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Abstract

This dissertation analyzes the effects of digital marketing techniques on consumer behavior using Netflix as a case study. Netflixs recommendation model of advertisement, which uses an algorithm-based marketing system to influence consumer-purchasing decisions, is the focus of the current investigation. From this background, this probe is designed to understand the experiences of Netflix users as they relate to the companys algorithm-based recommendations system. The goal is to find out its influence on consumer behavior and cultural preferences when making online purchases. A key part of this investigation discusses the extent that Netflixs product algorithm or recommendation model has influenced the cultural preferences of its consumers on the online streaming platform. Similarly, this investigation seeks to find out the extent that Netflixs recommendation system has aroused consumers curiosity about the streaming services recommendation system. Two focus group discussions involving five participants, each, helped to generate data on the above-mentioned research areas. The findings of the investigation revealed that the algorithm-based marketing model influenced the cultural preferences of Netflix consumers by playing the role of a choice architect. Furthermore, the system helped consumers to identify products that suit their needs from a seemingly endless list of options. However, the ability to manipulate consumer behavior using algorithms remains a risk in the implementation of the recommendation-based marketing system.

Introduction

Background of the Study

Technology has changed the way we live and work in the contemporary world. Consequently, its effects have been felt in different industries with far-reaching implications on profitability and consumer behavior (Xiaoping and Tao, 2021). The entertainment industry is perhaps one of the most affected sectors of the global economy because digital advancements in product development have revolutionized purchasing behaviors (Anderl and Witt, 2020). Particularly, the growth of entertainment streaming services has created a shift in the development of entertainment content across consumer entertainment platforms (Operational Delivery Profession, 2020). The most notable effect has been the collapse of the theatre film industry, which depended on foot traffic to make sales. Indeed, people no longer go to the theatres to watch their latest movies because they can do so at the comfort of their homes.

In the internet world, the live streaming of digital content has been traditionally associated with the gaming industry. Different sectors have since benefitted from this development through technological transfers (Duncan, 2020). For example, the events business sector thrived on the backdrop of live digital content streaming during the corona virus period (Gilpin, 2021). Marketing communications services were also sustained in the same manner during the crisis. Companies that are associated with the provision of these services are in a unique position to leverage the present market demand of online services to boost their businesses.

In this investigation, Netflix is selected as a case study to understand the impact of the algorithm-based marketing model on consumer behavior. The companys dominant presence in the digital entertainment streaming business justifies its selection in the study (Zhang and Zhang, 2018). The decision to use this company as a case study was likwise informed by the fact that the Californian-based firm controls about 44% of Europes on-demand market share of streaming services (Wayne and Uribe Sandoval, 2021). Its closest competitor Amazon only controls about 32% of the same market (Wayne and Sienkiewicz, 2022). Overall, today Netflix has a subscriber base of about 130 million people and a market presence that spreads across more than 190 countries (Roth, 2022). Its global operations provide a case study analysis of the impact of its marketing plan on consumer behaviors.

Problem Statement

Netflix has enjoyed a steady stream of customers from its conception. However, the firm has been unable to maintain the increase in subscription numbers due to cancellations, especially from long-term users (Cunningham and Scarlata, 2020). This reason explains why, in 2022, the company reported its first loss in subscription numbers (Roth, 2022). The problem was recently exacerbated when Netflix announced a plan to increase its subscription fees. In the first quarter of the year 2022, the firm lost more than 3 million subscribers due to this reason (Roth, 2022). Netflixs revenue model is based on the subscription format where users watch a variety of films, or television shows on a digital platform after paying a monthly or annual subscription fee (Zhang and Zhang, 2018). Therefore, a reduction in subscription numbers implies a similar decline in profitability.

