Researching entrepreneurship using big data: implementation, benefits, and challenges

International Entrepreneurship and Management Journal (2025) 21:85 https://doi.org/10.1007/s11365-025-01100-w Abstract Advancements in technology and digitalisation have paved the way for multidimensional possibilities in the availability, extraction, and implantation of data. Consequently, the application of big data in understanding the spectra of different fields of knowledge has been evolving. Entrepreneurship research is one such developing area. Within this field, research has called for the advancement and application of methodological approaches to understand entrepreneurial phenomena. This study attempts to respond to this call by addressing how big data can be applied to entrepreneurship research. Therefore, the use of big data analysis and analytics in entrepreneurship research was explored and several theoretical perspectives and methodological possibilities for applying big data to entrepreneurship research were investigated. Furthermore, benefits, challenges, and ethical considerations were considered with a focus on how to navigate these challenges and implement them. The study concludes with future directions for emerging technologies that can be adopted in entrepreneurship research. Finally, recommendations are offered to researchers on how to apply big data not only to collect information but also to analyse and present it in a meaningful way. Keywords Entrepreneurship · Big data · Artificial intelligence · Predictive analysis · Review · Technological innovation Accepted: 21 March 2025 © The Author(s) 2025 Researching entrepreneurship using big data: implementation, benefits, and challenges AdesuwaOmorede1· Juan FranciscoPrados-Castillo2· Amalia CristinaCasas-Jurado3 Adesuwa Omorede [email protected] Juan Francisco Prados-Castillo [email protected] 1 School of Innovation, Design and Engineering, Mälardalen University, Box 325, SE-63105 Eskilstuna, Sweden 2 University of Granada, C/ Santander, 1 52005 Melilla, Spain 3 University of Granada, University Campus of Cartuja, Granada 18071, Spain 1 3 International Entrepreneurship and Management Journal (2025) 21:85 Introduction As research on entrepreneurship grows, several streams have unfolded, leading to a theoretical expansion and comprehensive understanding of the entrepreneurship phenomenon within its ecosystem. Two significant factors contributing to the advancement of entrepreneurship in both practice and academia are technological advancement and digital transformation. This transformation has opened multiple possibilities to understand the complexities of the entrepreneurial environment and provide insights into the development of innovation, decision-making processes, the behavioural patterns of entrepreneurs, and forecasts (Mazzoni et al., 2021; Nambisan, 2017; Omorede, 2023; Xiao et al., 2023). One such technological advancement is the integration of big data and big data analytics into research and practice (George et al., 2014; McAfee et al., 2012). Research has highlighted the significance of integrating big data analysis in the advancement of entrepreneurship and management research, not just because of its interdisciplinary nature but also because of the possibility of changing how research and practice interact (Khan, 2020; Obschonka & Audretsch, 2020). Therefore, research has called for the integration of artificial intelligence (AI) and big data into entrepreneurship research (Giuggioli & Pellegrini, 2022; Lévesque et al., 2022). These technologies have become increasingly relevant as they offer innovative tools to tackle challenges in understanding and predicting entrepreneurial dynamics. The emergence of big data and artificial intelligence (AI) technologies is significantly changing entrepreneurship research and practices. These developments not only signify a change in basic assumptions in the ways we interpret entrepreneurial activities, but also in how these activities are evaluated and encouraged, rather than only providing minimal incremental improvements. Deeper insights into improved predictive capabilities and the ability to recognise new trends and obstacles in the entrepreneurial ecosystem are made possible by the unprecedented opportunities represented by the integration of big data analytics and AI into entrepreneurship research. Furthermore, big data analysis offers the potential to gain a deeper understanding and enhance predictive indicators of the entrepreneurial process (how entrepreneurs search for opportunities, how they initiate their start-ups, strategies for development and growth, as well as strategies to terminate their venture, Omorede, 2014) and its impact on entrepreneurial success through forecasting and anticipation of changes in market trends, identifying emerging opportunities and challenges, and implementing new strategies (Wamba et al., 2015). These predictive analytics can provide insights into venture sustainability and performance (Calvard, 2016). Additionally, the scalability of big data allows researchers to investigate broader patterns across industries and regions, thus offering a global perspective on entrepreneurial trends. First, it makes it possible to understand the entrepreneurial process on a large scale. Second, it improves our capacity