Data science for entrepreneurship research: studying demand dynamics for entrepreneurial skills in the Netherlands

We are drowning in data. Ninety percent of the world’s data today has been created in the last 2 years alone.¹ Most of it is unstructured text, images, and videos, which is hard to categorize, let alone understand, for human beings.² There are sensor data in (self-driving) cars, smart home and office equipment, social media data, mobile data, data on Internet and browsing behavior, or digital camera images, to name just a few. This explosion of data is accompanied by tremendous progress in data science methods, which can make sense of all the available information. Those methods are fueled by artificial intelligence (AI). And this may just be the beginning (Taddy 2018). The McKinsey Global Institute recently projected that the adoption of AI by firms may follow an S-curve pattern—a slow start given the investment associated with learning and deploying the technology and then acceleration driven by competition and improvements in complementary capabilities (Bughin et al. 2018). At the macro level, they expect that AI could potentially deliver additional economic output of around U$13 trillion by 2030, boosting global GDP by about 1.2% a year. The increased output from efficiency gains and innovations could be passed to workers in the form of wages and to entrepreneurs and firms in the form of profits.³

These rapid and ongoing changes in the economic, political, and social spheres also affect the domain of research. Massively improved AI offers better, i.e., cheaper, prediction (Agrawal et al. 2018). Improved prediction capabilities allow us to work with huge data sets that are representative for entire populations, simply because they contain nearly complete data on that population (see Section 4 for an example). Even more, the statistical methods relying on AI—data science methods—allow us to tackle novel types of questions, such as the following: How to study the role of geographic and social proximity for entrepreneurial interactions by using huge social media data sets, i.e., from Twitter, instead of traditional case studies? How can we classify the personalities of more than 1000 CEOs, identify the entrepreneurial ones, and study to what extent being entrepreneurial has positive effects on firm performance? To what degree are entrepreneurial skills and personality traits helpful for workers in all kinds of sectors and jobs? Crucially, these questions—if they could be asked at all—could not be seriously studied, let alone be answered, by traditional empirical methods that have been taught in graduate schools in economics and management in the past decades.

In their seminal article, Shane and Venkataraman (2000) defined entrepreneurship as the identification, evaluation, and exploitation of opportunities. Shane (2012) underlined that entrepreneurship is a process, not a one-time event. The questions listed above relate to opportunities initiated by very recent technological progress, which by itself is an ongoing process. Thus, the very object of entrepreneurship research changes along the development of the technological frontier. Today, due to the availability of much more data and computer power, this frontier is shaped strongly by the state of data science techniques. Today, we can analyze and interpret large amounts of complex and unstructured data and make predictions based on correlations and inductive modeling.

Researchers can benefit by understanding and—where appropriate—embracing statistical methods that are driven by AI algorithms. This process has already started and has had disruptive effects on the social sciences, such as economics (Einav and Levin 2014) and management (George et al. 2014). It has created the new field of computational social science, which may reveal new patterns of individual and group behavior and allow to model economic and social interactions more precisely (Lazer et al. 2009).

We contribute to the entrepreneurship literature in two dimensions. First, in the next section, we describe the most prominent data science methods suitable for entrepreneurship research. The goal is to give the interested reader a concise overview over what is possible technically today, with enough input and references to start educating oneself. Section 2 is complemented by the Appendix, where we provide links to literature and Internet resources and where we also delineate key technical terms and list the most relevant text mining tools and download resources for self-starters. Our second contribution comes in Sections 3 and 4. Section 3 surveys how data science methods have been applied in the entrepreneurship research literature and sketch how they have been used to study important research questions that could not—or not to the same extent—be studied without these techniques. Along these lines, in Section 4, we provide an original analysis of a data set with 7.7 million data points and study the dynamics of demand for entrepreneurial skills in the Dutch population. In Section 5, we conclude by discussing opportunities and risks of data science techniques and relate them to traditional empirical research methods and theory.

