2
Master’s Thesis
BUSINESS ADMINISTRATION (MSC)
TRACK: DIGITAL BUSINESS
A qualitative study on platform dynamics and competitive advantage within the vibrant and growing digital food-delivery industry
Author: Bob van Eerdewijk Student#: 11421657
Institution: Amsterdam Business School (University of Amsterdam) Thesis supervisor: dhr. prof. dr. P.J. van Baalen
Track coordinator: dr. H.P. Borgman Date: 23-06-2017
Competitive Advantage
of the
‘New Delivery’ Platform
Statement of originality
This document is written by Bob van Eerdewijk who declares to take full
responsibility for the contents of this document. I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it. The Faculty of Economics and Business is
Acknowledgements
First, I would like to express my gratitude towards my thesis supervisor, Peter van Baalen, for the useful feedback and interesting discussion sessions and for guiding me throughout the Master’s Thesis process.
Secondly, I would like to thank all the respondents for their openness and willingness to participate in this research. Without their contribution, I would have not been able to finish this study, and therefore I am very grateful for their effort and support.
After months of hard work, I can now proudly present my Master’s Thesis to obtain the academic degree of Master of Science in Business Administration with a specialization in Digital Business.
Yours sincerely,
Bob van Eerdewijk Amsterdam, 2017
Abstract
Purpose. The aim of this paper is to find out how competitive advantage and platform dynamics differ in distinctive business models in the platform economy, considering the online food-delivery market in the Netherlands.
Design/Methodology/Approach. This research follows an exploratory, theory-building, multiple, snapshot route, combining data gathered from different interview settings.
Contribution. Based on a theory-building approach, new propositions will be created about competitive advantage and platform dynamics of this new business model in the online food-delivery industry.
Findings. The findings show how the ‘new delivery’ business model may have competitive advantage over the traditional model and include different perspectives on competitive advantage and platform dynamics of the new business model.
Research implications and limitations. For academic purposes, this study adds to the existing base of platform literature by considering platform dynamics in different business models. It is argued that these concepts behave differently in different kinds of platforms. Practical implications. The findings of this study may help managers in the industry to decide on which factors to focus on, that shape competitive advantage for their firms. It seems that the ‘new delivery’ model has certain advantages over the traditional model.
Originality/Value of the paper. The research will contribute to our understanding of platform dynamics and will consider related concepts, such as switching costs and network effects. In different types of platforms, platform dynamics behave in other manners. Platform-based business models are very popular nowadays and more research is needed to increase our understanding, for that they heavily impact the lives of many. Nearly everyone uses at least one platform, regularly.
Table of contents
Statement of originality ... 3 Acknowledgements ... 4 Abstract ... 5 Overview of figures and tables ... 8 1. Introduction ... 9 2. Literature review ... 11 2.1 Emerging competition in on-demand food-delivery platforms ... 11 2.1.1 Three food-delivery business models ... 12 2.1.2 The ‘new delivery’ opportunity ... 14 2.1.3 Food-delivery platforms in the Netherlands ... 15 2.1.4 Trends in the industry ... 20 2.2 The platform economy ... 21 2.2.1 Two-sided and multi-sided platforms ... 21 2.2.2 Types of platforms ... 22 2.2.3 Network effects ... 24 2.2.4 Platform competition and leadership ... 25 2.2.5 Competition in a connected world ... 27 2.2.6 Competitive advantage in food-delivery platforms ... 29 2.2.7 Motivators of restaurants ... 30 2.2.8 Platform dynamics ... 30 2.3 Research gap and question ... 32 2.3.1 Theoretical framework ... 33 2.3.2 Relevance ... 34 3. Methodology ... 36 3.1 Research design ... 36 3.1.1 Approach ... 36 3.1.2 Case selection & participants ... 38 3.2 Data collection ... 39 3.2.1 Semi-structured interview & questionnaire (platforms) ... 40 3.2.2 Semi-structured telephone interviews (restaurants) ... 41 3.3 Data analysis ... 41 3.3.1 Semi-structured interview & questionnaire (platforms) ... 42 3.3.2 Semi-structured telephone interviews (restaurants) ... 43 3.4 Research quality ... 46 3.4.1 Credibility ... 46 3.4.2 Transferability ... 46 3.4.3 Dependability ... 47 3.4.4 Confirmability ... 474. Findings ... 48 4.1 Insights in the food-delivery market ... 48 4.1.1 The business model ... 48 4.1.2 The market ... 51 4.1.3 Trends in the industry ... 52 4.2 Motivators for restaurants ... 53 4.2.1 Benefits of platform connection ... 54 4.2.2 Cons of platform connection ... 56 4.2.3 Differences per platform ... 59 4.3 Platform dynamics ... 62 4.3.1 Platform perspective ... 62 4.2.4 Restaurant perspective ... 64 4.4 Summary of the findings ... 68 5. Discussion ... 69 5.1 Theoretical framework ... 69 5.2 Competitive advantage of the ‘new delivery’ model ... 72 5.3 Implications ... 73 5.4 Directions for future research ... 74 5.5 Limitations ... 75 6. Conclusion ... 77 References ... 79 Appendices ... 85 Appendix A: Cases selection: restaurants ... 86 Appendix B: Interview guide: online food-delivery platforms ... 87 Appendix C: Filled in questionnaire by Thuisbezorgd.nl ... 89 Appendix D: Interview guide: restaurants ... 91 Appendix E: Complete coding scheme: platform interview & questionnaire ... 92 Appendix F: Coding schemes: restaurant interviews ... 94 Appendix G: Quotation tables: interview & questionnaire ... 103 Appendix H: Quotation tables: restaurant interviews ... 107
Overview of figures and tables
FiguresFIGURE 1: DIFFERENT TYPES OF ON-DEMAND FOOD-DELIVERY FIRMS (DEALROOM AND PRIORIDATA, 2017). ... 13
FIGURE 2: BUSINESS MODEL: CONNECTING CONSUMERS & RESTAURANTS (TAKEAWAY.COM, 2016) ... 17
FIGURE 3: AUTONOMOUS DELIVERY BOT BY STARSHIP TECHNOLOGIES (STARSHIP TECHNOLOGIES, 2017) ... 20
Tables TABLE 1: CODING SCHEME: INTERVIEW & QUESTIONNAIRE ... 42
TABLE 2: CODING SCHEME: PROS AND CONS OF CONNECTING TO PLATFORM ... 44
TABLE 3: CODING SCHEME: DIFFERENCES PER PLATFORM ... 45
1. Introduction
Gone are the days of food delivery being limited to unhealthy fast food. Thanks to a new wave of third party delivery services, hungry citizens in many metropolitan cities in the world are now able to enjoy meals of their favourite restaurants, in the comforts of their own
homes. These ‘new delivery’ platforms backed up by huge delivery fleets, mostly on bikes, are altering the restaurant delivery landscape. Restaurants that did not own delivery logistics before, are now able to deliver meals, which is remodelling the dining culture in big urban centres.
