WHAT IS YET TO COME IS HERE: PREDICTING CONSUMER PREFERENCES AND DIFFUSION OF SELF-DRIVING CARS

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CONSUMER PREFERENCES AND DIFFUSION OF

SELF-DRIVING CARS

By:

Nikolay Petrov Aleksiev

2845423

A thesis submitted to the

Faculty of Economics and Business

In partial fulfilment of the requirements for the degree of

Master of science in Marketing

Specialization: Marketing Intelligence

UNIVERSITY OF GRONINGEN

Thesis supervisor 1: K. Dehmamy Thesis supervisor 2: F. Eggers

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ABSTRACT

This thesis attempts to measure consumer preferences for a radical innovation and its diffusion rate, specifically – autonomous cars. This task is hindered by several issues which have prevented researchers from doing this in the past. Some of those issues are, namely, lack of historical data for autonomous cars, distrust in the innovation and the methodological incompatibility that results from the “hypothetical nature” of the product. Those issues were handled by the implementation of two very popular techniques used in marketing science, namely, a conjoint analysis and the product growth model of Frank Bass. The study was conducted based on a sample of 109 people from the Netherlands. Based on that sample, conclusions for the Dutch population and its preference and adoption rate were estimated. It appears that, if self-driving cars would be introduced into the Dutch market in the form of a rental service, the market saturation would be reached in approximately 7 years, with a peak of adoptions in the 4th_{year. The total market size}

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Table of Contents

I. INTRODUCTION ... 3

II. LITERATURE REVIEW ... 4

Preferences ... 4

Preference measurement ... 5

Important attributes of self-driving cars ... 6

Hypotheses based on the self-driving car attributes ... 7

Direct effects ... 7

Indirect effects ... 7

Innovation diffusion theories ... 8

The product growth model for durable goods ... 9

Hypotheses based on the Bass diffusion model ... 10

Conceptual model ... 10

III. METHODOLOGY ... 11

Dual response choice-based conjoint (DR-CBC) ... 11

Study design ... 12

Choice design ... 13

Choice elicitation ... 14

Estimation ... 14

Bass diffusion model ... 14

Data collection ... 17

IV. ANALYSIS AND RESULTS ... 18

Description of the data set on autonomous cars ... 18

Analysis of the conjoint data ... 18

Predictions about the diffusion rate ... 26

V. DISCUSSION ... 28

Conclusions ... 28

Managerial implications ... 29

Limitations and suggestions for future research ... 30

Acknowledgements ... 30

VI. REFERENCES ... 31

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I. INTRODUCTION

During the last decade, artificial intelligence (AI) has taken over a lot of human tasks. There are algorithms that can lead human debates, remove check-out lines, spot plumbing leaks, and recognize various objects when presented with visual data. But the machines aren’t completely taking over just yet. How about self-driving cars? In literature, an autonomous car is defined as such that is able to detect its surroundings and operate with close to no human (Gehrig and Stein 1999). Such cars incorporate a plethora of sensors in order to analyse their surroundings. Some of those sensors are radars, Global Positioning System (GPS), inertial measurement units, computer vision, Light Detection and Ranging of Laser Imaging Detection and Ranging (LIDAR), sonars and visual odometry. Advanced control systems interpret the sensory information generated and identify correct navigational paths in addition to obstacles and the encountered (Lassa 2012). The concept of a self-driving car sounds futuristic but is actually not that new. Experimentations on automated driving systems (ADS) dates back to the 1920s (“The Milwaukee Sentinel” 1920.), with the a first real prototype developed in 1977 by Tsukuba Mechanical Engineering Laboratory in Japan. The car was tracing special white street markers, which were scanned by two cameras linked to an analog computer that did the signal processing (Vanderbilt 2012). Jumping forward to more recent years, in 2017, Audi made a statement that its latest A8 model would feature automated driving with a speed limit of 60km per hour. This feature would be supported by the "Audi AI." A driver would not be required to perform safety checks such as steering the wheel or giving gas input (McAleer 2017). Another car manufacturer, Waymo, also made an announced in 2017, that it starts testing driverless cars with no safety driver. A year later, the company made an announcement that the prototype model had driven in automated mode for more than sixteen million kilometres. Another year later, Waymo became the first company to commercialize a taxi service made out of fully autonomous cars in Arizona, America (Laris 2018).

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as an art is beginning to break-off as more and more scientific methods become available to marketing managers and analysts (Malhotra 2007).

This thesis will focus on identifying consumer preferences and predicting the diffusion of a hypothetical innovative product through the combination of two of the most widely used tools by marketing analysts, namely a choice-based conjoint study integrated with a Bass diffusion model. As it will be revealed to the reader, despite those approaches being popular in their own individual sense, combination of them both in such kind of research setting hasn’t been attempted before. The underlying reason is perhaps the fact that it is very hard to make predictions for a non-existing product without any historical data - in this case commercialized self-driving cars. Nevertheless, as this paper will reveal, the implemented approach here, could bring some very interesting insights. The target audience of this study are managers of the automobile industry and marketing academicians. This being the case, the paper makes the following contributions: An inexpensive market research on consumer preferences for self-driving cars in the Netherlands. Identification of the market potential that could be realized by corporations in the automobile industry and the expected diffusion time of automated cars. Finally, this thesis incorporates a new approach of combining choice-based conjoint analysis with a Bass diffusion model, which could become the focus of future research, since there is close to no literature on such kind of approach integration. Having introduced the topic and the research question, the thesis continues with a literature review on preference measurement and diffusion of innovations. Following that is the methodology section which describes all about the sample and the analytical procedures used in this study. Lastly, the thesis concludes with a discussion where key findings, limitations and suggestions for future research are summarized.

II. LITERATURE REVIEW

Preferences

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it does not assume stability over time. Preference are susceptible to changes even on a subconscious level (Coppin et al. 2010), especially by processes related to decision making, such as having more choices (Sharot, De Martino, and Dolan 2009). As a result, preferences could be influenced by the surroundings and the upbringing of the person, by education, culture and faith.

