An examination of moderator effects in the four-stage loyalty model

Oliver’s 1997 four-stage loyalty model proposes that loyalty consists of belief, affect, intention, and action. Although this loyalty model has recently been subject to empirical examination, the issue of moderator variables has been largely neglected. This article fills that void by analyzing the moderating effects of selected personal and situational characteristics, using a sample of 888 customers of a large do-it-yourself retailer. The results of multi-group causal analysis suggest that these moderators exert an influence on the development of the different stages of the loyalty sequence. Specifically, age, income, education and expertise, price orientation, critical incident recovery, and loyalty card membership are found to be important moderators of the links in the four-stage loyalty model. Limitations of the study are outlined, and implications for both research and managerial practice are discussed.

Dynamic causal modeling of load-dependent modulation of effective connectivity within the verbal working memory network

Neuroimaging studies have consistently shown that working memory (WM) tasks engage a distributed neural network that primarily includes the dorsolateral prefrontal cortex, the parietal cortex, and the anterior cingulate cortex. The current challenge is to provide a mechanistic account of the changes observed in regional activity. To achieve this, we characterized neuroplastic responses in effective connectivity between these regions at increasing WM loads using dynamic causal modeling of functional magnetic resonance imaging data obtained from healthy individuals during a verbal n-back task. Our data demonstrate that increasing memory load was associated with (a) right-hemisphere dominance, (b) increasing forward (i.e., posterior to anterior) effective connectivity within the WM network, and (c) reduction in individual variability in WM network architecture resulting in the right-hemisphere forward model reaching an exceedance probability of 99% in the most demanding condition. Our results provide direct empirical support that task difficulty, in our case WM load, is a significant moderator of short-term plasticity, complementing existing theories of task-related reduction in variability in neural networks. Hum Brain Mapp, 2013. © 2013 Wiley Periodicals, Inc.

Material and efficient cause interpretations of the formative model:resolving misunderstandings and clarifying conceptual language

This paper presents a causal explanation of formative variables that unpacks and clarifies the generally accepted idea that formative indicators are ‘causes’ of the focal formative variable. In doing this, we explore the recent paper by Diamantopoulos and Temme (AMS Review, 3(3), 160-171, 2013) and show that the latter misunderstand the stance of Lee, Cadogan, and Chamberlain (AMS Review, 3(1), 3-17, 2013; see also Cadogan, Lee, and Chamberlain, AMS Review, 3(1), 38-49, 2013). By drawing on the multiple ways that one can interpret the idea of causality within the MIMIC model, we then demonstrate how the continued defense of the MIMIC model as a tool to validate formative indicators and to identify formative variables in structural models is misguided. We also present unambiguous recommendations on how formative variables can be modelled in lieu of the formative MIMIC model.

The MIMIC model and formative variables:problems and solutions

The use of the multiple indicators, multiple causes model to operationalize formative variables (the formative MIMIC model) is advocated in the methodological literature. Yet, contrary to popular belief, the formative MIMIC model does not provide a valid method of integrating formative variables into empirical studies and we recommend discarding it from formative models. Our arguments rest on the following observations. First, much formative variable literature appears to conceptualize a causal structure between the formative variable and its indicators which can be tested or estimated. We demonstrate that this assumption is illogical, that a formative variable is simply a researcher-defined composite of sub-dimensions, and that such tests and estimates are unnecessary. Second, despite this, researchers often use the formative MIMIC model as a means to include formative variables in their models and to estimate the magnitude of linkages between formative variables and their indicators. However, the formative MIMIC model cannot provide this information since it is simply a model in which a common factor is predicted by some exogenous variables—the model does not integrate within it a formative variable. Empirical results from such studies need reassessing, since their interpretation may lead to inaccurate theoretical insights and the development of untested recommendations to managers. Finally, the use of the formative MIMIC model can foster fuzzy conceptualizations of variables, particularly since it can erroneously encourage the view that a single focal variable is measured with formative and reflective indicators. We explain these interlinked arguments in more detail and provide a set of recommendations for researchers to consider when dealing with formative variables.

Person-organization fit and incentives:a causal test

We investigate the effects of organizational culture and personal values on performance under individual and team contest incentives. We develop a model of regard for others and in-group favoritism that predicts interaction effects between organizational values and personal values in contest games. These predictions are tested in a computerized lab experiment with exogenous control of both organizational values and incentives. In line with our theoretical model we find that prosocial (proself) orientated subjects exert more (less) effort in team contests in the primed prosocial organizational values condition, relative to the neutrally primed baseline condition. Further, when the prosocial organizational values are combined with individual contest incentives, prosocial subjects no longer outperform their proself counterparts. These findings provide a first, affirmative, causal test of person-organization fit theory. They also suggest the importance of a 'triple-fit' between personal preferences, organizational values and incentive mechanisms for prosocially orientated individuals.

