Search results

The multidimensional structure of boredom poses unique measurement challenges related to scale length and statistical modeling. We systematically address these concerns in two studies. In Study 1, we use item response theory to shorten the 29-item Multidimensional State Boredom Scale (MSBS) (Fahlman et al., 2013). In Study 2, we use structural equation modeling to compare two theoretically consistent multidimensional structures of boredom (superordinate and multivariate) with the most commonly used, yet theoretically inconsistent, structure in boredom research (unidimensional parallel model). Our findings provide support for modeling boredom as multidimensional and demonstrate the impact of model selection on effect sizes and significance.

Entrepreneurial orientation (EO) has emerged as a core concept in the field of entrepreneurship. Yet, there continue to be questions about the nature of EO and how best to conceptualize and measure it. This chapter makes the case that EO has grown beyond its roots as a firm-level unidimensional strategy construct and that a new multidimensional version of EO is needed to capture the diverse manifestations and venues for entrepreneurial activity that are now evident around the world – global entrepreneurial orientation (GEO). Building on the five-dimension multidimensional view of EO set forth when Lumpkin and Dess (1996) extended the work of Miller (1983) and Covin and Slevin (1989, 1991), the chapter offers an updated definition of EO and a fresh interpretation of why EO matters theoretically. Despite earnest efforts to reconcile the different approaches to EO, in order to move the study of EO and the theoretical conversation about it forward, we maintain that as a group of scholars and a field, we need to acknowledge that two different versions of EO have emerged. Given that, we consider original approaches to measuring EO, evaluate formative measurement models, consider multiple levels of analysis, call for renewed attention to EO configurations, and discuss whether there is a theory of EO.

This paper seeks to detail an exploratory examination of a multidimensional, creativity‐based theoretical model of opportunity recognition originally proposed by Hills et al. and later refined by Lumpkin et al., but never empirically tested. The paper also aims to examine the relationship between individual dimensions of the model and creativity.

Analyses were conducted using AMOS software on a sample of 145 entrepreneurs. One structural equation model (SEM) and three confirmatory factor analysis models were tested.

The five‐dimensional model – consisting of preparation, incubation, insight, evaluation, and elaboration – was determined to be the best fitting model. The SEM model also indicated that incubation and elaboration were significantly related to creativity. Overall, a multidimensional, creativity‐based approach to modeling opportunity recognition is supported by this study.

Cross‐sectional data do not allow for testing of the process aspect of the model; however, they do provide evidence that the model can stand up to empirical tests of the five elements of the model. Future research should examine opportunity using multiple dimensions and a creativity perspective. Additional research is needed to examine the process aspects of opportunity recognition.

Fostering opportunity recognition processes that are iterative and involve multiple stages is likely to promote more creative entrepreneurial outcomes.

This study provides one of the few examples of a multidimensional perspective on opportunity recognition as well as an empirical examination of a creativity‐based theoretical model of opportunity recognition.

This chapter explores a descriptive theory of multidimensional travel behaviour, estimation of quantitative models, and demonstration in an agent-based microsimulation.

A descriptive theory on multidimensional travel behaviour is conceptualised. It theorizes multidimensional knowledge updating, search start/stopping criteria, and search/decision heuristics. These components are formulated or empirically modelled and integrated in a unified and coherent approach.

The theory is supported by empirical observations and the derived quantitative models are tested by an agent-based simulation on a demonstration network.

Based on artificially intelligent agents, learning and search theory, and bounded rationality, this chapter makes an effort to embed a sound theoretical foundation for the computational process approach and agent-based microsimulations. A pertinent new theory is proposed with experimental observations and estimations to demonstrate agents with systematic deviations from the rationality paradigm. Procedural and multidimensional decision-making are modelled. The numerical experiment highlights the capabilities of the proposed theory in estimating rich behavioural dynamics.

The purpose of this paper is to suggest a formalism given by an equation suitable for simulating discrete systems with space-time variation in addition to other change variables. With such formalism, multidimensional dynamical models of discrete complex systems, such as the social systems and ecosystems, can be built.

This formalism is named as discrete multidimensional dynamic system (DMDS). The DMDS provides a way to consider the variation of the density of a state variable with regard to the variables of the change space as a function of multidimensional rates. Multidimensional rates describe this evolution as a consequence of the relation of each multidimensional-point with a given set of other points of the change space. This relation contains the accessibility domains (sets of space points with which each space point is related).

This equation is compared with both the reaction-diffusion equation written in its finite difference form and the cellular-automata model, demonstrating its compatibility with them and an increase in generality, widening the scope of application. The steps to construct models of systems with multidimensional variation based on the equation that defines the DMDS are specified and tested.

Through the DMDS and a well-stated methodology, an application case is provided in order to describe the multidimensional demographic dynamics of an urban system. In this case, the numerical evolution of the population density by districts and cohorts is determined by the DMDS based on some hypothesis about functions of population diffusion between the different districts of the system.

