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This study examines what motivates firms to go and remain abroad despite uncertain profit potential. In a departure from probing traditional market-seeking, profit-driven motives, the authors explore how domestically driven, sociocultural motivations may shape the foreign market entry decisions of Korean commercial banks (KCBs). The authors argue that, due to the power imbalance between KCBs and their chaebol clients within the historical and cultural contexts of their relationships, KCBs' foreign market entries may depend more on their clients' presence in these markets than on their profit potential.

The authors focus on the foreign market entries of KCBs and their client firms. Using the data of 8 KCBs and their client firms belonging to the 60 business groups (chaebols) of Korea, the authors analyze 6,577 observations involving the dyadic relationship between a KCB and its client firm in 15 host countries from 2005 to 2014.

The authors find that the number of clients' subsidiaries operating in foreign markets may increase the likelihood of KCBs entering these markets. Moreover, when KCBs earn more domestic profit from client firms, the potential Korean market in the host country is greater, and the institutional distance between the host country and Korea is smaller.

In addition to the critical role of a bank-centered financing system in advancing a developing country and its firms, the authors’ findings suggest that firms should pay attention to the local diaspora and the institutional distance between the host and home countries in order to manage power-imbalanced relationships and make them sustainable.

The study contributes to the literature on foreign market entry by demonstrating how the home country's sociocultural factors may worsen the power imbalance, thereby pushing firms to make seemingly irrational decisions to go and stay abroad. That is, KCBs' foreign operations may be a way of seeking relational benefits with client firms, which would serve as a source of long-term domestic market profits. The authors’ findings thus highlight the need to consider how sociocultural factors may also shape firms' decision-making in their international business.

The literature stresses the importance of collaboration patterns and the role of public funding in regional competitiveness. This study aims to contribute to a better understanding of this subject by focusing on two key dynamics of technological cooperation. First, the authors focus on the ability of public funding to support regional technological demand through the promotion of science and technology-based innovation (STI) and innovation based on learning-by-doing, learning-by-using, learning-by-interacting (DUI) cooperation. Second, the authors investigate whether such cooperation patterns influence the companies’ ability to support the development of novel products through the effective transfer of knowledge.

The data used in this longitudinal study are taken from the Basque statistics agency’s (EUSTAT) technological innovation survey, which compiles activities, personnel, funding sources, support institutions and other innovation-related aspects of businesses in the Basque region. The survey was carried out following the methodology of the community innovation survey (CIS). CIS data are used to generate official innovation statistics for the EU and its member countries and have been used extensively for analysis in economics. The sample included an unbalanced panel of 17,431 companies that reported research and development expenditure for the period 2013‐2017.

The results of the analysis confirm that the relationship between STI cooperation and regional funding is positive (Piñeiro-Antelo and Lois-González, 2019), but regional DUI cooperation will have a greater impact than STI cooperation on a company’s ability to generate novel products. The authors can, therefore, say that public funding is successful at supporting cooperation between science and technology agents and firms but fails to promote the transfer of knowledge and subsequent development of novel products in companies in the region.

Following a quadruple helix approach, it is important to underline the need for public policies to strengthen the connections between all the key agents in the ecosystem (where the research community, industry, public sector and citizens are all active actors), promoting technology transfer and dissemination, as well as trust among the parties, absorptive capacity and business access to resources and financing. Thus, the design of public policies should be oriented to support a firm’s innovation, balancing the exploration and exploitation of STI and DUI regional cooperation.

The contribution of this research is threefold. First, it serves to emphasize the importance of the impact of regional innovation systems on business innovation modes and their performance. Second, it takes the study of innovation systems and their impact on companies a step further by examining the impact of public funding on the companies’ ability to explore and exploit regional innovation modes. Thirdly, the authors offer a dynamic view of the region’s ability to support its own demand for technology and study the impact of regional business modes on the firms’ ability to support novel products.

In this chapter we use data from industrial plants to find out whether seniority-based pay is used as a motivational device for production workers. Alternatively, seniority-based pay could simply be a wage-setting rule independent of incentives. Unlike previous papers, we use a direct measure of seniority-based pay as well as measures of monitoring devices and explicit incentives. We find that those firms that base their wages partly on seniority are less likely to offer explicit incentives. They are also less likely to invest in monitoring devices. We also discover that these companies are more likely to engage in other human resource management policies, which result in long employment relationships. Overall these results suggest that seniority-based pay is indeed used as a motivational device.