On the other hand, other social sciences and in economics, preferences represent the ranking of available alternatives in terms of the utility provided. This is a process that leads to an “optimal choice”, irrespective of whether it is a theoretical or a real one. The type of the individual preferences is created entirely by personal taste factors, unrelated to price, goods availability or income. By means of the scientific methods, a good amount of consumer decisions can be modelled, giving the possibility to make inferences about human behaviour (Arrow 1958).

Preference measurement

In marketing research, preference measurement is one of the major topics of interest. It facilitates managerial and marketing decision makers by informing about the intrinsic value customers put on products or services. In addition to quantifying consumer decisions, an analysis based on preferences also unravels the underlying motives of customers and their actions. As a result, such analysis provides reliable insights and a firm basis for consumers’ behaviour predictions, together with their purchase decisions (Slovic 1995).

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or service. The latter being one of the focal points of this thesis.

Practitioners together with academicians often times equate preference measurement to a conjoint analysis. Undoubtedly, since its introduction, almost half a century ago (Green and Rao 1971), the conjoint study and its variations have been turned into the preferred method for quantifying preferences. This being the case, it is not surprising that marketing science regards it as one of the primary contributions to marketing practice (Netzer et al. 2008).

Important attributes of self-driving cars

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Hypotheses based on the self-driving car attributes

Direct effects

While literature can show how and when it is best to use conjoint analysis, it is also interesting to hypothesize about the results that it can deliver in the current research setting. Naturally, this means that the direct effects of the attributes in the previous section can be hypothesized. Price is generally found to have negative effect on purchase intentions (Lichtenstein, Ridgway, and Netemeyer 1993) with some exception to the product category (Kukar-Kinney, Ridgway, and Monroe 2012). The safety level of a car, on the other hand, is always associated with greater preference and is encouraged by governments (Ross 1973). When researching self-driving cars, one could speculate that the desire for a fully autonomous car would be highest because it would entail greatest freedom for the user. Lastly, the car size is often associated with greater safety and utility (Evans and Frick 1992) thus it would make sense if it affects consumer preference positively. Consequently, the following hypotheses about the main effects of the attributes are formed:

H1a: Price has a negative effect on consumer preferences.

H1b: Higher safety rating has a positive effect on consumer preferences.

H1c: Higher level of autonomy has a positive effect on consumer preferences.

H1d: Bigger car size has a positive effect on consumer preferences.

Indirect effects

Self-driving cars are regularly frowned upon. Trusting a robot car is generally something that people are not so eager to do, however, this may especially be the case for women. The rise of females, owning an automobile after the 80s, is significant (Walsh 2011). The implication being that they would be a very important part of the market for self-driving cars as well. A study by Vrkljan and Anaby (2011), revealed that woman rank safety as the most important feature of a car. Their results were based on a sample of 2002 Canadian drivers aged from 18 and above. Knowing those facts and that safety is one of the biggest concerns when it comes to the level of automation of self-driving cars, the following hypotheses are drawn:

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H2b: Woman’s preferences for the safety ranking of a self-driving car will be higher than for men.

Assuming that gender continues to play a role, and that there could be a car segment appealing specifically to women, several more hypotheses could be derived from the other attributes. Women have been empirically shown to earn less money than man, with this trend slowly improving over the last decades. Nevertheless, still a long way to go before reaching equality (Giles and Walker 2000). This results in less disposable income and thus lower willingness to pay and purchasing threshold for females. An indirect implication of this could be the fact that women generally drive smaller cars, which is connected with the assumption that they are cheaper (Morton, Zettelmeyer, and Silva-Risso 2001). Based on those facts another two hypotheses are drawn:

H2c: Women would prefer smaller self-driving cars than men

H2d: Women would pay less for a self-driving cars than men

Innovation diffusion theories

Having reviewed what consumer preferences are and how they are captured by marketing researchers and practitioners, now it’s time to move on to the second part of the literature review which would reflect upon the diffusion of innovations literature. That reflection would be used as an elaboration to the approach that will be applied for the estimation of the diffusion of self-driving cars.

Historically, the first time the Diffusion of Innovation Theory was discussed in 1903 by Gabriel Tarde, a French sociologist (Toews 2003). He was the one that originally plotted the S-shaped diffusion curve. His followers were Ryan and Gross (1943) who suggested the inclusion of the adopter categories which were incorporated later on by Everett Rogers. Katz (1957) is also recognized for introducing the concept of opinion leaders, followers and media interaction which shape those groups of people.

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by, the product or service diffuses within the population until a point of saturation is achieved. Rogers identified five categories of adopters: innovators, early adopters, early majority, late majority, and laggards (Kaminski 2011). Those five categories can be depicted very closely by a Gaussian curve according to the estimations of Rogers. This claim is based on the percentage of each category that he estimated - appendix 1. The purpose of this theory, however, is not to shift people from one category to another but rather to help innovation serve the wants and needs of the identified categories.

Roger’s theory is a good starting point for getting a very basic idea about what the diffusion of the self-driving cars would look like. We can hypothesise based on his reasoning that the innovators would be comprised mainly by the people that actually work on self-driving car projects (employees of Audi, Google, Waymo etc.) and eventually everyone else will catch up until a point of saturation is reached. This, nevertheless, is not very practical information especially for managers that are interested in numbers and figures. The following section discusses a very famous diffusion model that is widely used for predicting the time it takes for an innovation to fully saturate the focal market.

The product growth model for durable goods

Developed by Frank Bass, the original Bass Model is comprised of a simple differential equation that depicts the diffusion of new products or services within the population (Bass 1969). His model formalizes the way current adopters of a new product or service interact with future adopters. The underlying assumption of the model is that adopters can be split into two groups, namely innovators or imitators – those are fitted in the latter four categories that Rogers (1995) defines – early adopters, early majority, late majority, and laggards.