to precisely forecast entrepreneurial results. Third, it facilitates recognition of new opportunities and difficulties that might not be visible using more conventional research techniques. To completely understand the field’s revolutionary potential, it is essential to examine specific instances of the use of big data in entrepreneurship research. One noteworthy instance is the research of Schwab and Zhang (2019), who showed how 1 3 85 Page 2 of 22 International Entrepreneurship and Management Journal… machine learning techniques can be used to predict the success of startups. They achieved unprecedented accuracy in identifying the critical elements that influence venture performance by examining sizable datasets of start-up characteristics and outcomes. Similarly, Prüfer and Prüfer (2020) showed how big data can help us understand how technology disruption affects entrepreneurial ecosystems by using big data approaches to analyse the effect of artificial intelligence on entrepreneurship. Obschonka et al. (2020) provide yet another groundbreaking use of big data in entrepreneurship studies. They mapped geographical variances in entrepreneurial personality using social media data. Their study serves as an example of how big data can shed light on the psychological and cultural underpinnings of entrepreneurship at a local level. Wang et al. (2017) used big data analysis to examine entrepreneurial social networks, providing fresh insights into the interpersonal elements of entrepreneurship. These examples demonstrate state-of-the-art big data applications for entrepreneurship research and pave the way for novel contributions in this field. Although integrating big data into entrepreneurship research can lead to endless possibilities, several barriers and challenges may arise in its implementation (Calvard, 2016). A major challenge in implementing big data is its associated ethical implications, which may violate individual data protection (Boyd & Crawford, 2012). Another challenge is how real-time analysis is conducted at different stages of the venture lifecycle (Kim et al., 2016). Other challenges associated with the integration of big data are how the data are gathered, analysed, and interpreted to yield adequate results (Kitchin, 2014). Bouwman et al. (2019) point out the methodological difficulties associated with using big data analytics in entrepreneurship research, such as the need for sophisticated analytical abilities and problems with integration and quality. Additionally, as mentioned by Obschonka et al. (2020), the use of social media data for research presents significant privacy and consent issues that require careful consideration in subsequent investigations. Notwithstanding these challenges, big data and AI have much to offer in entrepreneurship research. These techniques offer previously difficult-to-capture insights into regional differences in entrepreneurial activities. Furthermore, by considering the quick changes that frequently occur in these settings, it is possible to create more dynamic models of entrepreneurial ecosystems with the ability to analyse large-scale datasets in real-time (Stam, 2017). However, these advancements should be considered complementary to traditional methodologies to ensure a holistic approach to entrepreneurship research. This study aims to explore the use of big data analysis in entrepreneurship research by highlighting several factors that entrepreneurship researchers must consider when embarking on this type of research. Considering the rapidly evolving field of big data applications in entrepreneurship research, this study addresses the following research questions: i. How can big data analytics and AI be effectively integrated into entrepreneurship research to enhance our understanding of entrepreneurial processes and outcomes? ii. What are the key benefits and challenges of using big data in entrepreneurship research? 1 3 Page 3 of 22 85 International Entrepreneurship and Management Journal (2025) 21:85 iii. How can emerging technologies in big data analytics be applied to address the current gaps in entrepreneurship research? iv. What ethical considerations must be addressed when applying big data analytics in entrepreneurship research? The research questions above link to several streams of research within entrepreneurship. First, research question one is supported by the theoretical framework of interdisciplinary approaches to entrepreneurship, including the dynamic capabilities theory and resource-based view. Second, research question two is grounded in theories of innovation and dynamic capabilities, as highlighted by Wamba et al. (2015) and Teece (2018). Third, research question three links disruptive innovation theory and network theory to explore opportunities for technological advancement (Christensen et al., 2013; Hayter, 2013; Obschonka & Audretsch, 2020). Finally, the fourth question is framed by data governance theories and privacy guidelines, including Boyd and Crawford (2012). This study highlights the benefits and enablers that can facilitate the use of big data and further acknowledges several challenges and possible barriers when using this approach. Thus, this study contributes to the current discussions on conducting interdisciplinary research within entrepreneurship, as well