Table of contents(13 chapters)
Review of Marketing Research, now in its seventh volume, is a fairly recent publication covering the important areas of marketing research with a more comprehensive state-of-the-art orientation. The chapters in this publication review the literature in a particular area, offer a critical commentary, develop an innovative framework, and discuss future developments, as well as present specific empirical studies. The first six volumes have featured some of the top researchers and scholars in our discipline who have reviewed an array of important topics. The response to the first six volumes has been truly gratifying, and we look forward to the impact of the seventh volume with great anticipation.
Despite the diversity of all those involved within the marketing discipline, all have a stake in maximizing the advancement of marketing knowledge. Without a specific analysis it is difficult to reflect on where a field has been or where it might be heading. The purpose of this chapter is to examine who and what marketing scholars have been researching over the period 1977–2002 using content analysis. This chapter provides longitudinal benchmarking of the “inputs” (authors and institutions) and “outputs” (articles) examining the marketing literature in the four major marketing journals: the Journal of Marketing, the Journal of Marketing Research, the Journal of Consumer Research, and the Journal of the Academy of Marketing Science.
We develop an integrative conceptualization of how firms set and alter strategic goals, incorporating insights from goal-setting literatures across the disciplines of marketing, management, and psychology. Our framework accounts for the internal and external forces that impact the content of a firm's goals as well as the dynamic processes by which these goals are formed and changed over time. By proposing this framework, we strive to offer insights into the “black box” of organizational goals that connect firm resources and environmental context to firm strategies. Illustrative data to support our framework are provided from a case study of a Fortune 100 communication firm's entry into an emerging, high-technology, new product marketplace.
This chapter reports the findings of a large-scale study investigating the issues that arise when firms introduce a new Internet channel. Our analysis offers three key contributions. First, we provide a framework to guide firms in anticipating and understanding the unique challenges of introducing an Internet channel. Second, we present a menu of alternatives to address these challenges. Finally, we pose a series of questions which identify which solutions are most appropriate given the particular market and firm context.
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 referrals – customer-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.
This chapter reviews research on the question–behavior effect, the phenomenon that asking questions influences respondents’ behavior. Two distinct research streams, the self-prophecy effect, concerned with socially normative behaviors, and the mere measurement effect, dealing with purchase behaviors without socially normative significance, are identified. Despite the recent attempt at integration, it is argued that there are fundamental differences between the two effects. Distinctions are also drawn between lab-based and field-based mere measurement effects, and between normatively consistent and implicit attitude-driven, normatively inconsistent self-prophecy effects. Key studies, theoretical explanations, and moderators of each effect are discussed, potential unanswered questions and research opportunities are identified, and significant managerial and policy implications are highlighted.
This chapter addresses one aspect of the broad issue of the psychological foundations of the dimensions of multidimensional scaling (MDS) solutions. Using empirical data from three independent studies, it is shown that the dimensionality of MDS solutions is negatively related to individual differences in the level of cognitive differentiation and integrative complexity of individuals and positively related to the individual's ability to discriminate within dimensions. MDS dimensionality is also shown to be affected by a variety of task-related variables such as perceived task difficulty, consistency in providing similarity judgments, confidence, familiarity, and importance attached to the stimuli. The chapter concludes by raising the issue of whether MDS can be validly used to describe complex cognitive processes.
Alongside structural equation modeling (SEM), the complementary technique of partial least squares (PLS) path modeling helps researchers understand relations among sets of observed variables. Like SEM, PLS began with an assumption of homogeneity – one population and one model – but has developed techniques for modeling data from heterogeneous populations, consistent with a marketing emphasis on segmentation. Heterogeneity can be expressed through interactions and nonlinear terms. Additionally, researchers can use multiple group analysis and latent class methods. This chapter reviews these techniques for modeling heterogeneous data in PLS, and illustrates key developments in finite mixture modeling in PLS using the SmartPLS 2.0 package.