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been made is a linear function of the number of previous buyers (Bass 1969) “. In this way he tries

to account for the people that are less “innovative” who follow into the footsteps of the innovators Consequently, the pace and timing of the adoption of the innovation is related to the degree of imitation and the degree of innovativeness that adopters exhibit. Those he quantifies in terms of parameters p and q, which need to be estimated together with a market size m, and then applied to the model (Bass 1969).

Hypotheses based on the Bass diffusion model

Since the first part of the literature review was hypothesized about different gender effects for the different attributes, the assumption that gender plays a role will be maintained here as well. A study by Beede et al. (2011) found that women in science, technology and math (STEM) related jobs are highly underrepresented in the US. Despite the fact that females take up closely half of the overall jobs on the market, only 25% STEM related jobs are done by the weak gender. This raises the question whether women are less innovative than men or have less opportunities to be innovative. The exact explanation to those questions is not relevant for this study but the implications of the gender gap in innovativeness is. On the other hand, previous studies have shown that women tend to rely more on word-of-mouth (WOM) especially in the virtual environment. They use the internet more frequently than men, in order to receive or provide social support (Gefen and Ridings 2005). Thus those findings drive to the last 2 hypotheses, namely:

H3a: Bass parameter p (innovativeness) for self-driving cars will be smaller for females.

H3b: Bass parameter q (imitation – WOM) for self-driving cars will be larger for females.

Conceptual model

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Figure 1

III. METHODOLOGY

Dual response choice-based conjoint (DR-CBC)

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Study design

Price

The study design is represented by 5 attributes – table 1, based on relevant car manufacturing reports and websites. The first one is the price of the self-driving car, since this is almost always something that people consider when buying any product or service (Erickson and Johansson 1985). Current prototypes of robot cars exceed the overall base price of normal cars up to four times – for example, the self-driving Toyota Pirus Maham assembled at the cost of 320 000$ (Tannert 2014). The huge overhead comes due to all the tech that needs to be incorporated in order to allow for the autonomous driving. For this study design, however, the participants are presented with а hypothetical scenario – appendix 2, where a company called DigiDrive is renting out autonomous vehicles at hourly rates of 10, 12, 14 and 16 euros. This is a solution to the price issue of self-driving cars, since it is very likely that people would be unable or unwilling to pay for such an expensive product.

Brand

The second attribute is the car brand. The main reason is because brand image can hold a key role in sales, for example by defining the trends with which it is associated – in this case autonomous cars. Currently there are only very few companies that are extensively involved in self-driving R&D, namely Google, Waymo, Apple and Tesla (Preston 2018). Some studies have suggested that brand equity can account for up to 20% increase in spending, when it comes to a car’s image (Farquhar 1989). Thus the above-mentioned brands are used to represent the 4 different levels in this study design.

Autonomy

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Safety rating

The fourth attribute is the level of safety. The levels are defined based on the five-star safety rating system of the European New Car Assessment programme (NCAP). The number of stars is a reflection of how well the self-driving car handles Euro NCAP tests. An automobile that meets the minimum legal requirements would not receive any stars. This also implies that a car with a few stars is not necessarily unsafe, but it is not as safe compared to models with higher safety rating. Being aware that safety is one of the biggest concerns about self-driving cars, the attribute levels start from three stars until five, which is the maximum that NCAP awards (“Euro NCAP | How To Read The Stars” n.d.).

Car size

The last attribute of the autonomous car is its size class. This choice is justified mainly because the automobile market is already segmented into different size segments. The CBC study uses the definitions provided by the Euro Market Segment. The first level is B-segment small cars, which has a combined interior and cargo volume of roughly 2,41–2,8 m3_{. The second level is C-segment}

medium cars with a volume of 2.8–3.1 m3_{. The third level, D-segment large cars, with a volume of}

3.1–3.4 m3_{(“How are vehicle size classes defined?” n.d.). Visual and numerical aid is provided as}

part of the survey in order to facilitate the rating process. Table 1. Attribute levels

Level 1 Level 2 Level 3 Level 4

Price 10€/h 12/h€ 14/h€ 16€/h

Brand Google Waymo Apple Tesla

Autonomy Partial automation High automation Full automation

Safety 3-star rating 4-star rating 5-star rating

Size B - small C - medium D - large Choice design

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Choice elicitation

The choice sets from the optimal design are incorporated in a survey which is handed to random participants. They are familiarized with the attributes and their levels first by being introduced to a scenario that involves renting a self-driving car. The scenario can be found in appendix 2. The key advantages of the choice-based conjoint analysis is that it gives a no-choice option to the participant. This contributes two important things for this particular study, namely estimation of the potential market size for self-driving cars, and greater realism. Participant involvement is ensured by keeping limiting the number of choice sets to 12 – appendix 3 (Green and Srinivasan 1990).

Estimation

Initially, part-worth utilities are estimated based on an aggregate approach in order to achieve greater efficiency. The specifications of the random utility model and the random utility function are presented in equation 1 and 2. In order to incorporate more consumer heterogeneity several additional techniques are also applied. The first one is latent class analyses (LC). Its purpose is to derive segment preferences. The second one is Hierarchical Bayes analysis, which is used in order to calculate individual preference estimates. It’s important to mention that HB is chosen because it generates plausible estimates even when the amount of data is not very big (Arora and Huber 2002), which happens to be the case for this study.

