Mobile Service Bundles: The Example of Navigation Services

Bouwman, Harry; Haaker, Timber; Vos, Henny de

doi:10.1080/10196780601136757

Cited by 38 publications

(26 citation statements)

References 22 publications

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“…Specifically, services that reinforce the functionality of the core service are often regarded as more relevant (Bouwman, Haaker & de Vos, 2007). In a bundling situation, complementarities are reflected by products that functionally enhance each other, thus yielding a valuation that is super-additive (Telser, 1979).…”

Section: The Role Of Core Products and Product Complementaritymentioning

confidence: 99%

Strategic Bundling of Telecommunications Services: Triple-Play Strategies in The Cable TV and Telephone Industries

Chan‐Olmsted

Guo

2011

Journal of Media Business Studies

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Section: The Role Of Core Products and Product Complementaritymentioning

confidence: 99%

Strategic Bundling of Telecommunications Services: Triple-Play Strategies in The Cable TV and Telephone Industries

Chan‐Olmsted

Guo

2011

Journal of Media Business Studies

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“…Research first began with product bundling and extended to service bundling (for example, telecommunication services and software services), and then further to product-service mixed bundling. Service bundling has received wide attention regarding price discrimination, increasing sales, promoting customer lock-in and creating entry barriers [15].…”

Section: Related Workmentioning

confidence: 99%

“…These elementary services are combined in a network topology that is shaped by service configurations, interrelations, and dependencies [17]. Research on service bundling has included work on the principles and objectives of service bundling [18], the description and modeling of service bundles [15,19], including typical relationships between multiple elementary services, pricing of the service bundle [17,18], methods and processes of service bundling [4,[18][19][20], and quality evaluation of service bundling [8].…”

Section: Related Workmentioning

confidence: 99%

A sharing-oriented service selection and scheduling approach for the optimization of resource utilization

Wang

2011

SOCA

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We present a sharing-oriented service selection and scheduling approach capable of finding a trade-off between requirement satisfaction degree, service utilization rate and service sharing cost for limited quantities and capacities of available services. In traditional service selection approaches, each customer requirement is independently satisfied by optimally selecting a set of candidate service resources. However, in real-life service scenarios, it is usual for multiple customers to raise their requirements simultaneously, and available services need to be allocated between them. Especially, when available services are limited in both quantity and capacity, a traditional "first-come-first-serve" strategy would lead to a low service utilization rate, and some requirements cannot be satisfied at all (i.e., a low requirement satisfaction degree). Our approach makes use of the feature that some services can be shared by several customer requirements. Specifically, a virtualized service resource consisting of multiple candidate services is constructed and scheduled to satisfy multiple customer requirements simultaneously. Our approach searches for the global optimization on requirement satisfaction degree, service utilization rate, and service sharing cost. We build a mathematical model for this multiobjective optimization problem and propose a nested genetic algorithm mixed with a greedy strategy. Experiments in an ocean transportation service setting are conducted and our approach is compared with traditional approaches to validate its effectiveness.

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“…It can be particularly valuable to assess a customer's willingness to pay for bundled goods or services (Bouwman et al 2007). A conjoint analysis typically comprises three consecutive steps: First, a collection of distinguishing attributes (such as brand, performance, color, price) of an item under study (such as a laptop computer) is identified.…”

Section: Design Of the Servpay Recommendermentioning

confidence: 99%

Enabling individualized recommendations and dynamic pricing of value-added services through willingness-to-pay data

et al. 2010

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When managing their growing service portfolio, many manufacturers in B2B markets face two significant problems: They fail to communicate the value of their service offerings and they lack the capability to generate profits with value-added services. To tackle these two issues, we have built and evaluated a collaborative filtering recommender system which (a) makes individualized recommendations of potentially interesting value-added services when customers express interest in a particular physical product and also (b) leverages estimations of a customer's willingness to pay to allow for a dynamic pricing of those services and the incorporation of profitability considerations into the recommendation process. The recommender system is based on an adapted conjoint analysis method combined with a stepwise componential segmentation algorithm to collect individualized preference and willingness-to-pay data. Compared to other state-ofthe-art approaches, our system requires significantly less customer input before making a recommendation, does not suffer from the usual sparseness of data and cold-start problems of collaborative filtering systems, and, as is shown in an empirical evaluation with a sample of 428 customers in the machine tool market, does not diminish the predictive accuracy of the recommendations offered.

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Mobile Service Bundles: The Example of Navigation Services

Cited by 38 publications

References 22 publications

Strategic Bundling of Telecommunications Services: Triple-Play Strategies in The Cable TV and Telephone Industries

Strategic Bundling of Telecommunications Services: Triple-Play Strategies in The Cable TV and Telephone Industries

A sharing-oriented service selection and scheduling approach for the optimization of resource utilization

Enabling individualized recommendations and dynamic pricing of value-added services through willingness-to-pay data

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