5. Future management scholarship and the big data phenomenon

Digitization and the increasing value of big data analytics have led to a global disruption of immense proportions, similar to what was experienced during the industrial revolution. Business models and strategic thinking are changing as a result. Communication and computing technologies have developed so radically over the last 20 years that it is easy to forget we are living in an entirely new world. Decades ago, computers sat in rooms and on desktops, not in the palm of one's hand. Inboxes sat on desks, rather than residing in software. Data processing was a long, expensive, and arduous task. Accordingly, the context in which we conduct

#### Big Data and Strategy: Theoretical Foundations and New Opportunities DOI: http://dx.doi.org/10.5772/intechopen.84819

organizational research—and even how such research is conducted [5]—needs to change. What is more, it seems imperative to reflect and examine whether existing frameworks, variables, and measurements are still relevant in today's digitalized business environment. Through such reflection, the evidence of earlier paradigms specifically the important role that RBT plays—in today's digital era and the relationship with organizational learning is apparent. Yet scholars are now presented with an opportunity to conduct a renewed examination of how technology interacts with strategy. Although leaders of the field have iterated a call for research and theory development in this area, significant movement has lagged. To assist in the advancement toward this end, we present a number of avenues to begin addressing these gaps, not only in strategy, but across the field of management.

#### 5.1 Avenue A: theoretical development across management

Through this paper, we extend theoretical development by looking at how the big data phenomenon is interacting with RBT and organizational learning in new and novel ways. These are overtly and perceptibly not the only theoretical underpinnings found within the big data phenomenon, nor are they the only ones that may be challenged by the changing competitive landscape. As such, scholars have an exceptional opportunity to identify unique applications for existing theories, create new proposed boundary conditions across the field of management, or develop novel theoretical frameworks and extensions, such as in the domains of strategy, entrepreneurship, or human resources.

We make the case to further develop theory around existing streams of research in the extant knowledge creation, knowledge management, and exploration/exploitation literatures. The likelihood that management theories are universally true across all periods of time, contextual situations, and especially after radical innovations have been brought forth to the market is highly unlikely. It is our contention that when the assumptions used in developing theory are challenged by existing realities of the world, the management field should reconsider "what it knows" and look at its theories to drive forward more relevant understandings of the world. This is not to suggest that traditional theories of management will be invalidated. Rather, it is necessary to revisit paradigms, challenge assumptions, and explore alternative explanations. The digitization of business models, fueled by the big data phenomenon, is a massive economic transformation; therefore, a new and concerted effort to look at the underlying theories of our respective fields should be considered at this time.

### 5.2 Avenue B: investigating antecedents to data- and analytic-related capabilities

Beyond applying theory to better understand the nature of organizational decision making in the era of big data, it is imperative to explore the context and antecedents that allowed these organizations to leverage data and analytics for competitive advantage. What is it that allows for firms to transition into data-savvy organizations? Are there characteristics or nuances that propel firms and allow for the transformation into learning organizations? Externally, are their environmental factors that specifically trigger such adaptation?

Undoubtedly, we see application for traditional explanations such as visionary leadership, organizational culture, strategic resource heterogeneity, and environmental hostility. Yet scholarly examination should better explore the true characteristics and environmental stressors that elicit impactful organizational change that increases data and analytic capabilities. Tracking firms globally and

improved relationships with key stakeholders. Such relationships can be recognized and measured through a variety of financial and operational metrics likely already

For the learning-oriented firm, value is captured through the development of knowledge and dynamic capabilities to recognize, learn from, and act on large socio-cultural patterns, often with such scale as to offset traditional competitive forces in the creation, entrance, and/or development of new markets or industries with innovative business models. They are able to effectively integrate and disseminate new knowledge across organizational silos to drive further innovation and entrepreneurship. Their business models look to expand existing lines of business, building an increasing ecosystem of services that benefit customers and build brand loyalty [30]. Accordingly, these learning organizations follow a pattern that not only builds what they know into their business models, but also incorporates a means to facilitate learning while relentlessly increasing the data gap over

competitors. Despite these benefits, it is still evident that most industries still have not even scratched the surface of realizing the potential value of big data and