One of the main reasons for the cancellation of Netflixs subscription accounts is its contentious recommendations system, which promises to supply users with movies to to watch. At the same time, some subscribers have claimed to have access to similar movies on alternative streaming applications, such as Hulu (Potter, 2021). Part of the reason for Netflixs decrease in subscription numbers stems from a change in management attitude that affects consumer behavioral practices, such as password sharing (Roth, 2022). The increase in the subscription rate for existing users has worsened matters as well.

To mitigate these challenges, Netflix announced plans to develop cheaper ad-supported subscription plans for its users. However, critics have questioned the fairness of the firms algorithm system, which recommends movies and films to different groups of customers (Mattison and Brouthers, 2021; Xiao, 2020). These criticisms stem from the perception that the company intentionally misrepresents the contents of films to attract additional viewership (Al-Kwifi, Farha, and Zaraket, 2020). Linked to these concerns, Netflix has witnessed a significant drop in its share price due to increased numbers of cancellations. Its rivals are also riling under the same pressure.

Research Aim

As highlighted in this study, the focus of the present investigation is on understanding the effects of Netflixs marketing strategies on its users. However, given that the company has a broad-based marketing plan, the current study will focus on its recommendation system. It is designed to enable paying customers to refer their friends to the company for a reward.

Research Questions

In the course of formulating the research questions for this study, it is important to understand that the present study investigated the impact of Netflix on consumer behaviors and culture. The following research questions underpin the investigation:

What views do customers hold about Netflixs algorithm-based recommendations system?
How does Netflixs algorithm-based model influence consumer behavior?
To what extent has Netflixs marketing strategies influenced consumers cultural preferences?
To what extent has Netflixs recommendation system aroused consumers curiosity about the streaming services recommendation system?

Importance of Study

The findings of this study will be important in bridging the knowledge gap that exists between the implementation of marketing plans and the actual user experience of companies when using selected services. This analogy will be equally useful in helping to understand general trends in the marketing field, particularly as they relate to the consumption of entertainment content. Overall, the findings of this study are useful in generating new content for users worldwide.

Literature Review

This chapter examines the state of the extant body of literature on the research topic. It is fixated on understanding key areas of scholarly research that have been explored with links to the current purpose and objectives of the study. Key sections of this review explore the merits and demerits of the recommendations system and its use in developing marketing plans. This chapter also contains discussions about the impact of different recommendation systems on consumer behavior as well as the role of this system on choice facilitation. The influence of the recommendation advertisement model promotes cultural tastes and consumer autonomy to make decisions about purchasing behaviors that dominate discussions in this section of the study. However, before delving into the details of this analysis, it is important to understand the theoretical foundation of the present investigation.

Theoretical Framework

A dual theoretical framework is adopted for this review. It is predicated on understanding consumer-purchasing decisions based on a review of broad-based factors influencing the actions of users globally (Roth, 2022). At the same time, this theoretical review focuses on the cognitive processes influencing consumer purchasing decisions and the effects of digital marketing techniques on their outcomes (Deakin and Nicolescu, 2022). To this end, the actor network and critical consciousness theories form the basis for the dual theoretical framework mentioned above.

Actor-Network Theory (ANT)

The actor-network theory explains the nature of interactions between human and non-human actors. It suggests that both sets of players operate in a networked system where the actions of one group of people affect those of another. Developed by Michael Callon and Bruno Latour, this theory relies on findings from science, technology and society to explain how human actors engage with other players in the above-mentioned networked system (Syed, 2018). Based on this network of relationships, the ANT has been adopted in different social settings to explain human influences, behaviors, and actions (Ali Abbasi et al., 2022). Stated differently, every actor-network that is in place comprises of players who are known as actors. These players are not necessarily people because they may include things or intangible assets. However, the existence of these networked elements in a common system eventually creates action. Despite the presence of both human and non-human actors in the overall model of engagement, both sets of actors are treated equally.