as the discussion about the implementation of data analytics and artificial intelligence (AI) to research entrepreneurship. Ultimately, this study seeks to advance theoretical and practical understanding, providing a foundation for future studies in the field. The following sections present how big data is conceptualised and implemented in entrepreneurship research, followed by discussions about its benefits and challenges. The paper concludes with recommendations for implementation and considerations for future research. Literature review and theoretical background Overview of the state of the art Several authors have conceptualised the meaning of big data. De Mauro et al. (2016), describe big data using four essential elements: Information (the fuel of big data), Technology (a prerequisite for using big data), Methods (techniques for processing big data), and Impact (big data touches our lives pervasively). Here, they define big data as ‘the information asset characterised by such a High Volume, Velocity, and Variety to require specific Technology and Analytical Methods for its transformation into Value’. (pg. 127). They emphasised that the definition of big data encompasses big data technology and methods. Furthermore, researchers such as Laney (2001) adopt the same main dimensions of Volume, Velocity, and Variety. However, other researchers have defined big data as a popular term used to describe the exponential growth, availability, and use of information, both structured and unstructured (George et al., 2014; Khan, 2020). This information includes, but is not limited to, video and audio data originating from various sources, such as social media, transaction records, and sensors. 1 3 85 Page 4 of 22 International Entrepreneurship and Management Journal… Big data applications are becoming increasingly prevalent in various sectors owing to their potential to offer competitive advantages. These include healthcare, finance, marketing, supply chain management, and manufacturing (Benjelloun et al., 2015; Zhong et al., 2016; Sravanthi & Reddy, 2015). These applications use big data analytics to improve decision-making, optimise resources, and enhance strategies (Benjelloun et al., 2015). The adoption of big data technologies has led to the development of new business models, with organisations adapting their approaches to address the challenges and opportunities presented by large-scale data processing (Canan et al., 2013) in combination with other technologies such as Artificial Intelligence. As a result, both the service and manufacturing sectors have benefited from ongoing research and investment supported by the public and private sector (Benjelloun et al., 2015; Zhong et al., 2016). Furthermore, big data has emerged as a significant factor in business management and entrepreneurship research, offering new insights and opportunities for business growth, innovation, and business ideas (Sood et al., 2021; Shan et al., 2022). The integration of Big Data analytics affects various aspects of business activities, such as decision-making, innovation, and customer experience optimisation (Lull et al., 2024). Moreover, insights derived from Big Data analytics can lead to improved business strategy decisions (Kaur & Cheema, 2017; Paredes-Moreno, 2015). Big Data can empower knowledge-intensive entrepreneurship by aligning business processes with customer needs and market trends. As research in this field has advanced, the potential to revolutionise business practices and drive economic development has evolved. Big Data is seen as an essential investment for organisations seeking to maintain high quality and productivity, with 92% of executives reporting satisfaction with the results (Nascimento et al., 2021). However, recent research has highlighted the growing importance of big data in entrepreneurship studies. Big data analytics can enhance enterprise performance (Shan et al., 2022). Psychological big data offers insights into entrepreneurial culture and regional economic development (Obschonka, 2017). While big data presents new opportunities for advancing entrepreneurship theory and practice, researchers must navigate methodological challenges to ensure high-quality studies (Schwab & Zhang, 2019). As the field evolves, researchers are encouraged to employ sophisticated analytical methods to fully leverage the potential of big data in entrepreneurship research (Obschonka, 2017; Schwab & Zhang, 2019). This demonstrates the relevant potential for both the service and manufacturing sectors, sectors with ongoing research and development efforts supported by public-private development programs (Benjelloun et al., 2015; Zhong et al., 2016). In conclusion, the evolving role of big data in entrepreneurship research necessitates a dual focus: leveraging existing methodologies to maximise analytical capabilities and addressing gaps in the literature by integrating emerging trends and technologies. These considerations provide a robust foundation for the objectives of this study and its contribution to the field. Theoretical foundations of big data in entrepreneurship research In entrepreneurship, research using big data analytics is not only used to obtain meaningful insights into understanding the current state of events but also to make accu1 3 Page 5 of 22 85 