A conceptual model of ecological interactions in the mangrove estuaries of the Florida Everglades

A brackish water ecotone of coastal bays and lakes, mangrove forests, salt marshes, tidal creeks, and upland hammocks separates Florida Bay, Biscayne Bay, and the Gulf of Mexico from the freshwater Everglades. The Everglades mangrove estuaries are characterized by salinity gradients that vary spatially with topography and vary seasonally and inter-annually with rainfall, tide, and freshwater flow from the Everglades. Because of their location at the lower end of the Everglades drainage basin, Everglades mangrove estuaries have been affected by upstream water management practices that have altered the freshwater heads and flows and that affect salinity gradients. Additionally, interannual variation in precipitation patterns, particularly those caused to El Nin˜o events, control freshwater inputs and salinity dynamics in these estuaries. Two major external drivers on this system are water management activities and global climate change. These drivers lead to two major ecosystem stressors: reduced freshwater flow volume and duration, and sea-level rise. Major ecological attributes include mangrove forest production, soil accretion, and resilience; coastal lake submerged aquatic vegetation; resident mangrove fish populations; wood stork (Mycteria americana) and roseate spoonbill (Platelea ajaja) nesting colonies; and estuarine crocodilian populations. Causal linkages between stressors and attributes include coastal transgression, hydroperiods, salinity gradients, and the ‘‘white zone’’ freshwater/estuarine interface. The functional estuary and its ecological attributes, as influenced by sea level and freshwater flow, must be viewed as spatially dynamic, with a possible near-term balancing of transgression but ultimately a long-term continuation of inland movement. Regardless of the spatio-temporal timing of this transgression, a salinity gradient supportive of ecologically functional Everglades mangrove estuaries will be required to maintain the integrity of the South Florida ecosystem.

A conceptual ecological model to facilitate understanding the role of invasive species in large-scale ecosystem restoration

We developed a conceptual ecological model (CEM) for invasive species to help understand the role invasive exotics have in ecosystem ecology and their impacts on restoration activities. Our model, which can be applied to any invasive species, grew from the eco-regional conceptual models developed for Everglades restoration. These models identify ecological drivers, stressors, effects and attributes; we integrated the unique aspects of exotic species invasions and effects into this conceptual hierarchy. We used the model to help identify important aspects of invasion in the development of an invasive exotic plant ecological indicator, which is described a companion paper in this special issue journal. A key aspect of the CEM is that it is a general ecological model that can be tailored to specific cases and species, as the details of any invasion are unique to that invasive species. Our model encompasses the temporal and spatial changes that characterize invasion, identifying the general conditions that allow a species to become invasive in a de novo environment; it then enumerates the possible effects exotic species may have collectively and individually at varying scales and for different ecosystem properties, once a species becomes invasive. The model provides suites of characteristics and processes, as well as hypothesized causal relationships to consider when thinking about the effects or potential effects of an invasive exotic and how restoration efforts will affect these characteristics and processes. In order to illustrate how to use the model as a blueprint for applying a similar approach to other invasive species and ecosystems, we give two examples of using this conceptual model to evaluate the status of two south Florida invasive exotic plant species (melaleuca and Old World climbing fern) and consider potential impacts of these invasive species on restoration.

Examining moderators of the hindsight bias in the context of civil legal decision -making: Counterfactuals, causal proximity, and self -referencing

The current research sought to clarify the diverging relationships between counterfactual thinking and hindsight bias observed in the literature thus far. In a non-legal context, Roese and Olson (1996) found a positive relationship between counterfactuals and hindsight bias, such that counterfactual mutations that undid the outcome also increased participants’ ratings of the outcome’s a priori likelihood. Further, they determined that this relationship is mediated by causal attributions about the counterfactually mutated antecedent event. Conversely, in the context of a civil lawsuit, Robbennolt and Sobus (1997) found that the relationship between counterfactual thinking and hindsight bias is negative. The current research sought to resolve the conflicting findings in the literature within a legal context. ^ In Experiment One, the manipulation of the normality of the defendant’s target behavior, designed to manipulate participants’ counterfactual thoughts about said behavior, did moderate the hindsight effect of outcome knowledge on mock jurors’ judgments of the foreseeability of that outcome as well as their negligence verdicts. Although I predicted that counterfactual thinking would increase, or exacerbate, the hindsight bias, as found by Roese and Olson (1996), my results provided some support for Robbenolt and Sobus’s (1997) finding that counterfactual thinking decreases the hindsight bias. Behavior normality did not moderate the hindsight effect of outcome knowledge in Experiment Two, nor did causal proximity in Experiment Three. ^ Additionally, my hypothesis that self-referencing may be an effective hindsight debiasing technique received little support across the three experiments. Although both the self-referencing instructions and self-report measure consistently decreased mock jurors’ likelihood of finding the defendant negligent, and self-referencing instructions decreased their foreseeability ratings in studies two and three, the self-referencing manipulation did not interact with outcome knowledge to moderate a hindsight bias effect on either foreseeability or negligence judgments. The consistent pattern of results across the three experiments, however, suggests that self-referencing may be an effective technique in reducing the likelihood of negligence verdicts.^