The ANT model is relevant to the present investigation because the consumption of modern-day entertainment content is a part of a network system. Multiple players participate in the generation, dissemination, and marketing of associated content. Each player in this system has the ability to influence the overall health and outcomes of this model (Caredda, 2022). Therefore, the effect of one actor is analyzed within the boundaries of the impact it would have on other players. This relationship is useful in understanding the impact of Netflixs algorithm-based marketing model on consumer behavior (Mamatha and Geetanjali, 2020). Similarly, it is relevant in comprehending the effects of consumer behavior on a companys digital marketing strategy.

The insights highlighted above reveal that the actor-network theory provides a framework for judging the actions and activities of companies or consumers within a networked ecosystem. In this model, this ecosystem is designed to fulfill an intended objective of balancing the interests of multiple players in the decision ecosystem (Bu et al., 2021). The stability of the overall network depends on the ability of this system to balance the interests of different stakeholder groups (Sun, Habib and Huang, 2021). Stated differently, their interests should be aligned with the vision or purpose of forming the networked system in the first place (Demirtas, 2020). This structured framework is relevant to the current investigation because Netflixs digital marketing strategy is examined within a networked system designed to maximize user preferences, as the core objective.

This networked web of interests forms the basis for the inclusion of the actor network theory in this analysis. Its implication on the current investigation is the examination of Netflixs marketing strategy from two perspectives within and outside of the company. The latter analysis represents the networked model discussed above because it examines the companys interests as it relates with those of others that co-exist with it in the networked system discussed above.

Critical Consciousness Theory

As its name suggests, the critical consciousness theory focuses on explaining peoples cognitive understanding as a predictor of their behaviors. Developed by Brazilian educator, Paulo Freire, the critical consciousness theory uses common logic to evaluate consumer behavior. In the process, it can review marketing materials from companies that sell products or services influencing it (Latif et al., 2019). The justification for using the critical consciousness theory in the present study is fixated on its ability to explain the effects of Netflixs marketing strategies on consumers (MacKay, Chia and Nair, 2021). Particularly, its role is in helping the researcher to understand whether consumers understand the impact of Netflixs algorithm-based marketing strategy, or not is pertinent to its adoption.

The concept of critical awareness features prominently in the operationalization of the critical consciousness theory in marketing literature. This concept was founded on the assumption that people could overcome inherent biases in their decision-making systems by simply understanding the nature of processes that influence their purchasing decisions (Luckner, 2021). Proponents of the critical consciousness model notably used this argument to protect low-income people from predatory marketing campaigns that influence their buying or purchasing plans (Hamilton et al., 2021). Therefore, by understanding the workings of this system, they could become empowered to make incremental changes to their lives that would improve their wellbeing.

To achieve the above-mentioned objectives, the concepts of reflection and action are key tenets of the critical consciousness theory. Indeed, it is assumed that the two impressions could alter structures and conditions that affect consumer behavior (Kim et al., 2018). Reflecting on past practices helps consumers to think critically about the information they consume as marketing messages. This process allows them to detect nuances and hidden marketing messages that influence their decisions (Latif et al., 2019). At the same time, they are likely to identify the main objective of the company and relate it with marketing messages conveyed on marketing media platforms. This process could initiate action, which could align, or clash with the original intention of the marketing message. The outcome is useful in understanding Netflixs complex algorithm-based marketing model.

The concept of critical awareness is achieved in the critical consciousness theory when unique conditions are met within the consumer-marketing matrix of engagement. These circumstances are fulfilled in a multi-stage process characterized by three phases of cognition, which include crucial contemplation, political efficacy, and necessary action (Saleem, Shenbei and Hanif, 2020). In this tripartite framework of engagement, consumers can draw a link between their everyday experiences with the broader social challenges affecting their communities (Saleem, Shenbei and Hanif, 2020). This realization provides a basis for changing consumer behaviors as they experience different stages of critical awareness formation.

