Management and Marketing - Research Publications
Now showing items 1-12 of 607
‘There's Something About Sustainability’: The Discursive Dynamics Of Policy Reform
Sustainability is now among the hegemonic discourses used by government to construct problems and policy beyond the environmental domain. Detached from its origins, it functions as an ‘empty signifier’ whose flexibility and ambiguity can be harnessed in policymaking and political debate. This paper uses an Australian case study to show how sustainability discourse was mobilized to justify reversing a previous decision and raise the age at which publicly‐funded aged pension could be accessed. Overall, it contributes to understanding how hegemonic intervention is accomplished by tracing discursive processes over time and amongst different texts, helping to identify shifts and turning points in trajectories of policy reform and political debate. I conclude by arguing the use of sustainability discourse warrants particular critical attention because it signals broader difficulties in imagining alternative collective futures and considering the costs and consequences arising from current arrangements.
The potential of marketing communications to protect social workers in times of crisis
(Taylor & Francis (Routledge), 2020)
Socially stigmatized service workers (SSWs) like probation officers, social workers, and even aged care workers are often subject to negative media scrutiny when a crisis occurs, leading to public outrage and subsequent high attrition rates. The primary focus of this study is to examine the potential for marketing communication to generate a state of empathic concern amongst the public towards SSWs because an empathic public is less likely to want to punish, despite media calls to do so. A case is presented for the use of marketing communication explained through the lens of narrative execution and the general theory of emotion. Using a content analysis of public service announcements from representative bodies of social workers, in the US, UK and Australia we find little evidence of strategic intent to use narrative format or elicit empathic concern. A call and direction for further research is made in light of this finding.
Restrict, clean and protect: Signalling consumer safety during the pandemic and beyond
Purpose: Since the outbreak of the COVID-19 pandemic customers fear for their health when interacting with service providers. To mitigate this fear service providers are using safety signals directed to consumers and other stakeholders who make organizational assessments.The purpose of this article is to synthesize the range of safety signals in a framework that integrates signalling theory with servicescape elements so as to provide guidance for service providers to assist in their recovery. Design/methodology/approach: We extracted examples of how service providers signal safety to their consumers that the risk of infection is low in exchanging with their service. These examples were taken from secondary data sources in the form of trade publications resulting from a systematic search and supplemented by an organic search. Findings: In total 53 unique safety signals were identified and assigned to 24 different categories in our framework. Most of the signals fell into the default and sale independent category, followed by the default contingent revenue risking category. Originality: This study builds on signalling theory and service literature to develop a framework of the range of safety signals currently in use by service providers and offers suggestions as to which are likely to be most effective. Further, a future research inquiry of safety signals is presented which we believe has promise in assisting recovery in a post pandemic world.
What kind of donor are you? Uncovering complexity in donor identity
Identity is a useful lens to understand donation behavior. However, studies have typically conceptualized and examined donor identity as a generic, unidimensional concept. Through in‐depth interviews with 52 blood donors, this study sets out to discover if there is more complexity to donor identity, and what implications this might have for marketing communications, in the context of donation of the self (e.g., blood, organs, time, and effort). We use sentiment polarity and amplification analysis of inductive themes to uncover distinct patterns reflective of four different donor identities. We label these the Savior, Communitarian, Pragmatist, and Elitist, which are underpinned by theories of gift‐giving, sharing, pragmatism, and signaling, respectively. The typology offers a theory‐building mechanism to anticipate the effects of marketing stimuli on donation behavior. We conclude by presenting four theoretical propositions, for which we provide preliminary empirical evidence. The survey data is suggestive of action readiness for donation behavior when a marketing communication message is aligned with its intended donor identity.
Does Procedural Justice Increase the Inclusion of Migrants? A Group Engagement Model Perspective
(Academy of Management, 2018-07-09)
Workforces have become more culturally diverse due to globalization, skilled labor shortages, aging societies, and hardships in developing countries. One critical challenge associated with managing a culturally diverse workforce is ensuring inclusion. Migrant workers often experience discrimination, social exclusion, and lower organizational identification. Further attention is required to address these challenges and create inclusive workplaces for migrants. We integrate research on migrant workers with research on the group engagement model to create a model for understanding and enhancing migrant worker inclusion. We test our model using data drawn from employees in a large-scale survey of Australian workplaces. The results of our multilevel moderated mediation analysis indicate that, consistent with the group engagement model, a procedurally fair work environment tends to increase organizational identification, which in turn is associated with higher levels of work engagement. Importantly, our results also indicate that procedural justice climate is more important for migrant than for native workers. Our work has clear implications for practice. Organizations should establish a procedurally fair work environment in which cultural minorities experience consistent and unbiased policies and procedures, are able to express their opinions, and participate in decision-making.
An Alternative Framing of Organ Donation Registration: The Collective Donor Behavioral Model
(SAGE Publications, 2020)
Notwithstanding the prevalent use of donor registration prediction models grounded by the theory of planned behavior (TPB), registration behavior continues to remain low. A collective donor behavior (CDB) model underpinned by social exchange theory is introduced and its predictive ability is tested against a baseline TPB model using an online survey of adults ( n = 1,055). Individuals who indicated they were not registered donors were contacted 3 months later to track their registration status. The CDB model was found to explain 45% of variance in registration intentions which was comparable in performance to TPB. Normative commitment was found to be strongly associated with registration intentions, and both institutional trust and trust in others fostered this commitment. The CDB model provides different insights on how to increase donor registration intentions. Namely, interventions need to facilitate individual positive experiences with institutions such as hospitals and strengthen social inclusion perceptions.
Bayesian Estimation and Inference: A User's Guide
(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.
Multilevel Latent Polynomial Regression for Modeling (In)Congruence Across Organizational Groups: The Case of Organizational Culture Research
(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.
From Data to Causes II: Comparing Approaches to Panel Data Analysis
(SAGE Publications, 2019)
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.
From Data to Causes I: Building A General Cross-Lagged Panel Model (GCLM)
(SAGE Publications, 2019)
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.
Modeling interaction as a complex system
(Taylor & Francis, 2020-02-01)
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.