Anticipating acceptance of emerging technologies using twitter: the case of self-driving cars

The evolution of transportation has faced numerous trials as it has grown and expanded over time. It seems safe to assume that this steady chain of development of faster and safer vehicles with improved technological features continues (e.g., Burns 2013). Over the past decade, a vast amount of research has been conducted regarding the topic of self-driving cars (Fagnant and Kockelman 2015; Kyriakidis et al. 2015), which are being vigorously pursued by companies in the automotive and related industries. Even native IT companies such as Google are pursuing the development of self-driving cars (Spinrad 2014). However, for a technology to be successful, we must remember a significant key factor for the success of emerging technologies, technology acceptance (Davis et al. 1989).

In recent years, self-driving cars have become a controversially discussed topic. Ethical, regulatory, and liability concerns (Zmud and Sener 2017; Gogoll and Müller 2017), centering on who is driving and who assumes responsibility for accidents, are hotly debated issues. Nevertheless, the automotive and related industries seem convinced that self-driving cars will be the future of mobility and may underestimate the public’s concerns and misconceptions related to this emerging technology (Piao et al. 2016) that often differ from the perceptions of experts (Blake 1995). With the first intelligent vehicle handling systems, a pre-stage of technologies that enable self-driving cars, Conover (1994) already discussed that risk and benefit perceptions could be an issue. Research regarding other technologies also shows that risk and benefit perceptions are central determinants of their public acceptance (Siegrist 2000; Butakov and Ioannou 2015). Public perceptions, therefore, eventually determine whether self-driving cars will be used and, thus, are a crucial factor that needs to be considered especially for initial acceptance of emerging technologies such as self-driving cars (Butakov and Ioannou 2015; Pendleton et al. 2015; Bansal et al. 2016).

However, studies addressing public perception and acceptance of this emerging technology, especially across several countries and its change over time, remain scarce. We address this paucity of research by first outlining the results of previous research on public perception and acceptance of self-driving cars. Second, we describe a new approach to measure and react to public perceptions facilitating the voice of the customer (VOC). This approach gives us the opportunity to utilize the vast amounts of data publicly available in social media to anticipate acceptance of emerging technologies and answer the following research questions:

RQ1: How can we measure public perceptions of self-driving cars to anticipate acceptance?

RQ2: How do events influence the public perception of self-driving cars?

To address these research questions, we created an approach for automatically determining and monitoring perceived risks and benefits of emerging technologies from short 140-character text messages published on the social media platform Twitter. We build on scientific literature and text mining methods, which allow the extraction of knowledge from text documents (Tan 1999) and, more specifically, sentiment analysis (Hopkins and King 2010). We use the social media platform Twitter to collect a stream of opinions about self-driving cars, one instance of currently emerging technology. Based on the perceived risks and benefits of self-driving cars, we identify events and issues crucial for the future acceptance of this emerging technology and guide the management of emerging technologies.

The remainder of this paper is structured as follows: First, we provide an overview of current literature on technology acceptance, self-driving cars, and previous research on the acceptance of self-driving cars. Second, we describe the data extraction from Twitter, the preprocessing of the data, and the model generation including its evaluation. Third, we describe and discuss the results of extracting the relevant data and applying our machine learning model to this data. We conclude with a summary of the results, limitations of our work, possibilities for further research, and the contributions to research and practice.

In this section, we provide an overview of current literature disclosing the significance of acceptance towards self-driving cars from both an Information Systems (IS) and public acceptance perspective. An introduction to self-driving cars, the current scientific knowledge about them, and studies assessing the acceptance of self-driving cars are presented as well. We conclude this section by summarizing the theoretical background for our study.

We use a novel approach to identify risks and benefits by analyzing the vast amount of existing data about self-driving cars on Twitter encoded in tweets. This approach is theoretically founded in the quantitative content analysis (Neuendorf 2016), which allows conducting quantitative data analyses based on qualitative data and extends previous qualitative approaches (Fraedrich and Lenz 2014; Bazilinskyy et al. 2015). While content analysis has been used to analyze mainly unstructured social media before (e.g., McCorkindale 2010), we automate most of the coding process using machine learning. This method is similar to sentiment analysis in marketing research (Okazaki et al. 2014) and has the advantage that only a small portion of the data needs manual coding when using supervised machine learning classification. By using this method, we avoid certain issues with questionnaires and studying technology acceptance, for example, common method variance (Sharma et al. 2009).