U

ni

= V

ni

+

ϵ

ni (1)

• Uni: overall utility of consumer n for product i

• Vni: systematic utilitiy of consumer n for product i

•

ϵ

ni: error componentof consumer n for product i

𝑉_𝑛𝑖 = ∑ 𝛽_𝑛𝑘𝜒_𝑖𝑘

𝐾 𝑘=1

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• k: number of attributes (1=Price, 2 = Brand, 3= Autonomy, 4=Safety, 5=Size … K) • 𝜒: effect-coded dummy that indicates the specific attribute level of product i • 𝛽nk: part-worth utility for consumer n for attribute k

Bass diffusion model

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approach has been used broadly, especially for the purposes of sales forecasting of new products or the adoption of a new technologies (Dodds 1973). When using the model for a long-range forecasting, Bass (1969) identifies two scenarios, a limited-data case and a no-data case. Doing a market forecast for self-driving cars obviously falls into the latter category. This means that the market size (m), the innovativeness (p) and the imitation parameters (q) could not be estimated based on historical data. Either of the scenarios, however, may be addressed by guessing the sales curve of the autonomous cars or the three parameters necessary for the prediction. Bass did not attempt to answer which approach is better, but instead argued that it is likely that for some products it is possible to make plausible “guesses” of the three parameters needed by the model.

Since there is a decent amount of literature on the Bass parameters for the automobile industry (Abu and Ismail 2013; Al-Alawi and Bradley 2013; Massiani and Gohs 2015), an attempt for prediction is made. It is based on the self-reported innovativeness and imitation level of the participants which is then converted and weighted according to the minimum and maximum historical range of the p (0.0000365  0.0912) and q (0.09051.45) parameters for hybrid cars found in literature – appendix 5. Hybrid cars are chosen as a point of comparison because they share two key similarities with the autonomous cars. First, Hybrids were regarded as an innovation back in 1997 when they were first launched and second, there was a great deal of uncertainty about their commercialization, just as it is the case now with autonomous cars. The process of guessing the values of parameters p, q and m is defined as interpolation. It is elaborated further in the following paragraphs. The Bass model for predicting diffusion of autonomous cars is given in equation 3.

𝑛(𝑡) = (𝑝 + 𝑞𝑁(𝑡)

𝑚 ) [𝑚 − 𝑁(𝑡)] (3)

• n(t): number of adopters of autonomous vehicles at t

• N(t): total number of adopters of autonomous vehicles before t • m: potential market for autonomous vehicles

• p: innovation parameter for autonomous vehicles • q: immitation parameter for autonomous vehicles

Consumer innovativeness

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propensity to purchase new products faster and more often than others (Midgley and Dowling 1978). Social scientists have been busy developing different ways to capture that personality variable. Roehrich (2004) made a summary of concepts and measurements that have been used in practise. He analysed the results of 5 scales. From those, only one seemed to dominate in terms of predictive validity, namely the Goldsmith and Hofacker’s domain-specific scale which measures the

‘‘tendency to learn about and adopt innovations (new products) within a specific domain of interest

(Goldsmith and Hofacker 1991)’’. The above-mentioned findings prompt the decision to incorporate that scale in order to measure the innovativeness of participants – appendix 4a. Each participant’s innovativeness level is defined as a percentage based on the 5 questions they have to answer. Thus, for example if someone agrees completely with all five statements about their personality, they receive an index of 1 or 100% innovative. This index is then multiplied by the range of the p coefficient identified in the previous paragraph – 0.0911635. The results of each participants is then recorded, and the sample average coefficient is used as final value for p for autonomous cars – equation 4.

𝑝 =1

𝑛∑ 𝑖𝑛∗𝑝𝑟𝑎𝑛𝑔𝑒

𝑛 1

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• in: the innovation index of the nth participant

• prange= 0.0911635→ theoretical assumption of the range of the p parameter of self-driving

cars based on the literature for hybrid cars (Massiani and Gohs 2015). • n: the sample size

Consumer imitativeness

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paragraph – equation 5. 𝑞 = 1 𝑛∑ 𝑖𝑚𝑛∗𝑞𝑟𝑎𝑛𝑔𝑒 𝑛 1 (5)

• imn: the immitation index of the nth participant

• qrange= 1.3595→ theoretical assumption of the range of the q parameter of self-driving cars

based on the literature for hybrid cars (Massiani and Gohs 2015).

Market size

The interpolation of m is done based on the no choice option included in the study design of the CBC for self-driving cars. A similar attempt was made by Lee and collegues (2006), however, without a no choice option. They estimated the market share of a product at time t based on the average choice probability of each customer. In this study, however, the market share is estimated through a market simulation of the probability of people choosing the optimal self-driving car (the one that has highest utility) against the probability of a no choice. The assumption behind this method is that there are no other competitors on the market and that the optimal product competes only against the no choice option. The probability of the optimal product is then multiplied by the Dutch population since the sample was collected in the Netherlands. The formula for estimating the above-mentioned probabilities is given in equation 6.

𝑝𝑟𝑜𝑏(𝑖|𝐽) = 𝑒𝑥𝑝(𝑉𝑖)

∑𝑚𝑗=1𝑒𝑥𝑝(𝑉𝑗)

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Data collection

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IV. ANALYSIS AND RESULTS

Description of the data set on autonomous cars

The sample size for conjoint analysis differs significantly. Studies conducted for commercial purposes generally have between 100 to 1000 respondents. Such with lower number of attributes as this one generally requires between 100 to 150 participants (Cattin and Wittink 2006). The survey for this research was distributed to 241 respondents from which only 119 completed it. After inspecting the data several observations were dropped due to respondents taking either too long or not enough time to respond with the shortest time of 3 minutes and the longest - 33 minutes. This left the total sample size with 109 observations comprised of 54.62% females and 45.38% males. The mean age of the participants was 26 years, the minimum – 17, and the maximum – 65. The age distribution can be seen in appendix 6. The average time for completing the survey was estimated to be 7.3 minutes with a median of 6.2 minutes.