The concept of critical reflection, which is one of three states of the critical consciousness theory, will be used as the foundation for the present investigation. It is fixated on the need to promote social justice as the foundational aspect of marketing (Santander Trade, 2021). Therefore, it promotes that idea that peoples everyday experiences cannot be separated from broader social challenges affecting their communities (Leggett, 2020). This critical awareness model allows researchers to focus on larger systems that create injustices to consumers, as opposed to its actions or effects on victims (Santander Trade, 2021). The critical awareness approach to investigating marketing messages described in this analysis is similar to the ANT model mentioned above because they both focus on explaining the nature of interaction between consumers and companies within a larger system or framework that has other players as well. Therefore, there is a synchrony of approach, which makes both theories relevant to the current probe (Meng, 2021). In the context of the present study, the ability to understand Netflixs algorithm-based marketing model is the basis for acquiring critical consciousness.

The Recommendation Advertisement System as a Choice Architect Tool

The growth and spread of technology in the entertainment field has brought a selection of products that consumers can choose to buy. This development has created a surge in information flow to consumers, which has inhibited their ability to make informed decisions on the best type of product to buy or consume (Santander Trade, 2021). Consequently, a media bubble and group think ideology has emerged in the online digital streaming service (Eze, Chinedu-Eze and Awa, 2021). The recommendation advertisement system has been proposed as a solution to this problem because it attempts to match consumer needs and preferences with product types and categories (Meng, 2021). This contribution has created the perception that the recommendation model is an instrument of choice architecture. Stated differently, it can be used to manipulate consumer decisions without the buyers knowledge or understanding (Kornberger and Mantere, 2020). In this regard, it attracts specific risks that could be injurious to the role of the consumer as the choice architect. At the same time, as mentioned above, the recommendation model adopted by digital media companies helps customers to analyze multiple volumes of data to help them choose, that which suits their needs. To this end, there is a need to understand the merits and demerits of the recommendation advertisement model.

Advantages of the Recommendation System

The recommendation advertisement system is designed to favor certain products and not others because of its alignment with a customers core beliefs and preferences. Companies use this recommendation system to improve their marketing effectiveness because it helps them to identify and develop messages that are targeted for a key audience (Eze, Chinedu-Eze and Awa, 2021). From a consumer standpoint, the recommendation system is beneficial because it promotes autonomy. For example, it encourages the personalization of products to improve the overall user experience (Aasak, and van der Linden, 2019). Consumers also prefer to use this system because it minimizes their search costs and time (Eze, Chinedu-Eze and Awa, 2021). At the same time, it eliminates the uncertainty associated with online searches.

Disadvantages of the Recommendation System

Despite the advantages of the recommendation system described above, it can backfire if it fails to heed to the needs of consumers. For example, dissatisfaction could be registered if it undercuts consumer autonomy (Aasak, and van der Linden, 2019). This outcome could undermine the overall goal of introducing the system in the first place because the loss of consumer autonomy is detrimental to the credibility of the purchasing process. In some cases, clogging such systems may build resentment from users who may feel that they are being denied their right to make decisions (Eze, Chinedu-Eze and Awa, 2021). This challenge has not been solved because algorithms do not account for every aspect of a humans cognitive processes when predicting buying decisions (Gonzalez-Vicente, 2021). For example, it may not know ones aspirations, thereby making it difficult to predict whether the system would appeal to them, or not.

Effects of Digital Marketing on Consumer Culture

Culture refers to the set of beliefs, norms, values and attitudes that shape peoples behaviors and actions. In business circles, culture affects multiple aspects of corporate performance, including product development, strategy orientation, policy development, and recruitment strategy analysis, just to mention a few (Antonucci and Varriale, 2020). The introduction of digital marketing techniques in business has created cultural shifts in various areas of product development and marketing as well (DErman, 2021). Particularly, the advent of social media and the conversion of traditional marketing strategies into virtual ones have transformed the relationship between businesses and their customers (Bolat and Korkmaz, 2021). Scholarly research materials, which have investigated this subject, have focused on understanding the effects of social media marketing strategies on consumer behavior with little emphasis on technology-based companies that can use their internal marketing infrastructure to generate adequate data about their customers (Heindl, 2021; Marsden and Henig, 2019). The relatively few numbers of internet-based companies that can generate consumer data via their domestic infrastructure could explain the skewed findings.

