Search results

The challenge of truckload routing is increased in complexity by the introduction of stochastic demand. Typically, this demand is generalized to follow a Poisson distribution. In this chapter, we cluster the demand data using data mining techniques to establish the more acceptable distribution to predict demand. We then examine this stochastic truckload demand using an econometric discrete choice model known as a count data model. Using actual truckload demand data and data from the bureau of transportation statistics, we perform count data regressions. Two outcomes are produced from every regression run, the predicted demand between every origin and destination, and the likelihood that that demand will occur. The two allow us to generate an expected value forecast of truckload demand as input to a truckload routing formulation. The negative binomial distribution produces an improved forecast over the Poisson distribution.

To address the safety concerns generated by truck crashes occurred in big cities, this paper analyzes the zip code tabulation area (ZCTA)-based truck crash frequency across four temporal intervals – morning (6:00–10:00), mid-day (10:00–15:00), afternoon (15:00–19:00), and night (19:00–6:00) in New York City in 2010. A multivariate conditional autoregressive count model is used to recognize both spatial and temporal dependences. The results prove the presence of spatial and temporal dependencies for truck crashes that occurred in neighboring areas. Built environment attributes such as various types of business establishment density and traffic volume for different types of vehicles, which are important factors to consider for crashes occurred in an urban setting, are also examined in the study.

Purpose – This chapter provides an overview of issues related to analysing crash data characterised by excess zero responses and/or long tails and how to overcome these problems. Factors affecting excess zeros and/or long tails are discussed, as well as how they can bias the results when traditional distributions or models are used. Recently introduced multi-parameter distributions and models developed specifically for such datasets are described. The chapter is intended to guide readers on how to properly analyse crash datasets with excess zeros and long or heavy tails.

Methodology – Key references from the literature are summarised and discussed, and two examples detailing how multi-parameter distributions and models compare with the negative binomial distribution and model are presented.

Findings – In the event that the characteristics of the crash dataset cannot be changed or modified, recently introduced multi-parameter distributions and models can be used efficiently to analyse datasets characterised by excess zero responses and/or long tails. They offer a simpler way to interpret the relationship between crashes and explanatory variables, while providing better statistical performance in terms of goodness-of-fit and predictive capabilities.

Research implications – Multi-parameter models are expected to become the next series of traditional distributions and models. The research on these models is still ongoing.

Practical implications – With the advancement of computing power and Bayesian simulation methods, multi-parameter models can now be easily coded and applied to analyse crash datasets characterised by excess zero responses and/or long tails.

The keywords from patent documents contain a lot of information of technology. If we analyze the time series of keywords, we will be able to understand even more about technological evolution. The previous researches of time series processes in patent analysis were based on time series regression or the Box-Jenkins methodology. The methods dealt with continuous time series data. But the keyword time series data in patent analysis are not continuous, they are frequency integer values. So we need a new methodology for integer-valued time series model. The purpose of this paper is to propose modeling of integer-valued time series for patent analysis.

For modeling frequency data of keywords, the authors used integer-valued generalized autoregressive conditional heteroskedasticity model with Poisson and negative binomial distributions. Using the proposed models, the authors forecast the future trends of target keywords of Apple in order to know the future technology of Apple.

The authors carry out a case study to illustrate how the methodology can be applied to real problem. In this paper, the authors collect the patent documents issued by Apple, and analyze them to find the technological trend of Apple company. From the results of Apple case study, the authors can find which technological keywords are more important or critical in the entire structure of Apple’s technologies.

This paper contributes to the research and development planning for producing new products. The authors can develop and launch the innovative products to improve the technological competition of a company through complete understanding of the technological keyword trends.

The retrieved patent documents from the patent databases are not suitable for statistical analysis. So, the authors have to transform the documents into structured data suitable for statistics. In general, the structured data are a matrix consisting of patent (row) and keyword (column), and its element is an occurred frequency of a keyword in each patent. The data type is not continuous but discrete. However, in most researches, they were analyzed by statistical methods for continuous data. In this paper, the authors build a statistical model based on discrete data.

Copula modeling enables the analysis of multivariate count data that has previously required imposition of potentially undesirable correlation restrictions or has limited attention to models with only a few outcomes. This article presents a method for analyzing correlated counts that is appealing because it retains well-known marginal distributions for each response while simultaneously allowing for flexible correlations among the outcomes. The proposed framework extends the applicability of the method to settings with high-dimensional outcomes and provides an efficient simulation method to generate the correlation matrix in a single step. Another open problem that is tackled is that of model comparison. In particular, the article presents techniques for estimating marginal likelihoods and Bayes factors in copula models. The methodology is implemented in a study of the joint behavior of four categories of US technology patents. The results reveal that patent counts exhibit high levels of correlation among categories and that joint modeling is crucial for eliciting the interactions among these variables.

In recent years, the field of comparative and international education (CIE) has experienced an outburst of self-reflective papers wherein comparativists study the nature of the field and map its content. This study contributes to this trend by drawing attention to a previously unstudied aspect of CIE: its purpose. Using Arnove’s dimensions as a starting point to create five new purpose categories, four prominent CIE journals are surveyed to test whether the pragmatic history of CIE is evident in its current body of research. In this process, a complete and clear genetic mapping of the journals is created, which explores their similarities and differences, as well as the changes in their content over time. Findings indicate that the pragmatic purpose of CIE dominates, though it is primarily emancipatory and transformative in its prescription. Furthermore, articles rooted in specific situational contexts were more prominent than expected considering the comparative and international nature of the field.

