Competitive advantage | EPFL Graph Search

In business, a competitive advantage is an attribute that allows an organization to outperform its competitors. A competitive advantage may include access to natural resources, such as high-grade ores or a low-cost power source, highly skilled labor, geographic location, high entry barriers, and access to new technology and to proprietary information. Overview The term competitive advantage refers to the ability gained through attributes and resources to perform at a higher level than others in the same industry or market (Christensen and Fahey 1984, Kay 1994, Porter 1980 cited by Chacarbaghi and Lynch 1999, p. 45). The study of this advantage has attracted profound research interest due to contemporary issues regarding superior performance levels of firms in today's competitive market. "A firm is said to have a competitive advantage when it is implementing a value creating strategy not simultaneously being implemented by any current or potential player" (Barney 1991 cited

Technology Strategy in Dynamic Environments: A Computational Analysis of the Automation of Routines, the Organization of AI, and the Evaluation of Technology Risk

In this dissertation, I develop theory and evidence to argue that new technologies are central to how firms organize to create and capture value. I use computational methods such as reinforcement learning and probabilistic topic modeling to investigate three topics: the automation of routines, the organization of artificial intelligence (AI), and the evaluation of technology risk. Overall, I argue that new technologies are not a panacea for the firm but require deliberate strategic planning to manage the potential downsides of myopic automation, AI interdependencies, and the disclosure of technology risks.In the first essay, I argue that while automation can increase productivity by reducing the costs of coordinating individuals, the automation of routines can also incur an indirect opportunity cost due to slow adaptation to environmental change. I develop a reinforcement learning simulation to model the impact of automation on the returns from the division of labor in dynamic environments and to show how automation incurs opportunity costs through lost learning and slow adaptation. Moreover, automation can be suboptimal when it brings about myopic behavior, i.e. high returns from the division of labor in the short term, but negative returns in the long term. Given the simulation results, I argue that firms need dynamic routines to simultaneously balance learning and automation. I open-source the simulation platform as OrgSim-RL on GitHub.In the second essay, I argue that a data-driven culture - what I define as a Data Clan - can help to coordinate complex interdependencies between AI components within a firm. I analyze in-depth semi-structured interview data with a hierarchical stochastic block model (hSBM) and hand-coding to find that managers focus primarily on building a strong culture and establishing high-quality data assets when allocating resources to AI initiatives. Given the results, I inductively develop implications for theory and argue that the emergence of a Data Clan can be a governance mechanism to reduce coordination frictions and build a competitive advantage in the age of AI.In the third essay, I argue that investors require a higher initial return to take on more technology risk disclosure during an IPO. I quantify the magnitude of disclosed risk and the risk disclosure topics based on a latent Dirichlet allocation (LDA) topic model of IPO prospectus text and find a return-for-risk association between text-based technology risk disclosure and underpricing. The study also finds evidence that owning granted patents is associated with a lower return-for-risk association, suggesting that intellectual property allows the disclosure of risk without losing the competitive advantage. I open-source the code for quantifying risk disclosure as RiskyData-LDA on GitHub.In summary, this dissertation develops theory and finds evidence across three essays to argue that leveraging new technologies requires deliberate strategic planning to manage potential downsides of new technologies, such as the opportunity costs of automation, coordination costs, and costs associated with raising capital. The results suggest three mitigating solutions: dynamic routines to balance learning and automation, a Data Clan to improve coordination, and disclosure through patents to reduce underpricing.

EPFL2022

Open source corporate strategy (OSCS)

Juan Andrei Villarroel Fernandez

This doctoral dissertation defines and sheds light on a nascent phenomenon in management, informally referred to as "crowdsourcing". I term this concept Open Source Corporate Strategy, or OSCS, which borrows insights from the open source innovation literature, as well as from the strategy literature. This new concept explains how the firm may incorporate open source principles into its business model, as an effective diversification strategy to gain and sustain competitive advantage, in the context of a globally interconnected society. Firms implementing OSCS make use of a "loose" intellectual property regime to extend their operations outside the formal boundaries of the organization, and orchestrate knowledge work via informal globally distributed communities of practice. Empirical evidence discussed throughout this dissertation shows that these, among others, could be new and important sources of competitive advantage for the firm. Over the last few years we have seen a tremendous increase in the ability of firms to expand operations beyond their boundaries: (1) outside the focal organization, by formally trading knowledge with other organizations external to the firm (Chesbrough 2003, 2006), and more recently, (2) outside the scope of the formal interorganizational fabric, by informally sharing knowledge with external online communities of practice (Lakhani et al., 2007; Villarroel and Tucci 2006). The research developed in this doctoral dissertation was designed to explore this phenomenon, by means of: (1) case studies of firms implementing such open initiatives, (2) computational models of open innovation and open sourcing unveiling their impact on system-level learning performance, complemented by (3) a survey-based study of the online community associated to one such firm-sponsored initiative unveiling the motivation factors that determine participation and contribution performance.

EPFL2008

Technological innovation strategy in natural resource based industries

Bettina Lynda Bastian

This thesis analyses innovation and technology strategy within a primary industry, notably the upstream oil and gas industry. Industries that exploit and produce finite natural resources are challenged by specific external risks that do not receive much attention from management literature otherwise, especially with regard to organizational innovation behaviour, and thus merit such research focus. On one hand, primary sector firms are confronted with exhaustible nature of finite natural resources. On the other hand, those finite natural resources are fixed to certain locations and companies have to operate in numerous countries in order to exploit resource deposits. In this context, they are often confronted with tremendous socio-political constraints that may have a negative impact on their businesses. This research work raises questions specific to natural resource based industries, notably (1) how can firms defend or even expand their competitive position when they are challenged by the depletion of their core resources? How can firms compensate for an exhaustible resource? (2) How do we evaluate contingencies like political risk for competitive advantage and what role does innovation play in this context? This dissertation takes the format of a "compilation thesis", in this case, three compiled articles. Within the first paper we focus on the resource based view (RBV), a central theoretical approach in management science, which explains how unique firm resources and capabilities contribute to value creation and lead to sustained competitive advantage. We1 apply the assumptions of RBV to the context of finite natural resources and ask if its analytical power is sufficient to explain competitive advantage of companies active in the primary sector, such as hydrocarbon and ore production. Paper 2 analyses how firms respond to geophysical constraints, such as natural resource depletion, and how these depletion issues affect organizational capability building and operational performance in terms of resource exploration and recovery. Paper 3 asks if political risk as external threat influences firm technology and innovation strategies. We find that time horizons of political risk play an important role and analyse why mid-and long term threats positively influence the development of technology and innovation strategies. The dissertation makes several important contributions. First, it contributes to our understanding of innovation and technology strategy within a natural resource based industry. Second, the idea that finite natural resources with their exhaustible nature are different than the "usual" resources investigated by RBV is novel. Third, the analysis also shows how RBV applies to natural resources extracting industries, and how RBV would gain in explicatory power if it was able to integrate factors like "geophysical constraints" and 'exposure to political risk' into the approach. Fourth, the thesis applies RBV and capability approaches to the specific context of primary industries, which is important as it helps to extend the frameworks' usefulness. Fifth, the current thesis combines political risk theory and research on innovation and technology management, and answers why political risk influences firm strategies, differentiation, and entry into new fields. ______________________________ 1 I am going to use the personal pronoun "we" to refer to first person's actions or opinions in this paper. I do so because I want to acknowledge the contributory effort of all who provided ideas and support. Nevertheless, any errors and omissions are completely my own responsibility.

EPFL2009