Food delivery is not new; it has been around for over 20 years. However, this new business model is popping up in cities all over the world and seems more interesting than the traditional transaction model, in which the platform is solely passing through the orders to the restaurants. Therefore, the current research aims at investigating the competitive advantage of this new business model over the traditional one.
In this fast-paced world where consumer needs are changing all the time, it is important for managers to assess these needs from time to time and consider the business models that are delivering new value. New technologies make it possible to create different kind of business models. And these business models are meeting the needs of consumers in new, innovative ways. Therefore, it is deemed very interesting to see what shapes the competitive advantage of these new business models. What makes them more attractive for consumers?
For academic purposes, this study sheds light on different concepts from the literature on platforms. The literature on platforms is quite new and many different perspectives exist on what a platform is. However, it is agreed by legion that concepts, such as network effects and switching costs play important roles in the economy of platforms (e.g. Gawer and Cusumano, 2013). Therefore, it is interesting to see how these platform dynamics behave in this new industry and how they shape competitive advantage.
The goal of this study is to find out what the competitive advantage of the ‘new delivery’ business model is and how this is different from the traditional delivery model. Therefore, the research question of this study is as follows: How is the ‘new delivery’ model
advantageous over the traditional delivery model?
To assess competitive advantage of this new business model, the research will study the motivators of restaurants to join a ‘new delivery platform’ as well as the platform dynamics of the new model compared to the traditional model. Therefore, two sub questions emerged.
“What are the motivators for restaurants to join one or more ‘new delivery’ platform(s)?”
and “How do platform dynamics in the ‘new delivery’ business model differ from those in the
traditional model?”
Based on platform theory and on own interpretations of the researcher, five propositions emerged which have been tested through interviews with platforms as well as interviews with restaurants that are connected to a platform. The current study is qualitative in nature and follows an exploratory, theory-building, multiple, snapshot approach using case studies to answer the research question (Thomas, 2011).
The report is structured as follows. In chapter 2, the literature will be examined, including an analysis of the current state of the food-delivery industry in the Netherlands. In chapter 3 the chosen research methods will be justified. Chapter 4 reports the findings of the research. Chapter 5 sets forth a discussion, followed by chapter 6, a conclusion of the research.
2. Literature review
2.1 Emerging competition in on-demand food-delivery platforms
A new wave of tech-driven, on-demand food-delivery platforms is disrupting the restaurant delivery landscape. International firms such as DoorDash, Deliveroo and UberEATS are changing the market for food delivery, by taking over the delivery process from partnering restaurants, or delivering dishes for restaurants that do not own the
capabilities to do so themselves. These digital meal-delivery platforms broaden both choice and convenience, and allow consumers to place orders from a considerable line-up of restaurants, just with a single press of a button.
In a recent study, McKinsey (2016) predicts online delivery to grow by 25% from 2015 to 2018, after which it will decline to a growth rate of 14,9% per year until 2020. Based on a six-month research, the consultancy powerhouse estimates the worldwide market for food delivery at €83 billion, accounting for 1% of the total food market and 4% of food sold by restaurants and fast-food joints. 90% of the market is still served by the traditional way of delivery, where restaurants have their own fleets of delivery staff and most orders are still placed by phone.
Yet, digital technology is transforming the market. Technology-acquainted customers expect high levels of convenience in the process of ordering food, by using online apps and websites. Consumers of new online food-delivery platforms can be identified by a few key-factors; they rarely change platforms, speed of delivery is critical, most orders come from homes, and weekends are most popular.
2.1.1 Three food-delivery business models
Three distinct business models of on-demand food-delivery platforms are currently delivering the needs of hungry consumers. In this research, only firms that deliver prepared meals are addressed, therefore companies that deliver recipe boxes or groceries are excluded. Traditional model (delivery software)
First of all, the ‘traditional’ business models have been around for 15 years, and are platforms where consumers can choose between a wide array of restaurants and dishes. These kinds of marketplaces simply connect hungry citizens with restaurants, where the restaurants take care of the delivery process. These platforms charge around 10% to 15% service fee and have proven to be highly scalable. Examples that have had remarkable growth are JustEat, Delivery Hero, Foodpanda and GrubHub (Nextjuggernaut, 2015). These platforms do not facilitate the delivery logistics for the restaurants. Restaurants connected to this type of platform are listed on the website and orders are passed to the restaurants by fax or ticket machine. The restaurant then takes over the order and delivers the meal.
‘New delivery’ model (own fleet)
The more recent type of ‘new delivery’ firms, that made their entries in 2013, are digital platforms (e.g. DoorDash, Deliveroo, foodora and UberEATS) and use technologies to their advantage, bringing together consumers and restaurants through their websites and Uber-style apps. They also manage the delivery and therefore bring along a significant amount of
operational work, due to complex logistics, powered by the abundance of big-data. They control their own fleet of delivery workers, which makes these types of firms less scalable. However, they often charge higher commission rates of 20% up to 30% (Nextjuggernaut, 2015).
Cloud kitchens (vertically integrated)
The third category is the fully integrated business model, also known as ‘cloud kitchens’. They differ from the second model, by integrating the cooking process in the model,
therefore becoming the first step to fully digitized restaurants. These vertically integrated firms include start-ups like Sprig, Maple and Munchery. During the time of writing, no vertically integrated business models have been observed in the Netherlands. Please, see figure 1 for an overview of the different type of on-demand food-delivery business models.
FIGURE 1: DIFFERENT TYPES OF ON-DEMAND FOOD-DELIVERY FIRMS (DEALROOM AND PRIORIDATA, 2017).
2.1.2 The ‘new delivery’ opportunity
‘New delivery’ models are extending the market of food delivery to include new target groups of restaurants and consumers. Restaurants that initially did not offer delivery services can now offer the same dining experience at home. A so-called dispatch management system is at the base of an on-demand delivery platform. It provides the operational teams with an overview of the pickups and deliveries, as well as an end-to-end solution for the delivery process, as a whole. Furthermore, by using real-time tracking data of their drivers, these platforms are able to cut down delivery times, significantly. Therefore, these firms cater to time-sensitive consumers that want to eat well, but do not want to or simply are not able to spend too much time in the kitchen and are benevolent to spending a bit more money on convenience. In addition, the customer can track their delivery at all times and will receive push notifications about the journey, therefore increasing customer experience. Furthermore, these firms have the ability to advertise on delivery fleet workers and packaging creating a big advantage (Dealroom and Prioridata, 2017). Also, these firms are positioned as premium. The cons of these firms are that they are still highly loss making, but with significant growth numbers each year. Investors seem to believe in the opportunity as the number of investing rounds is still increasing. Top funded companies are Delivery Hero with over €1.2 billion in investments, Deliveroo with €431 millions of investments, HelloFresh with €353 million and Foodpanda with €293 million.
Online ordering penetration rates are expected to rise from 36% in 2016 to 58% in 2020 (McKinsey, 2016). This forecast is based on historical patterns; we saw the same trend happening in the flight-booking industry. This results mainly from high levels of funding in the industry and huge marketing expenditures. As users are increasingly adapting their behaviour to exploit new technologies, the use of smartphones is expected to grow in line
with these online penetration rates. McKinsey (2016) predicts the market for ‘new delivery’ business models to reach more than €20 billion by 2025.
