Order from us for quality, customized work in due time of your choice.
Executive Summary
Netflix is a streaming service for movies and television shows that charges a flat monthly fee for unlimited access to its library. Netflix and recommender systems will be thoroughly discussed in order to demonstrate the importance of the subject matter under consideration. Following the introduction of the background chapter, the theoretical framework will include the actor-network and critical awareness theories. The aim of the research will be to determine how customers perceive Netflixs and its recommendation scheme and its potential impact on their cultural preferences. This study will conduct a pre-study of the target audiences general understanding of Netflix and its recommendation system before conducting a focus group interview. In this case, the investigator will employ a qualitative, descriptive study design. The majority of the participants in the study will be drawn from the CBS student body.
Introduction
Recent years have seen a significant shift in how people consume cultural products including films, songs, art, and gaming. Netflix and recommender systems will be discussed in detail in order to demonstrate the significance of the subject matter under investigation. Actor-Network and critical awareness theories will be introduced in the theoretical framework after the introduction of the background chapter. After the theory is given, the procedure of conducting qualitative focus group interviews will be explained. There will be a final conclusion and recommendations made.
Background
When Reed Hastings and Marc Randolph launched Netflix in 1997, the company was based in California, USA. When it first debuted, it was a DVD rental business that allowed customers to rent an unlimited number of discs for a flat monthly fee. In 2007 Netflix became a streaming service, allowing customers to watch movies and television shows whenever they want (Myers, 2022). During the first three months of 2019, Netflixs member base increased to about 149 million from its original target of 76 million. There are an estimated 37 percent of the worlds internet users who have access to Netflix (Watson, 2019). The United States, where Netflix has 60 million users and accounts for more than a third of all Netflix subscribers, is the companys most significant market. Hulu and Amazon Prime Video are two of Netflixs key competitors in the United States (Waite, 2018). In spite of heavy competition, Netflix is presently preserving its dominance position by generating more original content than its rivals.
In this study, the researcher will examine how Netflix is seen in Europe, with a particular emphasis on the Nordic region. Surveys are conducted in Copenhagen, with the majority of the participants being from Europe. Europes streaming video on demand industry is dominated by Netflix, which has 44% of the market share, followed by Amazon, which has 32%. The remaining percentages are divided across many national corporations (André et al., 2017). ViaPlay (22 percent), HBO Nordic (14 percent), CMore (6%), and other services (12%) make up the rest of the SVOD market in the Nordics, which is dominated by Netflix. When Amazon Prime began in the Nordics in 2017, it took some of the other providers market share.
Automated Systems for Making Recommendations
Netflix is a movie and television series streaming service that charges a flat monthly fee for unlimited access to its library. In order to assist the user identify movies and shows that are of interest to them, Netflix uses a complicated recommendation algorithm (Netflix, no date). There are a number of factors that go into its recommendation system, but the most important are how users interact with it, how long they spend watching, what devices they use, and how long they watch for. It is also stated that demographic details like age and gender are not taken into account (Nguyen, 2018). In addition to catering to a single users preferences, the recommendation system is designed to meet the needs of a familys many members, each with their own unique set of preferences.
Netflix has implemented an algorithm that tries to persuade the viewer that a title is worth viewing by personalizing the artwork. Images for each title are generated using an algorithm that selects the best image to display based on user preferences (CBS, 2021b). The user is more likely to view a movie if they believe it belongs to a genre that they are interested in. A debate has erupted among users over whether Netflix is attempting to target specific demographics by displaying photographs of certain celebrities in a movie or series. Netflix has been under fire from viewers who claim the streaming service intentionally misrepresents some films in order to attract a diverse audience (History, 2021). In addition, viewers may feel mislead if the screen time of the characters is inaccurately represented.
Purpose
Customers views on Netflix and its recommendation methods will be examined in this study. In doing so, it hopes to close a knowledge gap in the field of recommendation systems by bringing in the perspective of actual users. Understanding how customers feel about recommendation systems can help us better understand how these systems may affect society as a whole.
Aim and Research Question
Prior to conducting a focus group interview, this study will undertake a pre-study of the target audiences general grasp of Netflix and its recommendation system. In the pre-study questionnaire, the researcher will learn how to test the focus group interview subjects perceptions of Netflixs recommendation algorithm and how to further detail the questions for the focus group interviews. The following research topic will be the focus of this study:
How do customers perceive the possible impact of Netflix and its recommendation scheme on their cultural preferences?
