Volatility, uncertainty, complexity and ambiguity, the acronym VUCA, has come to define the digital economy as highly disruptive environment and potentially punitive to incrementally focused organizations. Learning is cumulative, incremental and as such, the efficiency of learning is highly dependent upon learning structures and the richness of existing knowledge. To further complicate this incremental paradigm, organizational learning relies on the accumulation of individual knowledge as well as communication structures to external and between internal environments of the organization. As a result of the incremental accumulative nature of learning and the difficulty in communicating knowledge, novelty in problem solving and innovation has become much more challenging in the new VUCA environment. Successful organizations operating in the VUCA environment are breaking this incremental paradigm to develop structures to intensify external knowledge absorption.
While quantitative, big data, analytics have come to define the digital VUCA environment, prevalent use of data mining methods rely on historical customer behavior and do not solely yield competitive advantage. In response to disruption, organizations are increasingly pioneering new models, frameworks, methods, structures and processes to engage with the customer to sense and seize emerging patterns and future trends not easily defined from quantitative historical data. Design thinking has emerged as an absorptive capacity and integrative learning method to approach complex and often ambiguous problems from the perspective of the customer. Popularity surrounding design thinking in recent years has been largely due to its application within innovation which has resulted in anecdotal successes of design thinking practioners operating in highly disruptive VUCA environments. Therefore, it is incumbent upon researchers to further the study of design thinking and its usefulness in a variety of contexts and organizational settings to gain a deeper understanding of design thinking and its absorptive capacity capability to promote organizational learning.
This research study addresses how design thinking is used as an organizational learning process to pre-empt disruption of the VUCA environment. While absorptive capacity and organizational learning research is rich and extensive, providing a firm basis to explore design thinking, there exists a need for additional empirical research of design thinking in a variety of contexts including highly disruptive VUCA environments. Extending on previous research, the purpose of this empirical study is to describe and document the perspectives of subject matter experts on how design thinking is used to facilitate absorptive capacity and organizational learning in VUCA environments. As a result of data gathered and thematic investigation, this study confirms the ability of design thinking learning structures to facilitate absorptive capacity and promote organizational learning in a technology driven, highly disruptive, VUCA environments.
Volatility, Uncertainty, Complexity and Ambiguity, the acronym VUCA, was first developed by the US war college to define conditions military leaders encounter on the battlefield. Recently, the concept has come to define the competitive environment of the digital economy (Bennett & Lemoine, 2014) in which organizations must adapt past structures to match environmental change (Heugens & Lander, 2009). Desai (2010) affirms the role of technology and digital disruption noting advances in interactive technologies as reason for the changes in the way stakeholders learn. In a VUCA world, organizations can no longer focus on internal learning and instead should focus on co-creative and collaborative learning (Desai, 2010; Baltaci & Balci, 2017) outside the boundaries of the organization. Bartscht (2014) proposes that organizations must continually explore the VUCA environment, gaining situational understanding to sense and seize on opportunities and threats.
Volatility refers to large scale, frequent change having no predictable pattern (Bennett & Lemoine, 2014). Felin & Powell (2016) note stringent demands are being placed on organizations operating in volatile markets which require structures to obtain and process reliable and current information. Traditionally, in stable environments, organizations relied on experience, routines, learning and scale but volatility evident in the new VUCA environment is driving organizations to engage with stakeholders across external boundaries, drawing them into the learning and innovation process (Felin & Powell, 2016). Change is likely in volatile environments; however, the timing and extent of change are unknown. Therefore, Bennett & Lemoine (2014) suggest organizations should structure toward organizational agility as a countermeasure to volatility to increase their ability to sense and seize on opportunities in the market place. The more frequently and continuously an organization engages in exploring the VUCA environment, the more often the organization can update its situational understanding of the environment and thus minimize the effects of volatile change (Bartscht, 2015).
Uncertainty indicates lack of knowledge related to the frequency and significance of environmental change (Bennett & Lemoine, 2014). In uncertain environments, cause and effect are known however, timing and magnitude are unknown and may not occur at all. Uncertainty is solved by organizations investing in methods of collecting, interpreting and sharing of knowledge by devoting resources to boundary spanning activities. These boundary spanning activities are those actions which seek knowledge outside existing networks, data sources and analytic processes to gain knowledge from new partners providing new and richer understanding (Bennett & Lemoine, 2014). To understand an uncertain environment, organizations should proactively explore cause and effect factors impacting the uncertain environmental situation (Bartscht, 2015). Bennett & Lemoine (2014) note an uncertain situation is simply a lack of knowledge and therefore can be preempted by simply gathering more knowledge.
Drucker (2012) refers to the complex environment as a" threshold of chaos", characterized by technological disruption and globalization. Bennett & Lemoine (2014) define complexity as elaborate networks of interconnected parts being convoluted and multiform. Complexity is iterations of simple patterns (Bartscht, 2015) combined in a multitude of interconnections creating potential for information overload (Bennett & Lemoine, 2014). To simplify complex situations, organizations should structure themselves to the environment by adapting structures to align with and take advantage of complexity rather than struggle against it (Bennett & Lemoine, 2014). As such, organizations must adopt knowledge based strategies which facilitate immediate decision making (Drucker, 2012; Byrne & Callaghan, 2013; Adams & Stewart, 2015) by getting close to the environment and its stakeholders.
Ambiguity identifies a lack of knowledge of cause and effect where there is no precedent on which to base predictions (Bennett & Lemoine, 2014). Ambiguity typically involves new situations which are typically characterized by new strategies, products, markets or technological innovation. Newness is the challenge of ambiguous situations and therefore there is little quantitative and historical data on which to predict outcomes. Gathering information is vitally important in ambiguous situations but the challenge lies in knowing how to value the information collected as it is not apparent what information is useful (Bennett & Lemoine, 2014). Bartscht (2015) notes that organizations should shift paradigms from continual improvement and instead focus adaptability by being proactive in learning new knowledge to innovate and make better decisions.
Organizational Learning, Absorptive Capacity and Design Thinking
Learning capabilities and problem solving capabilities do not differ in necessary preconditions and are the same in their modes of development (Bradshaw, Langley & Simon, 1983; Simon, 1985; Cohen & Levinthal, 1990). According to Cohen & Levinthal (1990), learning is cumulative and the efficiency of learning is dependent upon learning structures and the richness of what is already known. Due to this incremental nature of learning, novelty in either problem solving, innovation or general knowledge is difficult to achieve and require strengthening of absorptive capability structures and increasing knowledge diversity (Harlow, 1959). Similarly, organizational learning is an incremental accumulation of individual stakeholder absorptive capacities to learn, as well as the organizations direct communication structures to the external and internal environments of the organization. Sun & Anderson (2010) take an integrative systems thinking view to propose a theoretical framework to identify interdependent nature between absorptive capacity capability and organizational learning processes to connect diverse knowledge across external organizational boundaries.
Absorptive capacity conceptualizes an organization's ability to utilize external knowledge through a sequential learning process that use existing internal organizational knowledge to recognize the value of external knowledge, assimilate this new knowledge through exploratory learning and apply this knowledge to create new knowledge and value (Lane, Koka & Pathak, 2006). Early research into absorptive capacity focused on learning and innovation with respect to the performance of the firm and the firm's ability to acquire, assimilate and apply external knowledge (Cohen & Levinthal, 1990). Todorova & Durisin (2007), drawing on learning theory, revised the absorptive capacity construct, introducing a reconceptualization which highlights social integration...