Search results

This paper aims to introduce the Schmid–Leiman solution (SLS) as a useful tool to interpret the results of higher-order factor analyses in marketing research irrespective of the type of higher-order factor structure used (formative or reflective).

Two studies, one with retail shoppers in India and another with undergraduate students in Hong Kong, are used to compare different types of higher-order factor structures to test the utility of SLS.

The authors show that whether a reflective or a formative model is used to operationalize a higher-order construct, using SLS as an additional analysis gives useful insights into the factor structure at different levels and helps isolate their unique contributions to the explained variance.

The authors test higher-order models for store environment and consumer impulsiveness with data from retail shoppers and undergraduate students in two Asian countries, which may restrict the generalizability of the study findings. Future research may try to replicate our findings with other higher-order constructs and consumers in other countries.

The authors offer a checklist that can be used by future researchers to evaluate alternate higher-order factor structures and choose the appropriate one for their research context.

The authors show that using SLS is especially useful when there is a lack of clarity on the nature of relationships between the factors at different levels or about the independent contribution of the factors at different levels, in a higher-order factor structure.

High‐dimensional model representation (HDMR) is a general set of quantitative model assessment and analysis tools for capturing the high‐dimensional relationships between sets of input and output model variables. It is an efficient formulation of the system response, if higher‐order cooperative effects are weak, allowing the physical model to be captured by the lower‐order terms. The paper's aim is to develop a new computational tool for estimating probabilistic sensitivity of structural/mechanical systems subject to random loads, material properties and geometry.

When first‐order HDMR approximation of the original high‐dimensional limit state is not adequate to provide the desired accuracy to the sensitivity analysis, this paper presents an enhanced HDMR (eHDMR) method to represent the higher‐order terms of HDMR expansion by expressions similar to the lower‐order ones with monomial multipliers. The accuracy of the HDMR expansion can be significantly improved using preconditioning with a minimal number of additional input‐output samples without directly invoking the determination of second‐ and higher‐order terms. As a part of this effort, the efficacy of HDMR, which is recently applied to uncertainty analysis, is also demonstrated. The method is based on computing eHDMR approximation of system responses and score functions associated with probability distribution of a random input. Surrogate model is constructed using moving least squares interpolation formula. Once the surrogate model form is defined, both the probabilistic response and its sensitivities can be estimated from a single probabilistic analysis, without requiring the gradients of performance functions.

The results of two numerical examples involving mathematical function and structural/solid‐mechanics problems indicate that the sensitivities obtained using eHDMR approximation provide significant accuracy when compared with the conventional Monte Carlo method, while requiring fewer original model simulations.

This is the first time where application of eHDMR concepts is explored in the stochastic sensitivity analysis. The present computational approach is valuable to the practical modelling and design community.

The theory of third order inference is a theory of how cultural beliefs influence individuals' decisions under conditions of interdependence and uncertainty. In this study, I build on prior work extending the theory to the role of third order information on social trust in public goods dilemmas. Namely, I argue that when second order information on the beliefs of those relevant to the group task are present, this information should influence decision-making over first and third order.

I test this argument in an experimental public goods game. After measuring first order social trust, participants are randomly sorted into one of four conditions – two that pair third and second order information on social trust as parallel and two that pair them as in conflict.

The results suggest that in the presence of second order information on social trust, third order information doesn't have an effect on cooperation.

The study extends the theory of third order inference to understanding the role of social trust at the first, second, and third levels in public goods dilemmas. It puts second order information in competition with third order in predicting cooperation. It suggests that resolving the uncertainty over the second order beliefs of a collective is key to preventing inefficient equilibriums when second and third order beliefs conflict.

This paper proposes that those who study diversity conflict recognize the distinction between first‐order diversity conflict and second‐order diversity conflict. The former refers to discrimination, while the latter refers to disputes over remedies designed to eliminate discrimination. First‐order disputes affect subordinant group members most strongly in the organization, are morally unambiguous for most, and are organized around set organizational and societal procedures. Second‐order disputes involve dominant as well as subordinant group members (so that more people are affected), are more morally ambiguous, and lack set procedures for dealing with them. As a result, second‐order disputes tend to remain hidden, despite being wide‐spread, resulting in autistic hostility. The presence of second‐order conflict may undermine efforts to resolve first‐order disputes, and lead to escalation of conflict between people from different identity groups. Recognizing this distinction is critical for understanding the dynamics of diversity conflicts.

Post-9/11 a first order terrorism narrative has been widely asserted. In this chapter, I explore the development of second order terrorism narrative or ideal-type.

The chapter begins by providing a brief synopsis of three highly mediated Australian counter-terrorism operations and of shortcomings in incident counting. It also relies on some U.S. research on counter-terrorism prosecutions in support.