Netflixs perceived impact on customers cultural consumption will be examined as a dependent variable in this research topic. Independent variables include the recommendation system. There is no objective truth to be found in this inquiry since the word perceive is at the heart of the investigation. The answer will be derived from the two focus group interviews that were conducted. The following sub-questions will serve as a framework for answering the research question:
Sub-questions
-
How are customers affiliation to Netflix and its proposal system?
-
How do Netflix subscribers evaluate their newfound understanding of how the services recommendation algorithm works?
-
How much influence does Netflixs recommendation algorithm have over users cultural preferences?
-
What percentage of Netflix subscribers are eager to learn how the streaming services recommendation engine works?
Theory
Theoretical Framework
The two focus group interviews will be analyzed using two different ideas. Actor-Network Theory (ANT) will be used to study the connection between Netflix and its customers. For the second time, the notion of critical alertness will be employed to elicit logical reasoning about how to reveal the ANTs black box.
Actor-Network Theory
Human and nonhuman actors in a network are defined and described by the Actor-Network Theory (ANT). Research on science and technology, as well as society, spawned Michael Callons and Bruno Latours work in the development of ANT (Bencherki, 2017). As a result, the theory has become a widely utilized sociological technique in a wide range of domains of social research. Every actor-network is composed of actants, according to ANTs core tenet.
It is possible to think of actors as people, things, or intangible assets, and they are all assemblages of different pieces. It is a universal fact that all actors are unable to act on their own, and must build a network with other actors in order to do so. The existence of other actors in a network consequently serves as a conduit for action (Helberger, Karppinen and DAcunto, 2018). Regardless of whether they are people or not, all members of a network have a same influence, and as a result, the same level of worth and agency. Modern-day societys intake of cultural commodities like movies and shows may be done in a variety of methods, including via streaming services like Netflix, which are part of an actor network. Smaller scale actors like Netflix and the customer are part of an actor-network in which each player has the power to impact the network in different ways. To watch Netflix, youll need to be in the same room as both of the other people involved. A users change in behavior will have the same impact on the network as Netflixs changes to its recommendation algorithm.
Essentially, the theory states that a network seeks to accomplish a certain purpose, while balancing the interests of all the participants. The interests of the actors must be constantly aligned in order to maintain network stability. As Netflix customers interests vary, Netflix must adapt its suggestion algorithm to better reflect the users preferences (Chandrashekar et al., 2017). New algorithms or tweaks to existing ones can be added to Netflixs recommendation engine, requiring consumers to approve a new interface. These black boxes of actors networks may appear simple at first glance, but the truth is that they are incredibly complex (Stoll, 2021b). The black box of the network can be uncovered by studying the interests of the networks actors. It is the Netflix users perspective of Netflix and its recommendation system that is the subject of this thesis. The networks interests will be examined by ANT, not Netflixs, as ANT is designed to look at the networks user interests.
Critical Consciousness
Using Freires notion of Critical consciousness, Netflix users will be able to better understand how the Netflix recommendation engine works. In order to encourage people to think critically about a subject, the term critical awareness was coined. Because of Freires teachings, he felt that impoverished people might obtain a critical knowledge of the repressive system in which they lived and begin to make good changes in their lives. Reflection and action are essential components of critical awareness since it aims to alter social structures and situations. As a means of promoting social justice, critical consciousness was first employed to educate the less educated (Möller et al., 2018). Critical consciousness may, however, be applied to a wide range of fields, from politics to technology. According to the researcher, the notion may also be used to examine Netflixs complicated recommendation algorithm. As a result, the researcher hypothesizes that by evoking critical awareness in the user, Netflix may be used as a tool to empower the user by giving them control over the recommendation system.
In order to achieve critical awareness, one must through a multi-stage process that includes critical contemplation, political efficacy, and critical action. Individuals improve their ability to recognize connections between their own experiences and broader social challenges, as well as how to alter their own behavior, as they progress through the three phases (Santini, 2018). This study will focus on critical reflection, which is one of the three phases. It is via critical reflection that one first develops the ability to evaluate injustice in light of ones broader social context. People who are more capable of critical thought do not place the responsibility on the oppressed, but rather on the system as a whole. Focus group members need to learn about Netflixs recommendation mechanism to acquire critical consciousness. The researcher hopes to broaden participants perspective on Netflixs recommendation system and influence their behavior toward it by revealing previously unknown algorithms to them. The initial step in critical thinking is triggered by the focus group interviews, which help the customer uncover the black box.
