Berend Wierenga

The effectiveness of different mechanisms for integrating marketing and R&D

textabstractThe integration of marketing and R&D is a major concern for companies that want to improve their new product performance (NPP). In order to integrate, companies are using mechanisms such as physical proximity, cross-functional teams, and job rotation. This study examines the relative effectiveness of these mechanisms by developing a model that distinguishes between indirect effects of mechanisms on NPP (i.e., through a higher level of integration) and direct effects. The model is tested with data collected from 148 pharmaceutical companies. By simultaneously studying a broad range of integrating mechanisms, we found that housing marketing and R&D closer to each other and using an influential cross-functional phase review board are highly effective mechanisms to increase integration. Using information and communication technology (ICT) more intensively, having equal remuneration and career opportunities for marketing and R&D and using more cross functional teams are also effective in producing more integration, although to a somewhat lesser extent. The effectiveness of personnel movement and informal social group events is rather low. Interestingly, ICT appears to be a very effective tool for enhancing NPP. ICT not only fosters integration, but in addition it has an independent direct positive effect on NPP, possibly through knowledge creation within marketing and R&D. We also found a direct effect on NPP of another mechanism: cross functional phase review boards. However, for this mechanism the direct effect is negative. So, notwithstanding its strong positive effect on integration, a price is paid in terms of NPP. This may be related to the amount of formalization and complexity accompanying this mechanism.

A Viral Branching Model for Predicting the Spread of Electronic Word of Mouth

In a viral marketing campaign, an organization develops a marketing message and encourages customers to forward this message to their contacts. Despite its increasing popularity, there are no models yet that help marketers to predict how many customers a viral marketing campaign will reach and how marketers can influence this process through marketing activities. This paper develops such a model using the theory of branching processes. The proposed viral branching model allows customers to participate in a viral marketing campaign by 1 opening a seeding e-mail from the organization, 2 opening a viral e-mail from a friend, and 3 responding to other marketing activities such as banners and offline advertising. The model parameters are estimated using individual-level data that become available in large quantities in the early stages of viral marketing campaigns. The viral branching model is applied to an actual viral marketing campaign in which over 200,000 customers participated during a six-week period. The results show that the model quickly predicts the actual reach of the campaign. In addition, the model proves to be a valuable tool to evaluate alternative what-if scenarios.

Agricultural Marketing and Consumer Behavior in a Changing World

Preface. Part I: The Changing Competitive Environment. 1. New Areas in Agricultural and Food Marketing: K.G. Grunert, et al. 2. Competing for the Future in the Agricultural and Food Channel B. Wierenga. 3. Marketing Analysis for Agricultural Development: Suggestions for a New Research Agenda W.G. Janssen, A. van Tilburg. 4. Impact of Changing Pig Welfare Preferences on the Economics of Pork Production-Marketing Chains M. den Ouden, et al. Part II: Evolution in Channels and Institutions. 5. Evolution of Agricultural Marketing Institutions, a Channel Approach M.T.G. Meulenberg. 6. The Impact of Changes in the Power and Information Balance Upon the Quality of Supplier-Reseller Relationships in Food Marketing Channels G.H. van Bruggen, L.P. Bucklin. 7. Hedging Risk in Agricultural Futures Markets J.M.E. Pennings, M.T.G. Meulenberg. Part III: Dynamics in Consumer Behavior. 8. Dynamics in Consumer Behavior with Respect to Agricultural and Food Products J.-B.E.M. Steenkamp. 9. The Identification of Sensory Dimensions of Food Products from Scanner Data Using the STUNMIX Methodology M. Wedel. 10. Quality Labeling as Instrument to Create Product Equity: The Case of IKB in the Netherlands H.C.M. van Trijp, et al. 11. Means-end Chain Theory and Laddering in Agricultural Marketing Research A. Audenaert, J.-B.E.M. Steenkamp. 12. Consumer Search and Surplus in Markets with Differentiated Food Products A. Bocker. 13. Testing for the Intertemporal Separability Hypothesis on Italian Food Demand J.A. Molina, F. Rosa.14. Analysis of Changes in Portuguese Meat Consumption: M.M. Barreira, M.F. Duarte. 15. A Test for Differences in Food Demand Among European Consumers. A Dynamic Approach A.M. Angulo, et al. About the Contributors. Subject Index. Author Index. List of Sponsors.

Dashboards as a service: why, what, how, and what research is needed?

Recent years have seen the introduction of a “marketing dashboard” that brings the firm’s key marketing metrics into a single display. Service firms across industries have created such dashboards e...Recent years have seen the introduction of a “marketing dashboard” that brings the firm’s key marketing metrics into a single display. Service firms across industries have created such dashboards either by themselves or together with a dashboard service provider. This article examines the reasons for this development and explains what dashboards are, how to develop them, what drives their adoption, and which academic research is needed to fully exploit their potential. Overcoming the challenges faced in dashboard development and operation provides many opportunities for marketing to exercise a stronger influence on top management decisions. The article outlines five stages of dashboard development and discusses the relationships among demand for dashboards, supply of dashboards, and the implementation process in driving adoption and use of dashboard systems. Key topics for future research include metrics selection, relationships among metrics, and the ultimate question of whether dashboards provide sufficient benefits to justify their adoption.

