Management and Marketing - Research Publications

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    How Does It Feel to Be Treated Like an Object? Direct and Indirect Effects of Exposure to Sexual Objectification on Women's Emotions in Daily Life
    Koval, P ; Holland, E ; Zyphur, MJ ; Stratemeyer, M ; Knight, JM ; Bailen, NH ; Thompson, RJ ; Roberts, T-A ; Haslam, N (American Psychological Association, 2019-06-01)
    Exposure to sexual objectification is an everyday experience for many women, yet little is known about its emotional consequences. Fredrickson and Roberts' (1997) objectification theory proposed a within-person process, wherein exposure to sexual objectification causes women to adopt a third-person perspective on their bodies, labeled self-objectification, which has harmful downstream consequences for their emotional well-being. However, previous studies have only tested this model at the between-person level, making them unreliable sources of inference about the proposed intraindividual psychological consequences of objectification. Here, we report the results of Bayesian multilevel structural equation models that simultaneously tested Fredrickson and Roberts' (1997) predictions both within and between persons, using data from 3 ecological momentary assessment (EMA) studies of women's (N = 268) experiences of sexual objectification in daily life. Our findings support the predicted within-person indirect effect of exposure to sexual objectification on increases in negative and self-conscious emotions via self-objectification. However, lagged analyses suggest that the within-person indirect emotional consequences of exposure to sexual objectification may be relatively fleeting. Our findings advance research on sexual objectification by providing the first comprehensive test of the within-person process proposed by Fredrickson and Roberts' (1997) objectification theory.
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    The I and We of Team Identification: A Multilevel Study of Exhaustion and (In)congruence Among Individuals and Teams in Team Identification
    Junker, NM ; van Dick, R ; Hausser, JA ; Ellwart, T ; Zyphur, MJ (SAGE PUBLICATIONS INC, 2021-04-13)
    The social identity approach to stress proposes that the beneficial effects of social identification develop through individual and group processes, but few studies have addressed both levels simultaneously. Using a multilevel person–environment fit framework, we investigate the group-level relationship between team identification (TI) and exhaustion, the individual-level relationship for people within a group, and the cross-level moderation effect to test whether individual-level exhaustion depends on the level of (in)congruence in TI between individuals and their group as a whole. We test our hypotheses in a sample of 525 employees from 82 teams. Multilevel polynomial regression analysis revealed a negative linear relationship between individual-level identification and exhaustion. Surprisingly, the relation between group-level identification and exhaustion was curvilinear, indicating that group-level identification was more beneficial at low and high levels compared with medium levels. As predicted, the cross-level moderation of the individual-level relationship by group-level identification was also significant, showing that as individuals became more incongruent in a positive direction (i.e., they identified more strongly than the average team member), they reported less exhaustion, but only if the group-level identification was average or high. These results emphasize the benefits of analyzing TI in a multilevel framework, with both theoretical and practical implications.
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    From Data to Causes III: Bayesian Priors for General Cross-Lagged Panel Models (GCLM)
    Zyphur, MJ ; Hamaker, EL ; Tay, L ; Voelkle, M ; Preacher, KJ ; Zhang, Z ; Allison, PD ; Pierides, DC ; Koval, P ; Diener, EF (FRONTIERS MEDIA SA, 2021-02-15)
    This article describes some potential uses of Bayesian estimation for time-series and panel data models by incorporating information from prior probabilities (i.e., priors) in addition to observed data. Drawing on econometrics and other literatures we illustrate the use of informative "shrinkage" or "small variance" priors (including so-called "Minnesota priors") while extending prior work on the general cross-lagged panel model (GCLM). Using a panel dataset of national income and subjective well-being (SWB) we describe three key benefits of these priors. First, they shrink parameter estimates toward zero or toward each other for time-varying parameters, which lends additional support for an income → SWB effect that is not supported with maximum likelihood (ML). This is useful because, second, these priors increase model parsimony and the stability of estimates (keeping them within more reasonable bounds) and thus improve out-of-sample predictions and interpretability, which means estimated effect should also be more trustworthy than under ML. Third, these priors allow estimating otherwise under-identified models under ML, allowing higher-order lagged effects and time-varying parameters that are otherwise impossible to estimate using observed data alone. In conclusion we note some of the responsibilities that come with the use of priors which, departing from typical commentaries on their scientific applications, we describe as involving reflection on how best to apply modeling tools to address matters of worldly concern.