Literature Review
A review of the literature on the social ramifications of recommendation systems, including how the study fills the information gap, will be provided next. Netflixs algorithmic recommendation engine, as well as how users perceive it, are discussed in this part, which also includes a thematic assessment of the relevant literature. This studys subject was drawn from current research for the reviews literature analysis. All of the research is tied to the studys specific research questions and goals.
Choice-Making Facilitator
The job of recommendation systems is similar to that of institutions in that they assist customers in navigating the multitude of options available to them while conducting internet searches. These networks take on the function of organizations, promoting habits and inclinations; they impact how priorities are developed as well as what material is created (Jones et al., 2019). Netflixs recommendation algorithm, known as a cultural data gathering procedures, provides evidence. By observing, gathering, and analyzing user activity data, recommendation systems serve as cultural information intermediaries, shaping how cultural commodities are offered to customers. Because of the rising availability of cultural information on the internet, it is vital to create recommendation systems for customers, which is why infomediaries are valuable (Lobato and Lotz, 2020). Infomediaries are increasingly accountable for determining how customers encounter and experience material as consumers become more reliant on them for relevant content.
Netflix not only serves as a cultural intermediary, but it also feeds user behavior into a program that may determine what kind of exclusive material to make next. The ability of recommender systems to affect customer behavior is demonstrated by their impact on purchase decisions. Institutions are becoming increasingly resemble each other because of recommendation algorithms (Siles et al., 2019). When firms start to use the same knowledge, they end up producing the same results since forecasts are made using the history of consumers (Pilipets, 2019). Although algorithms can benefit in everyday living, they may have a negative impact on original cultural material if they are made to be an integral part of society.
Recommendation Systems Influence on Cultural Tastes
Consumers perceptions of cultural commodities are shaped by the recommendation engines embedded into cultural infomediaries. System recommendations direct the user to specific cultural items that have the capacity to influence cultural interactions and, as a result, the tastes of the people who use them. Peoples lives are influenced when recommendation algorithms begin to influence their cultural preferences (Burroughs, 2019). People still live in black boxes since they do not know how the decision systems that impact their lives work. The black box society is the result of our daily contacts with decision support systems that make judgments on our account without human participation (Lobato, 2019). Companies, on the other hand, design them with a clear end goal in mind, most typically a monetary one.
Various Methods of Recommendation
Consumers are at danger of becoming trapped in media bubble and group think if optimization techniques are not diversified. This occurs when consumers get the same type of info and hence experience self-confirming control systems (Jenner, 2018). Efforts have been made to address this possible issue by designing recommendation systems that are more diverse. By integrating a random selection of options in the recommendation system, spontaneity adds a sense of excitement to the process. The most popular things are favored because they are more likely to meet the preferences of the majority of buyers, according to recommendation algorithms. Since less popular things tend to be underrepresented in recommendation systems, they will inevitably diminish in importance over time (Zarum, 2018). In spite of the fact that recommendation systems include variety into their design, the most often consumed commodities are still given preference by the public.
Autonomy for the Customers
Personalization and online decision-making are facilitated by recommendation systems, which minimize search costs and uncertainty. There are several advantages to using a recommendation system for consumers (Navarro, 2021). However, as much as customers profit from recommendation systems, they also put them to the test. Recommendation systems can backfire if they undercut customers feeling of autonomy in their decision-making, resulting in consumer resentment. In certain cases, customers may feel as if they are being denied of the ability to make their own decisions (Stoll, 2021a). Consumers ability to make their own decisions is critical to their well-being. Recommendation systems are unable to account for customers aspirational desires since they dont include their thinking processes. In this way, restricting customers freedom of choice makes it harder for them to change their minds.
Closing the Knowledge Divide
Consumers can benefit from recommendations systems in several ways, according to the above literature analysis. It shed light on the potentially harmful ramifications recommendation systems might have on society. However, no studies have been done on how consumers view recommendation systems. By analyzing how customers see Netflix and its inference engine, this study tries to address this vacuum in the literature. This research will bring consumers into the academic discourse and provides insight on whether clients have the same worries as the possible social implications described in previous studies.