We follow the analysis process suggested by Okazaki et al. (2014) for sentiment analysis. It consists of data extraction, data preparation, model generation, model validation, and model application. Our approach to risk and benefit perception analysis is similar to sentiment analysis, which allows us to follow a common sentiment analysis process. Variations in the sentiment analysis process, for example, combining the steps of model generation and validation into one step (Feldman 2013) usually do not differ much.

We implemented the process of Okazaki et al. (2014) as follows: First, we obtain tweets using the Twitter Search API (data extraction). Second, we preprocess the tweets to improve data quality, reduce dimensionality, and avoid misclassification (data preparation). Third, we train the machine learning algorithm (model generation) and evaluate it using cross-validation (model validation). Fourth, we apply the machine learning algorithm to classify the tweets (model application). We then analyze the classified tweets qualitatively and quantitatively to address our research propositions.

With an overall total of 642,033 tweets, we obtained 528,440 (82.3%) neutral tweets, 50,612 (7.88%) stated benefits (BT), and 62,981 (9.81%) stated risks about self-driving cars (RT). The risk ratio (RR) and benefit ratio (BR) were calculated as follows:

The RR describes the ratio of tweets about risks of self-driving cars to the total number of tweets excluding the neutral tweets that neither contain information about risks nor benefits. BR is defined analogously and can also be calculated as 1 − RR when RR is already known as RR + BR equals to one.

In 2015, 407,179 (83.0%) of the tweets were neutral, 37,891 (7.73%) stated benefits, and 45,214 (9.22%) stated risks about self-driving cars (Table 5). The RR in 2015 equals to 0.5441 and BR equals to 0.4559. In 2016, 121,261 (79.9%) of the tweets were neutral, 12,721 (8.38%) stated benefits, and 17,767 (11.7%) stated risks about self-driving cars. Overall, we see only slightly changing ratios over the years in our analysis.

We found a decrease of the ratio of neutral tweets, which might indicate that the SVM classifier performs consistently over time. The classifier does not fail to detect risk and benefit tweets among the huge number of neutral tweets as time progresses and topics change. Additionally, the discussion about self-driving cars might also focus more on risks and benefits of the technology as they become more known to the public.

The fact that RR and BR did not change substantially from 2015 to 2016 shows that RR and BR might be a robust measure to quantify risk and benefit perception. Closer inspection of RR shows that it did change between the months and might be an important indicator of issues in risk and benefit perception. Figure 1 shows the development of RR and BR over time.

By analyzing the tweets in detail, we can track how Twitter users react to specific news reports, announcements, or other events and whether changing perceived risks or benefits is influenced by these events. We found that there is a relationship between occurrences related to self-driving cars and the content of tweets. This finding allows us to state that, although being subjective, tweets regarding self-driving cars are related to facts and occurrences in the real world. In the following, we will give some examples.

In April 2015, we observed more tweets about benefits of self-driving cars than about risks as RR is smaller than 0.5 (Fig. 1). In this month, Google explained the use of solar energy and ridesharing for their self-driving car. Therefore, the Google Car has the potential to help protect the environment and ease congestion in major cities. Further, Google reported that their self-driving cars could reduce the number of people injured and killed in accidents. Tweets frequently discussed this report, which was one of the main reasons why in April 2015 RR was smaller than 0.5.

From May to July 2015, the tweets containing risks suddenly increased and outweighed the tweets containing benefits. The main reason for this is that Google released data showing that its self-driving cars had been involved in eleven minor accidents over the past 6 years. In June 2015, a website run by Google provided explanations for the accidents. In July 2015, Google reported its first accident with an injury. Although we have not explicitly filtered for Google-related posts, most tweets in this month were related to Google.

Evaluation of tweets from August 2015 revealed a spike in BR. We found that many tweets mentioned the announcement of self-driving crash trucks. These trucks are usually deployed on roadworks to protect the road workers from distracted drivers who would otherwise crash into them (Rubinkam 2015). Drivers of crash trucks are usually in a dangerous situation; replacing the driver with a driving automation system could save lives, which was obviously well-received by the public.

From September to October 2015, no noticeable changes in the RR occurred. In November 2015, Nevada approved regulations for self-driving cars, which lead to many tweets classified as benefit. The news that Google and Chrysler cooperated to produce self-driving cars was classified as benefit in April 2016. In June 2016, Volvo Cars & Ericsson released intelligent media streaming for self-driving cars, which lead to tweets classified as Neutral and, thus, did not influence RR.