The top five global companies account for an aggregated valuation of over €10 billion and the top two, GrubHub and JustEat, had their IPOs in 2014. It is expected that Delivery Hero and Deliveroo will go public within the next few years with current valuations of €2.7 billion and €1 billion, respectively. Valuation-to-equity ratios of both companies are high, with investments of €1.2 billion in Delivery Hero and €400 millions of funding in Deliveroo (McKinsey, 2016). It seems that investors expect rapid growth for these platform-based companies. As they do not share their financials, we can only speculate on their revenues and profits.
2.1.3 Food-delivery platforms in the Netherlands
Since 2000, food delivery rapidly evolved due to new digital technologies for computers and smartphones. In 2000, Jitse Groen’s platform Thuisbezorgd.nl was the most successful new market entrant. Thousands of restaurants joined his platform for delivering meals. After 2010, more web shops for food delivery have opened (FSIN, 2016). The fast-growing food and beverage delivery market can be segmented in two parts; meal delivery, and cold food & beverage delivery. In 2016, the total meal-delivery market equated around €1 billion (59% of total), where digital platforms covered €350 million (35%) of this part of the market (FSIN, 2016).
In the Netherlands, four companies are most known for on-demand food delivery; namely Thuisbezorgd.nl (2000), Deliveroo (November, 2015), foodora (May, 2015) and UberEATS (September, 2016). ‘Aggregator’ Thuisbezorgd.nl is the major player, but Deliveroo and foodora are already shaking up the market, by entering the market with their ‘new delivery’ business models, where after also Thuisbezorgd.nl is now partly using this strategy to cover restaurants that do not have delivery services. However, their core business remains focused
on being a transaction platform for ordering meals. UberEATS, the most recent entrant, brought its existing expertise into the Dutch market with an aggressive 2-month, free-delivery strategy. However, Deliveroo, foodora and UberEATS, presumably, still only cover a very small percentage of the market. TringTring is another small firm, but seems to be delivering less meals and is starting to focus on groceries and products from stores. Based on research from Dealroom and Prioridata (2017), Thuisbezorgd.nl is dominant in the Dutch market. However, they assess that Deliveroo is leader in the premium-segment. The research shows that Thuisbezorgd.nl has around 90% of the market. Deliveroo is accounting for almost 5%. And UberEats, foodora and ‘others’ share the remaining 5% (Dealroom and Prioridata, 2017).
In this research, I will focus on ‘new delivery’ business models in the Netherlands, such as Deliveroo, foodora and UberEATS. Thuisbezorgd.nl also delivers using their own delivery staff, but this covers only a small percentage of their total business. I will now elaborate on each specific firm.
Thuisbezorgd.nl (Takeaway.com)
Takeaway.com is one of the leading platforms in online food ordering, currently connecting consumers with restaurants in 9 countries in Europe and in Vietnam
(Takeaway.com | leading online food delivery marketplace, n.d.). Founded in 1999, in the Netherlands as Thuisbezorgd.nl, the platform connects hungry consumers to restaurants (see figure 2) and takes a flat average of 12,1% commission fee, per order, from the restaurant, in 2016. This fee has risen from 9,5% in 2014, to 10,2% in 2015. The reason can be found in their annual report of 2016: “We occasionally increase our commission rates to reflect the continuous improvement in our value proposition to restaurants, including our investments in marketing and technology as well as our ever-expanding network of both consumers and restaurants” (Takeaway, Annual Report, 2016, p. 27). The value they offer is a source of
orders for restaurants and online payment services. The platform is available through browsers and smartphones. Furthermore, to offer a broader selection of restaurants to the consumer, Thuisbezorgd.nl started to deliver on bicycles, under the name ‘Scoober’. As can be read in the annual report 2016 (Takeaway, Annual Report, 2016), in 2016, delivery expenses were almost 1/3 of the total costs of sales (worldwide).
In 2016, revenues were little over €55 million. In 2015, revenues were little under €42 million. Most important cost factor (marketing) was around €10 million in 2016, in the Netherlands. In Germany, revenues were €36 million and marketing costs €51 million, in 2016. Quite remarkable seems to be the fact that half of the company’s revenues is spent on marketing activities in Germany. It seems that Thuisbezorgd.nl has a well-established presence in the Netherlands and is currently focusing on promoting activities in Germany. The EBITD (Earnings Before Interest & Taxes) is €34 million in the Netherlands, which converts to a ±60% gross margin, which seems to be remarkably high. As variable costs are low to non-existent, the business of merely acting as a transaction platform seems highly scalable. In September 2016, Takeaway went public, raising €328 million, resulting in a company value of €993 million.
Deliveroo
Founded in 2012 by William Shu and Greg Orlowski, Deliveroo now delivers meals in 171 cities, in 12 countries, working together with over 20.000 restaurants (Deliveroo, 2017). Currently, deliveries are made in the UK, the Netherlands, France, Germany, Belgium, Ireland, Spain, Italy, Australia, Singapore, Dubai and Hong Kong. The concept is quite like Uber’s taxi platform. Deliveroo does not employ its riders directly, but the drivers are self-employed contractors. By bike, they pick up food deliveries from restaurants, that are accepted through popups on their mobile devices. The application uses real-time data and algorithms to create a good match between demand from restaurants and diners with riders in the respective area. Orders can be placed directly, or planned up to one day in advance. The journey of the delivery rider can be tracked down via GPS-tracking. It is a clever business model, however Deliveroo has had some attacks by the press and drivers about the
employment arrangements and profitability issues. Deliveroo focusses on luxury restaurants and gourmet food, but tries to offer a wide range of products. One burger is not the same as another. If a restaurant cannot handle the demand, more restaurants will be added to the network to balance the supply and demand. On average, Deliveroo is able to deliver meals within 30 minutes, charges a flat fee of €2,50 to the customer for delivery costs and takes a commission of 20-30% per order from the restaurant.
In the Netherlands, Deliveroo is currently delivering in 11 cities (Deliveroo, 2017). General Manager Philip Padberg, is running the Dutch part of the company based in Amsterdam. Currently, food is delivered in Amsterdam, Den Haag, Utrecht, Haarlem, Leiden, Delft, Breda, Den Bosch, Eindhoven, Rotterdam, Tilburg. Presumably the firm boasted around €150 million in revenues in 2016. However, the firm is recording significant losses. In 2015, the firm lost £1.4 million, in 2016 even £18.1 million (The Guardian, 2016). However, in 2016, Deliveroo received almost $275 million of investment in a Series E.
