges and that these are mainly created by comparison and competition among players (p. 2). Hence, when applying gamification to the design of an app, the user not only plays against himself but often also comes into contact with the performances of other users.

Whelan and Clohessy (2019) state that many gamification services are based on Fogg's Behavior model (p. 3). Fogg (2009) argues that motivation, ability and trigger

influence people's behavior (p. 1). That is why this philosophy can often be found in mHealth apps. Awards and leaderboards are used to motivate the user, tasks are made as simple as possible to increase the ability, and notifications are regularly sent to trigger the user into action. All these features can be considered as motivational affordances. Rivers (2019) explains affordances as follows:

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An affordance refers to the motivational properties emergent between an object (i.e., a technology) and an agent (i.e., a technology user). It is a concept premised upon the ways in which an individual perceives context in terms of action possibilities. Affordances encourage individuals to undertake action that satisfies inner-drives or needs resulting from an actual or imagined deprivation. Consequently, an affordance can be situated as the foundation of motivation intent. (p. 2)

The motivational affordances that are used in gamification are therefore the features that provoke users to display a certain behavior. Motivational affordances realize certain

psychological outcomes that lead to behavioral outcomes (Hamari et al. 2014, p. 3026). This shows how important motivational affordances are to provoke a certain behavior.

Creating a gameful experience is not only found in mHealth apps. Gamification is applied in all kinds of environments to achieve specific goals and to make activities more attractive. As a result, a lot of attention has been paid to gamification in research where the big question is whether the application of game elements and techniques in a non-game context actually delivers the expected effect. After reviewing 24 empirical studies, Hamari et al. (2014) conclude that gamification works, but the qualities of the user are critical (p. 3030). This will therefore always have to be looked at critically.

When conducting research into Strava it is important to draw attention to gamification as this is an influential design feature that is used in many mHealth apps. Research reveals that it can be assumed that gamification makes sense. However, it is important to take a critical look at the gamification features that are applied and to take into account the qualities of the user. By conducting a qualitative study, the personal experience of the motivational affordances that are applied will be discussed. This will most likely provide interesting insights into the experience of gamification as it will focus on professional cyclists who may interact with it in an alternative way compared to recreational cyclists.

2.2.2 Social Influence

Users of mHealth apps are initially confronted with their own achievements. Depending on the service used, they will see what they achieved and whether progress was made. However, most of the mHealth apps make it possible to get in touch with other users. In other words, there is a social dimension of the apps. By bringing users into contact with each other, mHealth apps create online communities in which like-minded users can support, motivate and inspire each other in different ways (Lupton, 2014, p. 615). Social influence is the result of this. The reason why mHealth apps facilitate social influence is because multiple studies show that getting in touch with other users, and the social support and benefits that

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can come from it, affects behavior that may help achieve goals in a more pleasant and effective way.

Indeed, in the Social Learning Theory of Bandura (1977) it is shown that behavior is adjusted by observing the social environment. A person who observes other people

considers some of them as models. Bandura argues that the model's behavior is most likely imitated by the observer because it is considered correct. Especially if the observer

recognizes a large part of himself in the model (for example age or gender) and if there are certain rewards or penalties for whether or not to adopt the behavior. Later this theory was further developed into the Social Cognitive Theory (Bandura, 1986) in which it is stated that certain behavior is created by the multidirectional influence of personal factors,

environmental factors and behavioral factors. Bandura makes it clear that these three factors influence each other in various ways, which ultimately results in a certain behavior. By bringing users of mHealth apps into contact with each other and creating a competitive environment through gamification, the aforementioned factors are influenced in various ways, so that it can be assumed that a different behavior is established that could potentially result in a healthier lifestyle.

In addition, Turner (1991) states that people have the psychological need to be part of a social community and want to be liked. According to Tuner, to achieve both, acceptance and adaptation to social influence take place. In other words, a person adjusts their behavior to be part of a social community that they would like to be part of. Cialdini and Goldstein (2004) add that people are prone to social influence when the community gives them

recognition and reciprocal benefits. People simply want to feel recognized by their peers. By creating online communities that individuals want to be part of, it can therefore be assumed that they will adjust their behavior. This can also have positive consequences for the health and well-being of users.

Characteristic of the online communities is that positive recommendations and compliments are often given. Cialdini (2009) states that people usually feel obliged to return a similar favor. When someone provides social support to another person, a self-imposed obligation is often created to try to repay in kind what the person has provided. As a result, people subsequently continue to make a positive contribution to each other. Social influence is thus created in this way by an obligation to give something back. mHealth apps apply this by users complimenting each other in a simple way or responding to each other. As a result, the urge to give something back remains, which means there is a good chance that the app will continue to be used.

The aforementioned studies and theories explain why mHealth apps have added a social side to their service and let users battle against themselves and others through gamification. Coming in contact with other users, wanting to be part of a community and

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observing other behavior increase the chance that behavior will be adjusted, which could possibly result in a healthier lifestyle. In short, social influence influences a person's attitudes, beliefs and actions. Exactly what mHealth apps are trying to achieve.