In this section, we highlight some recent papers using data science methods for research questions on various aspects of entrepreneurial characteristics, processes, and entrepreneurship (success). ML and/or text analytics have been applied to issues such as funding (via venture capital and via crowdfunding), (product) innovation, inventors’ disambiguation, and entrepreneurial traits. The fundamental contributions of these studies fall in two categories: the utilization of new sources of information and data, advancing the data frontier, and applying novel techniques to existing data and/or problems, thereby advancing the knowledge frontier. We now take them in order.

A classical question in entrepreneurship research relates to the factors predicting a start-ups’ success (Stuart and Abetti 1987; Hisrich et al. 2007). Today, the role of online and social media communication and information for the development, identification, and success of entrepreneurial activities and agents has received a lot of attention. To arrive at deeper, richer, and more fine-grained insights on the entrepreneurial mindset, the so-called digital footprints from social media are increasingly used. Lee et al. (2017) measure overconfidence of CEOs by classifying their messages sent on Twitter. They distinguish “professional CEOs” and “founder CEOs” and find that the latter use more optimistic language on Twitter and during earnings conference calls. Founder CEOs are also more likely to issue earnings forecasts that are too high.

Aggarwal and Singh (2013) show that social media can also be used as a means to an end for entrepreneurial success. They study company blogs across multiple stages of venture capitalists’ decision-making and find that blogging can help managers in getting their products and services selected at the screening stage, but that, beyond that, blogging does not help directly. The authors show that blogs can help indirectly in the last stage of the venture capital process when negotiating a contract with the venture capitalist: blogs (with good coverage) attract the attention of competing venture capitalists, which drives up venture prices, and hence improves the blogger’s outside option.

Since the success of the managerial “upper echelons” perspective, it is rather undisputed that the individual characteristics and values of decision makers have a significant impact on the performance of firms (Andrews 1980; Hambrick and Mason 1984). A key question is how certain managerial characteristics translate into better performance. A popular empirical approach to this question has been to measure actual behavior of decision makers through real-time personal observation (Mintzberg 1973). This time-consuming procedure, however, creates the problem of small sample sizes and suffers from selection issues.

Bandiera et al. (2017) tackle the issue by developing new methodology: First, via daily phone calls with 1114 CEOs or their assistants, they collected 42,233 data points about the decision makers’ diaries. Then they employed an unsupervised learning algorithm (a latent Dirichlet allocation, LDA), which provides them with a complete probabilistic description of time-use patterns, despite the high dimensionality of their data set. The algorithm posits that the actual behavior of each CEO is a mixture of a small number of “pure” behaviors and that the creation of each activity is attributable to one of these pure behaviors. In their case, the algorithm finds two “pure” behaviors and generates a one-dimensional behavior index that represents a CEO as a convex combination of the two pure behaviors. Following Kotter (1999), they classify the first pure behavior as “manager” and the second as “leader:” “manager” refers to more time of the CEO spent in meetings with production-level workers and one-to-one meetings with firm employees or suppliers; “leader” refers to more time spent with top-executives and in interactions with several participants and functions from inside and outside the firm together. Kotter associated “managers” with a focus on monitoring and implementation tasks, whereas “leaders” focus on the creation of organizational alignment and communication across a broad variety of characteristics. Clearly, the characterization of “leaders” is related to the characterization of entrepreneurial skills in the entrepreneurship literature (see Section 4).

As a final step, Bandiera et al. (2017) correlate their managerial behavior index with firms’ balance sheet data and find that “leader” CEOs are more likely to be found in larger and more productive firms: an increase of the behavior index by one standard deviation is associated with an increase of 7% in sales, controlling for a battery of factors. This not only suggests that decision makers with entrepreneurial characteristics can also do well in more established organizations. More important for the study at hand, Bandiera et al. (2017) show an innovative way how to use data science techniques to give a more robust answer to an existing question involving personal characteristics of decision makers. There is a lot of scope to apply this to a host of questions in the entrepreneurship literature.