This will be used for innovation in technologies, which can be seen in the new Deliveroo ‘Editions’ (earlier dubbed as RooBoxes). It is a delivery kitchen concept (Wired, 2017) that serves to deliver meals in areas where there are people but no restaurants.
foodora
Just like Deliveroo, foodora is an on-demand service for food delivery. Focused on high-quality restaurants, operating in 10 countries, offering meals from over 8.000 restaurants (foodora, 2017). The firm was known as Volo GmbH when it was founded in Munich,
Germany in 2014. After one year Rocket Internet acquired the company and changed the name to foodora (Rocket Internet, 2015). In September, 2015, Delivery Hero acquired the firm from Rocket Internet. Delivery Hero merged foodora with its existing brand Urban Taste, and both are now operating under the name foodora (South China Morning Post, 2015). Foodora delivers meals by bike and aims for an average delivery time of 30 minutes, and allows orders two days in advance. Delivery fee is €2,50 for the consumer and around 30% for the restaurant.
UberEATS
UberEATS is a subsidiary of Uber, launched in 2014 by the name of UberFRESH in California, currently delivering food in 83 cities, in all continents. UberEATS is also
charging €2,50 delivery fee to the consumer and around 30% to the restaurant. On the website, UberEATS claims that they deliver the food on average within 15 minutes
(UberEATS, 2017). UberEATS works with contractors and pays them €5 dollar per delivery. If you are already riding with Ubers’s taxi service, you are also able to deliver food with UberEATS.
TringTring
TringTring is a small Amsterdam based start-up, run by Roel Mos, established in 2015. Next to food they deliver groceries and packages, in Amsterdam and Utrecht. All by
bike to support the environment. They offer €5 per ride to their drivers and offer a delivery time of less than 60 minutes. Delivery fee for consumers is also €5.
2.1.4 Trends in the industry
Consumers are looking for convenience. On-demand delivery and out-of-home consumption of food is a result (Dealroom and Prioridata, 2017). The report from Dealroom and Prioridata (2017) says that according to a recent survey in the UK, most restaurants think this trend is good for business. Just Eat and Takeaway.com profited most from this trend, both with 50%+ margins. Takeaway.com managed to create a $1 billion business in a
relatively small market. Deliveroo, UberEATS and Amazon are now in the market offering a service with delivery logistics, increasing operating risk and requiring high population
density. These new models constructed a new premium market segment, instead of capturing a significant share of the existing market. Online penetration rates are still only at 15% in a large part of Europe, 30% in the Netherlands and 50% in the UK, so there is still a huge market potential. Dealroom and Prioridata (2017) expect that the different kinds of business models might converge soon. In addition, they prospect greatly the advancement of
autonomous delivery (e.g. Starship Technologies, please see figure 3). Furthermore, as is discussed before, Deliveroo is experimenting with Deliveroo ‘Editions’ that might be able to supply areas with meals that are currently out of reach for most restaurants, creating a whole new market.
2.2 The platform economy
In contrast to the traditional model where firms are competing for consumers, the new platform economy is giving rise to a whole new approach where consumers are directly connecting, interacting and engaging with one and each other through a platform, such as AirBnb, Amazon and Uber. This new trend has enabled different platform-based firms to achieve sky-high valuations, based on very asset-light firm structures. These firms do not own much capital-intensive machinery or other investments, but merely act as a transaction or marketplace platform and connect two groups of people that are able to fulfil a need or exclaim a demand. In this section, I will delve deeper into these platform dynamics. 2.2.1 Two-sided and multi-sided platforms
Platforms have become important in many different markets, especially high-technology ones (Gawer and Cusumano, 2015). However, it remains unclear to what the term platform refers to in academic research and management practice. Many scholars agree though, that platforms are different from regular products, in a way that they create positive feedback loops (or network effects), among the platform and its users. If more people use the platform, and more companies create complementary products and services for the platform, the more valuable it becomes for the platform owner and the users.
While the term platform is used in many ways, practitioners and scholars alike seem to agree that “multi-sided platforms (MSPs) aim at facilitating and enabling interaction between different groups of users.” More specifically, “the platform acts as a mediator between a group of end users and one (or more) groups of actors which hold the ownership of the products sold or the resources to deliver services (through the platform)” (Bhargava and Choudhary, 2004, Bhargava, 2014).
Unlike, for example a platform such as Windows on which third-party companies can build complimentary products for the operation system, on-demand food-delivery platforms
are different kinds of platforms. Restaurants can join the platform and receive a listing on the website, however they do not build additional services or products on the platform. In the next section, the main types of platforms will be discussed.
2.2.2 Types of platforms
Gawer and Cusumano (2015), both demonstrating a particularly large interest in
platforms, show that platforms have been studied from many different perspectives. Yet, they argue that there are two main types of platforms, namely product platforms and industry platforms. To summarize their findings, they define product platforms (or company/internal platforms) as “a set of assets organized in a common structure from which a firm can efficiently develop and produce a stream of derivative products” (Muffatto and Roveda, 2002, as cited in Gawer and Cusumano, 2015, p. 37). They define industry platforms as “products, services, or technologies that are similar in some ways to the former but provide the foundation upon which an entire industry ecosystem, including both the platform owner and third-party firms as well as users can develop their own complementary products, technologies, or services” (Gawer and Cusumano, 2002, and Gawer, 2009b, as cited in Gawer and Cusumano, 2015, p. 37/38).
Certain researchers in industrial organization economics started using the concept of platforms to define markets with two or more sides, known as two- or multi-sided markets, as is defined before (Evans, 2003, Rochet and Tirole, 2003, 2006, Parker and Van Alstyne, 2005, as cited in Gawer and Cusumano, 2015). The concept of these multi-sided markets can apply to both industry and supply-chain platforms, but it does not fully conform to either type of platform.
Gawer and Cusumano (2015) mention that a supply-chain platform is a special kind of product platform, defining it as follows: “In a supply chain, a set of firms follow specific guidelines to supply intermediate products or components to the product owner or the final
product assembler (Doran, 2004, as cited in Gawer and Cusumano, 2015, p. 38). To my account, this definition comes close to the concept of on-demand food-delivery platforms, where the supply-chain of food-delivery can be divided into three processes, namely
ordering, cooking and delivering. The on-demand food-delivery platform has contracts with different ‘users’ (i.e. restaurants and delivery staff) of the platform, each fulfilling their unique role in the supply-chain. Gawer and Cusumano (2015) concur with this point of view by demonstrating that two-sided platforms that are only used to facilitate an exchange, without the possibilities of other firms to innovate or add complimentary services to the platform, seem to fall within the supply-chain category.
There is still no widely agreed acceptance on the definition and different types of
platforms, thus other researchers have different viewpoints. Evans and Schmanlensee (2008) classify platforms into four types: ‘Exchange’ platforms aim at facilitating transaction of goods or services. ‘Advertiser-supported media’ aims at connecting advertisers and audience. ‘Transaction system’ platforms bring together sellers and buyers. The last type is
‘hardware/software platform’ andaims at connecting software developers to users. Ardolino, Saccani and Perona (2016) argue that this typology is relevant for platforms that enable interactions, therefore it is deemed useful for the current study. However, they argue that the transaction category can be integrated in the exchange category, therefore they propose the following platform typology, which aims at describing the interaction between platform sides: Matchmaking platforms, exchange platforms and maker platforms. For this research, on-demand food-delivery platforms are grouped under the exchange category, because these types of platforms facilitate a transaction of a service (home delivered meal) between restaurants and consumers.