The ongoing coronavirus pandemic has formed the basis for which scholars have recently explored the impact of culture on consumer behavior. Particularly, there has been a growing body of literature indicating the potential cultural shift that the pandemic has created on consumer behavior (Rendeci, 2022). The reasons given for this transformation have been linked with digitization and the role that technology plays in sustaining business operations during the pandemic. The need for mediated communication methods emerged during the pandemic as well (Jiao, Xu and Zhao, 2020). They influence processes associated with information dissemination between companies and their customers (Spijkman and de Jong, 2020). Consequently, brand narratives have changed with more consumers preferring to see do it yourself advertisements that empower them with knowledge to create products or services at home (Barrowman, 2019). This change in consumer behavior is a direct product of the lifestyle changes brought by the COVID-19 pandemic on consumer behavior.

The reliance on digital communications during the pandemic period has heralded a new phase of scholarly research, which affirms itself in the entrenchment of technology tools to sustain modern life. Business operations are integrated in this analysis because core activities have been influenced by digitization and automation (Bhandari and Bansal, 2019). The success of online streaming services, such as Amazon and Netflix, have equally thrived on the cultural shift brought by digitization on the entertainment industry (Antonucci and Varriale, 2020). The pandemic further elevated the importance of digital communications in advertisement (Xie et al., 2021b). This development has cemented the effects of digital marketing on consumer culture.

Impact of Recommendation Styles on Consumer Behavior

The recommendations model of marketing engagement works by proposing a predetermined list of products or services to customers for purchase or consumption. It thrives on the assumption that consumers are busy or lazy and prefer to buy products that other people have tried or tested, as opposed to novel ones (Rowley and Oh, 2019). Nonetheless, the use of a recommendations model to expand product outreach has been marred by criticism from scholars who believe it limits consumer options during purchasing (Lopez et al., 2020). Furthermore, a growing number of pundits argue that the algorithms used to recommend products and services on digital marketing platforms are unclear and cannot be used as an accurate predictor of consumer tastes and preferences (García-Canal et al., 2018). Research studies, which have focused on understanding the impact of digital communications on consumer behavior, have shown that changes in attention span have emerged among younger consumers due to extensive social media use (Gehlen, Marx and Reckendrees, 2020). Furthermore, it has been proven that consumers today desire shorter and more relevant advertisements on their feeds (Shapiro, 2020). Therefore, the nature and type of content that can be communicated to them has changed.

Data also shows that consumers desire organic content as opposed to paid advertisements to motivate them to buy goods and services. Therefore, companies that develop marketing campaigns with a social appeal or that can generate organic interest get the best outcomes out of digital marketing platforms (Liam, Kim and Choi, 2021). Research studies also suggest that consumers have become fussier when selecting goods both online and offline (Jena, 2020). However, the best results in digital marketing have been reported among companies that maintain consistency between their online and offline purchasing experiences (Sparre, 2020). Overall, these findings show that marketing campaigns can be recalibrated to suit different recommendation styles appealing to different groups of buyers. Based on the changing dynamics of the online consumer market highlighted above, the marketing development process ought to align with current consumer preferences.

Relationship between Digital Marketing and Consumer Autonomy

The use of technology in business and marketing has changed the nature of interaction between businesses and their consumers. Traditionally, people had the liberty to walk into a shop, choose an item of their liking, and pay for it on the spot. The variety of products or services to choose and pay for was open for everyone to see. However, the growth of the digital space has changed how businesses interact with their customers because the latter cannot physically inspect the goods available for sale within a specific product category (Tran and Smith, 2021). Instead, the customer relies on what a business owner posts online as the basis for making their purchasing decisions. This plan has attracted criticism for its potential to limit consumer autonomy in decision-making (Wang et al., 2020). Stated differently, it has the potential to take away a persons ability to make purchasing decisions based on what they can see or hear online. Instead, it transfers the same power to a business owner who chooses what to display to the customer.

