What technology enabled services impact business models in the automotive industry? An exploratory study

Athanasopoulou, Alexia; Reuver, Mark de; Nikou, Shahrokh; Bouwman, Harry

doi:10.1016/j.futures.2019.04.001

Cited by 49 publications

(21 citation statements)

References 48 publications

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“…In addition, automated pothole detection could generate cost-efficient maintenance measures of road conditions, which might attract the interest of city planners as further stakeholders [8,2]. Another safety-enhancing service, which was most frequently mentioned is the intelligent emergency call (eCall) (e.g., [14,11,44]). In case of an accident, the eCall system automatically contacts an emergency call-center and communicates the vehicle position, including relevant data (e.g., time of the accident, vehicle type).…”

Section: Ddbm Dimension 1: Value Propositionmentioning

confidence: 99%

“…While OEMs are launching digital services such as BMW ConnectedDrive and Mercedes me connect, they are threatened by major tech companies such as Google, introducing its own car operating system Android Automotive, or Apple, even planning its rumored electric car [10]. At the same time, several scholars work on related topics, such as novel connected services [8,11], required collaboration [12,13], technological enablers [14,15], or shifting revenue pools [16,17]. As our society is strongly driven by mobility, Kaiser et al state that "[...] it is almost our duty to examine the emergence of digital services based on vehicle usage data more closely" [18, p.40].…”

Section: Introductionmentioning

confidence: 99%

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Monetizing Car Data: A Literature Review on Data-Driven Business Models in the Connected Car Domain

Sterk¹,

Dann²,

Weinhardt³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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The amount of data generated by a single modern vehicle is exploding.Consequently, the entire global automotive industry is facing the question of how to monetize this valuable data. Triggered by the connectivity trend, data-driven business models disrupt the automotive ecosystem by changing mobility behavior, proliferation of technical enablers, new strategic collaborations, and shifting revenue streams.In this study, we analyze the existing body of literature on data-driven business models in the connected car domain and structure it according to four dimensions-value proposition, value architecture, value network, and value finance.Thereby, we contribute to the business model research by providing a comprehensive overview and categorization of existing works in this area and laying the foundation for future research.

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Section: Ddbm Dimension 1: Value Propositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Monetizing Car Data: A Literature Review on Data-Driven Business Models in the Connected Car Domain

Sterk¹,

Dann²,

Weinhardt³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…It is ambiguous what examining a CASE factor includes. All incumbent car manufacturers offer electric, to some extent autonomous and connected cars, and partly MaaS services today (Miao et al , 2019; Athanasopoulou et al , 2019). For example, in terms of the electric car capability dimension, battery technology make or buy decisions of OEMs differ greatly (Olivetti et al , 2017; Chae et al , 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing dynamic capabilities of incumbents in the face of unprecedented industry transformation: the case of the automotive industry

Hoeft

2021

JSMA

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PurposeThe purpose of this paper is to develop a holistic approach to the assessment of dynamic capabilities (DCs). Holistic refers to incorporating all DCs of an organisation relevant for determining and executing the firm's strategy.Design/methodology/approachA two-phase study was conducted. First, secondary sources, such as media, industry and annual reports, are being used to initially assess CASE (connected, autonomous, shared and electric) and implications for incumbent car manufacturers in a structured way. Second, semi-structured interviews with automotive managers and further automotive stakeholders offer in-depth insights into CASE, as well as incumbents' strategies and the underlying rationale.FindingsThe proposed framework for assessing DCs offers a holistic approach and provides new angles of analysis. First, the time dimension is considered using scenarios since timing is vital in strategy and implementation. Second, capabilities are broken down into technological and non-technological, sharpening strategic decision-making of automakers. Third, the analysis considers external VUCA (volatility, uncertainty, complexity and ambiguity) as they interplay with internal DCs.Research limitations/implicationsFurther testing of the proposed DC assessment approach offers a promising opportunity for future research. This paper focuses on the automotive industry, but it is worth investigating the extent to which the approach can be used in other dynamic industries, such as finance or retail.Originality/valueThe approach proposed highlights the importance and nuances of considering external perspectives in the DC assessment and the relevance of non-technological capabilities in the automotive industry. Thereby, it contributes to the literature on capability assessments and the operationalisability of the DC lens.

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“…The main objective is to enhance cluster stability (O.1) and QoS (O.5) among network entities. The clustering scheme addresses two challenges, namely high cost (X.2) and complexity whereby there are a high amount of overhead and a massive amount of data generated by a large number of nodes [167,168], and high dynamicity (X.3) whereby there are UEs with high mobility [169]. In this scheme, UEs form clusters among themselves and connect to BSs.…”

Section: Electing Backup Chsmentioning

confidence: 99%

Survey and taxonomy of clustering algorithms in 5G

Khan

Yau

Noor

et al. 2020

Journal of Network and Computer Applications

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The large-scale deployment of fifth generation (5G) is expected to produce a massive amount of data with high variability due to ultra-densification and the rapid increase in a heterogeneous range of applications and services (e.g., virtual reality, augmented reality, and driver-less vehicles), and network devices (e.g., smart gadgets and sensors). Clustering organizes network topology by segregating nodes with similar interests or behaviors in a network into logical groups in order to achieve network-level and cluster-level enhancements, particularly cluster stability, load balancing, social awareness, fairness, and quality of service. Clustering has been investigated to support mobile user equipment (UE) in access networks, whereby UEs form clusters themselves and may connect to BSs. In this paper, we present a comprehensive survey of the research work of clustering schemes proposed for various scenarios in 5G networks and highlight various aspects of clustering schemes, including objectives, challenges, metrics, characteristics, performance measures. Furthermore, we present open issues of clustering in 5G.

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What technology enabled services impact business models in the automotive industry? An exploratory study

Cited by 49 publications

References 48 publications

Monetizing Car Data: A Literature Review on Data-Driven Business Models in the Connected Car Domain

Monetizing Car Data: A Literature Review on Data-Driven Business Models in the Connected Car Domain

Assessing dynamic capabilities of incumbents in the face of unprecedented industry transformation: the case of the automotive industry

Survey and taxonomy of clustering algorithms in 5G

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