As can be read in the article by Ardolino et. al (2016), different agents are involved in multi-sided platforms. These different ‘sides’ are categorized in the following type of agents:
Platform manager, supply side, demand side, peer side, advertisement side and platform development side. In the current research, we deal with a supply side (restaurants), a demand side (consumers), and the platform manager (the platform, including its drivers).
2.2.3 Network effects
Coherent with the phenomenon of two-sided and multi-sided platforms is the concept of network effects (or network externalities). Gawer and Cusumano (2013) define network effects as follows: “as more users adapt the platform, the more valuable the platform becomes to the owner and to the users because of growing access to the network of users and often to a growing set of complementary innovations. In other words, there are increasing incentives for more firms and users to adopt a platform and join the ecosystem as more users join” (p. 417).
Two types of network effects are considered in the literature: direct network effects (same-side or intra-side) and indirect network effects (cross-side or inter-side) (Katz and Shapiro, 1985, Luchetta, 2013). Direct network effects play a role when the value that a user of one side receives increases with an increasing number of other users in the same side. Indirect network effects arise when the number of users on one side of the platform increases the number of users on another side of the platform.
Network effects can be very strong, especially direct network effects between the user and the platform (Gawer and Cusumano, 2015). These effects can become even more
powerful when a technical compatibility or interface standard makes it expensive or difficult for users to switch platforms or to use more than one platform simultaneously
(‘multihoming’). Indirect network effects can be as strong as or even stronger than direct network effects. This is the case in food-delivery platforms, where more restaurants attract more users due to a bigger offering, and vice versa, due to more potential customers on the website. However, as restaurants deliver only within a two-kilometre radius, this effect only occurs to a certain saturation point, where all restaurants/customers in the area are already
using the platform. Moreover, it seems that the platforms are more selective in which restaurants they allow access to the platform, than more open platforms, such as Facebook, that anyone can join. This might be an interesting subject to study, to see to what extend and in what way network effects play a role within the food-delivery platforms.
For platforms to achieve success, it is necessary to achieve a critical mass of end users to attain growth and scalability, which depends heavily on network effects (Katz and Shapiro, 1986). Also, the accumulation of data plays an important role to create strong network effects (Ruutu, Casey and Kotovirta, 2006). In the start-up phase, many companies face a common problem, called the ‘chicken-and-egg’ scenario, where too few users attract too few service providers and the other way around. One strategy to solve this problem is to use a free model on one side of the market and a paid model on the other side (Parker and Van Alstyne, 2005). 2.2.4 Platform competition and leadership
Platform competition is defined as the competition between groups or coalitions of firms, that contribute to the same platform, through the creation of products, services, or
technologies (Gawer, 2011). These coalitions do not belong to the same company, per se, not even to the same supply chain.
On-demand food-delivery platforms are not platforms where multiple companies are complementing the platform with their own products or services. In other words, these platforms are not industry platforms. Therefore, this section will refrain from using the term platform competition and, instead, will focus on the competition between on-demand food-delivery firms, as exchange platforms or supply-chain platforms.
“Platform leaders are organizations that manage to successfully establish their product, service or technology, as an industry platform. As such, they get to a position to drive the technological trajectory of the overall technological and business system of which the platform is a core element” (Gawer, 2011, p. 3). Because online food-delivery platforms are
not complemented with services or products from different firms, and thus are not industry platforms, based on the definition of platform leaders above, food-delivery companies cannot become platform leader. However, I disagree with Gawer, for that I believe that a platform leader can also be a market leader for a certain type of platform, and does not necessarily needs to be an industry platform. This might be a case of vague terminology.
For firms to be able to cope with the risks and investments in platform development, the platform needs to bear competitive advantage, which means it must lock in consumers. For the platform initiator, openness in platform development may reduce switching costs for users and increases competition levels (Eisenmann, Parker, and Van Alstyne, 2009). On the other hand, feedback loops are inducing a winner-take-all situation, when the platform leader can lock out competitors, which might have a negative effect on overall industry innovation.
Also, strong network effects and few differentiation possibilities accelerate a winner-takes-all scenario (Eisenmann et al., 2006). However, Ruutu et al. (2016) argue that
monopolies may be overruled by new entrants with new technologies or service concepts. In addition, Rysman (2009) argues that service providers offering complimentary services can differentiate their offering, which may lead to the same situation. Eisenmann et al. (2011) also mention that a company can leverage its assets in one industry to attain competitive advantage in another industry, just like Uber did by introducing UberEATS.
Firms that take advantage from the power of platform business models have grown considerably in the last decade. Some platforms are known by all, such as Facebook, Amazon, the Apple Store and Windows. But more platform ecosystems are advancing through the digitalization of products and services, in many different industries, and have proven to have true disruptive power (Evans and Gawer, 2016).
Although there have been important contributions to the economy (higher productivity, more efficient asset utilization, source of innovation), the fact that platforms can dominate
markets and erode competition has troubled some people (Evans and Gawer, 2016). Dominance of a platform leader in a certain industry may potentially negatively affect competition and therefore negatively affect innovation (Gawer and Cusumano, 2015).
Some of the preceding text implies that a winner-take-all situation is likely to form in digital platforms. However, the market leader in a certain platform industry may be
effectively challenged if young firms keep on investing in technologies and look for (new) ways to differentiate their services. This is in line with the argument of Cusumano (2010), who concluded that “as long as there is room for companies to differentiate their platform offerings, and consumers can easily buy or use more than one platform, then it is unlikely for one dominant platform to emerge—unless the direct or indirect network effects are
overwhelmingly strong” (p. 34).
Will it be possible that one firm is going to dominate the on-demand food-delivery market? Or is there room for different firms with distinct value propositions serving their own niche markets? What are these platforms offering to the restaurants and the consumers? 2.2.5 Competition in a connected world
The abundance of big data from the increasing number of smart products we embed in our business operations allows companies to optimize their services or products in many new ways (Porter and Heppelmann, 2014). Smart, connected products collect data by using algorithms and insightful analytics to increase production output, efficiency and utilization. In the case of ‘new delivery’ firms, a platform application for smart phones, is monitoring data from restaurants, drivers and consumers simultaneously, thereby aiming at service optimization.
As we know from Porter’s widely accepted ideas, in a given industry five forces drive competition. To develop an effective strategy that increases the firms’ long-term profits, the firm should understand how these five forces affect profits (Porter, 2008).
However, in a changing connected world, the competition within industries is being reshaped. A food-delivery company, for example, can now extend its business model of solely delivering meals on mopeds, by including a strong focus on information technologies into its core business model and day-to-day activities. Therefore, the firm needs to understand that it becomes only one actor in a much broader product system (Porter and Heppelmann, 2014), where the firm can now offer a bundle of connected products and services to satisfy the needs of their customers.
