Abstract
Competitiveness is essential for an organization's success in both the public and private sectors. Using three well-known mathematical methodologies, this work has centered its analysis on factors that affect primarily organizational competitiveness viewed through three different scales (global, local, and businesses). Using these methods, researchers and decision-makers gain insight to perform a qualitative analysis over key metrics of each level looking for improvements in business competitive performance and elucidate the importance of intangible assets like strategy. The authors identified that although each tool can only let us know some aspects about system agents, after combining the three tools as a toolbox, it might be used in volatile and rapidly evolving settings when strategic approaches concentrate on accurate resource allocation that demands a lot of time.
TopIntroduction
There is a growing interest in learning how management teams make quick strategic judgments in high-speed environments (Das, 2019; Elbanna et al., 2013). Proactive companies act before future demand to make a difference in the environment and to aggressively pursue excellent business opportunities against competitors (Eshima & Anderson, 2017; Wales et al., 2020). The latter situation produces considerable instability for enterprises in a transition economy, generating uncertainty and making it unfeasible for companies to forecast future market demand and the business climate (Luu & Ngo, 2019). Consequently, in an unpredictable and frequently changing context, strategic approaches that rely on both distinction and low financial risk may run counter to classic strategic frameworks (Slater & Olson, 2001).
Entrepreneurial-oriented firms exhibit innovative and proactive business behaviors that explore possibilities with unforeseen outcomes (Covin et al., 2006; Luu & Ngo, 2019). The relationships between entrepreneurial orientation strategies and performance can take more complicated paths than the straightforward and favorable ones (Luu & Ngo, 2019), prompting the development of new techniques that capture the underlying nonlinear nature of these situations.
Arbaugh et al. (2002) and Soininen et al. (2012) observe that entrepreneurially oriented companies grow more quickly that those that are not, but they suggest that defining how a company becomes entrepreneurial is difficult to determine. Therefore, to address this challenge, the author have combined various analytic techniques beyond the typical central tendency measurements with the purpose of linking growth and entrepreneurism, requiring that companies identify (1) changes in market expectation (Fombrun & Wally, 1989), (2) acquisition of additional resources, and (3) knowledge of how to employ these resources. These novel resource combinations improve their ability to adapt and uncover new business prospects that allow for the establishment of new businesses (Eshima & Anderson, 2017).
How do businesses respond effectively to a turbulent environment? It is an issue that has sparked the curiosity of both experts and strategic professionals, especially in light of the outbreak of the current COVID-19 pandemic (Wenzel et al., 2021). According to Wenzel et al. (2021), more research is needed to analyze the development of strategies in uncertain conditions during times of crisis, as well as to comprehend the temporal dynamics in the crisis response process. This means that one response may be superior to another at certain times, pointing to a window of opportunity in which the strategic performances are more or less successful. An organization’s competitive performance is a multidimensional concept in strategic management that intrigues both scholars and managers, since it can include variables that are not highly associated (Combs et al., 2005; Rust et al., 2004). This high dimensionality on decision-making suggests the use of more than one analytical technique at same time, at least to handle reductions in both performance and dimensionality.
The analytical information extracted from the data reveals the behavior of a system, providing decision points and assessment that can be used prospectively or reactively, depending on the strategic orientation and the organization’s responsiveness to the findings. However, Le Cam (1986) points out that summarized data reduces the information that comes from the data correlation, and a variety of techniques must be used to explore the raw data to exploit avoided correlation possibilities.
Key Terms in this Chapter
Long-Tailed Distribution: In statistics, it is a subset of heavy-tailed distributions. As its name indicates, the shape of the distribution in the case of large values of the random variable associated with the phenomenon described decreases logarithmically. The shape of the distribution in the logarithmic plane exhibits a linear and scale-free behavior. It is a question of study methods to demonstrate statistically that a distribution follows a long tail in the social sciences due to the lack of large volumes of data and because the system is, in principle, open. Models that explain unscaled behaviors can be used to infer properties about long-tail behaviors, such as least effort or preferential attachment, even the 80/20 rule. Many social systems in which agents are considered autonomous, such as wealth distribution, sales, and population, tend to exhibit long-tail behavior.
Organizational Competitiveness: This is an umbrella term used to define an organization’s ability to efficiently use its resources to offer products and services that exceed customer expectations. As a short-term capability, it refers to how a company manages its market position in both products and services, achieving corporate goals. As a long-term capability, it necessarily involves the investment and use of resources in an efficient way to reach corporate goals.
Emerging Economies: Emerging economies are found in countries that have some characteristics typical of a developed market; therefore, these countries may become developed markets in the future. The countries characterized by emerging economies experience a beginning of economic growth and a remarkable first phase of industrialization.
Competitive advantage: This describes the ability of a company to stand out from other companies or industrial sectors through a set of techniques or skills. Product or service innovation is usually the most common mechanism used in this context. Competitive advantages often cannot be maintained over the long term because markets are constantly changing, and companies must be alert to these changes.
Organizational Complexity: This describes how the diverse components of an organization (people, process, data, activities, and organizational structure) differ among themselves. This is often regularly operationalized as the number of distinctive proficient specializations that exist inside the organization, since the intricacy of the organizational structure relies on it.