Search results

This paper reviews the use of logit and probit models in marketing and focuses on demonstrating the use of ordered probability models. This type of model is appropriate for many applications in marketing and business where the dependent variable of interest is ordinal (e.g., likert scales). A comparison between the properties of the ordinary least squares (OLS) model and ordered logit and probit models is made using consumer satisfaction data on automobiles. This comparison between the two models shows that the use of OLS for ordered categorical data gives misleading results and produces biased estimates, leading to inaccurate hypothesis testing. The paper concludes that ordered probability models, such as the ones illustrated, should be employed in marketing and business research where the dependent variable is ordinal.

Genetically modified (GM) crops, particularly GM grain crops, have been controversial since their commercialization in 1996. However, only a few studies have investigated farmers’ attitudes toward adopting GM grain crops in China. The purpose of this paper is to explore farmers’ willingness to adopt GM insect-resistant rice prior to its commercial release in China and determines the factors that affect farmers’ prospective adoption decisions.

The data are collected using a questionnaire. Descriptive statistics are used to analyze the farmers’ potential willingness to adopt GM rice and level of awareness of GM rice and socioeconomic characteristics. Ordered and binary probit models are applied to identify the key factors that affect the farmers’ decision to adopt GM insect-resistant rice.

Descriptive statistics show that most farmers have little knowledge of GM rice, approximate 35.5 percent of farmers could plant GM rice, and over half of the respondents are uncertain whether or not they will adopt the new crops. The results of econometric analyses show that increasing output and income, and simplicity in crop management, have positive effects on prospective adoption, whereas the high-seed price of GM rice has a significantly negative effect. Health implications also have a significantly positive effect on the farmers’ decision to adopt GM grain crops. A comparative analysis of ordered and binary probit models demonstrates that farmers are more deliberate in their decisions when they have fewer choices. Aside from the above-mentioned variables, the following factors are also statistically significant in the probit model: government technicians’ recommendations, neighbors’ attitudes, level of environmental risks, and the farmer’s age.

Information on the major risks and benefits of GM rice was provided to the farmers in the questionnaire. The farmers were then asked to choose from the three ordered alternative answers, namely, “accept,” “uncertain,” and “reject”. Both ordered and binary probit models were applied to comparatively analyze the collected data. This study is one of a handful of studies that employ these econometric models to identify and explain the underlying factors that affect farmers’ decisions. The relevant findings have important implications for future agricultural policy in China.

This study aims to develop an ordered probit model to explain and predict small business relative performance in Chile, South America.

The design is survey research. The sample includes 403 small businesses classified as 158 failed firms, 101 mediocre firms and 144 successful firms within all economic sectors. The model variables are: internet, starting with adequate working capital, managing good financial and accounting records, planning, owner formal education, professional advice, having partners, parents owning a business, and marketing efforts.

The eight-variable model, tested with ordered probit, is a significant predictor of the level of performance at the 0.000 level. Also, six of the eight variables are significant predictors at the 0.05 level: internet, starting with adequate working capital, managing good financial and accounting records, owner, professional advice, having partners, parents owning a business, and marketing efforts. Two of the variables – i.e. planning and formal education – were not significant. ANOVA test of differences were run for each of the eight variables based on the level of performance were also run and results reported.

The model does in fact predict relative performance, so the model can be used to improve the probability of success. Thus, an entrepreneur can use the model to gain a better understanding of which resources are needed to increase the probability of success, and those who advise entrepreneurs can help them use the model. Investors and creditors can use the model to better assess a firm's potential for success. There is an extensive public policy implications discussion regarding how to use the model to assist entrepreneurial ventures so that society can benefit in direct and indirect ways via the allocation of limited resources toward higher potential businesses. Entrepreneurs and small business educators can use the model's variables to influence future business leaders, public policy makers, and their practices.

This study improves the Lussier 15 variable success versus failure prediction model by adding the use of the internet and taking out highly correlated variables. While Lussier and others ran logistic regression with only two levels of performance, this study uses the more robust ordered probit model with three levels of performance. It presents public policy with implications for Chilean institutions to promote entrepreneurship. Finally, it contributes to the literature because, to date, no empirical success versus failure studies have been found that were conducted in Chile or any small, open economies in Latin America

Asset sales can have opposing effects on firm credit quality. On the one hand asset sales could signal increased credit risk resulting from distress or on the other hand they could improve internal liquidity and hence credit quality. Therefore the impact potential asset sales can have on credit quality is an empirical question and one that has previously not been examined in the literature. The paper aims to discuss these issues.

Using credit ratings as a measure of firm credit quality, in ordered probit regressions, this study finds evidence consistent with the internal liquidity view of the asset sales-credit risk relationship.

Results from ordered probit regressions of credit ratings show that the likelihood of higher credit ratings is increasing in industry-level turnover of real assets

Credit-rating agencies often cite the impact of asset sales on firm credit quality as a motivation for their rating assignments. Distress-driven asset sales could reduce firm credit quality whereas other asset sales could result in increased internal firm liquidity and hence improve firm credit quality. This bi-directional expectation leaves the question of how asset sales affect credit quality to be answered empirically and has not been previously tested in the literature.

