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This paper aims to develop and argue for a new research path to advance theory on incumbent firm adaptation to discontinuous technological change. Integrating variance and process epistemologies, implications of distinguishing a firm's capacity to adapt from their adaptive choices are highlighted.

The concepts and argument presented are based on an extensive review and synthesis of the literature on the phenomenon.

Distinguishing resource-based capacity variables and behavioral-based choice variables can fuel progress in the literature on incumbent adaptation to technological changes. More attention is needed on the direct, proximate determinants of what occurs in the process of adaptation, e.g. the intermediate choices to adapt, the timing of adaptive actions and the selection of a means for adapting. Work must then associate specific choices with performance outcomes to complete both sides of the mediated cause-effect model connecting characteristics of the decision issue to performance.

Most studies toward understanding how incumbent firms adapt to discontinuous technological innovation have used variance analyses to identify firm and technology characteristics that explain adaptation outcomes. Focusing on characteristics and content, however, does not adequately explain why or how firms adapt. Scholars thus continue to lament the lack of clear, practical theory. I contend one heretofore unaddressed reason for this dissatisfaction is that too much of the research base neglects the importance of understanding choices and the factors affecting them.

The purpose of this paper is to critically analyze the influence of the algorithm used on scholarly search engines (Garfield’s algorithm) and propose metrics to improve it so that science could be based on a more democratic way.

This paper used a snow-ball approach to collect data that allowed identifying the history and the logic behind the Garfield’s algorithm. It follows on excerpting the foundation of existing algorithm and databases of major scholarly search engine. It concluded proposing new metrics so as to surpass restraints and to democratize the scientific discourse.

This paper finds that the studied algorithm currently biases the scientific discourse toward a narrow perspective, while it should take into consideration several researchers’ characteristics. It proposes the substitution of the h-index by the number of times the scholar’s most cited work has been cited. Finally, it proposes that works in languages different than English should be included.

The broad comprehension of any phenomena should be based on multiple perspectives; therefore, the inclusion of diverse metrics will extend the scientific discourse.

The improvement of the existing algorithm will increase the chances of contact among different cultures, which stimulate rapid progress on the development of knowledge.

The value of this paper resides in demonstrating that the algorithm used in scholarly search engines biases the development of science. If updated as proposed here, science will be unbiased and bias aware.