In August 2016, Tesla announced a $2.6 Billion deal with Solar City to combine solar energy with self-driving cars. People also tweeted about self-driving tractors that promise to work by themselves. Also, Ford announced self-driving taxis for 2021 and people started tweeting about self-driving living rooms, so nobody has to leave the sofa. However, many tweets about accidents with self-driving cars were reported in this month, resulting in a RR value of 0.5348, which shows that tweets classified as risk (N = 1269) slightly prevail over tweets classified as benefit (N = 1104).

In October 2016, tweets often mentioned that a self-driving car hit a truck in Singapore. This lead to an increased risk rate. The last tweets in the analyzed dataset were from October 21, so the analyses do not reflect events after this date.

In addition to the RR, we also inspected the absolute numbers of tweets classified as risk (Fig. 2) and benefit (Fig. 3). Plotting the tweets over time, we could observe several changes in the number of risk and benefit tweets. One characteristic month in our dataset is November 2015. A close inspection of the tweets shows that the annual International Driverless Cars Conference caused a general increase of tweets. This conference might not only have caused more tweets about self-driving cars but also led to an increased interest in reading tweets about self-driving cars, which were then retweeted—a self-reinforcing effect.

By analyzing the data, we recognized that the discussion about self-driving cars is dominated by the Google Car as found in previous research (Woisetschläger 2016). In contrast to currently available self-driving cars, the Google Car offers full self-driving capability (i.e., level 4 automation). So, the driver is not required to take over control of the car at any time. News such as a blind person driving the Google Car have been tweeted very often. Figure 4 shows the total number of tweets for each car manufacturer in the dataset. As described in Sect. 3.2, we specifically queried Twitter about tweets for those car manufacturers. The dominance of US-based companies is striking. The top five companies mentioned in tweets have their headquarters in the U.S. Furthermore, it is remarkable that IT companies are linked that strongly to this topic. Apple has not even officially announced a self-driving car but is only rumored to work on such a project, the Apple Car. However, we need to acknowledge that the dataset is biased for the U.S. market since we queried Twitter only for English tweets.

We also applied our analysis of the RR to the car manufacturer tweets. Again, we found interesting results as depicted in Fig. 5. The RR is lowest for five traditional car manufacturers. This might indicate that people tend to trust them to develop safe self-driving cars. However, we see Tesla is getting close to the traditional car manufacturers despite the accidents resulting from misuse of Tesla’s Autopilot. It is also interesting that traditional German car manufacturers such as Daimler-Benz, Volkswagen, and Audi, which are considered the leading innovators of self-driving cars in Germany, have a high RR value. The existing driving automation system of Daimler-Benz, Intelligent Drive, is much more restrictive than the system of Tesla. Therefore, misuse of the driving automation system is much less likely than with the Tesla system and, thus, should be considered safer. However, users might perceive the additional restrictions as further loss of control, which is one of the major concerns about self-driving cars (Rödel et al. 2014; Langdon et al. 2018). In the case of Google, the users also seem to be afraid of not being able to drive anymore since one version of the Google Car had no steering wheel. Furthermore, many minor accidents of the Google Car have received much public attention. Again, we need to acknowledge that the results could be biased as the number of tweets differs very much between the car manufacturers. Our dataset contains over 6500 times more tweets about Google than about Opel. The RR makes the results comparable, but the readers should keep this difference in mind before drawing any fast conclusions.

Before we discuss and interpret our results, we discuss their validity. First, we compare our findings to previous research. Fraedrich et al. (2016) found in a study conducted in Germany that 46% of their 1163 respondents had a positive connotation with self-driving cars. Schoettle and Sivak (2014) surveyed 1533 persons from the U.S., the U.K., and Australia for their opinions on self-driving vehicles and found that 56.8% of the respondents had at least a slightly positive opinion. Our BR ranges between 46% (2015) and 42% (2016), which is almost equal to the study of Fraedrich et al. (2016) and approximately 10% smaller than the rate of positive opinions from the study of Schoettle and Sivak (2014). Therefore, we are confident that our data resembles the public perceptions of risk and benefit perception and we found first support for our proposition P1 that machine learning algorithms can be used to analyze the publics’ risk and benefit perceptions regarding self-driving cars on Twitter. Second, our analysis shows that news and events were reflected in tweets as our in-depth analysis of tweets in the previous section has shown. Therefore, we also found support for our propositions P2a and P2b. In the following, we further discuss our findings and connect it to previous research on the acceptance of self-driving cars.