International Entrepreneurship and Management Journal (2025) 21:85 rate predictions of future events. For instance, to assess and mitigate risk rather than relying on heuristics, formulate strategies for opportunity identification, evaluation, and exploitation to achieve social good and sustainable change through social innovation (Kitchin, 2014; Pappas et al., 2017). Insights from fields such as economics, marketing, and business management further contextualise this data within the entrepreneurial ecosystem, including understanding demand, organisational behaviour, strategic management for competitive advantage, operational challenges, and indicators of economic growth (Porter, 1991; Varian, 2014). An interdisciplinary approach to entrepreneurship research using big data creates further possibilities for innovative methodological rigor that yields a richer understanding of new insights into entrepreneurship research. The effective application and implementation of big data in entrepreneurship research entails an interdisciplinary approach that can incorporate knowledge from data science, economics, business studies, and marketing (Khan, 2020). Through this interdisciplinary lens, researchers can take advantage of various methodologies and perspectives, leading to a thorough understanding of entrepreneurial phenomena. The core of data science, encompassing statistical analysis, machine learning, and computational techniques, offers tools to gather and interpret complex data and enrich entrepreneurship research with innovative methodological rigor (Provost & Fawcett, 2013; Shepherd & Majchrzak, 2022). The interdisciplinary approach to applying big data in entrepreneurship research also involves the application of theories and frameworks from other research fields, which cannot be overemphasised (Khan, 2020). For instance, in theories such as the resource-based view (Foss et al., 2008; Kellermanns et al., 2016) and knowledgebased view (Grant, 1996; Hayter, 2013), resources and knowledge are the data themselves, and the insights derived from these resources can be used to make strategic decisions, advance innovation capabilities, and gain competitive advantages (Wamba et al., 2015). Additionally, as a dynamic capability (Teece et al., 1997; Zahra et al., 2006), big data can aid organisations in not only sensing opportunities and reacting to threats more quickly, but also in seizing these opportunities and gaining competitive advantages. Therefore, research on corporate and strategic entrepreneurship can benefit from such integration. Moreover, in extremely dynamic markets, Big Data Analytics Capabilities (BDAC) have become essential tools for business competitiveness (Ciampi et al., 2021). For instance, BDAC can improve a company’s capacity to create both incremental and radical innovation, which is particularly important for entrepreneurial ventures (Mikalef et al., 2019). These capabilities allow for a more in-depth analysis of ecosystem dynamics, market trends, and entrepreneurial activities. Finally, disruptive innovation theory (Christensen et al., 2013) and network theory can be used to integrate big data into entrepreneurship research. Big data can reveal insights into the entrepreneurial ecosystem by facilitating the identification of collaboration opportunities, stakeholder relationships, and disruptive innovations, offering organisational tools to effectively navigate evolving markets. Recent studies of entrepreneurship using big data have examined the connection between local entrepreneurial activities and media coverage. For example, von Bloh et al. (2020) found that news coverage of entrepreneurial events can serve as a catalyst for regional venture creation and development. This demonstrates how big data 1 3 85 Page 6 of 22 International Entrepreneurship and Management Journal… analytics can be used to comprehend the complex relationships between media narratives and entrepreneurial activities. Furthermore, big data applications in entrepreneurship education are becoming increasingly popular. For instance, Ma et al. (2020) proposed a hierarchical framework that prioritises elements such as opportunity recognition, institutional environments, and psychological factors in their assessment of the use of big data technology in entrepreneurship education. These applications emphasise the transformative potential of big data, enabling researchers to uncover new patterns and forecast trends, and provide actionable insights for academics and practitioners. Methodology Our research sought to identify crucial studies published in peer-reviewed publications on entrepreneurship and big data analytics, focusing on articles written in English up to July 2024. The review process encompassed publications in various areas, including entrepreneurship, management, information systems, and data science. This methodology was employed in similar studies by Guttentag (2019) and Ip et al. (2011). To ensure the relevance of each article, we assessed it individually to confirm its focus on the confluence of entrepreneurship research and big data analytics. The review process for investigating the utilisation of big data in entrepreneurship was structured into five key phases. First, data sources and search strategies were identified using databases such as Web of