Dynamic Pricing for New Products Using a Utility-Based Generalization of the Bass Diffusion Model

The Bass Model (BM) has an excellent track record in the realm of new product sales forecasting. However, its use for optimal dynamic pricing or advertising is relatively limited because the Generalized Bass Model (GBM), which extends the BM to handle marketing variables, uses only percentage changes in marketing variables, rather than their actual values. This restricts the GBM’s prescriptive use, for example, to derive the optimal price path for a new product, conditional on an assumed launch price, but not the launch price itself. In this paper, we employ a utility-based extension of the BM, which can yield normative prescriptions regarding both the introductory price and the price path after launch, for the new product. We offer two versions of this utility-based diffusion model, namely, the Bass-Gumbel Diffusion Model (BGDM) and the Bass-Logit Diffusion Model (BLDM), the latter of which has been previously used. We show that both the BGDM and BLDM handily outperform the GBM in forecasting new product sales using empirical data from four product categories. We discuss how to estimate the BGDM and BLDM in the absence of past sales data. We compare the optimal pricing policy of the BLDM with the GBM and derive optimal pricing policies that are implied by the BLDM under various ranges of model parameters. We illustrate a dynamic pricing approach that allows managers to derive optimal marketing policies in a computationally convenient manner and extend this approach to a competitive, multiproduct case. This paper was accepted by Gui Liberali for the Management Science Special Issue on Data-Driven Prescriptive Analytics.

SPEED OF DIFFUSION, RETHINKING TIME AND FIRMS’ STRATEGY: ANALYSIS OF THE INTERACTIONS TO LEAP ACROSS THE CHASM

Although the literature has recognised short time to market and early entry as relevant factors, they are not enough alone to ensure success. In fact, an early entrant may successfully serve early adopters, but then fail in developing products suitable for those who adopt later (i.e., the early majority), dissipating the first-mover advantages previously gained. This paper argues that the likelihood of being successful in the early majority segment depends also on the rethinking time, defined as the time available to firms serving early adopters for planning and developing products that will be offered to the upcoming early majority segment. The rethinking time is here analytically defined through the Bass model and its relationship with product success is investigated. The paper shows that the market leader in the early majority segment is expected to be the incumbent when rapid diffusion occurs and, conversely, new entrants when rethinking time is longer.

Søren Johansen and Katarina Juselius: A Bibliometric Analysis of Citations through Multivariate Bass Models

We showcase the impact of Katarina Juselius and Søren Johansen’s contribution to econometrics using bibliometric data on citations from 1989 to 2017, extracted from the Web of Science (WoS) database. Our purpose is to analyze the impact of KJ and SJ’s ideas on applied and methodological research in econometrics. To this aim, starting from WoS data, we derived two composite indices whose purpose is to disentangle the authors’ impact on applied research from their impact on methodological research. As of 2017, the number of applied citing papers per quarter had not yet reached the peak; conversely, the peak in the methodological literature seem to have been reached around 2000, although the shape of the trajectory is very flat after the peak. We analyzed the data using a multivariate dynamic version of the well known Bass model. Our estimates suggest that the methodological literature is mainly driven by “innovators”, whereas “imitators” are relatively more important in the applied literature: this might explain the different location of the peaks. We also find that, in the literature referring to KJ and SJ, the “cross-fertilization” between methodological and applied research is statistically significant and bi-directional.

Supply chain resilience for managing the ripple effect in Industry 4.0 for green product diffusion

PurposeThe purpose of this paper is to examine (1) how the recovery speed using promotional investment and (2) distributed production using additive manufacturing (AM) improve the resilience of the supply chain to manage any disruptions in the diffusion of green products.Design/methodology/approachThe environmental performance, service level performance and economic performance are the measures of interest. These measures are studied through the integration of inventory and production planning (I&PP) of the reverse logistics system and consumer behavior using Bass (1969) model of diffusion of innovation under the paradigm of Industry 4.0 architecture. The Taguchi experimental design framework was used for the simulation analysis.FindingsThe adoption patterns based on the Bass model in conjunction with recovery speed and production on AM during the disruption period suggest that there exist tradeoff decisions between various combinations of information-sharing and I&PP policies.Practical implicationsThe extensive sensitivity analyses provide real-time support for managerial decisions. Besides the potentials of Industry 4.0 capabilities, the present research suggests paying close attention to the recovery speed in conjunction with the inventory management system.Social implicationsThe integration of consumers' behavior (Bass model) to digital technologies is an additional contribution of the present research toward sustainability issues from the social perspective.Originality/valuePrevious research studies have discussed resilience to manage the ripple effect. However, none of them have addressed the changing scope of resilience to manage the ripple effect caused by the disruption in the diffusion of green products in a reverse logistics setup.