Obschonka et al. (2017a) use Twitter data to identify the personality traits of superstar entrepreneurs and compare them to the characteristics of superstar managers, “a hitherto understudied population in entrepreneurship research” (p. 14). To do this, the authors use a sample of 106 Twitter accounts of (superstar) entrepreneurs and managers. They analyze information from these accounts by using a novel language-based personality assessment tool that is capable of dealing with the huge number of observations from social media data.⁸ Up to now, traditional, survey-based methods, such as a standard Big Five questionnaire, have been used to assess an individual’s personality traits. In contrast to these subjective and self-reported measures, digital footprints, where individuals willingly and unwillingly spread (personal) information to a large and diverse audience, can be used to derive objective and accurate information, revealing individuals’ true preferences. Obschonka et al. (2017a) show that this new tool delivers valid results for univariate and multivariate analyses of personality differences between (superstar) entrepreneurs and (superstar) managers and that, surprisingly and contrary to earlier findings, the latter category shows more entrepreneurial characteristics than the former one.⁹

Tata et al. (2017) use Twitter data to arrive at the “psycholinguistics of entrepreneurship” and demonstrate that even though entrepreneurs are fundamentally different from the general population, also the organizational life cycle matters for the emotions and sentiments attached to entrepreneurship and to the work-life balance in general. The use of language as a robust means for revealing individuals’ (work-life) concerns, motives, traits, and emotions is not new to the field. For instance, Tausczik and Pennebaker (2010) have shown that language is a robust means for revealing individuals’ work-life concerns and emotions. “Entrepreneurial emotion” is a topic in itself and describes a package of feelings that often come with being an entrepreneur (Cardon et al. 2012), a topic that has gained increased importance through big data and AI as enablers of new self-employed businesses: “Approximately 150 million workers in North America and Western Europe have left the relatively stable confines of organizational life—sometimes by choice, sometimes not—to work as independent contractors” (Petriglieri et al. 2018). However, by using Twitter data for these analyses, Tata et al. (2017) are able to overcome several limitations of traditional data sources, such as surveys. Social media data can not only avoid response and recall biases; they also offer a real-time window into peoples’ thoughts over long periods, for more actors than any existing alternative, at any point in time, and across diverse geographical locations. Moreover, content analysis of Twitter data allows collecting information on emotions, constructs, and concerns simultaneously.

Wang et al. (2017) use Twitter data for yet another type of entrepreneurship research. They apply social network analysis to entrepreneurial networks in the USA to identify and locate entrepreneurs jointly with important regional subtleties within the network. They find that although Twitter enables interactions across geographically (and socially) distant locations, the highest intensity can be detected in regional interactions characterized by similar socioeconomic and demographic profiles. This suggests that, even in our digitally connected world, geographic and social proximity are important for entrepreneurial interactions. Hence, earlier results about the important role of social relationships for entrepreneurship are still valid. See the work of Olav Sorenson (Rickne et al. 2018). For instance, Sorenson (2018) shows that both professional and private social relationships are original reasons for industry concentrations in a small number of places, even when firms do not benefit from this clustering. Wang et al. (2017) extend the research on the relevance of networks in various ways: they simultaneously examine the types of actors engaged in digital networks and the specific regions that are active on the Twitter entrepreneurship domain. Moreover, they analyze the regional characteristics that explain the intensity of activity on this social media platform. The use of big data allows for social network analyses on a much larger scale than when using data from primary survey collection efforts. Thereby, Wang et al. (2017) offer a seminal example of bringing data science into the entrepreneurial social networks literature that has mostly been dominated by case studies (Greve and Salaff 2003). They also demonstrate how the incorporation of additional quantitative and qualitative information can mitigate issues of representativeness inherent in social media data.