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    Income, personality, and subjective financial well-being: the role of gender in their genetic and environmental relationships
    Zyphur, MJ ; Li, W-D ; Zhang, Z ; Arvey, RD ; Barsky, AP (FRONTIERS MEDIA SA, 2015-09-29)
    Increasing levels of financial inequality prompt questions about the relationship between income and well-being. Using a twins sample from the Survey of Midlife Development in the U. S. and controlling for personality as core self-evaluations (CSE), we found that men, but not women, had higher subjective financial well-being (SFWB) when they had higher incomes. This relationship was due to 'unshared environmental' factors rather than genes, suggesting that the effect of income on SFWB is driven by unique experiences among men. Further, for women and men, we found that CSE influenced income and SFWB, and that both genetic and environmental factors explained this relationship. Given the relatively small and male-specific relationship between income and SFWB, and the determination of both income and SFWB by personality, we propose that policy makers focus on malleable factors beyond merely income in order to increase SFWB, including financial education and building self-regulatory capacity.
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    Bayesian Estimation and Inference: A User's Guide
    Zyphur, MJ ; Oswald, FL (SAGE PUBLICATIONS INC, 2015-02-01)
    This paper introduces the “Bayesian revolution” that is sweeping across multiple disciplines but has yet to gain a foothold in organizational research. The foundations of Bayesian estimation and inference are first reviewed. Then, two empirical examples are provided to show how Bayesian methods can overcome limitations of frequentist methods: (a) a structural equation model of testosterone’s effect on status in teams, where a Bayesian approach allows directly testing a traditional null hypothesis as a research hypothesis and allows estimating all possible residual covariances in a measurement model, neither of which are possible with frequentist methods; and (b) an ANOVA-style model from a true experiment of ego depletion’s effects on performance, where Bayesian estimation with informative priors allows results from all previous research (via a meta-analysis and other previous studies) to be combined with estimates of study effects in a principled manner, yielding support for hypotheses that is not obtained with frequentist methods. Data are available from the first author, code for the program Mplus is provided, and tables illustrate how to present Bayesian results. In conclusion, the many benefits and few hindrances of Bayesian methods are discussed, where the major hindrance has been an easily solvable lack of familiarity by organizational researchers.
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    Measurement and statistics in ‘organization science’: Philosophical, sociological and historical perspectives
    Zyphur, MJ ; Pierides, DC ; Roffe, J ; Mir, R ; Willmott, H ; Greenwood, M (Routledge, 2016-01-01)
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    Multilevel Latent Polynomial Regression for Modeling (In)Congruence Across Organizational Groups: The Case of Organizational Culture Research
    Zyphur, MJ ; Zammuto, RF ; Zhang, Z (SAGE PUBLICATIONS INC, 2016-01-01)
    This article addresses (in)congruence across different kinds of organizational respondents or “organizational groups”—such as managers versus non-managers or women versus men—and the effects of congruence on organizational outcomes. We introduce a novel multilevel latent polynomial regression model (MLPM) that treats standings of organizational groups as latent “random intercepts” at the organization level while subjecting these to latent interactions that enable response surface modeling to test congruence hypotheses. We focus on the case of organizational culture research, which usually samples managers and excludes non-managers. Reanalyzing data from 67 hospitals with 6,731 managers and non-managers, we find that non-managers perceive their organizations’ cultures as less humanistic and innovative and more controlling than managers, and we find that less congruence between managers and non-managers in these perceptions is associated with lower levels of quality improvement in organizations. Our results call into question the validity of findings from organizational culture and other research that tends to sample one organizational group to the exclusion of others. We discuss our findings and the MLPM, which can be extended to estimate latent interactions for tests of multilevel moderation/interactions.