Since Strava also allows users to come into contact with each other, it is important to pay attention to social influence when conducting research. For that reason, it will be

investigated how social influence takes place between professional cyclists who use Strava. Focus will be put on how the athletes apply social influence and are influenced by other users of the app. An important phenomenon that arises in this respect is self-presentation. This will be discussed in more detail later.

2.3 Surveillance

Another point that frequently comes up in research into mHealth apps is surveillance. Surveillance is known as monitoring a population by an authority for the purpose of

controlling the population and provoking appropriate behavior. Not surprisingly, surveillance is common in research into mHealth apps, as monitoring and adjusting individuals is a hallmark of healthcare. For example, doctors monitor their patients' developments and make adjustments where necessary. This form of surveillance can be seen as personalized health surveillance. The mHealth apps basically do the same thing. Surveillance is not time-bound, because it can take place constantly, through the monitoring of technological devices which are therefore not only used to get better, but also to prevent diseases. Thanks to mHealth apps, personalized health surveillance has become ubiquitous and extended with a preventive feature.

Lupton (2012) states that mHealth apps and their associated devices are part of today's surveillance society (p. 235). They simply belong to the many different surveillance devices that are used today to record, survey, monitor and discipline people. The ubiquity of surveillance can be explained by Foucault and his ideas about power operations in

contemporary societies. Foucault (1977) explains the effect of contemporary operations of power on the basis of the panopticon, which connotes a prison designed in such a way that a small number of authorities are capable of observing a large number of individuals. This is the idea that large groups of individuals can be monitored effectively by authorities, but also that monitored individuals will develop self-surveillance and discipline strategies in an effort to improve themselves because they know they are being monitored. This “double bind” principle can be found in the various forms of surveillance. Two types of surveillance have emerged in research into mHealth apps. These are self-surveillance and social surveillance.

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2.3.1 Self-surveillance

Self-surveillance is a form of surveillance that occurs when using mHealth apps. As mentioned before, self-tracking takes place when using them. Charitsis (2019) argues that self-tracking can best be characterized as a form of self-surveillance (p. 140). The apps confront the users with their own merits, so that the individual is surveilling himself. Like other forms of surveillance, self-tracking ensures self-responsibility. The users of the services are themselves responsible for achieving results. Hence, mHealth apps only

facilitate surveillance, which evokes this feeling because users are confronted with their own achievements and thus know that they are being monitored.

The mHealth apps promote the idea that individuals are primarily responsible for their own health and are able to work on it. This ties in well with contemporary neoliberalism. Neoliberalism is best known as a political-social movement with focus on deregulation and corporate privatization. As a result, there have been a lot of shifts in recent decades, with the result that market competition today has taken a prominent position (Read, 2009, p. 4). This can be found in almost every aspect of society. Almost everything is viewed from an

economic perspective with the idea that creating competition ensures positive developments and value. Read argues that these ideas have transformed individuals into competitive creatures whose tendencies to compete must be fostered (p. 5). Characteristic of this is that more and more people are starting to see themselves as entrepreneurs who calculate their achievements and work upon themselves in order to better themselves. Read concludes that this makes individuals regulate and governing themselves (p. 5). These

self-governing activities are at the core of the use paradigm of mHealth apps.

Looking at health care, the neoliberal ideology emerges in that private health is increasingly the focus of individual responsibility (Ajana, 2017, p. 9). Today individuals are primarily held responsible for their own health. mHealth apps tend to support these

individualistic approaches. As a result, Lupton (2013) states that there has been a shift in health promotion from “'My health is the responsibility of my physician' to 'My health is my responsibility, and I have the tools to manage it'” (p. 397- 398). In addition, the

aforementioned influential design features foster the competitive creature of neoliberalism. When using mHealth apps, self-surveillance evokes individual self-responsibility and the drive for competition that characterize neoliberalism’s impetus.

2.3.2 Social Surveillance

Social surveillance is the other form of surveillance that occurs when using mHealth apps. This form of surveillance is achieved through contact with other users of the app. As

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previously made clear, mHealth apps often facilitate social contact. This allows users to monitor each other's achievements, which results in social surveillance.

Marwick (2012) states that social surveillance differs from traditional forms of surveillance in three ways. First, Marwick assumes social surveillance to be a model of power flowing through all social relationships (p. 382). So there is no fixed form of power that would be in the hands of an authority. Instead, Marwick mentions an alternative form of power that is decentralized and present in all human relationships. For this reason, power within social surveillance is ever-present and fluid. This is because digital platforms offer users the same possibilities for surveillance. As a result, there is no central observer with all power, but power shifts between individuals through various push-pull interactions (p. 383).