Data science methods have also been applied to assess performance of crowdsourcing and crowdfunding platforms. Crowdsourcing taps into a crowd with talent to get a whole bunch of business projects or tasks solved by dividing them into microtasks. These microprojects assigned to a skilled on-demand workforce provide many business opportunities, but can also deliver interesting research questions. Crowdfunding, on the other hand, is a unique form of entrepreneurial finance that combines elements of private and public equity (Cummings et al. 2019). It taps into the power of the crowd to acquire financial support. Platforms match individuals or entities in need of funding with individuals or groups willing to contribute financially, often in the form of microfunding.¹⁰ Apart from being interesting sources of big data, these platforms themselves can be viewed as big data and datafication phenomena as a result of the ongoing digitization and automation. Both innovative developments use mass collaboration, mostly via online tools, to accomplish certain goals, for example the funding of an idea or a project. There is serious money involved in crowdfunding¹¹ and, therefore, reliable predictions on the success rates of these products or projects are important. Obviously, big data and data science methods play an important role also for the internal business processes at a crowdfunding or crowdsourcing platform. With social media promotions, statistics on earlier projects, market dynamics, and other activities, a huge amount of data is generated, which can predict the success of ideas or products based on past analytics results.¹²

Hoornaert et al. (2017) build a ML model to predict the success and failure of business and product ideas generated within the crowd based on 3C’s: its content, the contributor proposing it, and the crowd’s feedback on the idea. A non-linear, supervised algorithm identifies the variables that are most predictive of an idea’s distinctiveness and successful implementation. The authors find that considering immediately available information about the content and contributor improves the ranking performance by around 25% over random idea selection, while adding crowd-related information that accumulates over time further improves performance by nearly up to 50%. The last C, crowd feedback is, thus, the best predictor, but also the one that needs most time to develop.

Courtney et al. (2017) use data from Kickstarter, a large and popular crowdfunding portal. They examine the interplay of three signal types obtained from different sources within the platform on the viability of a certain idea: the direct actions a start-up takes regarding a proposed idea and/or product (the content), its characteristics (mainly crowdfunding experience; the contributor), as well as third-party endorsements (sentiments expressed in backer comments; the crowd). For the last type of signal, the authors implement a novel sentiment analysis technique, with which the underlying tone of textual comments by backers can be derived. This allows for a continuous feedback measure of a large and heterogeneous group of individuals commenting on a project, a major improvement on the dichotomous variable that is usually used to measure third-party endorsements (Courtney et al. 2017).

On a higher level, Hartmann et al. (2016) connect data science and entrepreneurship. They derive a taxonomy of business models used by start-up firms that rely on data as a key resource for business, which they call data-driven business models. Their taxonomy consists of six different types of such business models among start-ups and thereby develops a basis for understanding how start-ups build business models that capture value from data as a key resource.

Whereas the above-cited papers use new (big) data sources, Hoberg and Phillips (2016) apply a novel technique, text analysis, to study an existing administrative database.¹³ They use the product descriptions that firms filed with the US Securities and Exchange Commission (SEC) to develop new time-varying industry classifications. These new, more flexible measures of industry membership are better suited to explain differences in key characteristics across industries, such as profitability, sales growth, and market risk. Information on the text-based network classification is also informative about identifying rival firms. Moreover, these classifications show endogenously how industries and their competitors change due to external shocks and how R&D activities and advertisement are endogenously adjusted to the behavior of relevant competitors. Hoberg and Phillips (2016) combine two central ideas: first, that the product features and bundles a firm offers can be consistently derived from SEC product descriptions and that these descriptions can be used to assign a spatial location based on product descriptions, generating a Hotelling-like product location space for these firms. Second, this study uses text analysis to build a network of firms, in which the similarity of each firm to every other firm is calculated by firm-by-firm pairwise word similarity scores using the original product descriptions. Based on these pairwise similarity scores, firms are grouped into industries and the general industry classification can be interpreted as an unrestricted network of firms. There, a firm’s competitors are analogous to a group of friends on social media, with each firm having its own distinctive set of competitors.

Complementing the (admittedly selective) broad literature review in the previous section, now we go into some depth. To exemplify the general statements made above, we offer an original analysis of a novel big data set by using various data science methods. Specifically, we study the consequences of the ongoing technological and economic developments on the demand for entrepreneurial skills.

