Showing papers by "Philip M. Podsakoff published in 2012"
TL;DR: The meaning of the terms "method" and "method bias" are explored and whether method biases influence all measures equally are examined, and the evidence of the effects that method biases have on individual measures and on the covariation between different constructs is reviewed.
Abstract: Despite the concern that has been expressed about potential method biases, and the pervasiveness of research settings with the potential to produce them, there is disagreement about whether they really are a problem for researchers in the behavioral sciences. Therefore, the purpose of this review is to explore the current state of knowledge about method biases. First, we explore the meaning of the terms “method” and “method bias” and then we examine whether method biases influence all measures equally. Next, we review the evidence of the effects that method biases have on individual measures and on the covariation between different constructs. Following this, we evaluate the procedural and statistical remedies that have been used to control method biases and provide recommendations for minimizing method bias.
8,719 citations
TL;DR: This paper identifies 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.
1,567 citations
TL;DR: A model designed to predict when voice will positively or negatively impact raters' evaluations of an employee's performance found that at least one of the variables from each category had an effect on performance evaluations for the voicer and that most of these effects were indirect, operating through one or more of the mediators.
Abstract: The article contained a production-related error. In Table 5, the four values in the rows for Study 1 Prosocial motives and Study 1 Constructive voice should have been shifted one column to the right, to the Direct and Total Performance evaluations columns. All versions of this article have been corrected.] Although employee voice behavior is expected to have important organizational benefits, research indicates that employees voicing their recommendations for organizational change may be evaluated either positively or negatively by observers. A review of the literature suggests that the perceived efficacy of voice behaviors may be a function of characteristics associated with the (a) source, (b) message, and (c) context of the voice event. In this study, we manipulated variables from each of these categories based on a model designed to predict when voice will positively or negatively impact raters' evaluations of an employee's performance. To test our model, we conducted 3 laboratory studies in which we manipulated 2 source factors (voicer expertise and trustworthiness), 2 message factors (recommending a solution and positively vs. negatively framing the message), and 2 context factors (timing of the voice event and organizational norms for speaking up vs. keeping quiet). We also examined the mediating effects of liking, prosocial motives, and perceptions that the voice behavior was constructive on the relationships between the source, message, and context factors and performance evaluations. Generally speaking, we found that at least one of the variables from each category had an effect on performance evaluations for the voicer and that most of these effects were indirect, operating through one or more of the mediators. Implications for theory and future research are discussed.
177 citations
TL;DR: It has been more than 40 years since Blalock noted the distinction between what he called “cause” and“effect” (reflective) indicators of latent variables, and recently questions have been raised around the correct conceptualization and operation of formative indicator measurement models.
Abstract: It has been more than 40 years since Blalock (1964) noted thedistinction between what he called “cause” (formative) and“effect” (reflective) indicators of latent variables, and threedecades since the academic literature recognized that someSEM measurement models don’t fit classical test theory’sassumptions about the direction of causality of the relation-ships between constructs and their indicators (Bagozzi 1981;Fornell and Bookstein 1982). Since that time, interest inmodeling constructs with formative indicators has signi-ficantly increased in the business, social science, and informa-tion systems literatures (e.g., Bollen 2007; Cenfetelli andBassellier 2009; Diamantopoulos and Papadopoulos 2010;Diamantopoulos et al. 2008; Diamantopoulos and Winklhofer2001; Edwards and Bagozzi, 2000; Jarvis et. al. 2003; Lawand Wong 1999; MacKenzie et al. 2005; Marakas et al. 2007;Petter et al. 2007), and an increasing number of academicstudies are incorporating these types of measurement modelsin their substantive investigations. However, recentlyresearchers in a variety of disciplines (Franke et al. 2008;Howell et al. 2007; Kim et al. 2010) have raised questionssurrounding the correct conceptualization and operationa-lization of formative indicator measurement models.
27 citations