Given the previous study results and the RR and BR values determined in this study, we conclude that people have specific reservations regarding self-driving cars. It could be surmised that if self-driving cars were released today, technology acceptance could not be guaranteed. Public concerns need to be addressed before the public is confronted with a rising number of increasingly automated cars or even full self-driving technology. We calculated the BR and RR values at various time points to analyze the tweets over time and found an increase in RR of 7.11% from 54.4% (2015) to 58.5% (2016). This might indicate that people’s concerns have not been addressed. Furthermore, there might be a bias caused by a social amplification of risk perceptions (Kasperson et al. 1988), i.e., people tend to talk more about risks than about benefits. While it is an interesting finding that social amplification of risk perceptions might exist in social media, the results are troubling. We see in many instances that social media increasingly leads to exaggerated risk perceptions that lead to irrational behaviors. The consequence might be that one single accident could severely reduce acceptance of self-driving cars of all manufacturers and providers.

We found many tweets that indicated a distorted perception of a risk, for example, “[…] Google’s driverless cars have been involved in four car accidents” or “CAR CRASH Google Self Driving Cars to Decide if You Live or Die […]”. However, rather than increasing the number of accidents, self-driving cars could significantly reduce them (Fagnant and Kockelman 2015). Other studies also found distorted perceptions, which might change as people become more familiar with self-driving cars (Woisetschläger 2016; Bansal et al. 2016).

People also expressed distrust towards the self-driving car manufacturing companies and conveyed their love for driving. For example: “Sorry @google not going to buy a self driving car I like driving and don’t trust your technology.” In this case, benefit perception is distorted. While driving can be enjoyable in certain situations, we find ourselves often confronted with less enjoyable aspects of driving such as traffic congestion, long monotonous highways with speed limitations or searching for a parking space in increasingly crowded cities. We find these concerns also in marketing research that predicts a lack of emotional attachment due to the loss of driving pleasure when a car drives itself (Olson 2017). First studies are addressing the user experience in self-driving cars (Rödel et al. 2014; Pettersson and Karlsson 2015; Niculescu et al. 2017). Other research proposes to adapt the driving style of self-driving cars to the driving style of their users to mitigate this issue (Kraus et al. 2009; Butakov and Ioannou 2015; Kuderer et al. 2015). To mitigate trust issues and to increase driving pleasure, previous research also suggests allowing drivers to take back control of their vehicles if they like (Yap et al. 2016).

People also displayed fear for their safety and privacy, for example, “[…] Can #driverless #cars be made safe from hackers?” expresses the fear of hackers that could take control of your vehicle. Hackers might even implement viruses that could spread from car to car, a risk that could prove to be real as hacker activities have been noted on current cars and due to the increased connectedness of self-driving cars (Lee et al. 2016). These hacking attacks could cause financial and physical harm and even death to car passengers and other road users, which is certainly more severe than having a personal computer hacked. Manufacturers of self-driving cars need to be aware of hackers and provide strategies to avoid hackers successfully attacking their vehicles. Previous research shows that hacker attacks have become one of the biggest concerns about self-driving cars (Zmud and Sener 2017).

Regarding the tweets classified as containing benefits of self-driving cars, many users liked the fact that they could save time by using self-driving vehicles. For example: “Sleepy time in the car for a in back seat. Wish I had a self driving car & I coulda joined em……”. This tweet represents a case of distorted benefit perception since only full self-driving automation allows sleeping while driving. The current level of self-driving cars is level 2, and it is likely to take some years until we arrive at level 3 or even level 4 automation. Meanwhile, drivers are misusing current self-driving cars, by leaving the driver’s seat while driving on a public road using the Autopilot feature of a Tesla Model S (Krok 2015). Thereby, they, intentionally or unintentionally, risk their own and others’ lives and potentially affect acceptance of self-driving cars by the public as the fatal accident of a self-driving Tesla has shown. Manufacturers should, however, be aware that people would like to sleep while being in a self-driving car to prevent it in cars with lower automation levels and offer a comfortable interior in full self-driving cars to enable it. Several studies mention sleeping while being in a self-driving car to be a popular activity (e.g., Cosh et al. 2017).