In addition, Marwick states that social surveillance distinguishes itself because it takes place between individuals, rather than between structural entities and individuals (383). The result of this is that there is less extreme asymmetry (between individuals and State for example). However, there is a hierarchy since the relationship between individuals is different. Although platforms often label the relationship between users as friendships, this is often not the case. Marwick therefore argues that social surveillance exists between individuals, but that these individuals are not necessarily equal even if they do not represent structural entities (384).

Finally, social surveillance stands out because it works in both ways. Marwick

indicates that reciprocal participants both send and receive social information (384). In other words, they surveil and are being surveilled. This does not take place in other forms of surveillance.

Social surveillance ensures that participants engage in the strategic public disclosure and in the concealment of personal information (Marwick, 2012, p. 390). So, like other forms of surveillance, social surveillance results in self-management. It can also be concluded that the consequences of social surveillance tie in with the aforementioned theories that exist about social influence. Being able to observe each other's achievements ensures that behavior is adjusted.

When conducting research into the use of Strava by professional cyclists, both forms of surveillance are interesting to discuss further. By using mHealth apps, users are

confronted with their own achievements, the achievements of others, and the fact that others can observe their data. As already noted, this creates self-management upon which strategic choices are made. It is interesting to investigate how this is reflected in professional cyclists.

It is important to take a critical look at the consequences of surveillance that is taking place in mHealth apps. First, note that both types of surveillance (self and social) always take place in these type of apps. As a result, the use of mHealth apps always creates a so-called “data double” in which the achievements and developments of someone can be found

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and traced. French and Smith (2013) argue that this information can also be used against the individual (p. 388). The data double puts them, for example, at risk of being sanctioned by insurance companies. Employees can also be punished if it appears in their personal data that they display unhealthy and unproductive behavior. The data intended to improve individuals could therefore also be used against them. In other words, self-tracking becomes a weapon that discriminates between people who can produce good data in their everyday activities and those who cannot.

In addition, not everyone wants to be involved in self-tracking. As data is becoming increasingly valued, Charitsis (2019) therefore fears that it may end up penalizing those who do not participate in self-tracking by limiting their opportunities (p. 140). Cederstrom and Spicer (2015) make another point by arguing that self-tracking should not be seen as an attempt to locate and try to fix flaws or weaknesses. Instead, it is about reconstructing oneself according to specific market requirements (p. 4). The question is of course whether these actions are good and responsible. Lupton (2014) takes a critical look at the information and methods that are brought up by mHealth apps (p. 609). The sources these apps get their knowledge from are often unknown. With the extensive use of the apps, this is a critical point to clarify before incorrect information is distributed and users start behaving incorrectly. It is therefore important to take a critical look at the consequences of the use and

surveillance of mHealth apps.

2.4 New Media and Sport

As it was made clear in the introduction, many professional cyclists use Strava. It is not so remarkable that the service is frequently used by this group as research shows that many sports organizations, teams and athletes have embraced various new media (Hutchins, 2011; Sanderson, 2011; Smiths & Sanderson, 2015). Using online services such as Twitter, Instagram and Strava offers a lot of new opportunities to get in touch with the public and to build online relationships with supporters. In addition, research shows that athletes have much more control over their public presentation by being active on those digital platforms (Sanderson and Smith, 2015, p. 343). They largely control how, when and which information is disclosed. This has ensured that the sports world is no longer dependent on mainstream media coverage, but alternative channels are available to communicate with lovers of the sport. Sanderson (2011) therefore speaks of a game changer in the sports landscape.

Pegoraro (2010) makes it clear that by becoming active on online services, fans have come much closer to their sport heroes because it allows athletes to communicate openly and honestly as they wish without any third-party mediation (p. 501). This new transparency offered by digital media allows athletes to give fans a glimpse behind the curtain and to

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really see how their sport heroes live. Because of this fans will see a different side of the athletes and get a better idea of what it takes to become a top athlete.

The result of athletes becoming active on digital platforms is that self-presentation has become very important in today's sports world. Self-presentation was first noticed by Goffman (1959). Goffman states that people function as performers, expressing their identity through verbal and nonverbal messages with a goal to display the most credible image to others. Self-presentation is therefore always a battle between the true me and who someone wants to be and is tailored to a specific audience. Learly & Kowalski (1990) therefore argue that self-presentation is always goal-driven (p. 34). Because of that conscious choices are made that contribute to building an image that someone wants to create of themselves.

Hogan (2010) has researched how self-presentation takes place on digital platforms. He argues that there is no longer a performance, but an exhibition (p. 377). The reason for this is that content is often available online on demand and consequently a part of the audience that comes into contact with the content will remain unknown. Nevertheless, Hogan argues that this is also a form of self-presentation as conscious choices are made about what is and what is not shared online (p. 377). In contrast to a performance, a hidden and imaginary audience is taken into account when uploading content online. When creating an online exhibition, therefore, impression management takes place just as with a

performance. This is in line with the previously noted strategic choices made in social surveillance and social influence.

