Empirical Evaluation of Fair Use Flat Rate Strategies for Mobile Internet

Fritz, Marcel; Schlereth, Christian; Figge, Stefan

doi:10.1007/s12599-011-0172-6

Cited by 15 publications

(5 citation statements)

References 26 publications

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“…Finally, market simulation can also be employed in the context of a competitive market analysis. It was employed by seven design studies on the list (Abramova, Krasnova, & Tan, 2017;Choi et al, 2013;Daas et al, 2014;Fritz et al, 2011;Keen, Wetzels, De Ruyter, & Feinberg, 2004;Song, Jang, & Sohn, 2009;Weinreich & Schön, 2013) to predict the market shares of new products or modified existing products based on the preference models as well as to evaluate the contribution margin. In addition, the CA study on the preference structure for PaaS (Giessmann & Stanoevska, 2012) used the market simulation technique in the design of cloud business models.…”

Section: Discussionmentioning

confidence: 99%

“…Marketing research deploys commercial panels to identify target samples, while in IS research there are no established panels for this type of methodology. So far, very few studies have used existing online panels; examples include Fritz, Schlereth, & Figge (2011) and Mihale-Wilson et al (2017). In addition, Pu & Grossklags (2015) were first to use a crowdsourcing platform, Amazon Mechanical Turk, to hire participants and obtain a fast response rate, which can be considered a potential solution for future CA studies on mass-market systems.…”

Section: Study Administrationmentioning

confidence: 99%

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Leveraging Market Research Techniques in IS: A Review and Framework of Conjoint Analysis Studies in the IS Discipline

Naous¹,

Legner²

2021

CAIS

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With cloud and mobile computing, information systems (IS) evolve towards mass-market services. While user involvement is critical for IS success, the IS discipline lacks methods that allow integrating the "voice of the customer" in the case of mass-market services with individual and dispersed users. Conjoint analysis (CA), from marketing research, allows for understanding user preferences and measures user trade-offs for multiple product features simultaneously. While CA has gained popularity in the IS domain, the existing studies have mostly been one-time efforts and no cumulative research patterns have been observed. We argue that CA could have a significant impact on IS research (and practice) if it were fully developed and adopted as a method in IS. From reviewing 70 CA studies published between 1999 and 2019 in the IS field, we find that CA can be leveraged in the initial conceptualization, iterative design and evaluation of IS and their business models. We critically assess the methodological choices along the CA procedure to provide recommendations and guidance on "how" to leverage CA techniques in future IS research. We then synthesize our findings into a "Framework for Conjoint Analysis Studies in IS" that outlines "where" CA can be applied along the IS lifecycle.

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Section: Discussionmentioning

confidence: 99%

Section: Study Administrationmentioning

confidence: 99%

Leveraging Market Research Techniques in IS: A Review and Framework of Conjoint Analysis Studies in the IS Discipline

Naous¹,

Legner²

2021

CAIS

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“…Flat rate tariffs, also sometimes referred to as subscription pricing (Chen and Choi, 2014), have a fixed price, regardless of the number of units actually used within the account period (Diller, 2008, pp. 248-249; Fritz et al , 2011; Krämer and Wiewiorra, 2012; Köhler et al , 2014; Malone et al , 2014). Services offered under a bucket pricing scheme are periodically charged with a fixed price.…”

Section: Context Of the Modelmentioning

confidence: 99%

“…Preliminary work on ICT service offers (Lambrecht et al , 2007; Krämer and Wiewiorra, 2012; Gerpott and May, 2014) implies that private consumers exhibit similar preferences toward pricing options for CC services as they do toward established telecommunication services. However, flat rate tariffs are unsuitable for CC services because the provision of virtually unlimited resources poses erratic economic risks on providers[1] (Fritz et al , 2011; Gerpott and May, 2014). For providers, an avenue to lower these risks while taking advantage of the effects of the flat rate bias (Krämer and Wiewiorra, 2012) is to offer bucket tariffs.…”

Section: Context Of the Modelmentioning

confidence: 99%

Determining bucket structures in pricing plans for private user cloud computing storage services