Examining Moderators of the Hindsight Bias in the Context of Civil Legal Decision-Making: Counterfactuals, Causal Proximity, and Self-Referencing

The current research sought to clarify the diverging relationships between counterfactual thinking and hindsight bias observed in the literature thus far. In a non-legal context, Roese and Olson (1996) found a positive relationship between counterfactuals and hindsight bias, such that counterfactual mutations that undid the outcome also increased participants’ ratings of the outcome’s a priori likelihood. Further, they determined that this relationship is mediated by causal attributions about the counterfactually mutated antecedent event. Conversely, in the context of a civil lawsuit, Robbennolt and Sobus (1997) found that the relationship between counterfactual thinking and hindsight bias is negative. The current research sought to resolve the conflicting findings in the literature within a legal context. In Experiment One, the manipulation of the normality of the defendant’s target behavior, designed to manipulate participants’ counterfactual thoughts about said behavior, did moderate the hindsight effect of outcome knowledge on mock jurors’ judgments of the foreseeability of that outcome as well as their negligence verdicts. Although I predicted that counterfactual thinking would increase, or exacerbate, the hindsight bias, as found by Roese and Olson (1996), my results provided some support for Robbenolt and Sobus’s (1997) finding that counterfactual thinking decreases the hindsight bias. Behavior normality did not moderate the hindsight effect of outcome knowledge in Experiment Two, nor did causal proximity in Experiment Three. Additionally, my hypothesis that self-referencing may be an effective hindsight debiasing technique received little support across the three experiments. Although both the self-referencing instructions and self-report measure consistently decreased mock jurors’ likelihood of finding the defendant negligent, and self-referencing instructions decreased their foreseeability ratings in studies two and three, the self-referencing manipulation did not interact with outcome knowledge to moderate a hindsight bias effect on either foreseeability or negligence judgments. The consistent pattern of results across the three experiments, however, suggests that self-referencing may be an effective technique in reducing the likelihood of negligence verdicts.

Functional Evaluation of Causal Mutations Identified in Human Genetic Studies

Human genetics has been experiencing a wave of genetic discoveries thanks to the development of several technologies, such as genome-wide association studies (GWAS), whole-exome sequencing, and whole genome sequencing. Despite the massive genetic discoveries of new variants associated with human diseases, several key challenges emerge following the genetic discovery. GWAS is known to be good at identifying the locus associated with the patient phenotype. However, the actually causal variants responsible for the phenotype are often elusive. Another challenge in human genetics is that even the causal mutations are already known, the underlying biological effect might remain largely ambiguous. Functional evaluation plays a key role to solve these key challenges in human genetics both to identify causal variants responsible for the phenotype, and to further develop the biological insights from the disease-causing mutations.

We adopted various methods to characterize the effects of variants identified in human genetic studies, including patient genetic and phenotypic data, RNA chemistry, molecular biology, virology, and multi-electrode array and primary neuronal culture systems. Chapter 1 is a broader introduction for the motivation and challenges for functional evaluation in human genetic studies, and the background of several genetics discoveries, such as hepatitis C treatment response, in which we performed functional characterization.

Chapter 2 focuses on the characterization of causal variants following the GWAS study for hepatitis C treatment response. We characterized a non-coding SNP (rs4803217) of IL28B (IFNL3) in high linkage disequilibrium (LD) with the discovery SNP identified in the GWAS. In this chapter, we used inter-disciplinary approaches to characterize rs4803217 on RNA structure, disease association, and protein translation.

Chapter 3 describes another avenue of functional characterization following GWAS focusing on the novel transcripts and proteins identified near the IL28B (IFNL3) locus. It has been recently speculated that this novel protein, which was named IFNL4, may affect the HCV treatment response and clearance. In this chapter, we used molecular biology, virology, and patient genetic and phenotypic data to further characterize and understand the biology of IFNL4. The efforts in chapter 2 and 3 provided new insights to the candidate causal variant(s) responsible for the GWAS for HCV treatment response, however, more evidence is still required to make claims for the exact causal roles of these variants for the GWAS association.