Smith and Sanderson (2015) add that with the advent of digital platforms, athletes have gained much more control over their own self-presentation (p. 346). They can create their own identity and spread information whenever they want. This is very important for athletes because their image has a great influence on the opportunities they are offered within and outside the sport. Athletes becoming active on online services has triggered several number of developments.

First, there is a different relationship between sports organizations and teams and athletes (Sanderson 2015). Many sports organizations and teams want to keep as much control as possible over what comes out in the media about the sport. To keep the online communication and activities of athletes under control, various sports organizations and teams have therefore developed social media policies that athletes must adhere to. For example, the National Football League (NFL) does not allow athletes to upload online content 90 minutes before kickoff (Librizzi, 2009). This is to protect the existing relationships with traditional media as much as possible and to maintain the exclusivity of the

mediatization of the sport.

Another consequence of athletes becoming active on digital media is that traditional media pick up what is uploaded online by athletes. The personal channels of athletes have

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become an interesting news source. On the basis of the information that an athlete distributes, an interesting journalistic story can be created. In the case of Strava, this has already been made clear in the introduction with the example of Wout van Aert at the Omloop het Nieuwsblad. News reports are now regularly being published about the exceptional performances of cyclists uploaded on Strava. For example, it was recently announced that professional cyclist Oliver Naesen had completed a 350 km ride after it was decided to cancel all races in the spring of 2020 because of the Coronavirus outbreak (HLN, 2020). This indicates the important position that digital platforms, such as Strava, are

occupying today in the sports world.

In addition, becoming active on digital platforms offers new opportunities for athletes to generate income. By making good use of new media and establishing a close and

personal relationship with their followers, it has become very interesting for brands to

collaborate with athletes (Enoch, 2020). That is because by using their online channels, they can effectively reach and influence a lot of people. Brands, such as Nike, Adidas and

Specialized, are therefore looking to cooperate so that their products and services get recommended by athletes. A lot of money can be made through these collaborations.

Another consequence of athletes becoming active on online media is that they, too, can get in touch with other athletes and track their achievements, just like journalists and fans do. The social surveillance that is created through the use of the digital services allows athletes to keep an eye on each other, as it were. When something is uploaded, it can be assumed that not only the fans are addressed, but other professional athletes too. For example, research by Smith and Sanderson (2015) into the use of Instagram by various athletes shows that photos are often uploaded showing the entire body of the athlete (p. 354). According to Smith and Sanderson, this is because athletes take pride in their bodies. However, they overlook that this may also have the intent to show competitors how fit they are. Hardly any research has been conducted into the fact that athletes can also come into contact with each other on digital platforms. This is unfortunate because it certainly deserves attention as it could potentially have a major impact on athletes' use of digital platforms. For this reason, attention will be paid to this observation in this thesis.

So research shows that the sports world has embraced digital platforms like Twitter, Instagram and Strava. The result of this is that digital platforms have taken a prominent position in the sports landscape. Research into how professional cyclists use Strava could therefore make an important contribution to the knowledge that exists about this new reality. By examining how the cyclists present themselves on Strava and how they are influenced by the uploads of other athletes, potentially interesting insights can be revealed that tell

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2.5 Research into Strava

With more than 40 million users, Strava is the most popular fitness app among cyclists. Anyone who takes cycling a bit seriously uses the app. The result of this is that various studies have been carried out in which the app is the main object of study. To get a good picture of what has been researched so far, relevant scientific research will be briefly discussed and evaluated. This survey will clarify what research has been done already and position the contribution of this thesis’ research.

First, research was conducted on the communication that takes place between the users of Strava. The digital platform ensures that users come into contact with each other. Smith (2017) focused on the question of how Strava users interactively negotiate what it means to be an ethical user of the technology. The qualitative study shows that users see certain actions as indeterminate or disreputable (p. 171-174). Disreputable actions are considered outright cheating and indeterminate actions, however, remain in a gray area that is ethically questionable. Smith shows that the communicative features in Strava allow users to judge each other and intervene if an unethical activity has taken place (p. 175).

Smith and Treem (2017) have also researched Strava communication. This research focused on how individuals use the technology to communicate about their physical acts, and how the context of use facilitates organizing processes. The study shows that the choice to engage in an activity, coupled with descriptive communication about the activity, was influenced by awareness that others would view the communication (p. 142). Other users are therefore taken into account when carrying out, describing and uploading one’s activity. A result of this observation is that qualifying an activity takes place to give the activity a description and explain why a certain performance was delivered. This shows that strategic choices are made by coming into contact with other users.

Research into the communication on Strava shows that users intentionally, and often strategically, communicate to a community of others. Communicative features are used to report incorrect behavior and how performances have been delivered. All these actions indicate clearly there is self-surveillance, social surveillance and social influence as I have explained above. This shows that Strava is much more than just a fitness app for personal development.