To survey consumers living in Paris, France, to determine the extent to which they use nutrition labels, and to determine the percentage of French consumers who use nutrition labels, how often they use nutrition labels, and how they would like to see current nutrition labels improved. The researchers also wanted to determine the reasons why consumers do not use nutrition labels, and to try and assess users' and non‐users' perceptions about mandatory nutrition labelling.

French consumers (n  =  355) were surveyed in supermarkets in Paris and its suburbs, using a 21‐item questionnaire in May 2004. Interviewers used questionnaires to assess the frequency of respondents' nutrition label use, to investigate the specific nutrient information most commonly consulted on nutrition labels, the types of products on which consumers most often tend to consult nutrition labels, and to collect demographic information.

Only 45.1 per cent of the sample reported reading nutrition labels, with the majority of consumers reading labels only occasionally. Non‐label readers cited lack of interest as the primary reason why they do not read labels, but 95 per cent of the sample, when asked about mandatory nutrition labeling, felt that nutrition labeling should be required of food manufacturers. Research limitations/implications The sample size was small, participation was voluntary and was limited to the city of Paris and its suburbs, and therefore cannot be generalized to the French population.

This is the first study to collect data in a point‐of‐purchase setting in order to examine whether or not French consumers use nutrition labels.

It is globally accepted that climate change is presently the greatest threat to the sustainability of human livelihood and biodiversity. Most farmers in the study area are highly aware of climate change and its consequences on the farming system; however, mitigation strategies are clearly lacking. Among the mitigation, mechanism to reduce the threat is achieved by increasing the amount of carbon sinks and reducing greenhouse gas emission through the adoption of agroforestry practices. The purpose of this study is to determine if awareness on climate change leads to the adoption of agroforestry practices, and to examine the determinants.

A total number of 117 questionnaires were administered to the farmers in the district using stratified random sampling technique. Data were captured and analysed using STATA and XLSTAT software. Descriptive statistics and Heckprobit sample selection model were used to determine the objectives of the study.

The result established that climate change awareness does not lead to the adoption of agroforestry in the study area in which information source and member’s association were statistically significant at (p < 0.1) and (p < 0.05), respectively, and determine the adoption of agroforestry practices, while farming experience (p < 0.1), age (p < 0.05), extension visit (p < 0.05) and education (p < 0.1), were the determining factors that influence the awareness of climate change in the study area.

Regular number of extensions visit, information and training on agroforestry should be provided to the farmers in the study area.

Farmers’ association should be strengthened among the rural farmers.

The causal effect or relationship of climate change awareness on mitigation through the practice of agroforestry in South Africa, especially in the study area, has not been measured. This research set a pace in the area of climate change awareness leading to mitigation strategies through the use of agroforestry practices as an option to be used in the rural farming area of South Africa.

In this article we propose a multivariate dynamic probit model. Our model can be viewed as a nonlinear VAR model for the latent variables associated with correlated binary time-series data. To estimate it, we implement an exact maximum likelihood approach, hence providing a solution to the problem generally encountered in the formulation of multivariate probit models. Our framework allows us to study the predictive relationships among the binary processes under analysis. Finally, an empirical study of three financial crises is conducted.

This study aims to combine two fraud-related streams of the literature on guanxi and overconfidence into an integrated framework, which is the fraud triangle, to interpret the mechanism of fraud commission and detection.

A bivariate probit model with Partial Observability (POBi Probit) is applied. Moreover, the POBi Probit model is adjusted to the Chinese context. The China-specific POBi Probit model is constructed using data of Chinese A-share listed companies from 2008 to 2014, with a total of 15,109 firm-year observations.

Overconfidence induces fraud commission and worsens fraud detection; overconfidence mediates the relationship between fraud and guanxi; the “white side” of guanxi comes from alumni networks, while the “dark side” is derived from relatives-based networks; overconfidence induces fraud commission in accounting and disclosure and benefits the detection of disclosure frauds. Guanxi suppresses fraud commission in management and disclosure, however, it worsens fraud detection given fraud in management and disclosure; overconfidence induces fraud commission in both state-owned enterprises (SOE) and non-SOEs, and benefits fraud detection in SOEs. Guanxi suppresses fraud commission and worsens fraud detection in SOEs and city-owned firms.