Developing entrepreneurial skills is increasingly seen as important to foster entrepreneurship (Baumol et al. 2007). Several recent articles picked up the call and approached the topic from several disciplinary and methodological angles.¹⁴ As the purpose of this section is not to review the literature on entrepreneurial skills (for that, see the cited articles) but to exemplify data science methods, we restrict our notion to two observations. First, there is no generally accepted delineation, let alone definition, of “entrepreneurial skills.” Second, one of the restrictions of existing studies using traditional empirical methods is the small number of available data points: the cited articles report sample sizes of 39, 523, and 1126 subjects, respectively. Consequently, it is hard to draw general, robust lessons that can be applied to different contexts than those studied. An additional characteristic of these articles, which is interrelated with sample size, is that they focus on (would-be) entrepreneurs, which does not allow to make statements about the importance of and demand for entrepreneurial skills in the general population. Here, we try to alleviate these constraints.

The starting point is that digitization, automation, and the development of new (adaptable) technologies have an increasing impact on the labor market. The boundary between “ICT jobs” and other professions in which ICT-related skills are required is becoming increasingly blurred. Moreover, the specific skills demanded and the tasks that have to be fulfilled in all occupations have changed considerably in recent years (Spitz-Oener 2006).

These changes have led to increased demand for employees with sufficient digital skills in many countries, including the Netherlands (ROA 2017). For employees who, in the longer term, cannot acquire the necessary digital skills through training and retraining, suitable measures and career development paths are required that avoid insufficient qualifications and, eventually, unemployment. In contrast, research has shown that employees can adapt sufficiently to the changes on the labor market and that the negative effects of digitization and automation might be exaggerated, as many jobs may change but also new jobs will be created (Autor 2015; Arntz et al. 2016). Given that innovative capacity is directly related to economic growth, a lack of people with sufficient digital, technical, and ICT skills, in combination with a broader set of the so-called twenty-first century skills, limits innovative capacity (Obschonka et al. 2017b). Alternatively, abundance of these types of employees helps to mitigate the negative effects on innovative capacity and the labor market (Elliott 2017; McAfee and Brynjolfsson 2017).

What are the dynamics in skill demand on the labor market? What are the consequences for different occupations and for employees with different educational backgrounds and different levels of expertise? How do they affect certain types of professions such as managers, ICT professionals, and employees in non-IT/technical jobs? Prüfer et al. (2019) answer these questions by making use of a novel approach of “labor market analytics” in which information from online vacancies, thus from unstructured (big) Internet data, is combined with information from labor market forecasts, that is, with structured data from administrative sources.¹⁵ Thereby, an innovative and very rich source of information, as well as a unique dataset is created with which the authors analyze the impact of digitization and automation on the labor market in general, on specific economic sectors, on 371 different occupations, and on 3 types of professions. Prüfer et al. (2019) measure the change in skills requirements over time by taking into account digital, technical, and ICT skills compared to general cognitive and non-cognitive skills.

In an original extension to Prüfer et al. (2019), the current section derives insights on the consequences of the ongoing digitization and automatization on the dynamics of entrepreneurial skills. We distinguish three types of professions: managers, ICT jobs, and non-ICT jobs. This helps to understand how the requirements have changed over time and among types of professions and, thus, not only provides insights into ongoing skills dynamics but also on the need for additional qualifications and retraining of specific groups.

This approach is not without caveats, either. Vacancy data are not necessarily representative and we do not know who applies for a certain vacancy and who is employed in the end. On the other hand, (online) vacancies give a much more fine-grained and real-time picture of labor market demand. This data source can provide information over a longer period of time, for a larger sample, and across various locations. Moreover, vacancy data are less prone to response and recall bias, which are eminent in survey data—even more so as it is fairly expensive to place a (clearly visible and widely distributed) vacancy. Finally, vacancy data are much cheaper than other sources of information such as questionnaires within a representative sample or register data that have to be linked from multiple sources.