Analysis of the conjoint data

Part-worth and linear models

The analysis of the data included the estimation of several different models that gradually increased in complexity. The following paragraphs will describe them one by one and will include a discussion about the hypotheses that were framed earlier. The first and the simplest model was a part-worth one without any interactions based on a multinomial logistic regression and a maximum likelihood estimation – table 2. As it can be seen, the model has a decent explanatory power based on the significant parameters and a LL -1515.6 which is significantly smaller than the LL(0) -1813. On the other hand, however, the adjusted pseudo R2_{= 0.1571 falls under the acceptable range of}

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other car size, thus rejecting H1d. The last hypothesis about the main effects of the attributes was about the effect of price on utility. It is very obvious that the lower the price is the higher the utility for self-driving cars. There is also evidence to infer linearity about price effect on utility. This can be seen in appendix 7a, based on the linear graph of price and utility, the mixed model in table 3 and the insignificant value of the likelihood ratio test between the linear and part worth model (model 5 vs 4) in appendix 7b.

Table 2. Part-worth model without interactions

Coefficients Estimate Std. Error Z-value P-value

Google 0.066 0.071 0.941 0.347 Waymo -0.282 0.076 -3.737 0.000 *** Apple -0.086 0.073 -1.169 0.242 Tesla 0.301 0.067 4.481 0.000 *** Price.10 0.529 0.065 8.116 0.000 *** Price.12 0.218 0.069 3.149 0.002 ** Price.14 -0.084 0.074 -1.133 0.257 Price.16 -0.664 0.087 -7.619 0.000 *** Partial.automation -0.189 0.058 -3.231 0.001 ** High.automation 0.054 0.056 0.966 0.334 Full.automation 0.13 0.037 3.583 0.000 *** Stars.3 -0.856 0.077 -11.139 0.000 *** Stars.4 0.151 0.059 2.538 0.011 * Stars.5 0.705 0.055 12.808 0.000 *** Small 0.057 0.055 1.021 0.307 Medium 0.132 0.055 2.383 0.017 * Big -0.189 0.058 -3.2232 0.001 ** None_option 1.153 0.063 18.060 0.000 *** Log-Likelihood: -1515.6 LL(0): -1813

Pseudo R2 _{adjusted: 0.1571 AIC: 3057}

Significance: 0 ‘***’, 0.001 ‘**’, 0.01 ‘*’, 0.05 ‘.’

Red: Recovered reference levels

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automation. It reveals that women prefer cars with full automation → 0.332-0.373= -0.045, a lot less than men → 0.332, which is very much in line with H2a. The same thing was observed for the car size. There is a clear distinction between the preferences of man and women. The latter seem to find most utility in the small self-driving cars →-0.196+0.459 = 0.263, shown by the highly significant positive interaction, whereas the former find least utility → -0.196. This really confirms the paradigm that men drive bigger cars than women. Interestingly enough, however, on average men do not seem to choose the biggest self-driving cars that often, but rather stick with medium sized ones. In line with the preference for small cars women also seem to be more price sensitive than men → -0.198 vs -0.176 , respectively – confirming H2d as well.

Table 3. Combined model with interactions

Coefficients Estimate Std. Error Z-value P-value

Google 0.068 0.071 0.952 0.341 Waymo -0.289 0.076 -3.802 0.000 *** Apple -0.088 0.074 -1.197 0.231 Tesla 0.310 0.067 4.481 0.000 *** Price.Liner -0.175 0.019 -8.876 0.000 *** Partial.automation -0.307 0.088 -3.482 0.000 *** High.automation -0.026 0.084 -0.303 0.762 Full.automation 0.332 0.078 4.238 0.000 *** Stars.3 -0.914 0.114 -7.984 0.000 *** Stars.4 0.161 0.088 1.824 0.068 . Stars.5 0.753 0.111 6.741 0.000 *** Small -0.196 0.084 -2.320 0.020 * Medium 0.166 0.080 2.056 0.039 * Big 0.029 0.082 0.362 0.717 . None_option -1.270 0.241 -5.267 0.000 *** Gender*Price.Linear -0.022 0.009 -2.249 0.024 * Gender*Partial.automation 0.211 0.118 1.777 0.075 . Gender*High.automation 0.162 0.114 1.419 0.155 Gender*Full.automation -0.373 0.111 -3.337 0.000 *** Gender*Stars.3 0.079 0.155 0.515 0.606 Gender*Stars.4 -0.016 0.122 -0.140 0.888 Gender*Stars.5 -0.063 0.121 -0.566 0.571 Gender*Small 0.459 0.114 4.029 0.000 *** Gender*Medium -0.039 0.112 -0.337 0.735 Gender*Big -0.422 0.119 -3.538 0.000 *** Log-Likelihood: -1497.9 LL(0): -1813 Pseudo R2 _{adjusted: 0.1639 AIC: 3031}

Significance: 0 ‘***’, 0.001 ‘**’, 0.01 ‘*’, 0.05 ‘.’

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Attribute importance

The attribute importance – table 4, was calculated based on the input of the part-worth model without interactions. It was chosen because it provides an overall picture of the relevance of each attribute at a relatively simple level which is easier to interpret. It is clear that the importance may vary based on the gender of the person and even on the segment that he may belong to but effort to make more complex estimations was not made since the part-worth estimations already provides enough information for generalizations.

Table 4. Attribute importance for self-driving cars

Attribute Importance Brand 14.63% Price 29.98% Autonomy 8.12% Safety 39.20% Size 8.06% Total 100.00% Incremental willingness-to-pay (WTP)

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Table 5. Incremental WTP for self-driving cars Attribute level WTP (Euros/h) WTP (Worst/Best) Google € 0.38

Waymo € -1.60 From Waymo

Apple € -0.49 to Tesla

Tesla € 1.71 € 3.32

Partial A. € -1.08 From Partial A.

High A. € 0.31 to Full A.

Full A. € 0.77 € 1.84

3-stars € -4.88 From 3-stars

4-stars € 0.86 to 5-stars

5-stars € 4.01 € 8.89

Small € 0.33 From Big

Medium € 0.75 to Medium

Big € -1.08 € 1.83

Optimal product and market share estimation

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Table 6. Optimal product and its market share for different levels of the price

€10/h €12/h €14/h €16/h

Optimal product utility 1.802 1.490 1.188 0.608

None option utility 1.153 1.153 1.153 1.153

Prob(optimal)= J1 66% 58% 51% 37%

Prob(none)= J2 34% 42% 49% 63%

Prob(sum) = J1+J2 100% 100% 100% 100%

Revenue €6.57/h €7.00/h €7.12/h €5.87/h Optimal product: Medium Tesla car with 5 stars safety and Full A.