Methodology
Research Design
A qualitative, descriptive study design will be used by the investigator in this case. It is a better choice for this research since it encompasses a wider range of data, making it easier to interpret the findings. Because the descriptive survey design allows the research to be conducted in the participants natural environment, it ensures that high-quality and unbiased data will be obtained.
Target Population
The studys participants will mostly be drawn from CBSs student body. Three factors led the researcher to pick this particular crew. In the United States, the age range of 19-29 years old has the greatest number of Netflix users: 77 percent, according to market data (Lotz, 2021)). Second, students at CBS have easy access to facilities and networks made up of fellow CBS students. For the study, this will make it easier for participants to get in touch with researchers. As a third point, it may be argued that young university students, who have grown up in the digital age, have a viewpoint about how they apply and are influenced by technological services like Netflix. Members of CBSs Management of Innovative Business Operation program will participate in focus groups. Creative arts, such as movies, are the subject of this research. Due to their shared interest in the arts and entertainment sectors (including cinema), they are more likely to engage in stimulating conversation than the ordinary Netflix subscriber.
Questionnaire
In order to determine whether further investigation into a topic will provide new information, doing a pre-study is highly recommended. As a pre-study questionnaire, the researcher will inquire about the overall perspective of Netflixs recommendation system, based on the four possible social effects of recommendation systems highlighted in the literature review. Despite the fact that this is a pre-study, the researcher will pay close attention to the questions wording and sequence. Two face-to-face surveys will be used to conduct a pilot test of the questionnaire at CBS, the same location where the open-ended questionnaire interview will be held, to check that the respondents understand the questions and are intelligible for the population of interest. During the pilot test, if required, the questions will be reworded to verify that the respondent understands them correctly. As a result, queries that are vague or provocative will be avoided as much as feasible. Open-ended questions will be included in the questionnaire. As a way to keep the questionnaire brief and concise, as well as to maintain the interest of the responders, the researcher will select the most significant questions. Generalizations can be drawn from the data at the researchers choice.
Data collection
Facebook groups for students in CBSs Creative Business master program will be used to distribute a survey to a large number of potential responses. Physically handing out the questionnaire will also be done to prevent a skewed sample of only representing students of the Management of Innovative Business Operation program at Copenhagen Business School. Giving out surveys in person is a far faster way to collect data than posting them on Facebook. The study topic will not be quantifiably addressed because the focus will be on how customers view the effect of recommender systems. With all of these steps accomplished, the researcher has a solid grasp of how the student intended audience sees decision support systems and their impact on cultural expression before moving forward with detailed focus group discussions.
Focus Group Interviews in Qualitative Study
A qualitative approach will be used to learn more about how Netflix subscribers view the service and its recommendation system, as well as if they feel it has an impact on their cultural consumption. A focus group will be able to debate this issue since it is neither too wide nor too narrow. The focus group discussion will also allow respondents to communicate and reflect on their perspective of Netflix and its decision process, producing deeper information that could otherwise go unmentioned in a single interview.
There will be two focus groups, each with five members. Inexperienced facilitators should be able to handle five members in a focus group, which is the ideal quantity for creating an open conversation and a reasonable number for an experienced facilitator. It is more likely that the participants will be able to develop an informal and casual atmosphere in which they may openly express their thoughts and ideas to the group. No one is going to be scared to express their views or to challenge the views of others in this group.
Ethical Considerations
The interviewees are affected by their interactions with the interviewer, and the knowledge gained through interviews, such as focus groups, changes our understanding of human nature. This shows the need of understanding the ethical difficulties that may occur before, during, and after performing research. Because of this, ethical considerations are taken throughout every stage of the study process. Informed permission, confidentiality, penalties, and the researchers role will be examined by the researcher. It is important to return and reflect on these four areas during the research process since they are seen as fields of uncertainty, or potentially problematic regions.
At first, the researcher will ask the focus group members for their permission to tape the conversations and utilize the recordings for subsequent study in order to acquire their informed consent. Secondly, for privacy reasons, the researcher will carefully examine what data is needed and whether or not personal information is necessary. The researcher will ask participants to identify themselves and describe their relationship to Netflix, but this information will not be included in the coding process. As a result, the participants names will be obscured in the transcripts by using just their initials.