Although, industry structures are shifting, the basics of competitive strategy remain. When a firm can differentiate itself, operation costs might be lower and/or it can charge higher prices than its competitors, thus achieving competitive advantage and higher profitability and growth.
At the core of competitive advantages lays operational effectiveness (OE). “OE requires embracing best practices across the value chain, including up-to-date product technologies, the latest production equipment, and state-of-the-art sales force methods, IT solutions, and supply chain management approaches” (Porter and Heppelmann, 2014, p. 76).
Companies that do not operate effectively, will loose from competitors on both quality and cost, however, operational effectivity is highly imitable and, thus cannot be seen as a sustainable form of advantage (Porter and Heppelmann, 2014).
Operational effectiveness has to do with doing things right. Strategic positioning, on the other hand, focuses on differentiating yourself from the competition, by choosing between different ways of proposing a unique value set to the consumer. “Strategy requires making trade-offs: deciding not only what to do but what not to do” (Porter and Heppelmann, 201, p.76).
The emergence of smart products is raising the standard of operational effectiveness. Firms need to determine in what way they can include smart power into their products and
services, which has already resulted in an increasing amount of best practices across the value chain.
In the end, competitive advantage is achieved by implementing the right strategy. Porter and Heppelmann (2014) argue that firms need to face 10 new strategic, interdependent choices to construct a general strategic position. Please see the article to gain more
understanding on these 10 strategies.
2.2.6 Competitive advantage in food-delivery platforms
More and more people are integrating digital platforms into their lives. Moreover, it seems that people will tend to turn to one platform to rely on, the same happened with e.g. FaceBook, Uber and AirBnB. Currently, Thuisbezorgd.nl is the market leader in the Netherlands in food-delivery, however the new business models of Deliveroo, foodora and UberEATS are challenging the status quo. Consequently, it is deemed very interesting to look at the components that shape competitive advantage in this vibrant industry and especially when we focus on this ‘new delivery’ business model. Is there a competitive advantage of this new business model?
While the food-delivery platform has both the side of the restaurant (supply) and the side of the consumer (demand) on board, we can take two perspectives on competitive advantage of the platform. The platform might excel in the value it is offering to the
restaurants, and simultaneously offer a less than good experience to the consumer. However, these values might also intertwine, e.g. when a driver delivers a meal in good condition it is beneficial for both the restaurant and the consumer. The current research will offer an initial framework for factors that influence competitive advantage for ‘new delivery’ food-delivery platforms, focusing on one side of the market, i.e. the restaurant side. Based on this
framework, we will assess the competitive advantage of the ‘new delivery’ business model in comparison with the traditional model of market leader Thuisbezorgd.nl. To assess the
competitive advantage of the ‘new delivery’ business model this study looks at the motivators for restaurants to join a food-delivery platform. In addition, the study will consider different concepts that shape platform dynamics, such as network effects and switching costs, which are useful concepts to assess competitive advantage of the platform.
2.2.7 Motivators of restaurants
One of the most obvious reasons for restaurants to join a food-delivery platform could be related to the potential increase in revenue and larger customer base. However, there might be more reasons for restaurants to join these platforms. Expected motivators are an increased customer satisfaction due to faster delivery times. In addition, it is expected that the ‘new delivery’ model is attracting a different type of restaurant and is offering higher service levels to the restaurant. While the traditional model is solely acting as a transaction service for restaurants, the ‘new delivery’ model is offering a different value proposition by servicing the delivery process for the restaurant. This enables restaurants that do not have delivery logistics at hand to deliver meals and thus increases the service that these restaurants receive. In this way, the ‘new delivery’ platform is creating a different collaboration with the platform that is expected to be more durable and advantageous for both the platform as the restaurant. Therefore, platform dynamics such as switching costs and network effects seem related to the motivators of restaurants to join a platform in order to assess competitive advantage of this new business model. The next section will elaborate on this.
2.2.8 Platform dynamics
Switching costs are expected to be relatively low in this industry when we look from the perspective of the consumer. Almost all firms are charging the same amount of delivery fee. However, they seem to have, more or less, the same restaurant offer on their platforms. Therefore, it might be suggested that the switching need of consumers is also relatively low.
On the other hand, from personal experience it seems unreasonable to stick to one platform. After the restaurant is chosen it does not matter much which platform is delivering the selected meal.
For the platforms, it is therefore important to differentiate from their competitors by creating a unique portfolio of restaurants. In order to achieve this, one might argue that it is very important to create loyal partners, i.e. restaurants, that decide to stay connected to only one platform, on a contractual basis. Therefore, switching costs from the perspective of the restaurant are an important factor in competitive advantage for the platform. If the platform manages to succeed in creating high switching costs for the restaurant, through e.g. high service levels or marketing benefits for the restaurant, the platform might gain competitive advantage.
The other important notion that shapes platform dynamics in the literature is
concerned with network effects. The literature seems to agree upon the fact that when more actors join the network or platform the value either side of the platform will receive
increases, either through direct or indirect network effects. Think of for example the game console Xbox. If more developers create games on one side of the platform, more players join the demand side of the platform, because they can choose between more games to play. The other way around, if there are more players on the platform, the incentive for developers to create games increases. But how do these dynamics work in the food-delivery platform industry? When there are more restaurants on one side of the platform, this seems beneficial for the consumer, due to a broader choice. However, one could logically argue that too many of the same type of restaurants might create an exuberance of choice leading to the
assumption of lower quality. It also might increase competition on the restaurant side of the platform, which is lowering the incentive for restaurants to join the platform. Furthermore, if there are too many restaurants compared to consumers, the restaurants might receive too little
orders. The other way around might result in too little choice for the consumer. It seems that balancing the two sides of the platform is more important in this industry than achieving a high number of platform participants, especially because the point of critical mass is assumed to be lower than in traditional platforms with huge amounts of participants.
2.3 Research gap and question
There has been quite some research on the concept of platforms and platform dynamics. However, the literature did not dive into on-demand food-delivery platforms before and, neither in platform dynamics between different established companies as well as many food-tech start-ups in this industry. The industry is cutting-edge and growing, which makes this a highly interesting research area. Online food delivery is not a new thing; it has been around for over 15 years. However, the ‘new delivery’-model is a completely new business model, incorporating the delivery process into the value proposition, and
consequently, the management of complex logistics is becoming the core business of these young companies.
The current study will focus on these digital food-delivery platforms; i.e. platforms that facilitate a transaction of a service, in this case the delivery of meals from restaurants to consumers. This research attempts to find out what the competitive advantage of these ‘new delivery’ platforms is, over the traditional model of ‘aggregator’ Thuisbezorgd.nl.
Accordingly, the following research question materialized:
How is the ‘new delivery’ model advantageous over the traditional delivery model?
To assess competitive advantage of this new business model, the research is focusing on the motivators for restaurants to join a ‘new delivery’ platform, as well as the different platform dynamics that play a role in shaping competitive advantage. Therefore, two sub questions have been created:
1) What are the motivators for restaurants to join one or more ‘new delivery’ platform(s)?
