A Conceptual Analysis on the Different Approaches to New Product Development and the Variables Associated With Modelling the Process

Owens, Jonathan D.; Atherton, Andrew

doi:10.4018/978-1-5225-3401-3.ch001

Cited by 3 publications

(7 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our study contributes to the emerging third-generation digital innovation process literature by answering Owens and Atherton (2018) call for developing enhanced and practice-based third-generation models around the context-specificity of enterprises and their internal and external environments. It also contributes by answering innovation scholars' (Agostini et al, 2019;Nambisan et al, 2017;Vega and Chiasson, 2019) call for empirical evidence on the suitability of the need-solution fit approach for the emergence and evolution of digital innovations.…”

Section: Conclusion: Practical Implications and Limitationsmentioning

confidence: 89%

“…2. Literature review and theoretical foundations of the study 2.1 Innovation process models for the digitalization era Our literature review revealed that scholarly discussions have centered on the appropriateness of third as opposed to second-generation models for examining and understanding the digitalization of innovation processes (Owens and Atherton, 2018). The second-generation models emerged with the purpose of unlocking the innovation black box by bringing into focus certain processes for developing new technologies and stimulating learning.…”

Section: The Digital Innovation Processmentioning

confidence: 99%

“…The emerging conceptual third-generation innovation models that account for increasing digitalization emphasize that the innovation process is driven by the need to rapidly and efficiently develop and launch new products and to do so with greater flexibility and risk tolerance. These needs, for example, conflict with the nature of second-generation models driven by cycle times (Owens and Atherton, 2018). The third-generation models are additionally considered being dynamic since they integrate human and technological factors (including the technology push approach) with the market pull approach to create a more nuanced and holistic approach that accounts for additional actors and factors within the innovation process.…”

Section: The Digital Innovation Processmentioning

confidence: 99%

“…Hence, not all third-generation innovation models are deemed as being best suited for the digital innovation process either. Hence, Owens and Atherton (2018) state that enhanced third-generation models must be developed around the context-specificity of enterprises and their internal and external environments. Thus, research is needed to design appropriate digital innovation models that are based on empirical evidence derived from practice.…”

Section: The Digital Innovation Processmentioning

confidence: 99%

“…This implies that it is challenging to pinpoint at which stage under the digital innovation process, innovative activities begin or end. Consequently, Owens and Atherton (2018) assert that third-generation models, as opposed to second-generation ones, better grasp the nature of the digital innovation process. According to them, the conceptualization of emerging third-generation innovation models aptly conveys the involved pertinence of interactions, communications and iterations involved in the digital innovation process.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

How does the digital innovation process unfold in practice? A novel third-generation and empirical-based need–solution pairing model

Allataifeh

Moghavvemi

Peerally

2021

EJIM

View full text Add to dashboard Cite

PurposeThere is a lack of empirical-based models derived from practice to explain the digital innovation process. The authors investigate how the digital innovation process unfolds in practice.Design/methodology/approachThe authors undertake an exploratory and phenomenological study of 21 Malaysian small and medium enterprises (SMEs) in the information and communication technology (ICT) sector.FindingsThe findings show that the delineation between digital innovation process and outcome is blurred in practice, due to the process' iterative nature. Under this process, customers' role has changed from being passive receivers of innovative products to active reviewers, testers, influential decision-makers, initiators and co-creators at different review points in the innovation process. Enterprises' role has expanded from being the initiator of the innovation process to being a cogitative actor by seeking and absorbing knowledge from customer reviews into the digital innovation process. Market analysis is often the initiator of the digital innovation process, and the findings shed light on the underlying causative mechanisms of the initiation stage, which are understudied and not well understood in the existing literature.Originality/valueThe study contributes to academic knowledge by answering scholars' call for developing third-generation practice-based innovation models, which accounts for enterprises' context-specificities and internal and external environments, and for exploring the suitability of the need–solution fit approach for the digital innovation process. Such models have only been conceptually advocated in the literature. The study also informs practitioners on the organizational and operational activities involved in managing and strategizing for the digital innovation process.

show abstract

Section: Conclusion: Practical Implications and Limitationsmentioning

confidence: 89%

Section: The Digital Innovation Processmentioning

confidence: 99%

Section: The Digital Innovation Processmentioning

confidence: 99%

Section: The Digital Innovation Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

How does the digital innovation process unfold in practice? A novel third-generation and empirical-based need–solution pairing model

Allataifeh

Moghavvemi

Peerally

2021

EJIM

View full text Add to dashboard Cite

show abstract

Modeling Social Product Development Process, Technology, and Governance

Abhari

Davidson

Xiao

2022

IEEE Trans. Eng. Manage.

View full text Add to dashboard Cite

Untitled

2023

SPSUTOM

View full text Add to dashboard Cite

Artificial intelligence (AI) can be applied in various areas of crisis management. It can provide numerous benefits concerning speed, effectiveness and precision of actions. AI accelerates the process of massive amounts of data processing, such as sensor data, satellite images, social data streams and much more. This enables to make quick analysis of a situation and make quick decisions in order to monitor various sources of information in real-time and detect early signals of a crisis. Advanced algorithms can identify patterns and anomalies enabling a quick response. AI can be used to model various crisis scenarios based on available historical and current data. This way it is possible to predict the probable effects of a given crisis and prepare for various situations. AI can analyze social media data to monitor community sentiments, identify potential threats and manage crisis communication. Automatic sentiment analysis systems can help understand society's response to a crisis. Advanced AI systems can be used to predict human behavior in various crisis scenarios. This allows to understand better how people may react and what their needs are in a given time. AI can help detect and combat cyber attacks, which constitute a serious threat, especially when it is necessary to maintain critical infrastructures. Integrating AI into crisis management requires caution because decisions made by AI systems should be consistent with ethical standards and they must consider the human supervision and assessment of the crisis situation. Design/methodology/approach: The paper analyzes the AI methods applied during crises. The results of interviews conducted with students, teachers and authors' personal experiences are provided. Findings: AI is a new way of solving problems, especially during crises. The new technologies determine the way and the speed of problems solving. Originality/value: Originality based on the interviews and post-crisis (COVID) points of view are the value.

show abstract

A Conceptual Analysis on the Different Approaches to New Product Development and the Variables Associated With Modelling the Process

Cited by 3 publications

References 65 publications

How does the digital innovation process unfold in practice? A novel third-generation and empirical-based need–solution pairing model

How does the digital innovation process unfold in practice? A novel third-generation and empirical-based need–solution pairing model

Modeling Social Product Development Process, Technology, and Governance

Untitled

Contact Info

Product

Resources

About