This paper analyzes county-level firm births across the United States using a spatial count model that permits spatial dependence, cross-correlation among different industry types, and over-dispersion commonly found in empirical count data. Results confirm the presence of spatial autocorrelation (which can arise from agglomeration effects and missing variables), industry-specific over-dispersion, and positive, significant cross-correlations. After controlling for existing-firm counts in 2008 (as an exposure term), parameter estimates and inference suggest that a younger work force and/or clientele (as quantified using each county’s median-age values) is associated with more firm births (in 2009). Higher population densities is associated with more new basic-sector firms, while reducing retail-firm starts. The modeling framework demonstrated here can be adopted for a variety of settings, harnessing very local, detailed data to evaluate the effectiveness of investments and policies, in terms of generating business establishments and promoting economic gains.

The purpose of this paper is to provide an analysis of the risk factors influencing non-compliance in UK organic farming.

The paper uses a formal econometric model of risk analysis to provide empirical evidence on the determinants of non-compliance in organic farming. A panel of data from the archives of the largest control body in the UK for 2007-2009 is used, and specific analyses are performed for two types of non-compliances. A zero inflated count data model is used for the estimation, taking into account the fact that the occurrences of non-compliance are very sparse.

Results show the existence of strong co-dependence of non-compliant behaviours (i.e. the occurrence of major and critical non-compliance increases the probability of occurrence of the minor one; similarly the probability of occurrence of major non-compliance increases when minor non-compliance occur). Besides, livestock production and farm size are relevant risk factors.

Albeit highly representative, the findings are based on Soil Association data only and not on all UK organic farms.

The paper provides practical indications for control bodies, concerning aspects that could be strengthened for more efficient risk-based inspections. The paper advocates the use of financial information like turnover or capital stock, and of data concerning the characteristics of the farmers, that could substantially improve the probability of detecting the most severe non-compliances.

Certification is essential for organic farming, and an improvement of inspection procedures through a risk-based approach could add efficiency and effectiveness to the whole organic food system, with obvious advantages for consumers and the society as a whole.

This paper provides for the first time empirical evidence concerning the implementation of the organic certification system in the UK.

The purpose of this study is to investigate empirically what affects Global Reporting Initiative (GRI)-based sustainability reporting and its relationship with firm performance in the aviation industry between 2006 and 2015.

The authors derived data from the GRI Sustainability Disclosure Database and Thomson Reuters EIKON; from the former, they downloaded GRI-based reports, and from the latter, they obtained financial data. The authors performed four-level analysis – report existence, report count, application level of report and firm performance –using various regression models (i.e. logistic regression, Poisson regression, ordered logistic regression and ordinary least squares regression).

First, the authors based the analysis on the existence of GRI-based sustainability reports, which showed that firm size and leverage are positively associated with sustainability reporting. Contrary to expectations, ownership was negatively associated. Furthermore, free cash flow per share, growth and profitability do not have significant effects on sustainability reporting, in contrast to expectations. Subsequent analysis was based on report count (number of total published reports within the examination period) and application levels of reports. Compared to the preceding analysis, there were no notable surprises. In addition, we found evidence that growth is negatively associated with application levels of reports (partially supported). Thus, report existence, report count and application level results largely confirm each other. Finally, the authors tested the effect of sustainability reporting on firm performance, which did not produce significant results. Thus, in the aviation industry, sustainability reporting does not play a significant role in enhancing firm performance.

First, the findings show that larger and highly leveraged aviation firms can reduce agency and legitimacy costs through sustainability reporting. Surprisingly, the same assumption did not hold for ownership structure as the firms with diffused ownership base tend not to publish sustainability reports. Thus, boards are advised to establish and improve monitoring mechanisms in these types of firms. Second, although the number of aviation companies publishing separate sustainability reports has increased significantly over the years, almost half of the companies are not still producing sustainability reports. Hence, if the aviation industry believes the merits of engaging in sustainability issues and sincerely desires to enhance its sustainability reporting practices, the authors can suggest the following initiatives. Boards might encourage companies to incorporate sustainability issues into company operations by assigning the necessary financial and human resources. The boards might also establish a separate sustainability committee or department, which could focus on sustainability issues and reporting practices. Regulatory bodies could also encourage aviation companies to act in a socially and environmentally responsible manner by proposing legal requirements and providing guidance.

Relevant civil organisations and environmental activists might undertake more active roles to enhance awareness of sustainability issues in the aviation industry.

Most of the prior studies did not focus on standalone GRI-based sustainability reports, and they were conducted on limited samples and not the aviation industry in particular. This study aims to fill these gaps empirically by establishing testable hypotheses and attempting to demonstrate the validity of theoretical relationships in a wide range of data and among aviation companies worldwide. In this sense, this study is unique in what it undertakes. This study also tests whether sustainability reporting impacts firm value in the aviation industry which, to the best of the authors’ knowledge, has not been examined in prior studies to this extent.

Longitudinal regression analysis is conducted to clarify causal relations and control for unwanted influences from actor heterogeneity and state dependence on theoretically important coefficient estimates. Because strategic management contains theory on how firms differ and how firm actions are influenced by their current strategic position and recent experiences, consistency of theory and methodology often requires use of longitudinal methods. We describe the theoretical motivation for longitudinal methods and outline some common methods. Based on a survey of recent articles in strategic management, we argue that longitudinal methods are now used more frequently than before, but the use is still inconsistent and insufficiently justified by theoretical or empirical considerations. In particular, strategic management researchers should use dynamic models more often, and should test for the presence of actor effects, autocorrelation, and heteroscedasticity before applying corrections.