With the rapid development of big data over the past few years, researchers and practitioners need to consider the means by which they can incorporate the adoption of advanced technologies into their competitive schemes. Big data in company decision-making has recently garnered considerable attention [1
], and the number of firms investing in big data analytics to improve their competitive advantage and performance is growing [2
]. In order to take full advantage of the fast-expanding data volume, velocity, and variety, techniques and technologies for storing, analyzing, and visualizing data are required, but there has been noticeably less research attention on how firms can embrace these technologies for further improvement [1
Big data as a high volume, high velocity, and high variety of raw information needs a cost-effective and innovative information analysis technique to capture insights for decision making [3
]. Consequently, the topic of big data analytics arises when the concern is analyzing raw data that have not been processed for use and from which hidden information has not yet been extracted. Currently, big data analytics has been considered the predominant method for analyzing big data because of its superior ability to capture huge amounts of raw information and apply the best analytical practices to measure it. It has become a tool by which companies gather varied data and use automatic data analytics to inform appropriate decisions that had previously depended on the judgment and perceptions of decision makers [3
]. Thus, big data analytics revolves around three key features: the data itself, the analytics applied to the data, and the presentation of results in a way that allows the creation of business value for firms and their customers.
With the progress of digitalization, more companies are considering using big data and business analytics to analyze available data in order to (1) improve their products and services and (2) support smart decision-making [5
]. That is to say, organizations need to exploit the full potential of big data and business analytics to gain a competitive advantage in the market. Nevertheless, because big data analytics is still a rapidly developing technological and business practice, there is little research on how to effectively use and exploit it. Even though prior studies have shown the advantages of adopting big data analytics in various contexts, there remains a lack of evidence on how to apply this solution to create a competitive advantage. In the area of business and management, there are a few systematic papers focused on big data analytics, for example, Refs. [1
]. Rialti, Marzi, Ciappei and Busso [7
] stated that “minimal attention has been paid to systematizing the literature on big data and dynamic capabilities.” In this light, the current study attempts to identify the factors that may influence the use of big data analytics and the capabilities needed for improving firm performance. We, therefore, focus on summarizing and reviewing the available literature to pinpoint themes related to big data analytics and firm performance.
Despite all the benefits that big data may bring to an organization, many companies have decided not to invest in big data analytics. This occurs especially among companies that have not successfully adopted business intelligence [6
]. Big data comprises a large volume of data that are produced very rapidly from various sources, and sometimes it is difficult for companies to capture and store it; however, a series of novel technologies have been generated to deal with these mountains of data from various sources [11
]. Additionally, some business executives may question whether big data analytics is any different from business intelligence and the process of data mining or whether it represents a new capability whose use demands major funding.
Answering these questions is of vital importance to policy makers investing in the seeding of innovative data analytics projects as well as to business practitioners and scholars. To begin, the authors consider the dissimilarities between big data analytics and traditional business intelligence techniques. Although the era of big data started only in 2005, the volume of big data is growing fast, increasing around 50% annually [12
]. Interestingly, a substantial amount of this growth is represented by unstructured data, such as video, images, social media posts, user comments, and any type of data that cannot easily be grouped in recurring fields. Thus, big data is a collection of vast, complex datasets that challenge companies’ ability to capture and manage them in a timely manner using the most advanced data management techniques relevant to information processing [13
]. In some studies, big data analytics is seen as a fundamental leap from old business-intelligence techniques [3
], but it may still be new to researchers in the field of social science. According to Sundblad [14
], however, business intelligence is an integral part of most projects that adopt big data analytics, which can provide useful knowledge to companies [15
]. Herein, big data analytics is defined as “a collection of data and technology that accesses, integrates, and reports all available data by filtering, correlating, and reporting insights not attainable with past data technologies” [16
Overall, big data and its analytical methods represent newly emerged opportunities for companies to analyze available data to obtain more information about the status of their business in the market and thus make good decisions to stay competitive and increase their market share. Big data analytics has been used in diverse areas and sectors, such as e-commerce, e-government, and healthcare [17
], but other sectors and businesses would benefit from its adoption.