Table 2 summarizes how the Vs commonly attributed to big data influence firms resource- and learning-based orientations when employing digital business strategies. Figure 1 offers a visual to further describe how these orientations are

5. Future management scholarship and the big data phenomenon

Digitization and the increasing value of big data analytics have led to a global disruption of immense proportions, similar to what was experienced during the industrial revolution. Business models and strategic thinking are changing as a result. Communication and computing technologies have developed so radically over the last 20 years that it is easy to forget we are living in an entirely new world. Decades ago, computers sat in rooms and on desktops, not in the palm of one's hand. Inboxes sat on desks, rather than residing in software. Data processing was a long, expensive, and arduous task. Accordingly, the context in which we conduct

in service throughout an industry.

Visualizing RBT and OL in Digital Strategy.

Strategy and Behaviors in the Digital Economy

analytics [11, 52].

62

Figure 1.

staged across organizations.

longitudinally—through both qualitative and quantitative investigation—is necessary to properly uncover specifics about firms developing competitive advantage through a combination of big data analytics and strategic thinking. Are CEOs business school educated or do they have STEM (science, technology, engineering, and/or mathematics) backgrounds? Do they have brief or extended tenures in their organization? Were they founders? How was culture characterized before the shift, or was analytics core to the identity of the firm from inception? What was the nature of the industry cycle? Were resources plentiful in the environment? Was the company an industry leader, or falling behind? Were there disruptive innovations occurring (beyond digitization)? There is ample room for discovery of these and many more aspects to better understand the full scope of big data's impact on organizations.

methodologies. Learning-organizations have large scale human resource analytics capabilities developed through the recruitment of Ph.D.-level employees and research fellows. For insight into this, we need not look further than the great interest of managers, scholars, and students in Google's people analytics, where HR professionals work hand-in-hand with organizational scientists to identify the most effective fact-based solutions, rather than relying on individual experience and

Big Data and Strategy: Theoretical Foundations and New Opportunities

DOI: http://dx.doi.org/10.5772/intechopen.84819

More precise measurements in management are not only important from the perspective of the scholar hoping to create new knowledge, but also as a means to better understand both the phenomena of big data and, more generally, organizational entities. This translates into better research, teaching, and practice of business strategy and management. As scholars are able to more precisely measure what it is they are defining, the insight gained from research increases by magnitudes in its translation into teaching and practice, resulting in an important reconnection to the community, where business scholarship has strayed over the last several of decades. We believe that the big data being collected en masse by today's firms will be the scholar's playground tomorrow if the field positions itself to advance the practice as well as the theory of organizations. The big data phenomenon has the potential to bring organizational science back to life in a way that should be exciting

The link between the firm's IT and competitive advantage has long been discussed in the literature (e.g., [53–55]), but we proffer that technological

resources and capabilities are now dictating which strategic approach a firm can and will take to the market [27]. How firms choose to explore new markets is not done through traditional strategic planning, but instead evolves through opportunity recognition based largely upon information gleaned from consistently analyzing more and richer data flows and stocks. The emphasis on data and data analytics as strategically important to a firm's success has the potential to contribute to important developments in understanding organizations in a world where digital is rapidly overtaking traditional business models. While there is the possibility for considerable debate over whether big data practices can provide a sustainable competitive advantage, arguments can be made that continued advancements and innovation in infrastructure, analytical capabilities, and organizational processes will leave plenty of opportunity for proactive firms. What is more, while individual data stocks may be imitable, bundles that include proprietary data, dynamic data analytic capabilities, effective strategic decision making, and an entrepreneurial spirit will likely remain unique to a particular firm and translate into the creation of new knowledge and ambidextrous execution (i.e., both exploiting existing markets

Reinvestigating the interplay between technology and organizational strategy is needed, as big data is likely to play a role in changing the landscape of social and economic policy and research [5]. As such, the importance of beginning this line of study within the strategy literature is imperative. Closing the gap between traditional strategic thinking and how strategy is currently employed in superior

performing firms will test the ability of the field to match management theory with reality. In doing so, scholars can erase the perceived naiveté surrounding management theories and demonstrate the complexity witnessed in the real world through

to a diverse group of individuals, including future scholars.

debate.