Gerpott

May

2016

JM2

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Purpose Providers of cloud computing storage services (CCSS) charge offers in several unit bundles for a lump sum per bundle. This non-linear pricing approach is known as a bucket-pricing plan (BPP). If a customer exploits the purchased bucket, he/she can opt for the next higher bucket or refrain from further CCSS use. CCSS suppliers are faced with an optimization problem concerning the number of buckets as well as their lower and upper storage volume boundaries. The purpose of this paper is to develop a model, which supports CCSS suppliers in deriving a BPP-structure and which maximizes their profit in varying market constellations. Design/methodology/approach The authors develop a multi-period model of tariff choice decisions of private customers of CCSS. The model is applied in Monte Carlo simulations to determine profit-maximal tariff structures as a function of different market characteristics such as median demand saturation, demand heterogeneity, average price per storage unit and bucket ceiling allocation (identical size of each bucket within the frame set by the lower and upper overall boundary, varying sizes of the buckets offered, so that the interval between two ceilings consecutively increases for subsequent buckets) and type of a customer’s utility function. Findings The simulation analysis suggests that demand heterogeneity and average price per unit are the most influential factors for CCSS tariff structure optimization. Price plans with more than two buckets tend to generate higher profits than simple schemes with two buckets only if demand heterogeneity is low and the average price per storage unit is high and/or median saturation level of customers is low. Originality/value Despite the popularity of BPP among providers of CCSS for consumers, there is a lack of scholarly modeling work on the profit implications of the number of buckets entailed in a scheme and the size/ceilings of the various buckets on offer. The model suggested in this paper is a first step toward narrowing this research gap.

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“…In particular, discrete choice experiments have a firm foundation in sociology and behavioural research. Iyengar et al (2008), Fritz et al (2011), and Schlereth and Skiera (2012a) explain how to use discrete choice experiments to estimate demand for non-linear pricing plans, such as three-part pricing, fair-use flat rates, and bucket pricing. This appealing data collection method is inexpensive and allows for good control over the experimental setting to support tests of client reactions to new attribute ranges and pricing.…”

Section: Discrete Choice Experiments On Financial Servicesmentioning

confidence: 99%

Pricing plans for a financial advisory service

Schlereth

2014

European Journal of Marketing

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Purpose – In cooperation with a German online retail bank, the aim of this paper is to investigate how the bank should price a new fee-only financial advisory service. Two types of pricing plans differ in terms of their strategies for determining monthly prices: a fixed monthly price that is identical for all clients (i.e. a flat pricing plan) or a monthly price that varies as a function of each client's assets under management (i.e. a volume pricing plan). Design/methodology/approach – With a discrete choice experiment, this article studies client preferences for the two types of plans. To ensure that the respondents understood the financial consequences of their decisions, a price calculator was embedded into the discrete choice experiment to enable the respondents to determine their individual monthly prices based on their assets under management. Findings – Methodologically, the price calculator is useful for simplifying mathematically complex decisions, and it provides additional valuable information for analysis. Substantively, the results show that clients perceive both types of pricing plans as equally attractive; however, the service provider's revenues would increase by up to 12 per cent if it uses the volume pricing plan. Originality/value – This research extends the stream of literature on the measurement of pricing plan preferences and offers guidance for service industries, such as telecommunications, cloud computing services, insurances, or transportation. It extends the use of discrete choice experiments to study client preferences for different pricing plans and also integrates a decision aid, i.e. a price calculator, in the experiment to assist clients in comparing alternatives more effectively.

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Empirical Evaluation of Fair Use Flat Rate Strategies for Mobile Internet

Cited by 15 publications

References 26 publications

Leveraging Market Research Techniques in IS: A Review and Framework of Conjoint Analysis Studies in the IS Discipline

Leveraging Market Research Techniques in IS: A Review and Framework of Conjoint Analysis Studies in the IS Discipline

Determining bucket structures in pricing plans for private user cloud computing storage services

Pricing plans for a financial advisory service

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