The scope of application of the space-time dynamic system (STDS), given by the authors in a previous work, has been extended to discrete and multidimensional systems. STDS model produces better results than the reaction-diffusion model in validation.

The purpose of this paper is to explore the use of a multidimensional model from the field of media reception for analyzing how a value statement in a Danish windmill company led to employee identification as well as organizational antecedents that influenced this identification. Further, the paper evaluates whether the proposed model can enhance understanding of the study of employee identification in a specific organizational situation and context.

The identification model extends a multidimensional model for media reception originally proposed by Schrøder in the field of media reception studies. The proposed model includes the following reception dimensions: comprehension, discrimination, implementation, motivation, and position.

This paper illustrates how employees from a Danish windmill company receive a value statement. A systematic application of the multidimensional model makes it possible to gain detailed insight into the active and complex process of employee identifications with organizational texts and how they may fluctuate in a specific context.

This analysis only focuses on the reception of a value statement. Future research could include the analysis of employee readings of other types of organizational texts.

The multidimensional identification model is an extension of a media reception model and is new in the field of organizational identification. The model offers a method for analyzing the complexity and multiplicity of employee readings of different types of organizational texts. This may be crucial for both researchers and managers as the model may help to uncover the antecedents that influence how employees receive organizational texts while taking the historical and situational context of the organization into consideration.

This study seeks to investigate the multidimensionality person‐group (PG) fit. It first aims to examine values‐based, personality‐based, and KSA‐based fit as distinct PG fit dimensions. It then also aims to examine fit as an aggregate construct (each dimension combines to form a latent PG fit construct), and as a superordinate construct (an overarching assessment of compatibility drives the individual fit dimensions). It also aims to propose that the distinct dimensions or the overall perception predict commitment to team, employee voice, and knowledge sharing, resulting in a final outcome of employee task performance.

Data were collected using longitudinal survey methodology from three different sources (793 employees, their supervisors and the Human Resources department) in a manufacturing firm in Korea. The various models were evaluated using structural equation modeling.

The distinct dimensions model, in which values‐based fit predicted commitment to the team, personality‐based fit predicted voice behaviors, and KSA‐based fit predicted knowledge sharing, was mostly supported. Each of these intermediary factors predicted supervisors' ratings of individual task performance. Although each dimension had unique impact on the outcomes, results suggested that a superordinate PG construct might be driving the more specific fit assessments. The aggregate model was not supported.

This study is the first to show how different dimensions of PG fit may differentially influence affect and behavior, to predict task performance. It also shows the first evidence for PG fit as a superordinate multidimensional construct. Results provide a basis for new knowledge regarding the multi‐faceted relationship between fit perceptions and outcomes.

The purpose of this study is to analyse the influence of household and community characteristics on multidimensional poverty in communities hosting public universities in Ondo state, Nigeria.

The study constructs Global Multidimensional Poverty Index (MPI) using Alkire-Foster methodology and uses logistic regression to analyse the likelihood of experiencing multidimensional poverty.

Findings from the study suggest that child schooling attendance, child mortality and asset ownership are the indicators in which households are mostly deprived in education, health and living standards consecutively. In addition, using logistic regression, the study finds multidimensional poverty reducing effects of education, age (before old), household size (having more economically active members), income and residing in urban areas. The study, however, documents that living far away from the universities increases the likelihood of experiencing multidimensional poverty in those communities.

To reduce multidimensional poverty in the communities of study, there is need to implement policies that will improve child schooling, reduce infant mortality, increase gainful employment and create enabling environment for asset ownership. This is in addition to upgrading infrastructure in those communities especially in their fringe areas so that development can spread, and multidimensional poverty reduction can follow in no distant future.

Unlike capturing the effect of location on possibility of experiencing poverty using rural or urban dummy, the authors, in addition to that, incorporate distance to university variable on the premise of distance decay mechanism.

This paper seeks to discuss a mechanistic modeling concept for local phenomena governing two‐ and multi‐phase flows and heat transfer.

An overview is given of selected issues concerning the formulation of multidimensional models of two‐phase flow and heat transfer. A complete computational multiphase fluid dynamics (CMFD) model of two‐phase flow is presented, including local constitutive models applicable to two‐phase flows in heated channels. Results are shown of model testing and validation.

It has been demonstrated that the overall model is capable of capturing various local flow and heat transfer phenomena in general, and the onset of temperature excursion (CHF) in low quality forced‐convection boiling, in particular.

Whereas the multiphase model formulation is applicable to a large class of problems, geometries and operating conditions, the closure laws and results are focused on forced‐convection boiling in heated channels.

The proposed approach can be used to predict multidimensional velocity field and phase distribution in two‐phase flow devices and components used in thermal power plants, nuclear power plants and chemical processing plants.

A complete mechanistic multidimensional model of forced‐convection boiling in heated channels is given. The potential of a CMFD approach is demonstrated to perform virtual experiments that can be used in system design and optimization, and in safety analysis.