6. Conclusion

and exploring new opportunities).

65

contemporary and meaningful scholarship [56].

#### 5.3 Avenue C: reconsidering outcomes and consequences

Big data's emergence, in combination with disruptive business model innovations, has created an opportunity to reconceptualize organizational performance. Industry no longer uses a simple measure of profitability or traditional financial ratios, as success now relates to quantities of users on platforms, the richness of data flows, the collection of data stocks, or the knowledge created through the business activity. If we reconceptualize organizational performance more holistically, how does that open the definition of competitive advantage up to include the realities of a new contextual business environment?

Without understanding how senior management at digital-savvy firms perceive performance with regard to certain offerings, our current measurements may not allow us to properly test the hypothetical connections and theories that the big data phenomenon allows us to predict. Deep dive qualitative studies and case analyses surrounding digital transformations, as well as companies that have been founded digital, should be conducted to examine how these firms measure success. Additionally, companies that are founded and run by technological or analytical leaders should be more intimately compared and contrasted with companies founded and run by traditional operational management to better understand the underlying differences and subsequent impact on performance.

#### 5.4 Avenue D: refining and specifying the measurement of variables

The uses and application of big data have so thoroughly transformed methods and processes in the business environment that it is now necessary to not only reconceptualize theory, but also transform how we measure and model behavior, whether at the firm, meso-, or microlevel [5]. In the previous section, the change in how firms define desirable business outcomes was discussed, but future research will derive additional value when firm-level performance is measured in a manner that brings together the divergent ways that firm performance is now viewed by learning-oriented firms.

The same novel tools and data stocks that have digitized businesses can also be used for the qualitative testing of management theories. Therefore, macro level constructs that relied on poor proxies (or simply were unable to be measured) could come within scholars' range as they begin to open their perspectives to how business is conducted, what data stocks and flows are generated, and how they could capture them anonymously. Relying on changes in strategic human resource analytics capabilities in firms to create and predict behaviors will significantly impact our ability to understand organizational phenomenon beyond current

#### Big Data and Strategy: Theoretical Foundations and New Opportunities DOI: http://dx.doi.org/10.5772/intechopen.84819

methodologies. Learning-organizations have large scale human resource analytics capabilities developed through the recruitment of Ph.D.-level employees and research fellows. For insight into this, we need not look further than the great interest of managers, scholars, and students in Google's people analytics, where HR professionals work hand-in-hand with organizational scientists to identify the most effective fact-based solutions, rather than relying on individual experience and debate.

More precise measurements in management are not only important from the perspective of the scholar hoping to create new knowledge, but also as a means to better understand both the phenomena of big data and, more generally, organizational entities. This translates into better research, teaching, and practice of business strategy and management. As scholars are able to more precisely measure what it is they are defining, the insight gained from research increases by magnitudes in its translation into teaching and practice, resulting in an important reconnection to the community, where business scholarship has strayed over the last several of decades. We believe that the big data being collected en masse by today's firms will be the scholar's playground tomorrow if the field positions itself to advance the practice as well as the theory of organizations. The big data phenomenon has the potential to bring organizational science back to life in a way that should be exciting to a diverse group of individuals, including future scholars.