Mixture models (Latent class analysis)

The previous two models did not allow for a lot of heterogeneity in consumer preferences thus in order to overcome this disadvantage several latent class solutions were estimated in order to find discrete segments in which consumers differ in their preferences. In total, two separate latent class models without interactions were estimated and the optimal number of classes was determined to be 2 – table 7. This was concluded after several more classes were added to the analysis and were tested for significance. All additional ones turned out to be insignificant – appendix 7c. The two segments that were identified through the LC analysis differ mostly in price sensitivity → -0.318 vs -0.199, and in the preference for the no choice option → 0.552 vs -3.306. The first segment seems to be slightly smaller than the second → 40% probability to belong vs 60%. The observations that were used for segment one had a mean age of 24.1 years and the youngest person was 18 years old, whereas, the oldest – 54. The people were mostly females – 59.09%. The demographics of the second segment were somewhat similar: average age was 27 with a minimum and maximum of 19 and 65-year-old participants. The proportion of females was 52.31%. As a result, it can be concluded that the second segment is represented by a slightly older population and the distribution of males to females is almost the same which was not the case for the first one. Lastly, based on the market share estimate of 6 722 800 people, from the previous section, it can be inferred that segment one would roughly be comprised of 2 689 120 people and segment two of 4 033 680. Based on those characteristics the segments were labeled as “Sceptics” and “Believers”, fortunately for managers, the latter comprising the bigger part of the market.

The model fit seems to be significantly better than that of the model in table 3. The Pseudo R2

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decreased to 1071.7 compared to 1497.9 and the AIC is also lowest. All the three measure show a significant improvement thus indicating that this is the best model compared to the previously estimated ones.

Table 7. Latent class model with 2 segments.

Coefficients Estimate Coefficients Estimate

class.1.Price.Linear -0.318 ** class.2.Price.Linear -0.199 *** class.1.Google -0.052 class.2.Google 0.078 class.1.Waymo -1.291 * class.2.Waymo -0.285 *** class.1.Apple 0.516 *** class.2.Apple -0.151 . class.1.Tesla 0.722 *** class.2.Tesla 0.358 *** class.1.Partial.automation -0.075 class.2.Partial.automation -0.247 *** class.1.High.automation 0.227 class.2.High.automation 0.033 class.1.Full.automation 0.227 *** class.2.Full.automation 0.215 *** class.1.Small 0.260 class.2.Small 0.051 class.1.Medium 0.195 class.2.Medium 0.155 ** class.1.Big -0.455 * class.2.Big -0.195 ** class.1.Stars.3 -1.451 ** class.2.Stars.3 -0.885 *** class.1.Stars.4 0.476 class.2.Stars.4 0.139 * class.1.Stars.5 0.975 * class.2.Stars.5 0.745 *** class.1.None_option 0.552 class.2.None_option -3.307 *** Probability to belong: 40.4% Probability to belong: 59.96%

Log-Likelihood: -1071.6 LL(0): -1813 Pseudo R2 adjusted: 0.4269 AIC: 2189 Significance: 0 ‘***’, 0.001 ‘**’, 0.01 ‘*’, 0.05 ‘.’

Red: Recovered reference levels

Mixture models (Hierarchical Bayes)

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Figure 2. Boxplots HB utility estimates

Additionally, based on those individual parameters the incremental willingness to pay for each observation was calculated for some of the attributes: level of automation, brand and safety level. The HB WTP was estimated based on the assumptions of table 5. For example, overall, partial automation is preferred to full automation, the most preferred car is Tesla and the least is Waymo and 5 stars safety is preferred to 3 stars safety. The results are given in the histograms in figure 3. One can see that people have very differing desires for the different levels of those attributes. For example, most people are willing to pay around 2.5 euros extra for an upgrade from 3 to 5 stars but there are also individuals that are willing to pay as much as 10 euros extra per hour just to get that extra safety assurance. Similarly, for the more unknown brand Waymo, most people are willing to pay up to 1.5 euro extra per hour for a Tesla upgrade. Nevertheless, there are those that would pay as far as 8 extra additional euros just for a brand upgrade. The biggest consistency in the heterogeneity of people is observed in the upgrade from partial to full automation. This is indicated by the tall green bars that stretch up to 5.5 euros per hour. This shows that for each increment of 0.5 euros per hour there is approximately an even amount of people that would be willing to pay for it.

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Figure 3: Histograms of the WTP of people based on HB individual parameter estimates To conclude the HB section of the analysis, three random observations (41, 86 and 37) and their individual parameter estimates are shown in table 8. As it can be seen, observation 41 is a woman that prefers mostly a Google self-driving car with a high automation and 4 stars of safety rating. She is willing to pay only 2.87 euros extra for an upgrade from partial to full automation as opposed to observation 37 which is a male that is willing to pay 9.56 euros for such an upgrade. Overall the table demonstrates how each person has different preferences and how they could actually be identified with the HB estimation.

Table 8. Selection of observations and their HB estimates together with WTP

Predictions about the diffusion rate

Innovativeness and imitativeness parameters

Since market share was already estimated through a market simulation, the remaining innovativeness and imitation parameters were estimated for each individual and the sample average was used as an input for the diffusion model. Appendix 8c and 8d show the distribution of the p and q parameters among the sample that was collected for the conjoint analysis. H3a and H3b were tested with One-way ANOVAs. The results can be seen in tables 9a and 9b. It appears that women are indeed more imitative than men 0.892 > 0.777, based on the significant ANOVA test with a p-value of 0.015. On the other hand, however, the ANOVA for the innovativeness seems to be

N Age Gender Price None Google Waymo Apple Tesla Partial High Full Stars3 Stars4 Stars5 WTP:Partial>Full WTP:3>5 stars

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insignificant, even though it appears that men are slightly more innovative than women – 0.0474 > 0.0420. It is possible that this result becomes significant with more data. Additionally, it can also be seen that the sample consists of women slightly more than of men.