The ongoing datafication, coupled with gigantic technological progress in the domain of AI, is changing all aspects of our lives: work, politics, community interactions, economic transactions, and many more. Agrawal et al. (2018, p.194) summarize:

AI can lead to disruption because incumbent firms often have weaker economic incentives than start-ups to adopt the technology. AI-enabled products are often inferior at first because it takes time to train a prediction machine to perform as well as a hard-coded device that follows human instructions rather than learning on its own. However, once deployed, an AI can continue to learn and improve, leaving its unintelligent competitors’ products behind. It is tempting for established companies to take a wait-and-see approach, standing on the sidelines and observing the progress in AI applied to their industry. That may work for some companies, but others may find it difficult to catch up once their competitors get ahead in the training and deployment of AI tools.

Now, substitute “researchers” for “firms”/“companies” in this quotation and “research projects” for “products.”

The disruption occurring at the economy-level is mirrored in the world of research, fueled by developments in data science methods. Distinguishing themselves from traditional statistics and econometrics, these methods use algorithmic models and treat the data mechanism as unknown in order to discover complex structures that were not specified in advance. Where conventional statistics is deductive, data science is inductive. These inductive methods facilitate the automated collection of information, especially on, but not restricted to, the Internet. Via text analysis, computers can learn to understand the meaning of words, relate them to each other, and analyze them at scales that otherwise would require the help of hordes of research assistants. The new techniques and technologies also allow to use many more (unstructured) real-time data sources to conduct analyses that would not have been possible otherwise, for instance by using sensor data from mobile devices (Blumenstock et al. 2015). By making reliance on subjective and self-reported surveys largely unnecessary and substituting these sources with objective data on revealed preferences, they improve the accuracy, robustness and, hence, the value of entrepreneurship research.

Given that these methods are usually freely available and relatively easy to learn, data science techniques thereby contribute to a democratization of empirical research tools, where scholars or students with fewer resources have a higher chance to compete with established researchers from resource-rich countries regarding the types of research questions they can study.

However, given the current state of data science methods, they cannot completely substitute human creativity and research design skills.²⁴ According to Agrawal et al. (2018), AI algorithms are better than humans at factoring in complex interactions among different indicators if enough data are available. If this condition does not hold, however, humans are often better than machines when understanding the data generation process confers a prediction advantage. In the social sciences, data science methods appear to be especially well suited for first, inductive analyses that guide further research efforts. This occurs, for instance, by pointing researchers at relevant correlations and helping them to design better (field) experiments, to make better comparisons between more precise populations of interest, and to reveal behavior that was difficult to detect previously (Monroe et al. 2015). The inductive, data-driven approach can also point theorists at the key variables of interest for a specific question that deserve being modeled. This may alleviate the need for expert interviews or the use of small, unrepresentative surveys to obtain a first understanding of the main influence factors for a given research question. In Section 4, we showed the advantages of this approach—and the details how to apply it to a specific question from the domain of entrepreneurship research, the demand dynamics of entrepreneurial skills. Our study, based on a dataset of 95% of all job vacancies in the Netherlands over a 6-year period with 7.7 million data points, has visualized that with data science methods we can study questions that could not have been studied on smaller, non-representative data sets. It has allowed us to state that demand for both entrepreneurial and digital skills has increased for managerial positions but that entrepreneurial skills were significantly more relevant over the entire period 2012–2017 and that the absolute importance of entrepreneurial skills has even increased more than digital skills. This finding may serve as motivation for more research on the role of entrepreneurial skills in the general population—and not only among (would-be) entrepreneurs.

Moreover, data science techniques may also reduce the risk that theorists fall victim to confirmation bias (Mahmoodi et al. 2017). Dreaming ahead, this may lead to a norm for the best theoretical researchers having to motivate their models by the results of big data analyses. Notably, data science methods are no substitute for theoretical research or conventional statistics. They complement those established methodologies. A fruitful avenue for further research is to combine big data and ML with administrative and survey data. In all social sciences, data science techniques have been largely applied to Internet data (often by scraping and analyzing big social media data sets). Entrepreneurship research is no exception, as Section 3 has shown. However, this approach ignores both potential selection effects that are due to differences between online (social media) users and the entire population and measurement errors that are due to the unreliability of social media data as a representative measure of social phenomena. Comparing the results of a (small) representative survey with results of (big) unrepresentative data, of which the representativeness can even be assessed empirically, therefore looks like an ideal way forward for empirical research.²⁵