Thirdly, if any information gleaned from focus group interviews harms individuals or the group, it will be considered by the researcher. Because of the researchers increased awareness of the need of getting participants oral agreement before using their data in the study, the researcher will need to take extra precautions to protect participants privacy. Confidentiality concerns will not arise since the subject matter is not regarded to be of a particularly sensitive nature (CBS, 2021a). As a final point, the researcher will stay true to the duty of a researcher throughout the study. During the course of the study, the researcher will guarantee that all participants personal information is kept private and will provide a brief explanation of the studys purpose. In order to avoid directing them in a certain path, the researcher will not identify the researchs goal. Researchers will instead focus on gathering the participants unstructured responses to questions on the issue.
Conclusion and Recommendations
Netflix dominates Europes streaming video on demand industry, accounting for 44% of the market. This study will look at customers perspectives on Netflix and its recommendation methods. By doing so, it hopes to bridge a knowledge gap in the field of recommendation systems by incorporating the viewpoint of actual users. Two face-to-face surveys will be used to conduct a pilot test of the questionnaire at CBS to ensure that the respondents understand the questions and are understandable to the target population. The small sample size of this study could be remedied by conducting additional research on Netflixs and its recommendation systems perceptions by a different target demographic. Additional research should look into how people perceive various recommendation systems and employ a different research method, as well as the perceptions of people who have canceled their Netflix subscription.
Reference List
André, Q. et al. (2017) Consumer choice and autonomy in the age of artificial intelligence and big data, Customer Needs and Solutions, pp. 28-37.
Bencherki, N. (2017) Actor-Network Theory, The International Encyclopedia of Organizational Communication, 1(1), pp. 1-13.
Burroughs, B. (2019) House of Netflix: streaming media and digital lore, Popular Communication, 17(1), pp. 1-17.
CBS (2021a) Business Source Complete. Web.
CBS (2021b) Scopus. Web.
Chandrashekar, A. et al. (2017) Artwork Personalization at Netflix. Netflix TechBlog, Web.
Helberger, N., Karppinen, K. and DAcunto, L. (2018) Exposure diversity as a design principle for recommender systems, Information Communication and Society, 21(2), pp. 191-207.
History (2021) First Blockbuster store opens, Web.
Jenner, M. (2018) Netflix and the re-invention of television. Berlin: Springer.
Jones, J. et al. (2019) Nature documentaries and saving nature: reflections on the new Netflix series our planet, People and Nature, 1(4), pp. 420-425.
Lobato, R. (2019) Netflix nations. New York City: New York University Press.
Lobato, R. and Lotz, A. (2020) Imagining global video: the challenge of Netflix, JCMS: Journal of Cinema and Media Studies, 59(3), pp. 132-136.
Lotz, A. (2021) In between the global and the local: mapping the geographies of Netflix as a multinational service, International Journal of Cultural Studies, 24(2), pp. 195-215.
Möller, J. et al. (2018) Do not blame it on the algorithm: an empirical assessment of multiple recommender systems and their impact on content diversity, Information Communication and Society, 21(7), pp. 959-977.
Myers, V. (2022) Our progress on inclusion: 2021 update, Netflix, Web.
Navarro, J. (2021) Number of movies released in the United States and Canada from 2000 to 2020, statista, Web.
Netflix (no date) How Netflixs recommendation system works. Web.
Nguyen, N. (2018) Netflix wants to change the way you chill, BuzzFeed News, Web.
Pilipets, E. (2019) From Netflix streaming to Netflix and chill: the (dis) connected body of serial binge-viewer, Social Media+ Society, 5(4), pp. 1-12.
Santini, R. M. (2018) Recommender systems as tastemakers : collaborative filtering as a market strategy for online cultural products, Observatorio, 12(2), pp. 122-135.
Siles, I. et al. (2019) The mutual domestication of users and algorithmic recommendations on Netflix, Communication, Culture & Critique, 12(4), pp. 499-518.
Stoll, J. (2021a) Share of adults with a Netflix subscription in the United States as of August 2021, by generation, statista, Web.
Stoll, J. (2021b) Share of national movies available in SVOD catalogues in Europe as of November and December 2017, by service, statista, Web.
Waite, T. (2018) Apparently Netflix is targeting viewers based on race, Dazed, Web.
Watson, A. (2019) Netflix Statistics & Facts, Web.
Zarum, L. (2018) Some viewers think Netflix is targeting them by race. Heres what to know, The New York Times, Web.
Order from us for quality, customized work in due time of your choice.