Table 9a. ANOVA test for p Table 9b. ANOVA test for q

Feeding the parameters into the model

To recap, the following parameters were fed into the Bass diffusion model: m= 6 722 800, p = 0.0 444 and q = 0.8402. This resulted in the predictions observed in table 10 and figure 4. It appears that according to the results of this study, self-driving car adoption would reach a peak approximately at the 4th_{year after introduction to the market and saturation would be reached}

around the 7th_{year. Appendix 9 shows those calculations done in MS Excel.}

Table 10. Diffusion of autonomous cars Figure 4. Adoption of self-driving cars per year

Additionally, an effort to forecast autonomous car diffusion was attempted based on the two-segment solution estimated in table 7. The people comprising the first two-segment had an average p parameter of 0.037 and q of 0.85, whereas, the second segment 0.049 and 0.834 respectively.

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Intuitively, the diffusion curves did not differ too much from the overall diffusion rate estimated in the previous paragraph and also across those two different segments. The explanation is simple, namely because the parameters values are very similar.

The last step was to perform some sort of robustness check with regards to the diffusion model and see if it can still predict reasonable times even for extreme values of p and q. This was accomplished by defining 2 types of groups - people that are most likely and least likely to adopt the product according to the minimum and maximum p and q parameters estimated in the sample of this study. Those groups were defined as “Old schoolers” and as “Technology hunters”. The results revealed that the “Old schoolers” would not even consider autonomous cars for the first couple of years. Only after a decade or so they could begin to explore the possibility of using an autonomous vehicle. This was estimated based on p = 0.0000365 and q = 0.136. People in that category are expected to be mostly elders above 50 years. On the other hand the “Technology hunters” were forecasted based on p = 0.088 and q = 1.359. The results showed that the majority that fall in this category would have adopted the autonomous car already in the 2nd_{year after its introduction, this group is expected}

to be comprised mainly of people between 20 and 30 years. The diffusion curves of those two extremes can be found in appendix 10.

V. DISCUSSION

Conclusions

This thesis linked two very popular market research methods in order to find insights about a hypothetical innovative product – autonomous vehicles. Conjoint analysis and the product growth model for durable goods of Bass (1969) are popular in their individual usage but an attempt to combine them and derive additional insights has not been done – at least to the best knowledge of the author of this paper. This research gap led to the formulation of a research question that aimed to measure consumer preferences for autonomous vehicles in the Netherlands, and their potential diffusion in the market.

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service. The total market for self-driving rental cars was estimated to be around 6 722 800 people. The most preferred self-driving car was identified to be a Tesla car that is medium sized with 5-star safety rating and full automation . While there are still no competitors, the profit maximizing price per hour for such a car was estimated to be 14 euros, which would translate into 7.12 euros of revenue per hour. A dual response conjoint analysis provided the necessary link to the Bass diffusion model. A summary of the hypotheses that were tested can be seen in table 11.

Table 11. Overview of hypotheses

Hypothesis Supported

H1a Price has a negative effect on consumer preferences. ✓

H1b Higher safety rating has a positive effect on consumer preferences. ✓ H1c Higher level of autonomy has a positive effect on consumer

preferences. ✓

H1d Bigger car size has a positive effect on consumer preferences. ✗ H2a Man’s preference for the level of automation of self-driving cars will

be higher than that of women. ✓

H2b Woman’s preferences for the safety ranking of a self-driving car will

be higher than for men. ✗

H2c Women would prefer smaller self-driving cars than men. ✓

H2d Women would pay less for a self-driving cars than men. ✓

H3a Bass parameter p (innovativeness) for self-driving cars will be

smaller for females. (✓&✗)

H3b Bass parameter q (imitation – WOM) for self-driving cars will be

bigger for females. ✓

Managerial implications

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preferred car company seems to be Tesla, so this automatically provides it with an advantage that could be leveraged. It appears that people trust more established and renown brands and are less likely to give a chance to emerging brands such as Waymo. This observation seems to be an accurate representation of reality. When a new and distrusted innovation is presented to people, they would act under the influence of the herd behaviour and would bet on the most specialized and established companies, as it is the case here – Tesla.

Limitations and suggestions for future research

As with every research, this one also has its limitations. One of the potential issues is that convenience sampling was used during the data collection. An attempt to diversify the sample was done but it was not entirely successful in bringing an exact representation of the Dutch population. This in return might have an effect on the reported findings. Additionally, since there was no direct way of estimating p and q parameters for self-driving cars, an attempt to guess them was made. It is possible that the actual parameters deviate from the predicted ones thus resulting in a different diffusion rates. A suggestion for future research might be to compare the guessed values of those parameters with the actual values once there is some historical data for the adoption of self-driving cars. This in its turn would either support or disprove the robustness of the method used in this thesis. Another limitation is that the Bass model only included three variables in order to estimate the diffusion rate. Future research may attempt to incorporate additional variables such as the price of the product. This may contribute to a more accurate predictions. Last but not least, this study was conducted within a period of 4 months, thus limiting the findings to the degree of time allowed. It is unclear if the findings that were reported can be generalized to other countries similar to the Netherlands, for example Belgium. Perhaps future studies can look for patterns that can be generalized when it comes to the adoption of autonomous vehicles in first world countries.

