Strategic alignment between IT flexibility and dynamic capabilities: an
empirical investigation
- URL: http://arxiv.org/abs/2105.08429v1
- Date: Tue, 18 May 2021 10:37:33 GMT
- Title: Strategic alignment between IT flexibility and dynamic capabilities: an
empirical investigation
- Authors: Rogier van de Wetering, Patrick Mikalef and Adamantia Pateli
- Abstract summary: This paper develops a strategic alignment model for IT flexibility and dynamic capabilities.
It empirically validates proposed hypotheses using correlation and regression analyses on a large data sample of 322 international firms.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Dynamic capabilities theory emerged as a leading framework in the process of
value creation for firms. Its core notion complements the premise of the
resource-based view of the firm and is considered an important theoretical and
management framework in modern information systems research. However, despite
DCTs significant contributions, its strength and core focus are essentially in
its use for historical firm performance explanation. Furthermore, valuable
contributions have been made by several researchers in order to extend the DCT
to fit the constantly changing IT environments and other imperative drivers for
competitive performance. However, no DCT extension has been developed which
allows firms to integrally assess their current state of maturity in order to
derive imperative steps for further performance enhancements. In light of
empirical advancement, this paper aims to develop a strategic alignment model
for IT flexibility and dynamic capabilities and empirically validates proposed
hypotheses using correlation and regression analyses on a large data sample of
322 international firms. We conjecture that the combined synergetic effect of
the underlying dimensions of a firms IT flexibility architecture and dynamic
capabilities enables organizations to cope with changing environmental
conditions and drive competitive firm performance. Findings of this study
suggest that there is a significant positive relationship between the firms
degree of strategic alignment defined as the degree of balance between all
dimensions and competitive firm performance. Strategic alignment can,
therefore, be seen as an important condition that significantly influences a
firms competitive advantage in constantly changing environments. The proposed
framework helps firms assess and improve their maturity and alignment of IT
flexibility and dynamic capabilities.
Related papers
- Latent-Predictive Empowerment: Measuring Empowerment without a Simulator [56.53777237504011]
We present Latent-Predictive Empowerment (LPE), an algorithm that can compute empowerment in a more practical manner.
LPE learns large skillsets by maximizing an objective that is a principled replacement for the mutual information between skills and states.
arXiv Detail & Related papers (2024-10-15T00:41:18Z) - Cooperative Resilience in Artificial Intelligence Multiagent Systems [2.0608564715600273]
This paper proposes a clear definition of cooperative resilience' and a methodology for its quantitative measurement.
The results highlight the crucial role of resilience metrics in analyzing how the collective system prepares for, resists, recovers from, sustains well-being, and transforms in the face of disruptions.
arXiv Detail & Related papers (2024-09-20T03:28:48Z) - Towards Robust Federated Image Classification: An Empirical Study of Weight Selection Strategies in Manufacturing [0.0]
This study investigates the comparative effectiveness of two weight selection strategies: Final Epoch Weight Selection (FEWS) and Optimal Epoch Weight Selection (OEWS)
We employ various neural network architectures, including EfficientNet, ResNet, and VGG, to assess the impact of these weight selection strategies on model convergence and robustness.
arXiv Detail & Related papers (2024-08-19T14:18:21Z) - Balancing Similarity and Complementarity for Federated Learning [91.65503655796603]
Federated Learning (FL) is increasingly important in mobile and IoT systems.
One key challenge in FL is managing statistical heterogeneity, such as non-i.i.d. data.
We introduce a novel framework, textttFedSaC, which balances similarity and complementarity in FL cooperation.
arXiv Detail & Related papers (2024-05-16T08:16:19Z) - Hybrid LLM/Rule-based Approaches to Business Insights Generation from Structured Data [0.0]
The ability to extract actionable insights from vast and varied datasets is essential for informed decision-making and maintaining a competitive edge.
Traditional rule-based systems, while reliable, often fall short when faced with the complexity and dynamism of modern business data.
This paper explores the efficacy of hybrid approaches that integrate the robustness of rule-based systems with the adaptive power of Large Language Models.
arXiv Detail & Related papers (2024-04-24T02:42:24Z) - LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models [75.89014602596673]
Strategic reasoning requires understanding and predicting adversary actions in multi-agent settings while adjusting strategies accordingly.
We explore the scopes, applications, methodologies, and evaluation metrics related to strategic reasoning with Large Language Models.
It underscores the importance of strategic reasoning as a critical cognitive capability and offers insights into future research directions and potential improvements.
arXiv Detail & Related papers (2024-04-01T16:50:54Z) - Literature Review of Current Sustainability Assessment Frameworks and
Approaches for Organizations [10.045497511868172]
This systematic literature review explores sustainability assessment frameworks (SAFs) across diverse industries.
The review focuses on SAF design approaches including the methods used for Sustainability Indicator (SI) selection, relative importance assessment, and interdependency analysis.
arXiv Detail & Related papers (2024-03-07T18:14:52Z) - Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation [55.65482030032804]
Social robot navigation can be helpful in various contexts of daily life but requires safe human-robot interactions and efficient trajectory planning.
We propose a systematic relational reasoning approach with explicit inference of the underlying dynamically evolving relational structures.
Our approach infers dynamically evolving relation graphs and hypergraphs to capture the evolution of relations, which the trajectory predictor employs to generate future states.
arXiv Detail & Related papers (2024-01-22T18:58:22Z) - Variational Inference with Holder Bounds [68.8008396694788]
We present a careful analysis of the thermodynamic variational objective (TVO)
We reveal how the pathological geometry of thermodynamic curves negatively affects TVO.
This motivates our new VI objectives, named the Holder bounds, which flatten the thermodynamic curves and promise to achieve a one-step approximation of the exact marginal log-likelihood.
arXiv Detail & Related papers (2021-11-04T15:35:47Z) - A Deep Reinforcement Learning Approach to Marginalized Importance
Sampling with the Successor Representation [61.740187363451746]
Marginalized importance sampling (MIS) measures the density ratio between the state-action occupancy of a target policy and that of a sampling distribution.
We bridge the gap between MIS and deep reinforcement learning by observing that the density ratio can be computed from the successor representation of the target policy.
We evaluate the empirical performance of our approach on a variety of challenging Atari and MuJoCo environments.
arXiv Detail & Related papers (2021-06-12T20:21:38Z) - Dynamic enterprise architecture capabilities and organizational
benefits: an empirical mediation study [0.0]
This study focuses on EA-based capabilities, using the dynamic capabilities view as a theoretical foundation.
It develops and tests a new research model that explains how dynamic enterprise architecture capabilities lead to organizational benefits.
arXiv Detail & Related papers (2021-05-18T10:07:31Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.