## 6. Conclusion

longitudinally—through both qualitative and quantitative investigation—is necessary to properly uncover specifics about firms developing competitive advantage through a combination of big data analytics and strategic thinking. Are CEOs business school educated or do they have STEM (science, technology, engineering, and/or mathematics) backgrounds? Do they have brief or extended tenures in their organization? Were they founders? How was culture characterized before the shift, or was analytics core to the identity of the firm from inception? What was the nature of the industry cycle? Were resources plentiful in the environment? Was the company an industry leader, or falling behind? Were there disruptive innovations occurring (beyond digitization)? There is ample room for discovery of these and many more aspects to better understand the full scope of big data's impact on

Big data's emergence, in combination with disruptive business model innovations, has created an opportunity to reconceptualize organizational performance. Industry no longer uses a simple measure of profitability or traditional financial ratios, as success now relates to quantities of users on platforms, the richness of data flows, the collection of data stocks, or the knowledge created through the business activity. If we reconceptualize organizational performance more holistically, how does that open the definition of competitive advantage up to include the realities of

Without understanding how senior management at digital-savvy firms perceive performance with regard to certain offerings, our current measurements may not allow us to properly test the hypothetical connections and theories that the big data phenomenon allows us to predict. Deep dive qualitative studies and case analyses surrounding digital transformations, as well as companies that have been founded digital, should be conducted to examine how these firms measure success. Additionally, companies that are founded and run by technological or analytical leaders should be more intimately compared and contrasted with companies founded and run by traditional operational management to better understand the underlying

5.3 Avenue C: reconsidering outcomes and consequences

a new contextual business environment?

Strategy and Behaviors in the Digital Economy

learning-oriented firms.

64

differences and subsequent impact on performance.

5.4 Avenue D: refining and specifying the measurement of variables

our ability to understand organizational phenomenon beyond current

The uses and application of big data have so thoroughly transformed methods and processes in the business environment that it is now necessary to not only reconceptualize theory, but also transform how we measure and model behavior, whether at the firm, meso-, or microlevel [5]. In the previous section, the change in how firms define desirable business outcomes was discussed, but future research will derive additional value when firm-level performance is measured in a manner that brings together the divergent ways that firm performance is now viewed by

The same novel tools and data stocks that have digitized businesses can also be used for the qualitative testing of management theories. Therefore, macro level constructs that relied on poor proxies (or simply were unable to be measured) could come within scholars' range as they begin to open their perspectives to how business is conducted, what data stocks and flows are generated, and how they could capture them anonymously. Relying on changes in strategic human resource analytics capabilities in firms to create and predict behaviors will significantly impact

organizations.

The link between the firm's IT and competitive advantage has long been discussed in the literature (e.g., [53–55]), but we proffer that technological resources and capabilities are now dictating which strategic approach a firm can and will take to the market [27]. How firms choose to explore new markets is not done through traditional strategic planning, but instead evolves through opportunity recognition based largely upon information gleaned from consistently analyzing more and richer data flows and stocks. The emphasis on data and data analytics as strategically important to a firm's success has the potential to contribute to important developments in understanding organizations in a world where digital is rapidly overtaking traditional business models. While there is the possibility for considerable debate over whether big data practices can provide a sustainable competitive advantage, arguments can be made that continued advancements and innovation in infrastructure, analytical capabilities, and organizational processes will leave plenty of opportunity for proactive firms. What is more, while individual data stocks may be imitable, bundles that include proprietary data, dynamic data analytic capabilities, effective strategic decision making, and an entrepreneurial spirit will likely remain unique to a particular firm and translate into the creation of new knowledge and ambidextrous execution (i.e., both exploiting existing markets and exploring new opportunities).

Reinvestigating the interplay between technology and organizational strategy is needed, as big data is likely to play a role in changing the landscape of social and economic policy and research [5]. As such, the importance of beginning this line of study within the strategy literature is imperative. Closing the gap between traditional strategic thinking and how strategy is currently employed in superior performing firms will test the ability of the field to match management theory with reality. In doing so, scholars can erase the perceived naiveté surrounding management theories and demonstrate the complexity witnessed in the real world through contemporary and meaningful scholarship [56].