2) How do platform dynamics in the ‘new delivery’ business model differ from those in the traditional model?
2.3.1 Theoretical framework
Based on the theory above and own interpretations of the researcher the following assumptions have been made that will be empirically tested. It might be that other or
additional motivators are found during the research; therefore, this theoretical framework will return in the discussion section. The following propositions are related to the expected results of the research.
The most obvious reason for restaurants to join a food-delivery platform is related to an expected increased customer base and related revenues. When the restaurant is connected to the platform they gain higher online visibility and thus increase orders. Therefore, the first proposition emerged.
P1: Restaurants join ‘new delivery’ platforms to increase their customer base and revenues.
Due to decreased delivery times, it is expected that customer satisfaction levels of restaurants that are connected to a ‘new delivery’ platform are higher. The second proposition is as follows.
P2: The ‘new delivery’ model is increasing customer satisfaction due to decreased delivery times.
In addition, a new type of restaurant is attracted through the ‘new delivery’ model. It attracts a type of restaurant that did not have any delivery logistics at hand and therefore is creating a whole new market by offering delivery services in their value proposition and attracting a new customer base or increased customer base. It is argued that the ‘new
delivery’ model is collaborating with the restaurant on a new level in comparison to the traditional model. Therefore, the third proposition emerged.
P3: The ‘new delivery’ model offers higher service levels to the restaurant than the traditional model.
Because of the latter, it is argued that due to higher service levels, the switching costs of restaurants to change platforms is perceived as high, in contrast to the traditional model, where switching costs are expected to be lower. Therefore, proposition four is as follows.
P4: Switching costs of restaurants to change platforms is perceived as high.
Related to the concept of switching costs is the concept of network effects. The literature on platforms seem to agree upon the fact that an increase in one side of the platform will lead to higher value of the same side (direct network effects) or higher value on the other side of the platform (indirect network effects). However, this study argues that platform dynamics behave differently in distinct industries and business models. When more restaurants join the platform, this increases competition for the restaurant. Because the restaurant only delivers within a certain radius (around 2.2km), there is a limit on the number of restaurants and customers that can be connected in a certain area. Therefore, it is argued that in contradiction to increasing the number of users on both sides of the platform, the ‘new delivery’ model is more focused on balancing the two sides, in order to maintain a certain amount of orders for the restaurants and a broad set of choice for the consumer. The fifth and last proposition emerged.
P5: The ‘new delivery’ model is more focused on balancing the sides of the platform, than increasing the users on each side, infinitely.
2.3.2 Relevance
By answering the research question, this study will broaden our knowledge of the current situation of on-demand food-delivery platforms in the Netherlands and the
competitive advantage of the ‘new delivery’ business model. This explorative approach may be of great value for firms, currently operating in this industry. In this fast-paced world where consumer needs are changing all the time, it is important for managers to assess these needs from time to time and consider the new business models that are delivering new value. New technologies make it possible to create different kind of business models that are meeting the needs of consumers in new, innovative ways. The results of this study will help managers in the industry to decide on which factors to focus on, that shape competitive advantage for their firm.
For academic purposes, this research may shed light on these innovative business models and the ways these types of firms differentiate from existing market leaders. By considering the ‘new delivery’ business model, the current research will find out what factors determine competitive advantage of these new business models. The research will contribute to our understanding of platform dynamics and will take into account related concepts, such as switching costs and network effects. In different types of platforms, platform dynamics behave differently. Therefore, it is interesting to look at this new business model to assess how platform dynamics behave in order to increase our understanding of these notions in general.
3. Methodology
In this section, I will justify the chosen research methods that are used to answer the research question: How is the ‘new delivery’ model advantageous over the traditional
delivery model?
3.1 Research design 3.1.1 Approach
The current study is cross-sectional in nature and conducted within a predetermined time-frame, from January 2017 through to June 2017. After extensive consideration of different research methods, the current study will rely on a qualitative research approach. Whenever the current body of literature is quite vague or the study involves a new area of research, Blumberg, Cooper and Schindler (2008) argue that a qualitative approach is particularly useful. As is known, a qualitative approach is characterized by its explorative nature. Because the area of food-delivery platforms, and especially the ‘new delivery’ business model is a novel phenomenon, an explorative, qualitative approach is deemed most useful for this study, in order to gain a deeper understanding of the topic and develop the concept more clearly. This study will follow an inductive approach, where case study research may lead to new propositions and theories to understand the world from the perspective of the actors in the on-demand food-delivery industry. “Sometimes we simply have to keep our eyes open and look carefully at individual cases – not in the hope of proving anything, but rather in the hope of learning something” (Eysenck, 1976).
Accordingly, the current study is following a case study method approach. Case studies are particularly useful for addressing ‘why’ and ‘how’ questions (e.g. Yin, 2003; Blumberg et al., 2008). In addition, Yin (2003) mentions that case studies are useful when it is not possible to manipulate the respondents, the research intends to bring to light contextual
conditions that are deemed relevant to the phenomenon under study, and the boundaries between context and phenomenon are ambiguous. In addition, Santos and Eisenhardt (2005) concur with Blumberg et al. (2008) that case studies are an appropriate method for instances when the literature lacks understanding on a certain topic or phenomenon and theories are absent or vague.
The philosophical underpinnings are based on the constructivist paradigm, which claims that truth is dependent on one’s perspective and is relative (Baxter and Jack, 2008). A major benefit of this approach is the close cooperation between researcher and respondent, where the respondents can tell their stories (Crabtree and Miller, 2009), which are describing their perspectives on reality. This enables the researcher to interpret the actions of the
participants of the study (Lather, 1992).
To select the appropriate case for this study, one first must think about what a case is. Miles and Huberman (1994) define a case as “a phenomenon of some sort occurring in a bounded context. The case is, in effect, your unit of analysis” (p. 25). The current study tries to study the competitive advantage of a recently introduced new business model in in the food-delivery market in the Netherlands and make a comparison with the traditional model. The unit of analysis/subject, and thus the case, is therefore both this ‘new delivery’ business model, as well as the traditional model. Therefore, the current study is following a cross-case analysis approach, with two cases. The object of the study is the competitive advantage of this new business model. Based on the typology of Thomas (2011) the subject of this study is identified as an outlier case, because it may illustrate the difference of the new business model, compared to the traditional one. Beyond the subject and object of the study Thomas (2011) classifies case studies through purpose, approach and process. The purpose for this study is to explore the new business model, making it a heuristic/exploratory study. The study is theoretical because it is using theories from platform literature to test the new
business model. Therefore, one might argue that the study is of the type disciplined
configurative case studies, where existing theories are used to explain a case. While the study
is looking for differences in competitive advantage and platform dynamics, the study
approach is theory-building. The process of the study is comparative in nature, which makes this study a multiple case study. Based on the time-use varieties, the study is a snapshot type of study, because the cases are examined at a certain point in time. Ultimately, the current study follows an exploratory, theory-building, multiple, snapshot route. This typology is based on research by Thomas (2011).