Barratt (2017) and Rivers (2019) conducted research into the influence of Strava on performing the sport. This shows that due to the persuasive technologies and gamification that are applied in the app, cyclists are acting different when on the bicycle. Barratt makes it clear that a narrative has been added to cycling that affects the performance that is delivered (p. 330). Research by Rivers shows that cyclists have become dependent on the app, so to speak, and can no longer live without it (p. 7). The reason for this is that users like to keep

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track of their personal development and want to feel part of the cycling community. As a result, the use of the app and the data generated have an impact on cycling. For example, alternative routes are taken, users cycle more often, and even the direction of the wind is taken into account. Both studies thus show that using Strava actually affects the way people ride their bicycle.

Finally, Whelan and Clohessy (2020) have investigated the downside of Strava. Research was conducted into 270 cyclists and examined how Strava affects the wellbeing of the user. They specifically looked at Strava’s potential to lead to obsessive behavior and burnout in the long term. The results show that Strava can be seen as a double-edged sword in that the use of Strava can help create an exercise routine, but it can also develop

obsessive tendencies with too much exercise as a result (p. 16). The research by Whelan and Clohessy also makes clear that people who use self-tracking on fitness apps to support and encourage other users are more likely to have a healthy relationship with the app which will eventually lead to lower life stress, but that on the other hand, users who use the app to receive praise and public endorsements are likely to build an obsessive relationship with Strava and endanger themselves (p. 15). For example, these users would rest little and take more risks while cycling. The research therefore shows that the different social features can work both ways.

The studies that specifically focused on Strava make it clear that the use of the app does influence the behavior of the users. The ability to interact with other users means actions are taken with an audience in mind. In addition, the gamification features ensure that users remain challenged, go cycling more often, and take alternative routes. Research has revealed that this can have both positive and negative consequences. After all, users can become obsessive and push themselves to the limit, which can lead to complaints and risky behaviors. Strava is therefore much more than a fitness tool that helps individuals achieve a healthier life. The effect of the app is more comprehensive, which makes research into the use of the service even more interesting.

What the above studies have in common is that the participants they have

researched are mostly recreational cyclists. The special thing about Strava is that a large number of professional cyclists use the app. Now that existing research shows that Strava does indeed have an influence on cycling, it is interesting to research the use and effect of the app on professional cyclists. These cyclists are cycling with a different attitude and ideology than recreational cyclists, they train in a more disciplined manner in order to deliver the best possible performance during a competition when performance really matters. By researching this specific group of athletes, more will become known about the influence of Strava on the profession and the sport in general. In that vein, I will explore the use and experience of Strava by professional cyclists to gain more insight into how the app

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influences the development of sport in general. Attention will be paid to the points previously noted.

3. Methodology

This thesis focuses on the influence of the use of Strava by professional cyclists on professional cycling. To answer the question what influence Strava has on professional cycling, eight professional cyclists were interviewed about their use and experience of the app. After the interviews were conducted they were coded and analysed, from which certain results emerged. How exactly the interviews were conducted and how the results were analysed will be explained in this chapter.

3.1 Qualitative Approach

For this research a qualitative approach has been chosen. The reason for this is that the research is not aimed at producing numerical and quantitative insights. Instead, the focus is on generating in-depth information about the individual use of Strava and its experience by professional cyclists. Adopting a qualitative approach creates possibilities to uncover and analyse non-measurable information in order to generate a better understanding of the practices and experiences of using Strava.

That said, the disadvantage of a qualitative approach is that the research is often not very representative. This is because the samples studied are often small, which means that little can be said about the generality of the results. These types of materials often represent only a small group of people's ideas and experiences and because of that the data are highly specific to those individuals. The result of this is that the open-ended nature of these data raises issues for interpretation and representation. This also applies to this study, since only eight professional cyclists were interviewed.

On the other hand, a quantitative research is often limited because the questions asked are often closed or preformed. By using a qualitative approach, there is much more freedom to dive deeper into certain topics. This can provide valuable insights that would never have come to light by applying a quantitative approach. This approach is therefore more suitable for answering the research question.

3.2 Interviews

Talking with people is an excellent way of gathering information because it is well-suited for investigation of complex behaviour, opinions and emotions, and for collecting a diversity of experiences (Longhurst, 2010, p. 112). Following an interview method has a number of advantages. Firstly, the method can help to get spontaneous, honest and sincere answers.

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Hereby it is important to establish mutual trust between the interviewer and the interviewee (Fabien, 2016). It is also possible to create a heterogeneous sample (Fabien, 2016). By realizing variation in the profiles of the participants it becomes possible to collect diverse information which provides a better overview of the experience of professional cyclists who are using Strava. Thirdly, the method offers the possibility to avoid ambiguities around questions and answers (Fabien, 2016). In a conversation there is room to ask additional questions or to ask for an explanation. This will result in accurate and satisfactory answers from the participants. Non-verbal data can also be collected, which can provide interesting insights (Fabien, 2016). Being able to see exactly how someone physically responds to a question, for example by avoiding eye contact, can bring interesting insights to the surface that would otherwise never be known. In short, conducting interviews allows to carry out a deeper analysis compared to, for example, an online questionnaire. The interviews provide a glimpse of real life experiences of the respondents rather than statistics about them. In other words, it provides more precise and elaborated insights into the practices of respondents than those resulting from a survey.