Acknowledgements

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VII. APPENDICES

Appendix 1: Diffusion of innovations

Appendix 2: Scenario given to the participants

There is a new rental company DigiDrive, that works in close cooperation with governments across Europe and big corporations that are involved in the research and production of self-driving cars. The purpose of this grandeur project is to gradually integrate autonomous vehicles into the daily life of Europeans. DigiDrive has several self-driving models that are produced by different companies. The company is conducting a pre-launch market research in order to determine consumer preferences for self-driving cars. Based on this research the company will adjust its self-driving car fleet and would rent out vehicles based on an hourly rate. The autonomous cars will be allowed to self-drive in authorized locations only. This doesn't mean that the car would stop self-driving as soon as it moves out of its authorized area but rather, it means that the person renting the car assumes all legal responsibility in case of an accident since authorized areas are vouched by DigiDrive as "suitable and safe" for self-driving.

Now that you know about DigiDrive, you will be presented with the models that company has on offer and you will be asked to pick the one that you prefer the most. Good luck!

Appendix 3: The choice design used for the choice based conjoint analysis

Set 1 1 2 3

Price: 16 EUR/h 12 EUR/h 10 EUR/h

Brand: Waymo Google Apple

Autonomy: Partial automation Full automation High automation

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Set 2 1 2 3

Brand: Tesla Waymo Google

Autonomy: Partial automation High automation Full automation

Safety rating: 5 stars 4 stars 3 stars

Set 3 1 2 3

Brand: Google Waymo Apple

Autonomy: High automation Partial automation Full automation

Set 4 1 2 3

Brand: Apple Tesla Google

Set 5 1 2 3

Brand: Apple Tesla Google

Autonomy: High automation Full automation Partial automation

Set 6 1 2 3

Brand: Google Waymo Tesla

Set 7 1 2 3

Brand: Apple Tesla Waymo

Autonomy: High automation Full automation Partial automation

Set 8 1 2 3

Brand: Google Tesla Apple

Set 9 1 2 3

Brand: Waymo Google Apple

Set 10 1 2 3

Brand: Google Apple Tesla

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Set 11 1 2 3

Brand: Google Tesla Waymo

Autonomy: Full automation High automation Partial automation

Set 12 1 2 3

Brand: Tesla Waymo Google

Autonomy: Full automation High automation Partial automation

If available would you actually rent the selected self-driving car?

Yes, I would. No, I would stick to current modes of transportation.

Appendix 4a: Scales for measuring the innovation level of participants

Innovativeness

Please indicate the extent to which the following sentences describe you:

1. Compared to my friends, I own a lot of innovative products.

Strongly Disagree Strongly Agree

2. In general, I am the first in my circle of friends to know about the latest innovations.

3. In general, I am the first in my circle of friends to buy a new innovative product when it appears on the market.

4. New innovative products excite me.

5. I would be interested in a new innovative product, even if I haven’t seen it yet.

1 2 3 4 5 6 7

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Appendix 4b: Scales for measuring the imitativeness level of participants

Imitativeness

Please indicate the extent to which the following sentences describe you:

1. To make sure I buy the right product or brand, I often observe what others are buying and using.

2. If I have little experience with a product, I often ask my friends about it.

3. I often consult other people to help me choose the best alternative from a product class.

4. I frequently gather information from friends or family about a product, before I buy it.

5. I achieve a sense of belonging by purchasing the same products and brands that others purchase.

Appendix 5: Summary of p and q for hybrid cars (adapted from Massiani and Gohs 2015)

1 2 3 4 5 6 7

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Appendix 6: Distribution of the age of participants

Appendix 7a: Linear plot of the utility of price per hour

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Appendix 7c: Latent class model with 3 segments

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Appendix 8a: HB price utility distribution

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Appendix 8c: Distribution of p among the sample

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Appendix 9: Product growth model estimation (“The Bass Model Home Page” n.d.)

M p q t increment

6,722,800 0.0444108 0.840229 1

For info on this spreadsheet see: http://www.bassbasement.org/BassModel/WhichBassModelEquation.aspx

t

start t start

1 Adoptions Cum Adoptions 0 Adoptions Cum Adoptions Adoptions Cum Adoptions

t M * SM_f M * cF t M * f M * cF_asSumf DE_f DE_cF_as

Sum_DE_f M * DE_f M * DE_cF_asS um_DE_f 0 0 0 0 0 0 0 0 0 1 447,979 447,979 0 298,565 298,565 0.044411 0.044411 298,565 298,565 2 872,009 1,319,988 1 629,993 928,557 0.078097 0.122507 525,027 823,592 3 1,360,606 2,680,594 2 1,131,271 2,059,828 0.129294 0.251802 869,219 1,692,811 4 1,531,469 4,212,063 3 1,533,762 3,593,591 0.191525 0.443327 1,287,586 2,980,397 5 1,201,469 5,413,532 4 1,433,237 5,026,827 0.232081 0.675408 1,560,234 4,540,630 6 702,861 6,116,393 5 943,989 5,970,816 0.198621 0.874028 1,335,287 5,875,917 7 342,783 6,459,176 6 490,492 6,461,308 0.098106 0.972134 659,548 6,535,465 8 152,350 6,611,526 7 224,526 6,685,834 0.023999 0.996133 161,338 6,696,803 9 64,905 6,676,431 8 96,890 6,782,724 0.003408 0.999541 22,914 6,719,717

PEAK PEAK Bass Model Differential Equation

4.32 3.32

Bass Model SM Discrete Form Bass Model Little f Discrete Form F(t) is at the right side (end) of

time interval (t-1) to t. f(t) is average over interval (t-1) to t.

f(t) and F(t) are the values at the left side (start) of the interval t to (t+1).

Bass Model Differential Equation Form Bass Model Big F (SM)

Discrete Form

Bass Model Little f Discrete Form

Value at F(t-1). t is not used: it simply denotes the value of the F before this one.

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Appendix 10: Robustness check of diffusion – minimum and maximum p and q from the sample 0 500,000 1,000,000 1,500,000 2,000,000 2,500,000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Adoptions - p and q max

0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Adoptions - p and q min

0 2,000,000 4,000,000 6,000,000 8,000,000 1 3 5 7 9 11131517192123252729

Cumulative adoptions - p and q max 0 20,000 40,000 60,000 80,000 100,000 120,000 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29