3.1.2 Case selection & participants
In the initial phase of the research, the plan was to interview the major platforms for online food delivery in the Netherlands to find out what they offer to the restaurants.
Unfortunately, only one platform agreed to conduct an anonymous interview, and one other platform replied by answering the questions by e-mail. Therefore, it was decided in
conjunction with my thesis supervisor to shift the focus of the research onto the restaurants that are (or are not) collaborating with one or more platforms. In the end, the data that was gathered in this research is coming from multiple sources, as is quite common in qualitative research. It was decided to keep the platform interview data in the study, for that it might include useful information. The next paragraph elaborates on the participants.
There is one market leader in food delivery in the Netherlands, Thuisbezorgd.nl. They mainly deliver through the traditional business model of food delivery. Furthermore, there are currently four players operating via the ‘new delivery’ business model, i.e. Deliveroo,
foodora, UberEATS and TringTring. The research is focusing on this ‘new delivery’ business model, so based on a purposive or judgment sampling method, it was decided to approach all four ‘new delivery’ companies, plus Thuisbezorgd.nl. It was decided to include
Thuisbezorgd.nl, because they, partly, also deliver through the ‘new delivery’ model, however this is not their core business.
After the interviews with the platform were completed, the research focused on the restaurants that are connected to one or more ‘new delivery’ platforms. For the restaurant participants, a purposive or judgment sampling strategy was being used. Predefined criteria have been set to select the appropriate participants, in order to obtain data from specific target groups (Sekeran and Bougie, 2009). The cases were selected based on the location of the researcher. By entering the postal code of the researcher on the websites, a list was created and per restaurant it was noted to which platforms they were connected. All restaurants are based in Amsterdam, expect for one. After that a diverse sample was chosen.
The sample contained participants that are not connected to a platform, participants that are connected to one platform and participants that are connected to more than one platform. The ‘one-platform’ participants were chosen to include an equal number of participants per platform in the sample. However, after a while it became clear that this was a very hard task to achieve, as there are not many restaurants that are connected to only one platform. In the end, the sample should be large enough to obtain all different perspectives on the subject matter. The selection of cases that replied to the interview can be found in Appendix A.
3.2 Data collection
The data collection method of choice for this research is collection via interviews. Multiple forms of interview methods have been used, including a face-to-face interview, a filled in questionnaire by e-mail and interviews by telephone. This will be explained in the next sections. Semi-structured interviews were the chosen interview style for this research, because these are deemed particularly useful for assessing behaviours, opinions, emotions
and for collecting different experiences (e.g. Clifford, Cope, Gillespie and French, 2016). The interviews were collected in a specific time period, from April, 2017 until May, 2017.
3.2.1 Semi-structured interview & questionnaire (platforms)
In order to gain a better understanding about the industry of food-delivery in the Netherlands and on the business model of the different food-delivery platforms, it was decided to hold interviews with the major players in the Netherlands. An interview guide of 29 open and closed questions was created, based on the literature on platforms, plus literature from Osterwalder & Pigneur (2010) about business models and literature from Porter (2008). Please see Appendix B for the interview guide. Because one of the platforms agreed to collaborate on an interview, but under the condition of being anonymous, the data will be analysed in an anonymous and broad way to secure anonymity of this platform.
The first, anonymous, interview was hold with a firm that is solely operating with the ‘new delivery’ business model and has an established presence in the Netherlands. The interview was hold in the office building of the company and took approximately 45 minutes. The interview was recorded by iPhone and transcribed after. The transcription of the
interview will not be added to the appendix, due to confidentiality reasons. The coding and themes used in the data analysis will be a combination of the anonymous interview and the answers to the questionnaire from Thuisbezorgd.nl, otherwise it would be too easy to uncover unanimity. The anonymous platform will be denoted as ‘new delivery platform X’.
Mr. Imad Qutob, Director of Brand Marketing, from Thuisbezorgd.nl, could only reply by e-mail on the 29 questions, based on following comment: “Due to our presence on the stock market, I have to be very careful what I do and do not share, externally”. Therefore, the same interview guide was send to Mr. Qutob by e-mail and a part of the questions have been answered. However, because there was no physical interaction, the answers to the questions remain quite limited. The answers to the questions can be found in Appendix C.
3.2.2 Semi-structured telephone interviews (restaurants)
To gain a better understanding of motivators for restaurants to join a ‘new delivery’ platform, another interview guide (Appendix D) of 24 open and closed questions was created for the telephone interviews. The restaurants were contacted by phone and the interview was taken or an appointment for a later moment was arranged. The interviews were hold with owners or managers of the selected restaurants, to increase the quality of the data. The 29 interviews took between 15-30 minutes, depending on the willingness or knowledge of the participants to comply with the questions and the duration of the interview. The data have been recorded in an Excel sheet with the participants on the Y-axis and the interview questions on the X-axis.
In order to obtain all different perspectives on the subject, it is wise to keep on holding interviews until saturation takes place (e.g. Mason, 2010). The point of saturation takes place when each additional interview is not containing any new information, which is called the point of diminishing return. It is argued by Mason (2010), that frequency of data is not as important as quality. One occurrence of data can already be enough to create an analysis framework, which can be as useful as many data points to understand the
phenomenon or process of a subject. Therefore, it was chosen to keep on interviewing, until the point of diminishing return was found. The point of diminishing return was found after 29 interviews, when no interesting new facts emerged and the data was not overloaded.
3.3 Data analysis
By simultaneously collecting and analysing the data it was easier to spot patterns and emerging themes. It allows the researcher to benefit from flexible data collection (Eisenhardt, 1989). Also, the casual conversations with other students, friends, family and the thesis
supervisor helped in the development of relevant themes and patterns. Both the data from the interview and questionnaire, as well as the data from the restaurants was analysed by using three different coding techniques in a specific order, i.e. open coding, axial coding and selective coding. Open coding was used to gain a general understanding of the perspective of the participants and to structure the responses by adding meaningful labels to the text
fragments. Then, axial coding was used to spot relations among the data and locate patterns to see the connections among the codes. To create meaningful themes, selective coding was used and this concluded in the development of different motivational categories for
restaurants to join a ‘new delivery’ platform.
3.3.1 Semi-structured interview & questionnaire (platforms)
The goal of the semi-structured interviews was to sketch an image of the industry of food-delivery platforms in the Netherlands, and especially the difference between the ‘new delivery’ model in comparison with the traditional delivery model. The interview and questionnaire have been analysed by focusing on four distinct categories, i.e. the business model, the market, the platform dynamics and the trends in the industry. Based on these categories, the interview and questionnaire were coded to analyse what each respondent mentioned about each category. Because the coding schemes turned out quite elaborate, only the summarized coding schemes are presented below (excluding open coding). Please see table 1 for the coding scheme. Please see Appendix E, for the complete coding scheme.
Axial coding Selective coding
Value proposition
Business model Key Activities
Key Partners
Competition The market
Market size Switching costs
Platform dynamics Network effects
Technology trends Trends in the industry
Changing consumer behaviour
