Generalized knowledge comes from cumulating results across studies, a process known as meta-analysis. Efficiently increasing generalized knowledge in a defined area-estimates of price or advertising, for example-is one important goal for research. Because (1) most meta-analyses are based on highly inefficient and unbalanced natural experiments or designs and (2) additional studies are costly, carefully selecting the next study is important.
It has been suggested that evaluations may be based on a "How-do-I-feel-about-it?" heuristic, which involves holding a representation of the target in mind and inspect feelings that this representation may elicit. Previous studies have shown that reliance on such feelings depends on whether they are believed to be representative of the target. This paper argues that it also depends on whether feelings toward the target are regarded as relevant.
The authors discuss the long-run future of decision support systems in marketing. They argue that a growing proportion of marketing decisions can not only be supported but may also be automated. From a standpoint of both efficiency (e.g., management productivity) and effectiveness (e.g., resource allocation decisions), such automation is highly desirable. The authors describe how model-based automated decision-making is likely to penetrate various marketing decision-making environments and how such models can incorporate competitive dynamics.
This study investigates factors influencing causal attributions in managerial decision making. Three categories of factors are identified: (i) prior beliefs (ii) background frequencies, and (iii) covariation cues. The impact of factors in each of the above categories on causal attribution are studied in a marketing decision making context. Subjects demonstrated a bias toward assigning causality to variables that occurred infrequently or were controllable. Also, subjects were particularly influenced by the joint-occurrences of cause and effect variables.
A commentary on Barbara E. Kahn's article, Dynamic Relationships with Customers: High-Variety Strategies, published in the winter 1998 issue of Journal of the Academy of Marketing Sciences is provided. The purpose is to question Kahn's assumptions and hence to suggest some implications for research.
This article proposes a method that overcomes a number of problems associated with new product diffusion models noted in the marketing literature. We illustrate the methodology in the context of better understanding global variances in new product adoption. Building on existing diffusion models and sample matching principles from international consumer research, we suggest a "staged estimation procedure." The procedure provides both sensible and robust estimates and remains usable even if the diffusion process is in its earliest stage in most or all countries.
Effective communication requires that consumers attribute the message content to its intended source. The proposed framework distinguishes four types of source identification processes-cued retrieval, memory-trace refreshment, schematic inferencing, and pure guessing-and delineates their contingencies. Two experiments examine portions of the framework, and experiment 2 introduces a new methodology for decomposing multiple processes. Findings suggest that when cued retrieval fails, consumers try to refresh the original memory trace for the learning episode-a process that is effortful.
Marketers are making increasing use of very brief messages that mention just a brand name or a brand name with a short headline, as in event sponsorship and program endorsements. There has been debate over the effectiveness of these "advertising fragments." This paper introduces an approach for controlled testing of the effects of advertising fragments. Using a reaction-time based procedure, we show that a key effect of advertising fragments is to revive established brand associations, even though these associations are not explicitly communicated.