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[Display omitted] ► We identify factors that cause method bias. ► We discuss the psychological mechanisms through which they produce their biasing effects. ► We propose procedural remedies to counterbalance or offset these effects. There is a great deal of evidence that method bias influences item validities, item reliabilities, and the covariation between latent constructs. In this paper, we identify a series of factors that may cause method bias by undermining the capabilities of the respondent, making the task of responding accurately more difficult, decreasing the motivation to respond accurately, and making it easier for respondents to satisfice. In addition, we discuss the psychological mechanisms through which these factors produce their biasing effects and propose several procedural remedies that counterbalance or offset each of these specific effects. We hope that this discussion will help researchers anticipate when method bias is likely to be a problem and provide ideas about how to avoid it through the careful design of a study.

During the past 30 years, interest in organizational citizenship behaviors (OCBs) has grown substantially. Although much of the early empirical research in this domain was directed at the individual level of analysis, more recently, researchers have focused their attention on identifying the outcomes of group-level or unit-level OCBs, as well as the mediating mechanisms and boundary conditions of the relationships between OCBs and unit-level outcomes. Therefore, the purpose of this article is to provide a summary of the extant literature in this area. First, we discuss the applicability of the types of OCB to the unit level of analysis. Following this, we provide a summary of the literature examining outcomes of unit-level OCBs, with particular attention paid to the mediators and moderators of the relationship between OCBs and unit effectiveness. Next, we examine the methodological characteristics of studies conducted in this research domain. Finally, we make a series of conceptual and methodological recommendations regarding future research on the consequences of unit-level OCBs.

A review of the literature suggests that few studies use formative indicator measurement models, even though they should. Therefore, the purpose of this research is to (a) discuss the distinction between formative and reflective measurement models, (b) develop a set of conceptual criteria that can be used to determine whether a construct should be modeled as having formative or reflective indicators, (c) review the marketing literature to obtain an estimate of the extent of measurement model misspecification in the field, (d) estimate the extent to which measurement model misspecification biases estimates of the relationships between constructs using a Monte Carlo simulation, and (e) provide recommendations for modeling formative indicator constructs.

It is difficult to get a clear picture of the relative influence of management journals because previous studies have focused on a single sub-area in the field over a relatively restricted number of years, and/or have used inconsistent criteria to judge journal influence. Therefore, the purpose of this study is to examine journal influence using citations from 28 journals over the past two decades. The findings show that the top seven journals accounted for 61 percent of all of the citations in the journals included, and that the three journals that showed the greatest increase in influence over the past 20 years were AMJ, AMR, and SMJ.

Most authors give a great deal of consideration to questions of internal and external validity, but far less to issues related to construct and statistical conclusion validity. The downward spiral for many manuscripts begins with the failure to adequately define the focal constructs of the study. This fundamental failure produces a series of subsequent problems: 1. Because the construct was never defined, it is difficult to develop measures that faithfully represent its domain. 2. This failure makes it difficult to specify how the construct should relate to its measures. 3. Inadequate construct definition undermines the credibility of a study's hypotheses. To break this cycle, researchers should carefully define the constructs of interest, develop measures that adequately represent the constructs of interest, think carefully about the relations between the constructs and the measures, and defend the construct domain and insist on the conceptually appropriate measurement model.

Consumer perceptions of advertising creativity are investigated in a series of studies beginning with scale development and ending with comprehensive model testing. Results demonstrate that perceptions of ad creativity are determined by the interaction between divergence and relevance, and that overall creativity mediates their effects on consumer processing and response.

Despite the fact that validating the measures of constructs is critical to building cumulative knowledge in MIS and the behavioral sciences, the process of scale development and validation continues to be a challenging activity. Undoubtedly, part of the problem is that many of the scale development procedures advocated in the literature are limited by the fact that they (1) fail to adequately discuss how to develop appropriate conceptual definitions of the focal construct, (2) often fail to properly specify the measurement model that relates the latent construct to its indicators, and (3) underutilize techniques that provide evidence that the set of items used to represent the focal construct actually measures what it purports to measure. Therefore, the purpose of the present paper is to integrate new and existing techniques into a comprehensive set of recommendations that can be used to give researchers in MIS and the behavioral sciences a framework for developing valid measures. First, we briefly elaborate upon some of the limitations of current scale development practices. Following this, we discuss each of the steps in the scale development process while paying particular attention to the differences that are required when one is attempting to develop scales for constructs with formative indicators as opposed to constructs with reflective indicators. Finally, we discuss several things that should be done after the initial development of a scale to examine its generalizability and to enhance its usefulness.

This article discusses several advantages of latent variable structural equation modeling (LVSEM), and the potential it has for solving some fundamental problems hindering research in the field. The advantages highlighted include the ability to control for measurement error; an enhanced ability to test the effects of experimental manipulations; the ability to test complex theoretical structures; the ability to link micro and macro perspectives; and more powerful ways to assess measure reliability and validity. My hope is to sensitize researchers to some of the key limitations of currently used alternative methodologies, and demonstrate how LVSEM can help to improve theory testing and development in our discipline.

The present study develops and tests a conceptual model of consumer response to different types of price-matching characteristics (i.e., refund depth, length, and scope) across consumer segments with varying levels of price consciousness. A computer shopping simulation results show that a deep refund is interpreted as a signal of low prices by nonprice conscious consumers. However, price conscious consumers are found to associate deep refunds with increased prices, an unintended outcome for the retailer. The effects of price-matching characteristics on search and purchase behavior were also found to vary across more and less price conscious consumer segments.