EL TIPO DE COMISIÓN POR OMISIÓN

As related in the literature review conducted for this study, (Cepeda and Vera 2007; Protogerou et al., 2010; Wilden and Gudergan 2015; Braganza, et al., 2017) suggested that in a digital environment where technological innovation necessitates fast organisational responses to be made, dynamic capabilities become a tool that allows a firm to build and renovate operational capabilities faster and cheaper than its competitors. For example, Wilden and Gudergan (2015) note that when organisations face environmental changes even valuable marketing and IT capabilities can become liabilities, widening an organisation's capability gap. In such a case authors (Leonard-Barton 1992; Day 2011; Wilden and Gudergan, 2015) argue that dynamic capabilities become important because they reflect an organisation’s ability to ‘engage in

market-based learning and use the learned insight to reconfigure existing resources and enhance its capabilities in a ways that reflects organisation’s dynamic environment’ (Morgan

2012, p. 108).

This study conforms to the standard set by Teece (2007, p.1344) who defined dynamic capabilities as consisting of a series of managerial and organisational processes that ‘orchestrate’ the firm’s resources and ordinary capabilities in order to adapt and evolve to rapidly changing environments. As illustrated in figure 3.1, Box 3.2, this research explores an

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organisation’s managerial and organisational processes (strategic competitive response, learning, reconfiguration, and coordination) that are likely to better match the digital environment and make effective use of Big Data for their operational activities (Teece, 2007; Ambrosini and Bowman, 2009; Protogerou et al., 2010). Making connections between dynamic capabilities (strategic competitive response, learning, reconfiguration, and coordination) and operational (Marketing and IT) capabilities in the adoption of Big Data initiatives within MNCs in the pharmaceutical would help explain how the company prepared for Big Data adoption and how it identified and developed its capabilities for Big Data.

-Strategic Competitive Response (Box. 3.2.1)

Adopting the term strategic competitive response from Protogerou et al. (2010), whose definition incorporates sense, seize, and transformation abilities (Teece, 2007), this study holds that, as a core process of dynamic capabilities, strategic competitive response (box. 3.2.1) is particularly important in relation to the Big Data initiatives adoption process. Protogerou et al. (2010, p. 619) explain that strategic competitive response is the firm’s ability to scan the environment, identify new opportunities, assess its competitive position, and respond to competitive strategic moves. In other words, strategic competitive response grants organisations the ability to sense new opportunities (Teece, 2007) offered by Big Data. The

seize ability enables the mobilization of resources to address such opportunities and to capture

value from doing so (Teece, 2007), and to maintain competitiveness through enhancing, combining, and reconfiguring (where necessary) an organisation's existing resources and capabilities (Teece, 2007).

Sensing is an important part of dynamic capabilities. The ability to sense threats and opportunities helps to identify new market opportunities, evaluate competitors, and recognise resource and capability gaps (Roberts et al., 2016). Therefore, it has a significant impact on a firm’s capacity to redefine its operational capabilities for Big Data initiatives. The seizing

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ability is an equally important characteristic of dynamic capabilities that implies evaluation of various options to accommodate the identified opportunity (Protogerou et al., 2010). Thus, these abilities are important as they enables the firm to reconfigure (creating, extending, and modifying) certain capabilities before they become core rigidities (Teece et al., 1997; Eisenhardt and Martin, 2000).

Dynamic capabilities have an inherently transformative ability, in that they allow a company to adjust its capabilities in response to change (Wilden and Gudergan, 2015). To benefit from sensing and seizing activities, a company's asset orchestration must be reconfigured appropriately (Teece, 2007). In digital environments, where technological innovations are introduced by external sources, it is important to constantly scan and learn from the environment and to respond to detected changes. Thus, to look at how an organisation identifies, drafts, and carries out potential solutions in response to threats and opportunities that emerge from Big Data initiatives it is important for this study to explain how the company approached the issue of using Big Data and how it identified and developed its Big Data capabilities.

-Learning (Box. 3.2.2)

Organisational learning is defined as ‘the capacity or process within an organisation to improve

or maintain performance based on experience’ (Nevis et al., 1995, p 73). It has been argued

(Teece, et al., 1997; Winter, 2003; Ethiraj et al., 2005) that capabilities reflect the evolutionary process of experiences that firms engage. Teece et al. (1997) suggest that learning is a very important process which through experimentation and repetition leads to quicker and better resolution of specific problems, and which at the same time enables firms to identify new opportunities.

Learning processes that promote, enhance, and renew technological knowledge are critical to the success of Big Data transformations in high-technology industries like Big Pharma (Helfat,

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1997). This is especially true of sharing and combining new knowledge with existing skills and experience within an organisation, and cross-functional teams have been acknowledged to have a positive impact on the transformation and recombination of marketing and technological capabilities (Protogerou et al., 2010; Pavlou and El Sawy, 2011; Wilden and Gudergan, 2015). Therefore, examination of these processes will be important to this research.

-Reconfiguration (Box. 3.2.3)

The introduction of new resources like Big Data and advanced technology enhances a firm’s efforts to use dynamic capabilities in order to reconfigure those operational capabilities that are most likely to support the ongoing development of valuable products and services. Reconfiguration is an organisation’s ability to extend and modify existing capabilities in response to changes introduced in the market and technologies that are generated through new knowledge based on Big Data (Teece, 2007; Kwon, et al., 2014). It was evidenced in the literature that reconfiguration of existing marketing and IT capabilities was necessary for the MNCs in the pharmaceutical industry in order to implement Big Data opportunities in their operational activities. On the other hand, as has been explained several times in this thesis, in a digital environment the potential value of dynamic capabilities lies in enabling firms to renew and reconfigure their operational capabilities and introduce new configurations that better fit shifting environmental conditions. Therefore, looking at the reconfiguration ability is central to this study and its goal of explaining what new capabilities are needed for Big Data.

-Coordination (Box. 3.2.4)

Teece et al. (1997) suggest that efficient coordination of different resources and tasks may help an organisation overcome some of the challenges introduced by the digital environment. Moreover, one of the most suggested solutions for the Big Data transformation in the pharmaceutical sector was the coordination ability (e.g. cross-disciplinary and cross-functional specialisations). It was assumed that this capability would help Big Pharma to shift from the

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current functionally-divided approach to a data science-enabled approach to take advantage of continuing digital innovation (Marwaha et al., 2018). Protogerou et al. (2010) argue that coordination processes connect and interface single activities through communication, scheduling, task assignment, collaboration, and other related activities. This capability is an effective means of dealing with a changing business environment, especially in the case of Big Data, about which most firms have neither substantial experience nor established 'best

practices' to call on.