Science, Scopus, and Google Scholar. Subsequently, search terms including but not limited to ‘entrepreneur*?, ‘big data’, and ‘digital entrepreneur*’ were applied, incorporating only articles specifically analysing the utilisation of big data in entrepreneurship. In the second phase, an initial review of the titles and abstracts was conducted, followed by a comprehensive text review. Subsequently, data were extracted and synthesised, and content analysis was performed to identify common trends and gaps in the literature. Finally, a quality assessment was conducted to ensure that only high-quality studies aligned with the research objectives were included. Figure 1 outlines the steps involved in the selection of the papers analysed. Findings Methodological framework for big data research in entrepreneurship When processing big data for entrepreneurship research, the use of an appropriate research design cannot be overemphasised. Big data deals with high volume, velocFig. 1 Review process for big data in entrepreneurship research. 1 3 Page 7 of 22 85 International Entrepreneurship and Management Journal (2025) 21:85 ity, and variety of information. This indicates that the design for conducting such research must be structured to gather meaningful insights from the data. Several research designs can be adapted to use Big Data in entrepreneurship research. Strategies, for instance, can be exploratory in nature, where exploratory design can be used to identify new patterns, ideas, trends, and previously unrelated factors/concepts as well as to test and confirm hypotheses (Shah et al., 2012). Exploratory designs can include activities related to the mining of data and machine learning algorithms (MLA) to detect these patterns. One aspect that could be of interest to entrepreneurship researchers is the exploration of firm failure and success factors using big data. Furthermore, predictive research design is another strategy by which researchers can adopt big data analytics. Specifically, predictive analytics such as machine learning algorithms can use historical data to forecast future trends in the market, potential venture performance, and possible investment outcomes (Provost & Fawcett, 2013). Additionally, using big data in a longitudinal research design can facilitate tracking a venture’s activity and provide insights into what it can improve, eliminate, or implement to maintain its performance (George et al., 2014). For such designs, the methods by which data are collected and curated are also important. Data for this type of research can be extracted from diverse sources using both structured and unstructured methods. A structured means of big data collection can involve accessing the database of the organisation to extract customer transaction logs, financial records, digital footprints of customers, videos, images, and the social media of ventures and organisations to make predictive analytics and strategic decisions. Data collection tools such as application programming interfaces (API), Internet of Things (IoT) devices like sensors and actuators, and web scraping tools such as bright data, scrapping-dog, and AvesAPI can be used to track information and interactions and gather data to understand different dimensions within entrepreneurship research (Fan & Gordon, 2014). Considering the volume of data collected through big data analytics, it is also important to clean, organise, and structure the data using suitable formats to effectively manage the data. Data management ensures the reliability and validity of the collected data (Kitchin, 2014). This is because it establishes grounds for accuracy, completeness, and the quality of the data. In doing so, it becomes less complicated to sort through the data, seek connections, and identify patterns so that relevant information can be extracted. Artificial intelligence (AI) in the form of natural learning processing (NLP) tools and algorithm tools such as Gensim and SpaCy can be used to interpret and quantify qualitative data from social media and customer reviews as well as analyse visual content related to entrepreneurial products and services (Halevy et al., 2009). In today’s digital age, entrepreneurship researchers will benefit from adopting some of these tools and methods to gather information about factors that are important to advance the research field (Gandomi & Haider, 2015). Analytical techniques in using big data for entrepreneurship research Quantitative and statistical analyses are commonly associated with big data research as they involve large datasets with computational algorithms for pattern identification, hypothesis testing, and prediction (Fan & Bifet, 2013). For example, when 1 3 85 Page 8 of 22 International Entrepreneurship and Management Journal… working with structured data, a quantitative approach is more suitable for effectively handling large volumes of numerical data. Given the nature of big data, entrepreneurship research can use analytical tools, such as regression analysis, cluster analysis, and network analysis, to allow for correlations within large populations and generalise such findings (Fan & Bifet, 2013), provide insights into venture startup performance and operational efficiency, facilitate data-driven decision-making (Wamba et al., 2015), and ensure the rigor and objectivity