Chapter 4 aims to characterize a mutation already known to cause a disease (seizure) in a mouse model. We demonstrate the potential use of multi-electrode array (MEA) system for the functional characterization and drug testing on mutations found in neurological diseases, such as seizure. Functional characterization in neurological diseases is relatively challenging and available systematic tools are relatively limited. This chapter shows an exploratory research and example to establish a system for the broader use for functional characterization and translational opportunities for mutations found in neurological diseases.

Overall, this dissertation spans a range of challenges of functional evaluations in human genetics. It is expected that the functional characterization to understand human mutations will become more central in human genetics, because there are still many biological questions remaining to be answered after the explosion of human genetic discoveries. The recent advance in several technologies, including genome editing and pluripotent stem cells, is also expected to make new tools available for functional studies in human diseases.

Identifying treatment effects and counterfactual distributions using data combination with unobserved heterogeneity

This paper considers identification of treatment effects when the outcome variables and covari-ates are not observed in the same data sets. Ecological inference models, where aggregate out-come information is combined with individual demographic information, are a common example of these situations. In this context, the counterfactual distributions and the treatment effects are not point identified. However, recent results provide bounds to partially identify causal effects. Unlike previous works, this paper adopts the selection on unobservables assumption, which means that randomization of treatment assignments is not achieved until time fixed unobserved heterogeneity is controlled for. Panel data models linear in the unobserved components are con-sidered to achieve identification. To assess the performance of these bounds, this paper provides a simulation exercise.

CAUSAL INFERENCE WITH A CONTINUOUS TREATMENT AND OUTCOME: ALTERNATIVE ESTIMATORS FOR PARAMETRIC DOSE-RESPONSE FUNCTIONS WITH APPLICATIONS.

Causal inference with a continuous treatment is a relatively under-explored problem. In this dissertation, we adopt the potential outcomes framework. Potential outcomes are responses that would be seen for a unit under all possible treatments. In an observational study where the treatment is continuous, the potential outcomes are an uncountably infinite set indexed by treatment dose. We parameterize this unobservable set as a linear combination of a finite number of basis functions whose coefficients vary across units. This leads to new techniques for estimating the population average dose-response function (ADRF). Some techniques require a model for the treatment assignment given covariates, some require a model for predicting the potential outcomes from covariates, and some require both. We develop these techniques using a framework of estimating functions, compare them to existing methods for continuous treatments, and simulate their performance in a population where the ADRF is linear and the models for the treatment and/or outcomes may be misspecified. We also extend the comparisons to a data set of lottery winners in Massachusetts. Next, we describe the methods and functions in the R package causaldrf using data from the National Medical Expenditure Survey (NMES) and Infant Health and Development Program (IHDP) as examples. Additionally, we analyze the National Growth and Health Study (NGHS) data set and deal with the issue of missing data. Lastly, we discuss future research goals and possible extensions.

Causal factors of anxiety symptoms in children

Aim: The present work aimed to investigate the impact of the child’s cognitions associated with ambiguous stimuli that refer to anxiety, both parents’ fears and anxiety, and parents’ attributions to the child’s interpretations of ambiguous stimuli on child anxiety. The influence of parental modelling on child’s cognitions was also analyzed. Method: The final sample was composed of 111 children (62 boys; 49 girls) with ages between 10 and 11 years (M = 10.6, SD = 0.5) from a community population, and both their parents. The variables identified as most significant were included in a predictive model of anxiety. Results: Results revealed the children’s thoughts (positive and negative) related to ambiguous stimuli that describe anxiety situations. Parents’ fears and mothers’ anxiety significantly predict children’s anxiety. Those variables explain 29% of the variance in children general anxiety. No evidence was found for a direct parental modeling of child cognitions. Conclusion: Children’s positive thoughts seem to be cognitive aspects that buffer against anxiety. Negative thoughts are vulnerability factors for the development of child anxiety. Parents’ fears and anxiety should be analyzed in separate as they have distinct influences over children’s anxiety. Mothers’ fears contribute to children’s anxiety by reducing it, revealing a possible protective effect. It is suggested that the contribution of both parents’ fears to children’s anxiety may be interpreted acknowledging the existence of “psychological and/or behavioral filters”. Mothers’ filters seem to be well developed while fathers’ filters seem to be compromised. The contribution of mothers’ anxiety (but not fathers’ anxiety) to children’s anxiety is also understood in light of the possible existence of a “proximity space” between the child and parents, which is wider with mothers than with fathers.