Handbook of Marketing Decision Models

Marketing models is a core component of the marketing discipline. The recent developments in marketing models have been incredibly fast with information technology (e.g., the Internet), online marketing (e-commerce) and customer relationship management (CRM) creating radical changes in the way companies interact with their customers. This has created completely new breeds of marketing models, but major progress has also taken place in existing types of marketing models. The HANDBOOK OF MARKETING DECISION MODELS presents the state of the art in marketing decision models, dealing with new modeling areas such as customer relationship management, customer value and online marketing, but also describes recent developments in other areas. In the category of marketing mix models, the latest models for advertising, sales promotions, sales management, and competition are dealt with. New developments are presented in consumer decision models, models for return on marketing, marketing management support systems, and in special techniques such as time series and neural nets. Not only are the most recent models discussed, but the book also pays attention to the implementation of marketing models in companies and to applications in specific industries.

Marketing decision support systems: Adoption, use, and satisfaction

This paper deals with marketing decision support systems (MDSS) in companies. In a conceptual framework five categories of factors are distinguished that potentially affect adoption, use, and satisfaction: external environment factors, organizational factors, task environment factors, user factors and implementation factors. Hypotheses are developed and tested on data from a survey of 525 companies. The factors that determine the adoption of an MDSS are different from the factors that affect the success of an MDSS, once it has been installed. For adoption, support from inside the company, communication and knowledge about MDSS are crucial. For the subsequent success of an MDSS, involvement of the user, sophistication, adaptability of the system and the possibility of direct interaction with the system are important. MDSS are primarily adopted to obtain information and not to upgrade (add value to) existing information. Companies in consumer goods are ahead of companies in the business-to-business sector, but this difference is likely to decrease in the future.

Case-based reasoning systems: from automation to decision-aiding and stimulation

Over the past decade, case-based reasoning (CBR) has emerged as a major research area within the artificial intelligence research field due to both its widespread usage by humans and its appeal as a methodology for building intelligent systems. Conventional CBR systems have been largely designed as automated problem-solvers for producing a solution to a given problem by adapting the solution to a similar, previously solved problem. Such systems have had limited success in real-world applications. More recently, there has been a search for new paradigms and directions for increasing the utility of CBR systems for decision support. The paper focuses on the synergism between the research areas of CBR and decision support systems (DSSs). A conceptual framework for DSSs is presented and used to develop a taxonomy of three different types of CBR systems: 1) conventional, 2) decision-aiding, and 3) stimulative. The major characteristics of each type of CBR system are explained with a particular focus on decision-aiding and stimulative CBR systems. The research implications of the evolution in the design of CBR systems from automation toward decision-aiding and stimulation are also explored.

The dependent variable in research into the effects of creativity support systems: quality and quantity of ideas

Creativity support systems (CSS) aim at enhancing the creativity of users. There is an emerging stream of research in which the effects of CSS on the creative output of respondents are measured. In this research, it is important to make a clear distinction between the dependent variable, creative output, and the independent variable use of CSS. Furthermore, the research design should take the potential effect of other factors on creative output into account, most notably, creative ability as a trait of the respondents. An experimental study on the value of creativity support systems was recently reported in MIS Quarterly (Massetti 1996). That study yielded interesting insights with respect to the value of CSS. However, because of the methodology applied in analyzing the data, the study underestimated the effects of CSS on the creative output of decision makers. In this note, Massettis experiment is positioned in the broader perspective of current research in the area of CSS, and an alternative framework for analyzing the data is proposed.

Broadening the perspective on marketing decision models

Marketing models are of invaluable importance for the advancement of marketing science. Regarding the role of models in marketing decision-making in practice we question the claim that marketing models are routinely used by many companies. Marketing models are suitable for certain types of marketing decision situations but much less for others. We advocate the development of integrated marketing management support systems (MMSSs), in which the strong points of marketing models are combined with the strengths of other types of MMSSs.

On academic marketing knowledge and marketing knowledge that marketing managers use for decision-making

In his article ‘What is marketing knowledge?’, Rossiter (2001) offers a very explicit view about what marketing knowledge is and what marketing knowledge is not. According to Rossiter, marketing knowledge is declarative knowledge (‘know what’), and should be distinguished from procedural knowledge (‘know how’). Marketing skills and ‘tacit knowledge’ are excluded, and there is a clear distinction between marketing knowledge and marketing abilities. By delineating marketing knowledge in this way, it seems that Rossiter deliberately restricts the meaning of marketing knowledge to academic marketing knowledge, which is mostly taken as a synonym for ‘marketing science’. Rossiter sees marketing knowledge as ‘true’ knowledge (epistemologically: ‘best beliefs’), implying that marketing knowledge has a prescriptive role, and should enable its user to find the best course of action in a given situation. In the remainder of his article Rossiter discusses a classification of four ‘forms’ in which marketing knowledge can appear. This is a praiseworthy and useful endeavor to provide more structure for the difficult stuff that marketing knowledge is, but we should realize, that due to the limitation to academic marketing knowledge, this classification of forms only refers to the subset of marketing knowledge so defined. I think that restricting marketing knowledge to academic marketing knowledge is unnecessary and not productive. Marketing decision-makers in practice have a much richer treasure of marketing knowledge at their disposal than the codified ‘body of knowledge’ that has emerged from systematic academic research. However, I do agree with Rossiter that ‘marketing knowledge is absolutely fundamental to our discipline’ (2001: 9). So, there is every reason to engage in a debate on this topic. I will contribute my view on marketing knowledge in the next few pages.