of findings. Although quantitative analysis has proven to be an adequate analytical tool for big data analysis, researchers can also adopt qualitative research analysis, such as content analysis and thematic analysis. These can be used to synthesise unstructured text, images, and videos (Fan & Gordon, 2014).) to interpret patterns and themes, as well as to understand the underlying factors behind a phenomenon. These analytical tools can provide further understanding of why and how a phenomenon occurs. Other advanced tools (earlier discussed in methodsMLA and NLP) that can be applicable for entrepreneurship research create avenues to analyse data that would be difficult for traditional methods of analysis. These tools not only provide information on leveraging long-term success and the potential for making venture funding decisions and potential market opportunities, but can also create avenues into insights for customer interaction, leadership communications, and access to new markets, which are vital aspects of entrepreneurship research (Müller et al., 2018). Benefits of big data in entrepreneurship research There are several benefits to using big data to research entrepreneurship. Some prominent ones are highlighted below. Enhancing analytical depth and breadth Integrating big data analysis in entrepreneurship research can help understand several elements of this field, especially because it provides a comprehensive analysis that may be challenging for traditional methods to implement. Owing to the attributes of the 4Vs (velocity, variety, volume, and veracity), big data can enable entrepreneurship research and practice to draw nuanced, holistic, and timely insights into the studied phenomena. More specifically, with its volume, entrepreneurship research can examine a complex entrepreneurial phenomenon that cuts across countries, customers, and professionals in different contexts. For instance, Gupta and George (2016) emphasise that big data can provide insights from initial contact points to long-term purchasing behaviour, uncovering insights that drive personalised marketing and customer retention strategies. Furthermore, the variety of big data can provide a range of numeric transactional data as well as text-based reports from social media, thereby collecting both quantitative and qualitative data. This information provides an avenue for addressing research questions using diverse approaches, as it incorporates everything from real-time microeconomic trends to macroeconomic trends that affect the entrepreneurial ecosystem (Gandomi & Haider, 2015; Kitchin, 2014). Additionally, the veracity of big data presents an opportunity for entrepreneurship researchers to apply techniques to clean and process data before analysis, enabling them to use accurate insights from the data. Thus, ensuring that the findings do not just reflect the real world but also enhance 1 3 Page 9 of 22 85 International Entrepreneurship and Management Journal (2025) 21:85 Practical implications and recommendations Although an ambitious endeavour, applying big data in entrepreneurship research has the potential to advance the study of entrepreneurship. In doing so, guidance is needed for researchers. We offer guidance and actionable plans for entrepreneurship researchers intending to apply big data to their research. First, similar to applying a traditional research methods, researchers must begin with a clear plan and purpose for their research. This includes defining research questions that Big Data can answer, ensuring data quality and representativeness, and adopting advanced analytical techniques that mitigate bias. The first step was to develop clear objectives and answer the research questions. Owing to the complexity of working with big data, having a clear, narrow scope increases the complexity of applying big data. Entrepreneurship researchers should utilise the relevant literature and provide theory-driven hypotheses that guide their data collection and analysis (George et al., 2014). Additionally, identifying research questions that big data can answer, as well as its limitations, will direct researchers in data gathering and analysis. Second, when gathering data, researchers must consider the challenges associated with data quality, integrity, and representativeness. They must ensure that the data they gather are reliable and relevant to address their research questions. To ensure the reliability of the data, they must be scrutinised for accuracy, representativeness, and completeness. One focus of this stage is how they clean and store their data. In doing so, they must apply tools that are necessary for handling large datasets. Furthermore, entrepreneurship research must avoid the potential trap of thinking that all data are relevant, which stems from the belief that more data leads to better insight. Therefore, it is important to use validation techniques to avoid this problem (Kitchin, 2014). At this stage, ethical considerations must be at the forefront of research to ensure that individuals’ data privacy is protected and that data protection guidelines are adhered to (Martin, 2019). Third, when processing data, researchers must be aware of the biases that may arise during this stage. Questions such as how we process the data and how we determine the quality