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    From Data to Causes II: Comparing Approaches to Panel Data Analysis
    Zyphur, MJ ; Voelkle, MC ; Tay, L ; Allison, PD ; Preacher, KJ ; Zhang, Z ; Hamaker, EL ; Shamsollahi, A ; Pierides, DC ; KOVAL, P ; Diener, E (SAGE Publications, 2020-10-01)
    This article compares a general cross-lagged model (GCLM) to other panel data methods based on their coherence with a causal logic and pragmatic concerns regarding modeled dynamics and hypothesis testing. We examine three “static” models that do not incorporate temporal dynamics: random- and fixed-effects models that estimate contemporaneous relationships; and latent curve models. We then describe “dynamic” models that incorporate temporal dynamics in the form of lagged effects: cross-lagged models estimated in a structural equation model (SEM) or multilevel model (MLM) framework; Arellano-Bond dynamic panel data methods; and autoregressive latent trajectory models. We describe the implications of overlooking temporal dynamics in static models and show how even popular cross-lagged models fail to control for stable factors over time. We also show that Arellano-Bond and autoregressive latent trajectory models have various shortcomings. By contrasting these approaches, we clarify the benefits and drawbacks of common methods for modeling panel data, including the GCLM approach we propose. We conclude with a discussion of issues regarding causal inference, including difficulties in separating different types of time-invariant and time-varying effects over time.
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    From Data to Causes I: Building A General Cross-Lagged Panel Model (GCLM)
    Zyphur, MJ ; Allison, PD ; Tay, L ; Voelkle, MC ; Preacher, KJ ; Zhang, Z ; Hamaker, EL ; Shamsollahi, A ; Pierides, DC ; KOVAL, P ; Diener, E (SAGE Publications, 2020-10-01)
    This is the first paper in a series of two that synthesizes, compares, and extends methods for causal inference with longitudinal panel data in a structural equation modeling (SEM) framework. Starting with a cross-lagged approach, this paper builds a general cross-lagged panel model (GCLM) with parameters to account for stable factors while increasing the range of dynamic processes that can be modeled. We illustrate the GCLM by examining the relationship between national income and subjective well-being (SWB), showing how to examine hypotheses about short-run (via Granger-Sims tests) versus long-run effects (via impulse responses). When controlling for stable factors, we find no short-run or long-run effects among these variables, showing national SWB to be relatively stable, whereas income is less so. Our second paper addresses the differences between the GCLM and other methods. Online Supplementary Materials offer an Excel file automating GCLM input for Mplus (with an example also for Lavaan in R) and analyses using additional data sets and all program input/output. We also offer an introductory GCLM presentation at https://youtu.be/tHnnaRNPbXs. We conclude with a discussion of issues surrounding causal inference.
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    Modeling interaction as a complex system
    van Berkel, N ; Dennis, S ; Zyphur, M ; Li, J ; Heathcote, A ; Kostakos, V (Taylor & Francis, 2021)
    Researchers in Human-Computer Interaction typically rely on experiments to assess the causal effects of experimental conditions on variables of interest. Although this classic approach can be very useful, it offers little help in tackling questions of causality in the kind of data that are increasingly common in HCI – capturing user behavior ‘in the wild.’ To analyze such data, model-based regressions such as cross-lagged panel models or vector autoregressions can be used, but these require parametric assumptions about the structural form of effects among the variables. To overcome some of the limitations associated with experiments and model-based regressions, we adopt and extend ‘empirical dynamic modelling’ methods from ecology that lend themselves to conceptualizing multiple users’ behavior as complex nonlinear dynamical systems. Extending a method known as ‘convergent cross mapping’ or CCM, we show how to make causal inferences that do not rely on experimental manipulations or model-based regressions and, by virtue of being non-parametric, can accommodate data emanating from complex nonlinear dynamical systems. By using this approach for multiple users, which we call ‘multiple convergent cross mapping’ or MCCM, researchers can achieve a better understanding of the interactions between users and technology – by distinguishing causality from correlation – in real-world settings.