Impact of a Late Entrant on the Diffusion of a New Product/Service

Krishnan, Trichy V.; Bass, Frank M.; Kumar, V.

doi:10.1509/jmkr.37.2.269.18730

Cited by 149 publications

(104 citation statements)

References 16 publications

(18 reference statements)

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“…Most previous empirical applications of the Bass model reported an innovation parameter p lower than the imitation parameter q [43]. Since parameter λ r is positive, the condition expressed in Eq.…”

Section: Putting It All Togethermentioning

confidence: 96%

“…Our twophase structure accounts for the fact that the diffusion process may deeply change after the launching of the product and may be based on new innovation and imitation parameters. Krishnan, Bass, and Kumar [43] also developed a two-phase diffusion model to account for such changes in the diffusion of a product after a major event by changing some parameters before and after the event. However, the event they focus on is the late entry of a new brand, while we analyze a complete different issue.…”

Section: The Second Phase Of the Modelmentioning

confidence: 99%

See 1 more Smart Citation

A Diffusion Model for Preannounced Products

Manceau

Eliashberg

Rao

et al. 2013

Cust. Need. and Solut.

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Strategic preannouncement of a new product launch by a firm creates a pent-up demand (or consumers committed to purchase prior to launch) for the new product. The level of the pent-up demand depends, among other factors, on the timing and the reputation of the firm announcing the new product; it is critical in shaping up the diffusion process of the new product after launch. In this paper, we develop a two-phase diffusion model that describes both the impact of the announcement on consumers' purchase commitments and the diffusion process since launch starting with a strictly positive number of new product orders. We illustrate the empirical performance of the model with an old but classic dataset that captures both advance purchase orders as well as sales after launch for a new audio CD. We discuss how our model can guide a firm's decision on when to preannounce the introduction of its new product or technology, relative to the time of launch, so as to maximize the total benefit during the planning horizon.

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Section: Putting It All Togethermentioning

confidence: 96%

Section: The Second Phase Of the Modelmentioning

confidence: 99%

A Diffusion Model for Preannounced Products

Manceau

Eliashberg

Rao

et al. 2013

Cust. Need. and Solut.

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show abstract

“…These models address, among other things, (1) replacement purchasing and repeat purchasing [9][10][11][12]; (2) supply restriction [13]; (3) and diffusion at the brand level [14]. Diffusion models, however, have some limitations when forecasting the demand for new technology because they usually depend on historical time series data.…”

Section: Literature Reviewmentioning

confidence: 99%

Demand Forecasting in the Early Stage of the Technology’s Life Cycle Using a Bayesian Update

Lee

2017

Sustainability

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Abstract:The forecasting demand for new technology for which few historical data observations are available is difficult but essential to sustainable development. The current study suggests an alternative forecasting methodology based on a hazard rate model using stated and revealed preferences of consumers. In estimating the hazard rate, information is initially derived through conjoint analysis based on a consumer survey and then updated using Bayes' theorem with available market data. To compare the proposed models' performance with benchmark models, the Bass model, the logistic growth model, and a Bayesian approach based on analogy are adopted. The results show that the proposed model outperforms the benchmark models in terms of pre-launch and post-launch forecasting performances.

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“…Supplier-initiated referrals perform the same function for potential customers in B-to-B markets (Ruokolainen & Igel, 2004). For products in the earlier stages, product diffusion theory finds that customer-to-potential customer referrals (the contagion effect, in diffusion theory 5 ) have a significant influence on potential customers' decision to buy a product (e.g., Bass, 1969;Krishnan, Bass, & Kumar, 2000). Therefore, we expect that the influence of referrals on potential customers is higher during earlier, versus later, stages of the product life cycle.…”

Section: A21 Product Characteristicsmentioning

confidence: 99%

Referral equity and referral management: the supplier firm's perspective

Hada¹,

Grewal²,

Lilien³

2010

Review of Marketing Research

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From the supplier firm's perspective, a referral is a recommendation from A (the referrer) to B (the potential customer) that B should, or should not, purchase from C (the supplier firm). Thus, as referrals are for a specific supplier firm, they should be viewed as part of the supplier firm's marketing and sales activities. We recognize three types of referralscustomer-to-potential customer referrals, horizontal referrals, and supplier-initiated referrals -that have critical roles in a potential customer's purchase decision. We develop the concept of referral equity to capture the net effect of all referrals for a supplier firm in the market. We argue that supplier firms should view referral equity as a resource that has financial value to the firm as it affects the firm's cash flows and profits. We offer strategies firms can use to manage referrals and build their referral equity and suggest a research agenda.

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Impact of a Late Entrant on the Diffusion of a New Product/Service

Cited by 149 publications

References 16 publications

A Diffusion Model for Preannounced Products

A Diffusion Model for Preannounced Products

Demand Forecasting in the Early Stage of the Technology’s Life Cycle Using a Bayesian Update

Referral equity and referral management: the supplier firm's perspective

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