The Usefulness of Marketing Strategies in a Regulated Market: Evidence from the Spanish Tobacco Market

This paper outlines possible useful marketing strategies for companies to develop in a regulated market. The empirical analysis aims to highlight whether companies can use marketing strategies to create competitive advantages and scale positions in sales leadership once regulation policies are introduced. To this end, we apply two econometric approaches to sales variables for 138 best-selling cigarette brands: the Bass Model (1969) to estimate the parameters that determine the way clients adopt, and the methodology proposed by Hartigan and Wong (1979) for a further cluster analysis that groups brands. The empirical results suggest that restrictions on demand introduced to the Spanish tobacco market during the years 2005 and 2006, have generated a new scenario in which innovation parameters have no effect on the process of adoption. Therefore, the imitation coefficient is the parameter which makes discrimination between brands possible, which demonstrates the existence of heterogeneity among brands based only on recommendation. The results show the inability of direct marketing strategies to create advantages and scale positions in sales leadership after the introduction of regulation policies, however companies have other marketing options such as imitation among clients and these have proven to be effective since imitation shows heterogeneous behaviour among brands across the diffusion process. This has implications that should be taken into account in markets which are on the verge of being regulated, specifically the modification of marketing strategies if the intention is to lead and scale position in a regulated market. In particular, in the case of Spain, policymakers must acknowledge that the measures implemented in law 28/2005 have had an effect on marketing strategies by cancelling, in practical terms, the diffusion of brands based on innovation and homogenizing the diffusion process based on direct marketing.

Diffusion Mechanism of Leading Technology in the New Energy Industry Based on the Bass Model

With the increasing difficulties associated with heating, the new energy industry has become the mainstay for property development. The effective diffusion of leading technologies supplies a social edge for enterprise core technologies, and this is also a necessary topic for industrial transformation and optimization. Within the international context of energy conservation and emission reduction, the scientific and in-depth study of the diffusion mechanisms underlying leading technologies in the new energy industry have vital theoretical significance for the promotion of the diffusion of leading technologies. Based on the introduction of the Bass model and one extension model, this paper constructs the diffusion model of the new energy industry’s leading technology and analyzes its diffusion mechanism. The identified mechanism indicates that in the case of imperfect market and policy environments, the diffusion of the leading technology of the new energy industry is mainly influenced by the “expected utility” of innovators and the “actual utility” of imitators. The diffusion of the leading technology in innovator enterprises of the new energy industry is mainly affected by the “expected utility,” while the diffusion in imitator enterprises is affected by the “actual utility.” These influences are verified by simulation analysis. Based on the diffusion mechanism, several suggestions are presented for the promotion of the diffusion mechanism of leading technology, with the aim to provide references for the government, industry associations, and enterprises for relevant decision-making.

BASS Model Analysis in “Crossing the Chasm” in E-Cars Innovation Diffusion Scenarios

In the face of contemporary ecological problems, including the influence of transport and mobility on the greenhouse effect, policy makers see a solution in the technology of electric vehicles (EVs). This innovation has appeared in the automotive market around the world. In this article, Rogers’ innovation diffusion concept was used to determine if and when EVs could replace combustion engine vehicles. The aim of the article is to analyze the current situation of innovative electric vehicle (EV) technology on the European Union (EU) market and the potential and possible directions of its market diffusion. As a result of the analysis of the current situation of the EU automotive markets, three groups of countries with different market conditions regarding the development of EV technology were distinguished. The classification was carried out using the K-means clustering algorithm, based on the current level of innovation and the level of development of the LPG-(Liquefied Petroleum Gas) powered car market. Then, a group of countries with the greatest advancement in EV technology was selected for further research and market diffusion scenarios were created for this innovation using the Bass model, assuming the target market size of 70–100% of all passenger cars.

Bass Models for EHR, RIS and PACS Diffusion in Finland and Germany

Building on Rogers’ Diffusion of Innovation Theory, Bass models describe the diffusion processes distinguishing between innovation (p) and imitation (q). This study aimed at modelling the uptake of RIS, PACS and EHR systems in Germany and Finland. The Bass models revealed a quick and almost identical uptake process across all three systems for Finland. In contrast, the Bass models mirrored a slower uptake in Germany. Consequently, the Finnish “imitation” coefficients were larger than the German ones. While in Germany almost free market forces were driving the adoption through imitation but without tail wind from policy, the adoption process in Finland was centrally governed. This suggests that the diffusion process in Finland reflected a well-managed roll-out of the systems rather than imitation behaviour. Thus, in order for Bass model coefficients to be understood properly, additional contextual information is required.