The purpose of this paper is to revisit the ordered probit model of Hausman et al. after the NYSE decimalization.

The changed ordered probit model.

The model can somewhat capture the different impact of trading-related “explanatory” variables on price changes among three different decimals but does not explain much about price discreteness and irregular transaction intervals among the existing models of stock price discreteness. Overall 1/16th and 1/24th range of the dependent variable is better explained by trading-related explanatory variables than 1/8th range of the dependent variable for small firms and there is not much difference in large firms among three decimals. The results imply that finer specification in decimalization and smaller firm size matters in trading after the decimalization project.

First paper to revisit the ordered probit model of Hausman et al. after the NYSE decimalization.

This study aims to investigate the safety and liability of autonomous vehicles (AVs), and identify the contributing factors quantitatively so as to provide potential insights on safety and liability of AVs.

The actual crash data were obtained from California DMV and Sohu websites involved in collisions of AVs from 2015 to 2021 with 210 observations. The Bayesian random parameter ordered probit model was proposed to reflect the safety and liability of AVs, respectively, as well as accommodating the heterogeneity issue simultaneously.

The findings show that day, location and crash type were significant factors of injury severity while location and crash reason were significant influencing the liability.

The results provide meaningful countermeasures to support the policymakers or practitioners making strategies or regulations about AV safety and liability.

This chapter compares the performance of the maximum simulated likelihood (MSL) approach with the composite marginal likelihood (CML) approach in multivariate ordered-response situations. The ability of the two approaches to recover model parameters in simulated data sets is examined, as is the efficiency of estimated parameters and computational cost. Overall, the simulation results demonstrate the ability of the CML approach to recover the parameters very well in a 5–6 dimensional ordered-response choice model context. In addition, the CML recovers parameters as well as the MSL estimation approach in the simulation contexts used in this study, while also doing so at a substantially reduced computational cost. Further, any reduction in the efficiency of the CML approach relative to the MSL approach is in the range of nonexistent to small. When taken together with its conceptual and implementation simplicity, the CML approach appears to be a promising approach for the estimation of not only the multivariate ordered-response model considered here, but also for other analytically intractable econometric models.

Recent developments in the literature on international trade and foreign direct investment (FDI) emphasise the role of firm characteristics in shaping firm participation in exports and FDI. The seminal work of Melitz and Helpman-Melitz-Yeaple (HMY) places heterogeneity in firm productivity at the heart of exporting and FDI. While the HMY hypothesis finds support for firms in the industrialised economies, the evidence from developing economies is limited. This paper attempts to contribute empirical insights into the theoretical framework laid out by Melitz, Helpman et al., Head and Ries with evidence from India.

While related literature takes into account several firm-specific and country-specific characteristics to explain outward FDI, the paper unifies the firms ' choice of markets (domestic versus foreign) and mode of serving foreign markets (export versus FDI) in a single framework in the line of the HMY model. The paper uses an ordered probit model that combines domestic market-oriented, exporting and outward FDI-oriented firms in a quality ladder.

The findings are that there are strong differences between the characteristics of domestic firms, exporting firms, and firms that invest abroad. The differences between these firms are consistent with the HMY model. The most productive firms appear to walk up this ladder of quality and graduate to globalisation through exporting and then through FDI.

A key innovation of this paper is an ordered probit model that combines domestic market-oriented, exporting and outward FDI-oriented firms in a quality ladder. The paper also brings empirical insights into the theoretical framework laid out by Melitz, Helpman et al., Head and Ries with evidence from India.

Research on strategic choices available to the firm are often modeled as a limited number of possible decision outcomes and leads to a discrete limited dependent variable. A limited dependent variable can also arise when values of a continuous dependent variable are partially or wholly unobserved. This chapter discusses the methodological issues associated with such phenomena and the appropriate statistical methods developed to allow for consistent and efficient estimation of models that involve a limited dependent variable. The chapter also provides a road map for selecting the appropriate statistical technique and it offers guidelines for consistent interpretation and reporting of the statistical results.

This study aims to explore patterns in e‐tail spending across different demographic groups and to predict which households are the most frequent shoppers and highest spenders. Further, it aims to investigate which households are least likely to purchase from unfamiliar online stores.

Using a random sample of Florida households, the study is the first to use probit and ordered probit models to study Internet purchasing behavior.

Younger, college educated, higher income households living in suburban, rural and small towns spend and shop the most online. Caucasians purchase online more often than African Americans and Hispanics but spend about the same amount. The study also finds that male, Hispanic, college educated and younger consumers are more willing to purchase from unfamiliar online stores.

This study provides new evidence on factors affecting household online spending and buying decisions. Previous studies have not used an ordered probit to model different levels of spending and this new specification provides information about which demographic groups are the most (or the least) frequent buyers as well as which demographic groups are the highest (or the lowest) e‐tail spenders. This study also investigates which demographic groups are most likely to shop only at stores with whom they are already familiar.