In first order terrorism, crime appears as a spectacular irruption or original sin on a tabula rasa of innocence and there is a clean division between us and them, non-state and state, victim and offender. In the second order terrorism narrative there is a contrasting claim that 9/11 is blowback, in kind, for U.S.-led interventions and does not offer a clean division between how we and they behave, blurs non-state and state culpability in big crimes, and sees victims and offenders trading places over time. As we adjust our perspective from the presumptive first order to second order event-acts, terrorism and counter-terrorism, event-act and interdiction, is merged as one.

The concept may be useful in accounting for assumptions pertaining to this category of crime, including its relation with precaution and security.

Summarizes some of the important concepts and developments in cybernetics and general systems theory, especially during the last two decades. Shows how they can indeed be a challenge to sociological thinking. Cybernetics is used here as an umbrella term for a great variety of related disciplines: general systems theory, information theory, system dynamics, dynamic systems theory, including catastrophe theory, chaos theory. Also considers the emerging “science of complexity”, which includes neural networks, artificial intelligence and artificial life, and discusses the methodological drawbacks of second‐order cybernetics.

The purpose of this paper is to describe and analyse a class of finite element fractional step methods for solving the incompressible Navier-Stokes equations. The objective is not to reproduce the extensive contributions on the subject, but to report on long-term experience with and provide a unified overview of a particular approach: the characteristic-based split method. Three procedures, the semi-implicit, quasi-implicit and fully explicit, are studied and compared.

This work provides a thorough assessment of the accuracy and efficiency of these schemes, both for a first and second order pressure split.

In transient problems, the quasi-implicit form significantly outperforms the fully explicit approach. The second order (pressure) fractional step method displays significant convergence and accuracy benefits when the quasi-implicit projection method is employed. The fully explicit method, utilising artificial compressibility and a pseudo time stepping procedure, requires no second order fractional split to achieve second order or higher accuracy. While the fully explicit form is efficient for steady state problems, due to its ability to handle local time stepping, the quasi-implicit is the best choice for transient flow calculations with time independent boundary conditions. The semi-implicit form, with its stability restrictions, is the least favoured of all the three forms for incompressible flow calculations.

A comprehensive comparison between three versions of the CBS method is provided for the first time.

Concepts equip the mind with thought, provide our theories with ideas, and assign variables for testing our hypotheses. Much of contemporary research deals with narrowly circumscribed concepts, termed simple concepts herein, which are the grist for much empirical inquiry in the field. In contrast to simple concepts, which exhibit a kind of unity, complex concepts are structures of simple concepts, and in certain instances unveil meaning going beyond simple concepts or their aggregation. When expressed in hylomorphic structures, complex concepts achieve unique ontological status and serve particular explanatory capabilities. We develop the philosophical foundation for hylomorphic structures and show how they are rooted in dispositions, dispositional causality, and various mind–body trade-offs. Examples are provided for this emerging perspective on “Big concepts” or “Big Ideas.”

The purpose of this paper is to investigate the best frequency description of a chain dependent Markov process for the daily simulation of precipitation. The influence of the order of the Markov chain model to simulate daily precipitation occurrence is evaluated. A mixed‐order model is constructed and compared to a simple first‐order model to evaluate the importance of the model order for the pricing of a rainfall index put option.

For the first time a mixed‐order Markov chain model is presented where the monthly varying order was chosen based on a Bayesian information criteria analysis of rainfall data for one weather station in the US. The outcome of this model is compared to simpler Markov models and to burn analysis results.

The comparison indicate that there is only a slightly better representation of the rain statistics in the theoretically best mixed‐order Markov chain model compared to a more simple first‐order model. Clear differences between the daily simulation and the burn method are found when pricing a put option on a rainfall index. All daily simulation models underestimate the volatility of the monthly rainfall amount especially in the summer months.

To assess the robustness and any geographical dependence of the bias in the volatility a systematic analysis could be applied to more weather stations across the US in further studies.

The bias in the volatility has significant influence on the price of the put option considered here and limits the use of such a model for risk analyses, e.g. for an extreme event cover.

For the first time a multi‐order Markov chain model is applied to price a precipitation derivative. While the focus of previous studies was the appropriate choice for the intensity process, the importance of the frequency process is investigated in this paper.

This article explores producing and managing change within conversationally constructed realities. Conversations are proposed as both the medium and product of reality construction within which change is a process of shifting conversations in the network of conversations that constitute organizations. In this context, change entails bringing new conversations into a sustained existence and the job of change managers is to create the conversational realities that produce effective action rather than to align organizations with some “true” reality.