Dunn (2005) states that there are three types of interviews: structured, semi-structured and unsemi-structured (p. 79). Structured interviews follow a predetermined and standardized list of questions that are almost always asked in exactly the same order. An unstructured interview is just the opposite: there is no specific set of predetermined

questions. Because of that, the interview is more like an informal, everyday conversation. In the middle of these kinds of interviews is the semi-structured interview, which is the type of interview chosen for this study.

3.2.1 Semi-structured Interviews

Semi-structured interviews have some degree of predetermined order but still ensure flexibility in the way issues are addressed by the informant (Dunn, 2005, p. 79). The interviewer does not strictly follow a prepared list of questions but has established certain guidelines in advance. As a result, semi-structured interviews are often informal or conversational in nature, allowing information to be gathered in a pleasant way.

The choice for this type of interview has been made because the use of Strava is quite a complex subject, since it has a large variety of use. By talking about the app with the participants based on predetermined themes, it became possible to collect a good amount of diverse information, especially since this left enough freedom to deviate from certain topics or to dive deeper into something interesting being said. Besides, this way of interviewing allowed the participants to answer the questions they were asked in a pleasant and relaxed way. This certainly contributed to the open and honest answers that were given.

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In preparing and conducting the interviews, Longhurst's (2010) guidelines were used to achieve appropriate results. The method described by Cope (2010) was used to analyse the data. In order to achieve credibility, the actions that were taken by following the methods that ultimately led to the results will be described below.

3.3 Preparing the Interviews

It is a lot more pleasant for both the interviewer and the interviewee if the interview is properly prepared (Longhurst, 2010, p. 106). As a result, the interviewer will appear a lot more confident and the interviewee will feel taken seriously, which can lead to more spontaneous and sincere answers. To ensure that the semi-structured interviews run smoothly, Longhurst's guidelines and instructions have been carefully followed.

3.3.1 Preparations

The first step to be taken is to prepare the interviews. Longhurst argues that each interview requires its own preparation, thought and practice (p. 106). Although each interview requires a separate approach, there are a number of aspects that should be taken into account in each preparation. First, it is important that the interviewer knows enough about the topic of conversation and is well aware of what will be discussed. Since I frequently use Strava and follow professional cycling closely, I already had a good deal of knowledge about the topic. Particularly for the interviews, however, I wanted to know more about the participants. Therefore I analysed their Strava profiles and looked up their results in professional cycling. In addition, I conducted the literature study prior to the interviews, so that I was aware of the debates surrounding mHealth apps and Strava. All this together ensured that I entered the interviews with sufficient background knowledge and knew exactly what I wanted to talk about.

Longhurst argues that when conducting semi-structured interviews it is important to work out a list of themes or questions (p. 106). This ensures that the interviews are fairly structured, that there is always something to fall back on, and that it is possible to check whether important topics have been skipped. For my interviews, four themes with a number of accompanying questions have therefore been compiled. The themes were (1) General use of Strava, (2) Gamification, (3) Social Influence and (4) Impact on Cycling. The semi-structured interviews with the participants were conducted based on these four themes and a number of predetermined questions. Before this could take place, however, the eight professional cyclists had to be selected and approached.

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3.3.2 Finding Participants

Finding the right participants is important because the interviews must provide valuable and useful information (Longhurst, 2010, p. 108). I have chosen to approach a diverse mix of professional cyclists instead of one type of rider. By realizing variation in the profiles of the participants, it became possible to get diverse information which provided a better overview of how professional cyclists use and experience Strava. The method used to find and approach participants is described below.

First, I looked up various Dutch cyclists via the website procyclingstats.com (“Cycling Statistics, Results and Rankings,” n.d.) and made a shortlist of the riders.

Procyclingstats.com is a website containing the statistics of professional cyclists. In addition to their results, the profiles of the riders also show their Strava and social media accounts. This made it possible to find riders who make frequent use of Strava and who could be approached via social media.

After compiling the shortlist of riders that I would like to interview, I contacted the riders via direct messaging on Instagram and Twitter. The message that was sent out briefly stated what the research involved and asked whether they were open for an interview. Fortunately, I received only one rejection from all the invitations. This has led to interesting conversations with a diverse group of professional cyclists. The eight participants are between 19 and 31 years of age, are specialized in different disciplines of cycling, and are active on the three different levels of professional cycling (Continental, Pro-Continental and WorldTour). The similarities of the participants are that they are all Dutch men and of course make frequent use of Strava. The riders have jointly uploaded almost 11.000 rides, existing of more than 850.000 kilometers on Strava. The choice for Dutch nationality was made because I am Dutch myself. Being able to conduct the interviews in my native language made me feel a lot more confident and I was also able to understand the answers of the participants a lot better and faster. Important statements and answers to my questions were therefore translated from Dutch into English in the development of the results (see below). The names of the riders are not included in the research. This is because it has no added value.