It has been reported that big data analytics can increase the effectiveness and efficiency of firms by allowing them to set appropriate strategies through the lens of data [5
]. Big data analytics has become a vital element of the decision-making processes of agile organizations [19
], and it is claimed that big data analytics produces impressive results in diverse industries. For instance, the majority of retailing companies are currently extending big data capabilities to enhance the customer–relationship management (Tweney [20
]), while in the healthcare industry, big data analytics is likely to moderate operational costs and improve quality of life [21
]. In some sectors, such as manufacturing, it is expected to facilitate and improve business-process monitoring [22
]. Furthermore, it has become a catalyst for the improvement of supply-chain management, the enrichment of industrial automation [23
], and the acceleration of business innovation [16
]. In addition, big data analytics can optimize prices; increase profit [25
]; and maximize sales, financial productivity, and market share [26
] as well as return on investment [27
]. In their research in the context of healthcare, Srinivasan and Arunasalam [30
] claim that gaining capability in big data analytics will help firms maintain their competitiveness through cost reduction; for instance, it will help them to reduce waste and fraud. Furthermore, it supports companies to improve their quality of care by improving safety in treatment. In this vein, firms using big data technologies are more likely to convert data into intelligence and insights, improving their productivity and business growth [31
Big data analytics has been considered a primary capability that can improve a firm’s performance [5
]. An organization that increases its big data analytics capability should be able to maximize its performance. This can be done by developing big data analytics capability and identifying the factors that may positively influence that capability building. Thus, superior firm performance in a big-data–driven environment derives from a perfect combination of all resources, including organizational resources (big data analytics management), physical resources (Information Technology (IT) infrastructure), and human resources (analytics skill or knowledge), which should be unique and inimitable [27
Notably, the available studies of big data analytics are still few and fragmented, especially in the social sciences. Furthermore, the implementation of big data analytics among practitioners is also in its initial phase; therefore, through the lens of a systematic literature review, this study attempts to gain a broad overview of big data analytics and its relationship to firm performance. This study provides direction to researchers and businesses by categorizing the diverse existing models of big data analytics. To assess the use of big data analytics by firms, it is essential to identify its main drivers. Doing so will provide grounds for the claim that the proper implementation of big data analytics allows organizations to effectively exploit big data.
This paper aims to make the following contributions: First, identifying the number of papers available on the Web of Science (WoS) that focus on the use of big data analytics; second, determining the factors that the published papers have identified in the successful use of big data analytics to improve a firm’s performance. As such, this paper provides a broad review of big data analytics and firm-performance studies. The next section describes the research methodology of the systematic review, followed by a presentation of the results of the literature analysis, showing the frequency-related findings of the selected papers. A discussion, directions for future research, and a succinct conclusion are provided in the final section.
4. Discussion, Future Research Directions, and Conclusions
This study presents an overview of publications on big data analytics and firm performance by means of the descriptive and content analysis of highly-ranked articles. To extract the most relevant articles, the authors used predefined keywords to search for studies in the WoS database. The papers were screened by assessing the articles through titles, abstracts, objectives, and conclusions. In the screening stage, we excluded those that did not fulfill the inclusion criteria. For example, the reviewed papers must first have been in the Science Citation Index, Social Science Citation Index, or Arts & Humanities Citation Index. Second, they had to be completely related to big data analytics and firm performance. Third, they necessarily had to be ISI- and Scopus-indexed journal articles. To give a precise view of big data analytics and firm performance research, we extracted and analyzed a set of 33 articles. Through the lens of the systematic review method, we identified the key contributing factors that may influence the adoption of big data analytics and consequently improve firm performance. These factors include individual aspect, organizational aspect, big data analytics capability, data-related aspect, business analytics capability, absorptive capacity, open innovation, and market orientation. Furthermore, the similar terms used across a broad spectrum of disciplines were identified. This will help future researchers, in particular social science researchers, to appreciate what terms are related to big data analytics and firm performance, allowing them to categorize the similar and different definitions developed by other studies. Thus, this paper generates knowledge through its systematic review in the area of big data analytics and provides directions for future researchers. We can see from the descriptive results that big data analytics capabilities/assets
is the term most frequently used by scholars other than big data analytics
. The latter term is used in almost all the articles reviewed. Two of the three papers that did not use the term big data analytics
are those of Ghasemaghaei, Hassanein and Turel [5
] and Arnaboldi [55
], who use the keyword data analytics
. Another is that of Ashrafi and Zare Ravasan [6
], who use the keyword business analytics
. However, the authors of the present review included those papers because the contents of the studies fulfilled the objective of the current study. Furthermore, business intelligence and data analytics are related to big data, both of them contributing to the decision-making process in organizations by taking advantage of big data [14
]. In this light, Santoro et al. [70
] believed that big data is compatible with the business intelligence techniques that are needed to provide intelligent assistance for organizational processes.
According to the statistics documented in our study, an interest for future researchers is the study of the factors influencing the adoption of big data analytics and the creation of business value for organizations. Empirical research that looks at the value of big data analytics remains insufficient, and, therefore, leaves industries insecure once confronted in employing such investments in their businesses [54
]. To substantiate theoretical and practical implications of research for future scholars, researchers must understand the core elements that may influence the implementation of big data analytics and how such investments lead to business value [71
]. The adoption of big data analytics has become common in large companies, such as those in healthcare [72
]. Recent researchers, such as Amato et al. [74
], also look for new applications of big data in that industry to improve the performance of the healthcare industry. The use of big data analytics can be external or internal. For example, if some firms, such as SMEs, have little ability to directly use the tools and techniques required for analyzing big data, they can seek help by outsourcing the analysis of their acquired data and thus build business value for their firms. Previous researchers, for instance, Ghobakhloo, et al. [75
], have discovered the factors that may influence the adoption of information technology among SMEs.
