Impact of Artificial Intelligence on Firm Performance: Exploring the Mediating Effect of Process-Oriented Dynamic Capabilities

Wamba-Taguimdje, Serge-Lopez; Wamba, Samuel Fosso; Kamdjoug, Jean Robert Kala; Wanko, Chris Emmanuel Tchatchouang

doi:10.1007/978-3-030-47355-6_1

Cited by 14 publications

(9 citation statements)

References 40 publications

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“…The resource-based view of firm contends that firms make use of superior resources to develop capabilities either as a reaction to uncertainty or as a proactive approach to exploit opportunities (Jajja et al ., 2018) that help the firms to achieve competitive advantage (Eisenhardt and Martin, 2000). Further, the notion of resources is not confined to tangible or physical resources, in fact, the resources include knowledge, skills and know-how of key processes (Wamba-Taguimdje et al ., 2020; Votto et al ., 2021; ur Rehman et al ., 2022) that result in either resource-driven or process-driven capabilities. Firms address environmental rigor by configuration, reconfiguration and integration of resources that drive positive performance (Teece et al ., 1997).…”

Section: Theoretical Developmentmentioning

confidence: 99%

“…DCV argues that organizations deploy superior resources to enhance their performance in uncertain environment (Jajja et al ., 2018; ur Rehman et al ., 2022). Dynamic capabilities can be bifurcated in resource-driven and process-driven capabilities (Wamba-Taguimdje et al ., 2020; ur Rehman et al ., 2022). We posit SF and BSI as key resource-driven dynamic capabilities that firm develop to mitigate the impact the of EU and drive performance.…”

Section: Introductionmentioning

confidence: 99%

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The interplay of integration, flexibility and coordination: a dynamic capability view to responding environmental uncertainty

Rehman

Jajja

2022

IJOPM

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PurposeSupply chain literature highlights that environmental uncertainty (EU) encourages firms to integrate their business strategies and develop strategic flexibility (SF). The authors’ use the dynamic capability view (DCV) to explain why the EU motivates firms to develop SF and integrate business strategies for better supply chain coordination (SCC) and enhanced performance. This study tests the role of SCC as a mediator between (1) business strategy integration (BSI) and operational performance, and (2) SF and operational performance. The study also measures the contingent effect of structural constructs on the relationship between EU, BSI and SF.Design/methodology/approachData of 356 firms drawn from the continuous innovation network (CINet) are used in this paper. Structural equation modeling (SEM) is used to test the direct, indirect and moderation hypotheses.FindingsThe paper finds that EU enhances SF and BSI. Further, BSI has a significant positive impact on SCC that drives operational performance. The paper also finds that SF has no direct impact on SCC rather it affects SCC through BSI. SCC mediates the relationship between (1) SF and operational performance, and (2) BSI and operational performance. The organization's structural factors including connectedness and formalization moderate the effect of EU on BSI and SF.Originality/valueThe paper extends debate at the interface of supply chain risk and strategy literature and provides a specific understanding of the “external-internal-external” mechanism in the context of the turbulent business environment. This mechanism helps to understand why and under what structural conditions EU (external) leads to BSI and SF (internal). Further, BSI and SF (internal) help firms to manage SCC (external) effectively that in turn improve operational performance.

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Section: Theoretical Developmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The interplay of integration, flexibility and coordination: a dynamic capability view to responding environmental uncertainty

Rehman

Jajja

2022

IJOPM

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“…As a result, this methodology has proved a high potential for advanced improvement in technological growth [52]. AI assists in the enhancement of performance and the development of new enterprise models [53]. Furthermore, we can use embedded AI solutions to optimize manufacturing processes and intelligent functions to extend the machines and services [54].…”

Section: Cao Et Al Experimentally Analyzed Pem Fuel Cells Using a New...mentioning

confidence: 99%

Artificial Intelligence-Assisted Optimization and Multiphase Analysis of Polygon PEM Fuel Cells

Jabbary¹,

Pourmahmoud²,

Abdollahi³

2022

Preprint

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This article presents new PEM fuel cell models with hexagonal and pentagonal designs. After observing cell performance improvement in these models, we optimized them. Inlet pressure and temperature were used as input parameters and consumption and output power as target parameters of the multi-objective optimization algorithm. Then we used artificial intelligence techniques, including deep neural networks and polynomial regression, to model the data. Next, we employed the RSM (Response Surface Method) method to derive the target functions. Furthermore, we applied the NSGA-II multi-objective genetic algorithm to optimize the targets. The optimized Pentagonal and Hexagonal models averagely increase the output current density by 21.819% and 39.931%, respectively, compared to the base model (Cubic).

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“…As a result, this methodology has proved to have a high potential for advanced improvement in technological growth [52]. AI assists in the enhancement of performance and the development of new enterprise models [53]. In addition, embedded AI solutions can be used to optimize manufacturing processes and extend machines and services with intelligent functions [54].…”

Section: Cao Et Al Experimentally Analyzed Pem Fuel Cells Using a New...mentioning

confidence: 99%

Artificial Intelligence-Assisted Optimization and Multiphase Analysis of Polygon PEM Fuel Cells

Jabbary

Pourmahmoud

Abdollahi

et al. 2022

Preprint

View full text Add to dashboard Cite

This article presents new hexagonal and pentagonal PEM fuel cell models. The models have been optimized after achieving improved cell performance. The input parameters of the multi-objective optimization algorithm were pressure and temperature at the inlet, and consumption and output powers were the objective parameters. The output data of the numerical simulation has been trained using deep neural networks and then modeled with polynomial regression. The target functions have been extracted using the RSM (Response Surface Method), and the targets were optimized using the multi-objective genetic algorithm (NSGA-II). Compared to the base model, the optimized Pentagonal and Hexagonal models increase the output current density by 21.8% and 39.9%, respectively.

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Impact of Artificial Intelligence on Firm Performance: Exploring the Mediating Effect of Process-Oriented Dynamic Capabilities

Cited by 14 publications

References 40 publications

The interplay of integration, flexibility and coordination: a dynamic capability view to responding environmental uncertainty

The interplay of integration, flexibility and coordination: a dynamic capability view to responding environmental uncertainty

Artificial Intelligence-Assisted Optimization and Multiphase Analysis of Polygon PEM Fuel Cells

Artificial Intelligence-Assisted Optimization and Multiphase Analysis of Polygon PEM Fuel Cells

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