Understanding the S-Curve of Ambidextrous Behavior in Learning Emerging Digital Technologies

Kar, Sudatta; Kar, Arpan Kumar; Gupta, Monika

doi:10.1109/emr.2021.3107344

Cited by 10 publications

(3 citation statements)

References 37 publications

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“…In exploratory learning, knowledge is gained through experimentation. Individually, the ambidextrous learning behaviour of employees involves concurrently investigating new abilities while making use of the skills they have already learned from the project at hand (Kar et al 2021). Higher levels of individual exploration and exploitation lead to higher levels of innovation performance, which impacts aspects of individual, group, and firm performance (Schnellbächer et al 2019).…”

Section: A Appendix: Questionnaire On the Intention Of Residents To S...mentioning

confidence: 99%

Proceedings of SW21 The OR Society Simulation Workshop

2023

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Simulation, and now digital twins, excel in estimating parameters associated with complex and timedependent stochastic processes, e.g., large airport, highway, and warehouse operations. Within such contexts, we consider statistical inference, whereby one seeks to quantify the error in the obtained simulation estimate. Historically, statistical inference in simulation has been considered challenging because parameters needing estimation are often complicated, and simulation output often autocorrelated and non-normal. However, we argue that the remarkably simple idea of batching can be used as an "omnibus" inference device in simulation settings. Batching for inference works in three simple steps: (i) divide simulation output data into overlapping batches; (ii) construct parameter estimates from each batch; and (iii) use the batch estimates, after accounting for dependence, to perform statistical inference. As we describe in this paper, the resulting procedures are usually trivial to implement in software, and are provably correct and efficient. Batching ideas originated in the 1950s, and have enjoyed steady development in the simulation community since the 1970s mostly within the problem of variance estimation. Our thesis is that batching ideas have much wider utility, and that batching should be considered alongside other resampling ideas such as the bootstrap or the jackknife in modern statistics.

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Section: A Appendix: Questionnaire On the Intention Of Residents To S...mentioning

confidence: 99%

Proceedings of SW21 The OR Society Simulation Workshop

2023

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“…The source of power arrangement in an organization and the role and responsibilities of employees also influence the development of EA (Kar et al, 2021). The diversity of stakeholders with different roles and responsibilities in a large organization often presents a complex interrelation among them with multiple attitudes and conflicting interests to be addressed in EA development.…”

Section: Ea Failure Factors From the Literaturementioning

confidence: 99%

Why Organizations Fail in Implementing Enterprise Architecture Initiatives?

Gong

Janssen

2022

Inf Syst Front

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Enterprise architecture (EA) initiatives consist of functions, processes, tools, instruments, and principles to guide the design of IT and its alignment with business. EA is often presented as a silver bullet to ensure that IT contributes to business. Yet, many EA initiatives do not work out or even fail, but in the literature this area is undertheorized. This study aims to understand the factors influencing the failure of EA initiatives. We identified 15 factors and invited 8 EA experts to evaluate the factors and their influence based on an approach combining grey systems theory, Decision-Making and Trial Evaluation Laboratory (DEMATEL), and Interpretative Structural Modeling (ISM). The findings indicate that the factors are correlated and interwoven in complex causal chains. This study reveals the root factor and suggests enhancing high-level managers' EA knowledge and ensuring communication and leadership skills of enterprise architects as the starting point to avoid EA failure. Only later, organizing the EA function becomes important.

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“…Individually, the ambidextrous learning behaviour of employees involves concurrently investigating new abilities while making use of the skills they have already learned from the project at hand (Kar et al 2021). Higher levels of individual exploration and exploitation lead to higher levels of innovation performance, which impacts aspects of individual, group, and firm performance (Schnellbächer et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Modelling the Impact of Ambidextrous Learning on Team Performance Using Agent-Based Simulation

Yan¹,

Onggo²

2023

Proceedings of SW21 the OR Society Simulation Workshop

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In an increasingly competitive environment, organizations need to continuously innovate (explorative learning) while making steady improvements to their existing operations (exploitative learning). The capacity to pursue both exploratory and exploitative learning simultaneously is called ambidexterity. Therefore, ambidexterity has become one of the important research topics in the field of organizational study. This paper focuses on ambidexterity learning at the team level. The objective is to propose a generic agent-based simulation model that can be used to examine how ambidextrous learning affect team performance under different levels of task complexity, communication intensity and communication cost. The experiment shows that the model can reproduce what have been reported in the team performance literature.

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Understanding the S-Curve of Ambidextrous Behavior in Learning Emerging Digital Technologies

Cited by 10 publications

References 37 publications

Proceedings of SW21 The OR Society Simulation Workshop

Proceedings of SW21 The OR Society Simulation Workshop

Why Organizations Fail in Implementing Enterprise Architecture Initiatives?

Modelling the Impact of Ambidextrous Learning on Team Performance Using Agent-Based Simulation

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