Broadly, the concept of consumer autonomy has been mentioned fewer times in current academic literature compared to other concepts, such as satisfaction and engagement. This trajectory in research discourse could be caused by the complex blend of factors accounting for ones purchasing decisions (Xie et al., 2021a). Therefore, examining the concept of consumer autonomy in decision-making appears to stretch the limits of understanding regarding a persons cognitive processes.

Summary

This literature review has investigated the state of scholarly literature on factors that influence consumer-purchasing decisions. The research has mostly focused on the recommendations-based model of advertisements that relies on algorithms to suggest products and services for purchase. The concepts of consumer autonomy, consumer culture, and stylistic differences in recommendation emerged as core tenets of the review. The ability of the recommendations model to act as a choice-making facilitator in marketing research has equally been investigated in this review. The evidence suggests that the recommendations model has its advantages and disadvantages. Thus, it can have a mixed impact on consumer purchasing behaviors. Consequently, it is important to undertake a context-specific understanding of the research topic. The focus of the present investigation is on the entertainment business using Netflix as a case study. It will help to bridge the information gap highlighted above.

Methodology

This chapter of the study highlights techniques adopted by the researcher in answering the study questions. Key sections of this chapter discuss the research design, data collection techniques used in the study, processes adopted by the researcher in defining the target population, and a review of ethical implications of including human subjects in the probe. These areas of the investigation highlight core tenets of the overall research methodology of the study.

Research Design

The researcher employed the qualitative descriptive research design in the present investigation. This technique focuses on gathering subjective data relating to consumer purchasing decisions and marketing data. The justification for employing this research design stems from its ability to provide researchers with a range of data to use in their investigation (Melnikovas, 2018). Given the unstandardized nature of qualitative information, this research design was consistent with the exploratory nature of the present investigation. Similarly, the research technique was appropriate in obtaining unbiased data because it enabled the researcher to interact with the respondents in their natural environment.

Data Collection

The researcher collected data from one primary source focus group interviews. This type of data was collected in Copenhagen and among a respondent group that was majorly European. Respondents who took part in the focus group investigation were pre-screened to test their understanding of Netflixs recommendation system. Emphasis was made to assess their understanding of the companys algorithm-based model and marketing systems. Those who demonstrated sufficient level of understanding of these marketing instruments were included in the discussion. Those who failed to demonstrate the same level of competence were excluded from the review.

Target Population

The researcher collected data from Copenhagen Business School (CBS) students. This population comprises of people who are between the ages of 19-29. The distribution of respondents across this age range informed the decision to select college students as the target population because the same age group comprises the highest users of Netflix (van Wingerde and van Ginkel, 2021). Additionally, the researcher selected students of CBS because they shared an intra-college communication network that enabled the investigator to interact with the informants safely and freely. The above-mentioned target age group was equally selected for the present study because Netflix subscribers are mostly young people whose views could arguably be used as a premise for developing the companys future products and services (Pande and Kumar, 2020). Therefore, the characteristics of the target population were consistent with the objectives of the study.

To get context-specific data from the student population, the researcher recruited students who have a business background. To this end, members of CBS Management of Innovative Business Operation program were given the first priority in the study. The recruitment process was initiated from a class Facebook program. Students who developed initial interest in the study sent links to potential participants who then responded by signing up for the study. Furthermore, students who were studying in an art-related major were similarly given preferential treatment in the study. Their participation was based on the assumption that they would engage in more creative conversations about art and entertainment, which are the core of Netflixs services, better than others student groups (Leroy, 2022). Collectively, the informants were categorized into two focus groups, which answered unique questions relating to the research process.

Data Collection Instrument

The focus group discussions highlighted above were guided by a collection of questions stipulated in the interv

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