Enchantment in Business Ethics Research

This article draws attention to the importance of enchantment in business ethics research. Starting from a Weberian understanding of disenchantment, as a force that arises through modernity and scientific rationality, we show how rationalist business ethics research has become disenchanted as a consequence of the normalisation of positivist, quantitative methods of inquiry. Such methods absent the relational and lively nature of business ethics research and detract from the ethical meaning that can be generated through research encounters. To address this issue, we draw on the work of political theorist and philosopher, Jane Bennett, using this to show how interpretive qualitative research creates possibilities for enchantment. We identify three opportunities for reenchanting business ethics research related to: (i) moments of novelty or disruption; (ii) deep, meaningful attachments to things studied; and (iii) possibilities for embodied, affective encounters. In conclusion, we suggest that business ethics research needs to recognise and reorient scholarship towards an appreciation of the ethical value of interpretive, qualitative research as a source of potential enchantment

WHEN MINDSETS COLLIDE: SWITCHING ANALYTICAL MINDSETS TO ADVANCE ORGANIZATION SCIENCE

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Institutionalizing identities: Communication skills and the making of doctors and patients

Annual Academy of Management Conferenc

Bayesian Estimation and Inference: A User's Guide

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

Multilevel Latent Polynomial Regression for Modeling (In)Congruence Across Organizational Groups: The Case of Organizational Culture Research

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

Modeling Measurement as a Sequential Process: Autoregressive Confirmatory Factor Analysis (AR-CFA)

To model data from multi-item scales, many researchers default to a confirmatory factor analysis (CFA) approach that restricts cross-loadings and residual correlations to zero. This often leads to problems of measurement-model misfit while also ignoring theoretically relevant alternatives. Existing research mostly offers solutions by relaxing assumptions about cross-loadings and allowing residual correlations. However, such approaches are critiqued as being weak on theory and/or indicative of problematic measurement scales. We offer a theoretically-grounded alternative to modeling survey data called an autoregressive confirmatory factor analysis (AR-CFA), which is motivated by recognizing that responding to survey items is a sequential process that may create temporal dependencies among scale items. We compare an AR-CFA to other common approaches using a sample of 8,569 people measured along five common personality factors, showing how the AR-CFA can improve model fit and offer evidence of increased construct validity. We then introduce methods for testing AR-CFA hypotheses, including cross-level moderation effects using latent interactions among stable factors and time-varying residuals. We recommend considering the AR-CFA as a useful complement to other existing approaches and treat AR-CFA limitations