Just as all technologies based on AI, data science methods come with risks. Agrawal et al. (2018) conclude their insightful book on the consequences of AI by focusing on three trade-offs. The first is productivity versus distribution. Bughin et al. (2018) note: “A key challenge is that adoption of AI could widen [performance and outcome] gaps between countries, companies, and workers.” Applied to research, data science methods can increase the number, breadth, and speed of questions we can work on, increasing our productivity. But researchers who neglect technological progress or who miss the train may feel very disadvantaged as some traditional methods may be dominated by data science techniques. Consequently, there may be a watershed moment for every researcher, where she either invests some time to familiarize herself with data science methods (for these the above-described democratization of research tools may kick in), or not (which saves time and effort in the short run but may come at significant risk for the relevance of her research in the long run).

The second trade-off is innovation versus competition. In business, the successes of Google and Facebook, both of which are highly data-driven firms that have embraced AI early, have shown that data-driven markets display first-mover advantages and are prone to market tipping. Importantly, watching the dismal fate of their competitors underlines how important it is not to fall behind.²⁶ To some degree, data science methods could introduce a similar spiral, where those researchers who embrace them early could produce higher-quality research, which may have positive feedback effects on their consecutive projects. As long as data sets from one project can be merged and, hence, be partly reused in future projects, the prediction power of those researchers’ models might outcompete latecomers repeatedly, discouraging entry of new researchers in their fields.²⁷ The quality of top researchers’ work might be(come) stellar but the competitive supply of answers to important research questions might decrease, giving the top researchers significant opinion leadership.

The third trade-off is performance versus privacy. Using AI successfully depends on huge amounts of data because it is the very power of personalization of services and inference about an individual’s preferences and characteristics that can be made if only sufficient data about other individuals are available.²⁸ But the benefits of aggregate data may come at individuals’ costs, especially for privacy.²⁹ Doing research by analyzing big data sets with data science methods is subject to the same trade-off as running a firm in a data-driven market. Therefore, such research is subjects to the same laws. As a direct policy response to datafication and AI, the General Data Protection Regulation (GDPR) has become effective in the EU in May 2018, regulating the legal use of privacy-sensitive data, especially those relating to Internet services.³⁰ The GDPR is already affecting researchers doing empirical research that uses data from the EU or about EU citizens.³¹

Crucially, the one-to-one translation of the three trade-offs listed by Agrawal et al. (2018) from the business to the research domain is subject to further scrutiny. For instance, it is unclear whether empirical research using data science methods is subject to the same indirect network effects as competition on data-driven markets (which leads to market tipping and one highly dominant firm per market).

By contrast, what is certainly true is that we as researchers need to keep up the standards of verifiability, reliability, and replicability of research results. However, this is particularly difficult when ML algorithms are used because, by definition, the algorithm is learning: it adapts based on feedback.³² Therefore, it is harder than with conventional research methods to reproduce predictions (read: results) based on ML. What is necessary, thus, is to make the decision-making processes of algorithms more transparent. This would facilitate trust in the new technologies and replicability would be easier.

One option to achieve this goal is to build algorithms with an internal self-evaluation or calibration stage such that the machine can test its own certainty and report back to the researcher. One attempt in this direction is the Automatic Statistician, which was developed at Cambridge University.³³ The tool is set up with funding from Google and helps researchers to analyze their datasets while also providing a report in a human-understandable form that explains what it is doing and how certain it is about its predictions. This technology is related to a recent development within ML, Automated Machine Learning (AutoML). This approach tackles the fundamental problems of accountability and verifiability. Here, ML methods and hyper-parameter settings are automatically selected and, thereby, reduce the necessity of handcrafted human interventions. Apart from substantial performance improvements, AutoML can provide evaluations of all tested methods and specifications. Thereby, it can help non-experts to effectively and reliably apply ML techniques.

In all social sciences, including entrepreneurship research, there is a lot of ground to cover.