and integrity of the data are important to ensure that biases are mitigated. Researchers must consider the implementation of rigorous cleaning and processing protocols to deal with missing values, outliers, and duplicated data accurately and completely. The use of automation tools that are trained to avoid bias is relevant to ensure data quality (Batini et al., 2009). Another recommendation is for researchers to use a combination of data sources and analytical techniques to triangulate findings and apply advanced statistical methods to adjust for and mitigate bias (Boyd & Crawford, 2012). Fourth, in analysing big data, adequate use of tools and techniques is required. For instance, to analyse data for prediction, machine learning is considered. Furthermore, when analysing data for gathering sentiments from the data, NLP is appropriate. Network analysis can be considered by analysing data that can potentially seek interconnectivity within the entrepreneurial ecosystem. Researchers must understand the underlying assumptions and limitations of tools applied to analysing big data (Pro1 3 85 Page 16 of 22 International Entrepreneurship and Management Journal… vost & Fawcett, 2013). This emphasises the need for researchers to have the appropriate skills and competencies to appropriately utilise and apply the suggested tools. Finally, for the actionable application of big data in entrepreneurship research, a multidisciplinary team must be established to engage experts and ensure the relevance and applicability of big data analysis in entrepreneurship research. Additionally, clear data governance that ensures data management must be created. This will ensure that researchers have policies that guide the collection, storage, processing, and sharing of big data. Moreover, maintaining transparency in the research process cannot be overemphasised. Detailed records must be kept not only to maintain transparency but also to allow for the replicability of the research. A schematic representation of a roadmap for the implementation of Big Data in entrepreneurship is shown in Fig. 2. Conclusion This study explored the use of big data and data analytics in entrepreneurship research and defined big data by considering variables such as sheer volume, velocity, and the variety of data available. Different methodological frameworks and analytical techniques to be used, such as predictive modelling and natural language processing (NLP), were introduced in this research to guide researchers in the application of big data in their future work. These tools are built on the foundation of the ability to discover patterns, predict trends, and extract valuable information in real time, thus facilitating a deeper understanding of business and entrepreneurship phenomena. This document highlights several challenges, especially in terms of data quality, representativeness, and the interpretative complexity of massive datasets. Ethical issues related to privacy, consent, and data ownership are also crucial, given that big data are often sourced from online platforms and social networks. There is also a problem with the correlations found in large datasets, as these structures do not always provide a clear understanding of the underlying causal relationships, which poses the challenge of ensuring methodological rigor. From a future research perspective, the integration of emerging technologies such as the Internet of Things (IoT) and artificial intelligence (AI) could further expand the capabilities of big data analytics in entrepreneurship. Thus, it seems clear that Fig. 2 Guidance and actionable plans for applying big data in entrepreneurship research. Source: the authors 1 3 Page 17 of 22 85 International Entrepreneurship and Management Journal (2025) 21:85 fostering interdisciplinary collaboration between entrepreneurship researchers, data scientists, and AI experts is key to making the most of these technologies. Thus, research in this field will be able to advance not only from an ethical point of view and methodological rigor, but also from a perspective on the impact on theories and practices applicable to entrepreneurship. Although this study presents a literature overview of the application of big data in entrepreneurship research, it is not without its limitations. First, the research, as discussed above, has not applied any form of primary data, such as collecting big datasets in qualitative or quantitative settings. Future research can, therefore, attempt to use our suggestions and recommendations to test the use of big data in entrepreneurship research while highlighting the further benefits and limitations of its application. Second, the research is limited to the field of entrepreneurship; other fields of research, such as marketing and business management, can apply these recommendations in their fields. Finally, future research can further apply big data analytics to determine the extent of its usage in developing the field of entrepreneurship research. Funding Open access funding provided by Mälardalen University. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit h t t p : / / c r e a t i v e c o m m o n s . o r g / l i c e n s e s / b y / 4 . 0 / . References Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. 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