Thus, future scholars can focus more on the factors that may help SMEs to adopt big data analytics, thus ensuring that they can benefit from the adoption of big data analytics. As SMEs contribute substantially to the economic growth of nations, more studies in the area of big data should be conducted on this type of firm and the industries in which they operate. Recently, research was performed by Mikalef, Boura, Lekakos and Krogstie [54
] which focused on the big data analytics and firm performance including SMEs. Mikalef, Boura, Lekakos and Krogstie [54
] found out that technological resources in terms of technological and technical assets contribute more towards a firm performance improvement in an environment with moderate uncertainty, whereas organizational resources such as managerial aspects and individual skills play essential roles in a highly uncertain environment. In addition, in line with the result obtained from the current systematic review, Mikalef, Boura, Lekakos and Krogstie [53
] discovered that technical skills are key elements in enabling firms to leverage the potential of big data analytics. Technical skills, as an individual aspect, are the factors that have received much more attention in recent years from data scientists, although attention on the importance of organizational aspects to benefit big data analytics has also increased.
In line with the result obtained from the current study, future scholars of business and management are encouraged to deliver more empirical studies on the related topic and particularly the impact of different factors such as the impact of the data-related aspect, absorptive capacity, open innovation, and market orientation. In addition, the results reveals that middle-income countries are currently less studied. Generally, studies on big data and big data analytics have concentrated on large companies in high-income nations; therefore, it will be interesting to find more empirical research on SMEs in middle-income nations. Additionally, this study tried to determine the terms that are used synonymously with big data analytics. Future research could investigate those terms to find more similarities and differences between the terms to avoid confusion among novice social science researchers new to the field of big data.
This study provides a reference for scholars and practitioners identifying the challenges related to big data analytics. Future researchers can identify the journals that fit their research approach to facilitate the diverse publication of conceptual and empirical papers with different methodologies. As the field is still new to social scientists, future scholars may attempt to publish articles about big data analytics in various areas of social science and management. In addition, with the evolution of big data in a digitalized world, businesses and entrepreneurs may be inspired to learn how to adopt and implement big data analytics and business analytics rather than merely using devices that produce big data. Firms, especially startups and new firms supported by incubators, must continually improve their performance, so studying the elements that contribute to the adoption of big data analytics will help them to use it in a manner befitting their contexts. Ferraris, et al. [76
] stated that big data analytics has the potential to change the way companies practice and enhance their performance through better understanding, managing, processing, and using of vast amounts of raw data obtaining from various sources (internal and external). Those companies that developed their big data analytics capabilities more in terms of technological and organizational aspects have been able to improve their performance subsequently. More precisely, firms must first initiate a coherent and unambiguous data-driven strategy if they aim to benefit from big data analytics. Second, firms have to employ the right human resources, with the right skills and expertise in big data. Finally, despite the importance of the technological aspect in big data analytics adoption, the organizational aspect should not deny a data-driven culture [54
]. For instance, firms need to provide a robust infrastructure to maintain their resilience and take advantage of the data-driven culture. It will also increase their ability to collect and analyze data from different sources [77
Notwithstanding, there are other factors in the successful adoption of big data analytics that are not discussed in this paper. For example, Ferraris, et al. [76
] have found that knowledge management orientation can play a significant role in increasing the impact of big data analytics capabilities and firm performance [76
]. In another study, Dremel, et al. [78
] found that actualizing big data analytics affordances in companies can be affected by various social and technical elements, for example, human resources expertise, organizational processes, and social capabilities. Thus, studying the socio-technical aspect can be a direction for future research on big data analytics at the organizational level. In addition, Ghasemaghaei [79
] showed that other than the organizational and technical aspect, social factors such as psychological readiness are core elements which can influence an organizations’ decision to be able to create value from big data analytics adoption. On the other hand, Conboy, et al. [80
] identified the temporal factors that may affect the business value of analytics in a setting. Conboy, Dennehy and O’Connor [80
] defined these factors to include the followings: “appropriate use of clock and event time in analytics, appropriate use of subjective and objective time in analytics, appropriate use of analytics to predict challenges around social constructions of time, appropriate management of multiple speeds of analytics, appropriate communication of real-time data in analytics, appropriate management of different perceptions of time in analytics, appropriate management of different temporal personalities in analytics”.
Based on the result obtained from current systematic research work, this study recommends that future scholars conduct more systematic and empirical studies on the use of big data analytics in diverse types and sizes of companies to explore what other factors may help an organization to amplify the adoption of big data analytics for improvement of their performance. Future scholars may expand the domain of search of big data analytics and business analytics in the area of business and management to capture more studies including those missing in WoS Core Collection as other research works have not appeared in our search result but deserve to be reviewed systematically (for example, Refs [7
Although most articles in this study are mainly business- and management-oriented, there are other reputable ones [55
] which tie social science and managerial issues in the context of big data analytics. As such, this systematic review provides a multidisciplinary stream of research and opens an avenue for future researchers to explore social-science-related factors tied with big data analytics usage. Furthermore, it will be possible to make more comprehensive studies from an integrated perspective of social science, behavioral, and managerial issues. The increasing popularity of big data analytics in different areas such as business, science, engineering, and social science signifies its multidisciplinary nature to be appreciated by different groups of societies, businesses and policy makers around the world.