There are two drawbacks of the partial observable bivariate probit (POBi-Probit) method that must be mentioned here. On one hand, the ex ante variable selection is one of the most difficult parts of applying the POBi-Probit model and different variables are included in different studies. On the other hand, the POBi-Probit model might not converge if too many variables are included. Thus, many widely accepted factors can be included in the model. Thus, this study initially sets the POBi-Probit model based mainly on Khanna et al. (2015) and then adjusts the model for the Chinese context (e. g. considering government ownership) according to Yiu et al. (2018) and Zhang (2018) and the local study of Meng et al. (2019). Considering the observability of fraud, on one hand, the observability of fraud commission is a widely accepted limitation, especially when accounting opacity comes across with regulatory efficiency (Yiu et al. (2018). On the other hand, the observability of relationships is another obstacle to this study. Future studies can go further by revealing the presently unobservable relationships using Big Data technology.

This paper theoretically and practically contributes to the literature on both corporate fraud and corporate governance. Theoretically, by introducing integrated principal-agent resource-reliance theory (IPRT) and upper echelon theory (UET), this paper broadens the framework of fraud triangle theory (FTT) and testifies the availability of the broaden FTT in the transitional and emerging-market context of China. Practically, this paper provides evidence that guanxi and overconfidence are two of the factors affecting corporate fraud. Thus, this paper provides a governance approach opposing corporate fraud in China, which may help the other emerging economies in transition.

This paper explored factors that impact insurance choices of demand (farmers) and supply (insurance companies) side, respectively.

Specially designed survey questions allow one to fully observe the demand tendency from farmers and partially observe the supply tendency from insurance companies. Using bi‐vairate probit model, a joint estimation of insurance decisions of both supply and demand sides suggested that factors perform different roles in affecting insurance participation.

Farmer's age and education have positive impacts on insurance demand, but are indifference to insurance providers. Insurance suppliers care about farmers' experience in the fields when providing insurance services, however, on the demand side, farmers' experience occasionally results in overconfidence and hence, impedes farmers' insurance purchasing. Production scales, proxy by sow inventory, are put more weight by farmers than insurance suppliers when making decisions. Production efficiency measures perform as incentives for farmers to purchase insurance. While suppliers prefer customers who use vaccine, farmers tend to treat vaccine as a substitute for insurance to prevent disease risk.

Results from bi‐vairate probit model offer deeper understandings about livestock insurance choices and provide further insights to improve policy design and promote participation.

The study designed a special questionnaire and firstly used bi‐vairate probit model to offer more understandings about demand and supply sides of livestock insurance.

The purpose of this paper is to empirically examine the role of perceived ability to participate in decision making in the workplace, with respect to job satisfaction.

Data from the fourth wave of the European Value Survey, is utilised, and a bivariate probit model is employed to account for unobserved heterogeneity.

Empirical analysis comparing univariate and bivariate probit models reveals that the results from the former are negatively biased; potentially indicating that prior research may have underestimated the impact of participative decision making (PDM) on job satisfaction. Additionally, it appears clear that the magnitude of the marginal effects for both socio-demographic and work characteristics do not differ when comparing workers with above and below average participation. More importantly, the authors find a substantial negative marginal effect of below average participation on job satisfaction (close to three times the magnitude of the next largest marginal effect estimated in the model), indicating how crucial it is for employers to actively pursue programmes that enhance PDM.

This study contributes to the growing literature aimed at understanding drivers of satisfaction in the workplace. Adding to the scant empirical investigation of the influence of PDM on job satisfaction, the authors find strong evidence of a direct and positive impact, which is further amplified after controlling for unobserved heterogeneity.

This paper formulates and analyzes Bayesian model variants for the analysis of systems of spatial panel data with binary-dependent variables. The paper focuses on cases where latent variables of cross-sectional units in an equation of the system contemporaneously depend on the values of the same and, eventually, other latent variables of other cross-sectional units. Moreover, the paper discusses cases where time-invariant effects are exogenous versus endogenous. Such models may have numerous applications in industrial economics, public economics, or international economics. The paper illustrates that the performance of Bayesian estimation methods for such models is supportive of their use with even relatively small panel data sets.