相关论文: Which Discoveries Are Paradigm Shifting?

Marketing scholars have underscored the importance of conceptual articles in providing theoretical foundations and new perspectives to the field. This paper supports the argument by employing two network-based measures -- the number of…

In many countries and at European level, research policy increasingly focuses on 'excellent' researchers. The concept of excellence however is complex and multidimensional. For individual scholars it involves talents for innovative…

This study proposes the concept of disruptive firms: they are firms with market leadership that deliberate introduce new and improved generations of durable goods that destroy, directly or indirectly, similar products present in markets in…

Novelty detection is the machine learning task to recognize data, which belong to an unknown pattern. Complementary to supervised learning, it allows to analyze data model-independently. We demonstrate the potential role of novelty…

Academic success is distributed unequally; a few top scientists receive the bulk of attention, citations, and resources. However, do these ``superstars" foster leadership in scientific innovation? We introduce three information-theoretic…

We respond to Holst et al.'s critique that the decline in scientific disruptiveness documented in Park et al. (Nature, 2023) is an artifact of including works with zero backward citations. Using their advocated dataset, metric, and…

Interestingness measures provide information that can be used to prune or select association rules. A given value of an interestingness measure is often interpreted relative to the overall range of the values that the interestingness…

A martingale framework for concept change detection based on testing data exchangeability was recently proposed (Ho, 2005). In this paper, we describe the proposed change-detection test based on the Doob's Maximal Inequality and show that…

Despite the growing importance of teams in producing innovative and high-impact science and technology, it remains unclear how expertise diversity among team members relates to the originality and impact of the work they produce. Here, we…

Time series similarity measures are highly relevant in a wide range of emerging applications including training machine learning models, classification, and predictive modeling. Standard similarity measures for time series most often…

Classical designs of randomized experiments, going back to Fisher and Neyman in the 1930s still dominate practice even in online experimentation. However, such designs are of limited value for answering standard questions in settings,…

Change of measure inequalities translate divergences between probability measures into explicit bounds on event probabilities, and play an important role in deriving probabilistic guarantees in learning theory, information theory, and…

The current work applies a methodology for mapping the supply of new knowledge from public research organizations, in this case from Italian institutions at the level of regions and provinces (NUTS2 and NUTS3). Through the analysis of…

We propose an indicator to measure the degree to which a particular news article is novel, as well as an indicator to measure the degree to which a particular news item attracts attention from investors. The novelty measure is obtained by…

This study elaborates a text-based metric to quantify the unique position of stylized scientific research, characterized by its innovative integration of diverse knowledge components and potential to pivot established scientific paradigms.…

This study explores the relationship between R&D intensity, as a measure of innovation, and financial performance among S&P 500 companies over 100 quarters from 1998 to 2023, including multiple crisis periods. It challenges the conventional…

Testing for causation, defined as the preceding impact of the past values of one variable on the current value of another one when all other pertinent information is accounted for, is increasingly utilized in empirical research of the…

The surge in scientific publications challenges the use of publication counts as a measure of scientific progress, requiring alternative metrics that emphasize the quality and novelty of scientific contributions rather than sheer quantity.…

Traditional recommendation algorithms develop techniques that can help people to choose desirable items. However, in many real-world applications, along with a set of recommendations, it is also essential to quantify each recommendation's…

Scientific novelty is a critical construct in bibliometrics and is commonly measured by aggregating pairwise distances between the knowledge units underlying a paper. While prior work has refined how such distances are computed, less…