Algorithms for operational decision-making: An absorptive capacity perspective on the process of converting data into relevant knowledge

Neirotti, Paolo; Pesce, Danilo; Battaglia, Daniele

doi:10.1016/j.techfore.2021.121088

Cited by 21 publications

(8 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this sense, AC works as a filter that enables to select the insights with the greatest value-in-use potential from the vast amounts of data captured and managed by BDAC (Lam et al. , 2017; Neirotti et al. , 2021).…”

Section: Theoretical Background and Hypotheses Developmentmentioning

confidence: 99%

“…AC plays an indispensable role in identifying what kind of insights should be drawn from the process of acquiring new data. In this sense, AC works as a filter that enables to select the insights with the greatest value-in-use potential from the vast amounts of data captured and managed by BDAC (Lam et al, 2017;Neirotti et al, 2021). The company's ability to understand new external information it has acquired through assimilation capability is also crucial to interpret the insights offered by BDAC.…”

Section: Mediating Effect Of Absorptive Capacitymentioning

confidence: 99%

“…NoSQL, BigQuery, Map Reduce, Hadoop, Flume, Mahout, Spark, WibiData, Skytree, Python) and more complex analysis techniques (e.g. machine learning, fuzzy logic, evolutionary algorithms, artificial neural networks, deep learning), AC is essential because it enables individuals to understand and make sense of the insights generated by BDAC and identify key value-generating elements in them (Neirotti et al, 2021). Furthermore, the tools employed by BDAC to visualize the generated insights (e.g.…”

Section: Mediating Effect Of Absorptive Capacitymentioning

confidence: 99%

See 2 more Smart Citations

Unveiling the effects of big data analytics capability on innovation capability through absorptive capacity: why more and better insights matter

Lozada

Pérez

2023

JEIM

View full text Add to dashboard Cite

PurposeDespite the increase in studies focused on analyzing the potential of big data analytics capability (BDAC) as a driver of product and process innovation, it is still necessary to understand how the use of insights generated by BDAC in innovation may be maximized through articulation with individuals' intellect and other processes involving the assimilation and transformation of knowledge. This study thus aims to analyze the impact of BDAC's deployment on innovation capability (IC – process and product innovation capabilities), taking absorptive capacity (AC) as mediating variable in this relationship.Design/methodology/approachStructural equations were used to test the research model with survey data from 112 firms located in an emerging country that is one of the digital transformation leaders in the region.FindingsThe results show that 37% of process IC variance is explained by the indirect relationship via the variable mediator (AC), while in the case of product IC this percentage is 34%.Originality/valueThese results allow us to ascertain the extent to which individuals continue to be relevant to generating product and process innovation in the digital age at a time when the literature anticipates a total loss of prominence due to the arrival of new digital technologies. However, in the case of the relationship between BDAC and ICs, the existence of a partial mediation of AC indicates that individuals continue to play a role that, albeit not being the most prominent, remains relevant in ensuring that a company maximizes the assimilation and transformation of the insights generated by BDAC in new products and processes.

show abstract

Section: Theoretical Background and Hypotheses Developmentmentioning

confidence: 99%

Section: Mediating Effect Of Absorptive Capacitymentioning

confidence: 99%

Section: Mediating Effect Of Absorptive Capacitymentioning

confidence: 99%

See 1 more Smart Citation

Unveiling the effects of big data analytics capability on innovation capability through absorptive capacity: why more and better insights matter

Lozada

Pérez

2023

JEIM

View full text Add to dashboard Cite

show abstract

“…Hence, a sequential idea management system with AI enhances innovation capacity, which may conclusively increase the likelihood of radical innovation. In this scenario, we must first acknowledge that Articicial Intelligence and machine learning models use algorithms (mathematical models) to find patterns in digitally available information (data) and convert them into relevant knowledge (Neirotti et al, 2021). We use the notions AI and machine learning interchangeably.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Unraveling Data From an Idea Management System of 11 Radical Innovation Portfolios Key Lessons and Avenues for Artificial Intelligence Integration

jakobsen¹,

Brix

Jakobsen

2023

Preprint

View full text Add to dashboard Cite

In strategic and radical innovation, the degree of uncertainty and the amount of complexity is much higher compared to ‘business as usual’. Therefore, idea management systems are often used to support such innovation processes. An interesting question is what we can learn from studying data in such idea management systems and what potential implications we can derive from the innovation management literature. In this study, we were allowed to access and analyze data from the same idea management system used in 11 radical innovation projects from the year 2012-2018. Our analysis unravels 8 findings that in different ways nuance or challenge current research on innovation management. Finally, we discuss how the integration of artificial intelligence (AI) in idea management systems can support innovation team members in increasing the innovation potential of the ideas that are elaborated.

show abstract

“…According to their statement, absorptive capacity (AC) was the key mediator between R&D and innovation. In other studies, as an essential ability to identify, assimilate, transform, and exploit external knowledge, AC was recognized as an important component of corporate competence [ 61 , 62 ]. When firms make efforts in R&D, the absorption of external knowledge can facilitate innovative activities and enhance innovation capability and efficiency.…”

Section: Theories and Hypothesesmentioning

confidence: 99%

The impact of R&D effort on business model innovation: Evaluating chain mediation through collaboration breadth and depth

Chen

2023

PLoS ONE

View full text Add to dashboard Cite

Drawing on a novel theoretical framework, we explored the impact of research and development (R&D) effort on business model innovation via external collaboration breadth and collaboration depth in sequence. We empirically analyzed a sample of 94 Chinese innovative enterprises by applying hierarchical regression analysis and chain mediation analysis. The results indicate that R&D effort positively influences business model innovation. The influencing mechanism is that R&D effort positively affects external collaboration breadth, which in turn positively stimulates external collaboration depth, and ultimately benefits the implementation of business model innovation. Therefore, the breadth and depth of external collaboration play a chain-mediating role. The study develops a new framework for understanding the relationship between R&D effort, external collaboration, and business model innovation. It combines enterprises’ internal behavior (R&D) and external behavior (collaboration) to establish an inside-out mechanism for predicting business model innovation. It enriches the theory of business model innovation. It also provides insights for managers and governments to optimize policies in innovation-driven development.

show abstract

Algorithms for operational decision-making: An absorptive capacity perspective on the process of converting data into relevant knowledge

Cited by 21 publications

References 70 publications

Unveiling the effects of big data analytics capability on innovation capability through absorptive capacity: why more and better insights matter

Unveiling the effects of big data analytics capability on innovation capability through absorptive capacity: why more and better insights matter

Unraveling Data From an Idea Management System of 11 Radical Innovation Portfolios Key Lessons and Avenues for Artificial Intelligence Integration

The impact of R&D effort on business model innovation: Evaluating chain mediation through collaboration breadth and depth

Contact Info

Product

Resources

About