Research Gap

Adopting the dynamic capabilities perspective, the purpose of this research is to generate knowledge on the process of Big Data adoption within a pharmaceutical company. To achieve this purpose, an investigation is needed on how companies perceive Big Data, and what capabilities they employ to integrate it within their company strategy in order to extract the most value from such an initiative.

Despite the great interest surrounding Big Data, exploiting its potential value has still not yet been fully developed or examined (McAfee et al., 2012). Much attention to date has been on the technical aspects of Big Data, such as storing, securing, and analysing the ever-increasing volumes of data obtained by organisations. The literature review in this chapter shows that the current literature is still at a developmental stage in terms of explaining how organisations, in particular MNCs in the pharmaceutical industry, realise value from Big Data. Examination of this shortfall is the core of this study.

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Even though there are acknowledged opportunities and challenges associated with Big Data initiatives in the pharmaceutical sector, there are few examples in the literature of why and how MNCs prepare for Big Data adaption in practice and what are obstacles they face during this journey. Consequently, there is a limited academic body of knowledge on the organisational capabilities that Big Data exploitation encompasses in pharmaceuticals and how they can be developed to gain full benefit for the industry (Brynjolfsson et al., 2011).

As with any new technology, it is important to understand the mechanisms and processes through which Big Data can bring business value to companies. Thus, a major discussion over the years has focused on the opportunities and challenges that Big Data brings to organisations (Chen et al., 2012; McAfee et al., 2012; Davenport et al., 2012). However, there is less empirical evidence in the literature relating to how MNCs in pharmaceuticals can overcome these challenges. Although suggestions exist that Big Pharma needs to change, renew, and innovate its capabilities, and/or fundamentally change existing operational models and enhance internal data management capabilities (Deloitte, 2015; Teece, 2010) successful Big Data initiatives are still rare within MNCs in the pharmaceutical industry, despite their access to resources, market knowledge, and key technologies. Thus, it is not clear what are the required capabilities that pharmaceuticals need to develop for Big Data initiatives.

Agarwal and Dhar (2014. p. 445) looked at challenges and opportunities pertaining to Big Data in information systems research and noted that the Big Data phenomenon expanded opportunities for inquiry into any type of phenomenon researchers may want to study, ranging from all functional areas of the business to the broadest perspective on the enterprise as a whole. However, it is important to consider and bring new insight into how the different pieces fit together. Various authors suggest that research on today’s data-rich environment should be more focused on business transformation and value creation through data, and less on algorithms or frameworks without consideration of business value. Adding to this conversation,

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it is clear from the literature (Bharadwaj et al., 2013; Constantiou and Kallinikos, 2015; Erevelles et al., 2015; Wamba et al., 2015) that some current predictions offer valuable ideas about how MNCs in pharmaceuticals might approach Big Data initiatives, although these ideas need to be explored for greater insight, without being limited to existing knowledge and practices.

It has also been acknowledged (Chen et al., 2012; McAfee et al., 2012; Davenport et al., 2012; Galbraith, 2014; Gandomi et al., 2014) that Big Data transforms the way that companies relate both to their customers and to the way the companies enact and perform business operations. The growing interest in Big Data, however, means there is a need to explore how it can be exploited within organisations. Constantiou and Kallinikos (2015) note that decision-makers have a limited understanding of how to adopt and implement Big Data initiatives that can drive their business strategies. Potential adopters also are struggling to make key decisions in relation to Big Data. For example, recent findings from Woerner et al. (2015) show that many chief information officers (CIOs) and business executives have hesitated to make major investments in Big Data, specifically after direct experience of disappointing results, or observing other firms failing in Big Data investment. Therefore, some industries, including Big Pharma, remain heavily reliant on traditional research techniques and have been slow to follow the example of other industries (e.g. retail, financial) in employing innovative techniques to generate deep insights from Big Data initiatives (Costa, 2014; McKenzy, 2014). This justifies a call for further empirical studies that carefully examine how MNCs in pharmaceuticals actually prepare for Big Data adoption in practice and how a company may strategically approach the issues of using Big Data. For example, there is a need to investigate how MNCs in the pharmaceutical industry identify and develop their capabilities for Big Data. Specifically, future research needs to empirically examine how different resources and capabilities within organisations work with Big Data in practice.

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While acknowledging the importance of investment in the improvement or development of capabilities, there are differences between competitors in the timing of this kind of effort, and in the amount of investment, in the managerial undertakings, and in the internal organisational culture that supports this process (Kale et al., 2002; Ethiraj et al., 2005). It is also assumed that organisations take different positions when it comes to implementing Big Data initiatives, such as industry context, organisational size, and the business environment. The nature of the pharmaceutical industry, coupled with the fact that most of Big Pharma consists of MNCs (Schotter and Beamish, 2011), perhaps differentiates pharmaceutical organisations from those who have achieved success in Big Data adoption. There is thus increased demand for more empirical approaches that look at what new capabilities MNCs in pharmaceuticals develop for Big Data and how they develop it in practice.

The overall findings from the literature (McAfee and Brynjolfsson, 2012; Davenport et al., 2012; Chen et al., 2012; Wamba et al., 2015) suggest that managers can realise great benefits from establishing a Big Data-driven culture and capabilities. Indeed, Big Data as a resource in a vacuum is meaningless. To uncover hidden patterns within data, organisations need to develop capabilities around it. Capabilities and organisational processes are interlinked, as it is the capability that enables the operational activities in a business process to be carried out (Day, 1994). With the correct capabilities in place, a firm can better adapt to the digital environment, and make better use of Big Data for its operational activities (Marketing and IT.). Ethiraj et al. (2005) argue that while a resource like Big Data is available to all firms, the capability to exploit it effectively is not uniformly distributed. To meet Big Data requirements there is a need for the enhancement of existing capabilities, or investment in developing new capabilities.

Although dynamic capabilities (organisational and managerial abilities) have been acknowledged as a possible tool to enhance organisations’ existing capabilities, they are mainly investigated and explored in the context of an organisation's performance. There is a limited

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body of knowledge on their role in the capabilities development process, especially in the context of Big Data. Moreover, Teece (2012) argues that although some elements of dynamic capabilities may be embedded in the organisation, the capability for evaluating and approaching changes to the configuration of assets rests on the abilities of senior management. However, there is only a limited number of studies that directly develop this topic, making it an obvious candidate for future research.