In general, people are impressed by the innovation put into the self-driving cars. For example: “[…] That hyper-futuristic driverless Mercedes has been spotted in San Fran – again […]”. Most benefit tweets reflected that people were simply excited to try something new. For example: “[…] A perk of living near Google… We saw the self-driving car today on the highway!” Developers of self-driving cars have recognized that people are excited about this new technology and the benefits it could provide. Consequently, they are investing in the development of self-driving cars and already promise features that will only be implemented in several years. If communication strategies are not adjusted, this excitement could cause a misunderstanding of the potential benefits and exaggerated risk perceptions of self-driving cars. Focusing only on the benefits and even generating exaggerated benefit perceptions could also have adverse effects on public acceptance of self-driving cars (Nees 2016).

By analyzing 642,033 tweets, we could show that using machine learning to classify social media automatically is a promising approach to analyze acceptance of emerging technologies such as self-driving cars. Even if data from Twitter is prone to certain biases, our results are in line with previous research. Our approach mitigates some of the methodological shortcomings regarding data collection, time-consuming manual coding, and biases of online surveys for emerging technologies. Thereby, we follow the call by several researchers to provide new impetus for acceptance research (e.g., Benbasat and Barki 2007). Furthermore, our approach allows measuring the effect of certain events on public perception of emerging technologies. The identified perceived risks and benefits can be incorporated in traditional acceptance models for survey-based research.

Our research has several managerial implications. Based on our results, we identified the need for developers and manufacturers to listen to the voice of future potential customers. Even the objectively best solution or superior new technology development can fail if it does not appeal to the customer or does not create public acceptance. Therefore, an active management of the public acceptance is mandatory to reduce the failure rate of new technologies. In the case of self-driving cars, companies need to rethink their communication strategies to address distorted perceptions of both the benefits and risks of self-driving cars (Kasperson and Kasperson 1996), already obvious with the first available level 2 automated cars. An overestimation of benefits might lead to a misuse of self-driving cars, the disappointment of initial users, and could have fatal consequences. An overestimation of risks by the public could lead to a resistance against self-driving cars before they even become widely available (Kleijnen et al. 2009; König and Neumayr 2017). Furthermore, practitioners should make sure to exploit the full potential of self-driving cars by implementing the benefits discussed in social media as described in this paper. First field studies show that people are more accepting of self-driving vehicles after having used prototypes (e.g., Alessandrini et al. 2011; Pendleton et al. 2015; Christie et al. 2016; Portouli et al. 2017; Madigan et al. 2017). Early personal experience with prototypes may lead to less susceptibility to distorted perceptions of self-driving cars and, therefore, should be made available more publicly by, e.g., self-driving car events of manufacturers, establishing additional model regions and test tracks, or creating driving experience centers for self-driving cars. We also found that user interface design will play an important role for the next generation of level 2 and level 3 driving automation systems as the risk perception of Daimler's Intelligent Drive system shows. Rather than the increased safety due to system restrictions, users primarily perceive the increased loss of control caused by them, which could reduce acceptance.

This research has some limitations. By using machine learning algorithms other than SVM and additional training data, analyses might be further improved. However, we do not expect major improvements since our analysis of the SVM classifier performance already showed good results and SVM are usually among the strongest performers for text classification (Socher et al. 2011). Further research should rather focus on more detailed risk and benefit categories. For example, Slovic (1987) describes risks as a combination of two factors: unknown risks, which are not observable and unknown to those exposed, and dread risks, which can be globally catastrophic and fatal. Hohenberger et al. (2017) divided the benefits of self-driving cars into the sub-categories of economic, time, and safety benefits. Further research could analyze risk and benefit perceptions using sub-categories of risks and benefits or identify new categories influencing technology acceptance based on data from social media.

For further research, it could be interesting to use topic modeling (Debortoli et al. 2016) to support the in-depth qualitative analysis of the tweets after classification. A next step could be to include other social media sources into the analyses such as Facebook, Reddit, or blogs. While our Twitter sample appears to be comparable to existing survey samples, extending the analyses to other platforms would allow analyzing differences in the explication and dissemination of perceptions between social media platforms. However, this would require extending the technical platform as the structures of other social media platforms differ from Twitter significantly. Besides analyzing opinions expressed in written text, VOC can also be extracted from other media such as recorded speech and videos, as mentioned by Brown (2017), who analyzed YouTube videos about experiences with driving automation. Including diverse sources may lead to a broader overview of opinions and perceptions and helps to improve our understanding of the various aspects of emerging technologies, which are relevant for potential customers and society.