3.3.3 Where to Meet

Longhurst argues that it is important to think carefully about where the interviews are being conducted (p. 109-110). The participants must be able to feel at ease. A neutral, informal and easily accessible place is therefore recommended. In the period in which the interviews were conducted, however, the Coronavirus broke out, prohibiting all unnecessary travel by the Dutch government. This led to the interviews being conducted via Skype.

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Using Skype provided several benefits. First, the interviews could be scheduled very easily, since the conversations could be conducted at any time of the day. In addition, geographical restrictions no longer played a role. This has made it possible to interview riders who live abroad. And third, the location was pleasant for both of us as the interviews could simply be conducted from home.

On the other hand, it became more difficult to observe non-verbal communication. In order to be able to capture such non-verbal communication in a certain way, it was therefore decided to make video calls so that facial expressions and body language could still be picked up.

3.3.4 Recording and Transcribing

In order to analyse the interviews properly, it is important that the conversations are recorded so that they can be listened to and transcribed (Longhurst, 2010, p. 110). A positive side effect of using Skype was that the video calls could also be recorded via the digital service. As a result, I did not have to use any external recording devices or software.

Transcribing the interviews is an important, but very time consuming process.

Fortunately, I came up with a way to do this quickly, but very accurately. It is possible to type by voice via Google Docs. Simply put, this means that everything you say is automatically transcribed by the software. I therefore listened to the recordings of the interviews through headphones and then repeated out loud what was said, sentence by sentence. Everything I repeated out loud was automatically converted into text. Because I spoke each sentence out loud, I could keep a close eye on whether everything was accurately transcribed and was able to intervene where and when necessary.

3.3.5 Ethical Issues

Finally, Longhurst argues that two ethical issues are important when conducting interviews. First, participants must be assured that the collected data is well protected and that no one can access it without permission (p. 111). Second, participants must always retain the right to withdraw from the research at any time without explanation (p. 111). When approaching the participants, these two points were therefore explained to them and I made clear to adhere to them. All participants have agreed to the terms and conditions.

3.4 Conducting the Interviews

The interviews were conducted via Skype on a day and time chosen by the participant. During the interviews I tried to listen to the answers as best as I could and asked questions when necessary. Although the first interview was a bit awkward, I am very satisfied with the

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eight interviews that were conducted. Due to the structure of the four themes and asking the corresponding questions, the main points were covered in each interview. This resulted in more than 190 minutes of information. During the interview process, no technical problems occurred.

3.5 Data Analysis

After the interviews were conducted, they were transcribed. By putting the conversations on paper, it became possible to gain a better insight into the answers of the participants. A thematic content analysis has been carried out to actually obtain certain results (see below). This means that the data was closely examined to identify common themes, topics, ideas and patterns of meaning that came up repeatedly. The coding method described by Cope (2010) was used for this. Cope states that coding makes it possible to make sense of

subjective data in a rigorous way (p. 441). This method helps to identify categories, patterns, themes and connections in the data, which ultimately results in understanding meanings in the transcripts. Hereby it is important that the researcher approaches the data with an open mind and allow the data to "speak" for itself. I've always kept this in mind.

Reading the transcripts several times and taking general notes were the first steps taken. The purpose of this is to become immersed in the data and to become more fully aware of the "life world" of the participants (Cope, 2010, p. 445). The transcripts were then read again, this time all relevant pieces from the texts were encoded by means of a label. This stage is known as open coding and is a descriptive process. I did this by highlighting interesting passages from the transcripts and by adding a descriptive keyword. This resulted in a wide variety of labels.

Reducing the number of labels is the next step indicated by Cope (p. 446). This is done by deciding which labels are most important and by creating categories by bringing several codes together. This stage is known as axial coding and is an analytical process. As a result, a short list of categories to which common codes belong has been created.

Important to mention is that this is a very dynamic process which can be chaotic. In order to keep an overview, I therefore gave the categories and their codes different colors. This made it possible to quickly find in the transcripts where something was said and to see what was related to each other.

The next step was selectively coding. This means that the different categories were labelled and a selection was made by deciding which ones are most relevant for my

research and by looking at how the categories are connected. The topics that were not relevant for my research were dropped, resulting in a shorter list of labels. The remaining passages from the transcriptions of the same color I then put together so that the different

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results related to the same theme could easily be compared with each other. The result of this all is that a number of themes have arisen to which different labels belong that connect to the sub-questions. The last step was to write the results down and to interpret them, which is described in the next three thematic chapters.

4. Gamification: KOMs and Badges

Like many other mHealth apps, Strava uses different gamification features to challenge users and enrich the experience. Strava's main gamification features are the aforementioned KOMs (“King or Queen of the Mountain,” the user-generated sections of a cycling route used to create a leaderboard of the fastest riders) and badges that can be earned. Among many Strava users, conquering KOMs and earning badges is a prestigious achievement. This is mainly because it often requires a big effort. Existing research therefore shows that recreational cyclists are influenced by the gamification features and therefore practice the sport in a different way (Barratt, 2017; Rivers, 2019). Since professional cyclists also use Strava and therefore automatically come into contact with the main gamification features, they have been asked how they deal with it and whether their performance on the bike is affected as well.

4.1 No Influence

First of all some riders admitted not to be conquering KOMs and earning badges. They announce that, in consultation with their personal trainer, they have put together a well-thought-out training plan that they want to adhere to as rigorously as possible. As a result, they are not consciously engaged in conquering KOMs or earning badges. That is because it could mean that during a training ride they will deliver efforts that are not in line with the imposed training for that day. KOMs are clearly not the intention. In addition, they also attach little value to conquering a KOM as it is based on time. Rider 1 says about this:

Yeah of course it's cool when you're the fastest and when you conquer a KOM, but I don't really value it. What I’m really interested in are the wattages that have been produced. You can set the fastest time, but if you achieved that with wind force five in your back you only got to 200 watts. That's not special at all. [...] It doesn't say much about your fitness. That’s why I attach little value to conquering a KOM and why it doesn’t really interest me.

Regarding the specific earning of badges, most riders indicate that they do not pay attention to this. The long-term challenges have little meaning for professional cyclists

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because they make a lot of miles in a month. The long-term challenges can therefore be completed very easily. Rider 4 says about this:

At our level we kind of stand above that because we follow our training schedule and therefore automatically make a lot of hours on the bike. As a result, the challenges are completed in no time. So maybe earning badges doesn't quite apply to us. So I don't really pay attention to that.

For a number of riders, the main gamification features of Strava therefore have no influence on how they practice their sport. The main reason for this is that they focus on their training plan and follow it very carefully. As a result, they are not influenced by the

gamification of Strava.

4.2 Conquering KOMs

However, the fact that the main gamification features have no influence does not apply to everyone. The other cyclists indicate that they are regularly busy conquering KOMs.

However, they also focus on the training plan and they also try to adhere to this as much as possible. The difference with the other riders is that they are regularly engaged in

conquering KOMs because they add this to the training if the effort that has to be made for this is in line with the training for that day. Rider 7 says about this:

Yes, I do use the KOM feature sometimes. For example, if I know I’m going to do a tough but not too heavily structured interval training, especially when I am in Spain, I look up certain segments of which I know oh hey this is in the margin of what I am allowed to do in terms of intensity. In such cases it can be a challenge to measure my performance by attacking the KOM.

Rider 5 uses the KOM feature in a similar way. He states the following:

It really depends on what kind of training you’re going to do and where. The place I live is of course as flat as a pancake and if you’ve got wind force seven in your back on a segment of two kilometers then anyone could break the record. I don't really look at it that way. But I had to do long training blocks in recent years and when I did those on the Lekdijk, for example, and I saw in advance that there was a certain time on it, on the segment, that time was in the back of my mind. I made it a goal. That can be fun.

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How seriously conquering a KOM is taken differs between the riders. For example, the quote from Rider 5 just mentioned shows that he is looking for the fastest time for a training so that he can take that into account. Rider 7, however, regularly goes a step further. He says:

Sometimes I really make a serious attempt. I check how and where the segment starts and how I can best organize it. I think it through in advance. I do a kind of race simulation, as I normally would if I were to ride a real race or explore a parcours.

Finally, two riders even indicate that they regularly complete a training with the

intention to conquer a KOM. These are the so-called “KOM Attack” training sessions. Rider 3 says the following about this:

Sometimes my trainer adds a KOM Attack to the training programme – giving

everything you have to try to conquer segments. In that case, we go full gas for once. We’re instructed to do so in the training.

Later he adds that the KOM attacks are also completed as a team:

I remember last year we did a training for a day and the goal was just trying to get as many KOMs as possible. One by one we attempted to conquer a segment. That’s what we did all day long: trying to conquer all kinds of KOMs. That was the training for that day. We spent the night in Limburg and tried to conquer even more KOMs the next day.

The KOM feature is therefore regularly used by some riders during training sessions. Some even use it as part of their training plan to keep challenging themselves and to make training sessions more fun and attractive at times.

4.2.1 Benefits

Trying to conquer a KOM is done by a number of riders. The reason for this is that the riders experience several positive consequences. Firstly, participants announce that conquering a KOM provides makes one feel good. Rider 3 says about that:

Usually they're pretty prestigious KOMs – someone has set a really sharp time. And to break it, yeah, that's quite an achievement. That surely make me feel good.

Dealing with KOMs and Badges: A Qualitative Study into the Use and Experience of Strava by Professional Cyclists