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Branching is not the only way for foreign banks to enter a national market, and it is impractical when there are informational and cultural barriers and asymmetries among countries. The purpose of this paper is to analyze the determinants of cross-border branching in the Latin American banking sector, a region with regulatory disparity and political and economic instability, offering elements to a grounded strategic decision.

This study uses data from six Latin American countries. To account for the preponderance of zero counts, classes of zero-inflated models are applied (Poisson, negative binomial, and mixed). Model fit indicators obtained from differences between observed and estimated counts are used for comparisons, considering branches in each region established by banks from every other foreign region of the sample.

Branching by foreign banks is positively correlated with the population, GDP per capita, household disposable income, and economic freedom score of the host country. The opposite holds for the unemployment rate and entry regulations of the host country.

Few paper address cross-border banking in emerging economies. This paper analyzes cross-border branching in Latin America in the context of the current financial integration and bank strategy. Econometrically, its pioneering design allows modeling of inflation of zeros, over-dispersion, and the multilevel data structure. This design allowed testing of a novel country-level variable: the host country’s economic freedom score.

The purpose of this paper is to indicate trade characteristics of Foreign direct investment (FDI) inflows in China and examine the dynamic interaction between FDI inflows and China’s international trade through empirical analysis.

At first, this paper builds the probability distribution model (Poisson and negative binomial (NB)) to capture the characteristics of spatial distribution of all kinds of FDI firms in Chinese cities and provinces based on count data, so as to indicate the potentials for further introducing FDI inflows in China; Second, this paper investigates the effects of trade on FDI firms inflows based on probability regress model (Binary Logit, Tobit, NB, Poisson, zero inflated negative binomial) and shows how international trade accelerates the different kinds of FDI firms to agglomerate in Eastern, Middle and Western region by the endowments of factors; third, this paper empirically examines the magnitude and characteristics of trade effects generated by FDI inflows by building dynamic panel model based on continuous data.

First, statistical tests of probability distribution model based on count data show that there are characteristics of spatial agglomeration of FDI firms such as manufacture firm, R & D firm, managing and marketing firm and total sectors, which obey NB distribution as whole; Second, this study indicate that FDI inflows have strong positive effects on the international trade in China’s provinces and on China’s regional trade, and that most of foreign firms in China are export oriented being strongly characterized as labor-intensive industries, especially, contributions of FDI to imports are greater than the contributions of FDI to exports in China’s Middle and Western trade, and the growth of FDI trade in China’s trade volume has been strong over the past years; third, the empirical results of models based on count data and continuous data indicate that FDI inflows have significantly positive relationship with international trade, that is, the relationship between FDI and international trade in the case of China is the characteristics with complement and imports substituting relationship.

Because of mixed data set for FDI inflows of processing and assembling trade and production-oriented FDI, efficiency-seeking and knowledge or technology – intensive FDI inflows in the past 36 years, the paper only investigate characteristics of FDI inflows in China before the turning point of financial crisis, but it is important for capturing the whole picture of trade characteristics of FDI inflows in China.

The derived quantitative results imply that there are still greater potentials for further introducing FDI inflows in China, and decision-maker should make policy of introducing FDI inflows which are favorable to supporting innovative activities and economic agglomeration, and preferably encourage efficiency-seeking and export-oriented FDI inflows so as enhance quality and efficiency of economic growth, which are also helpful to accelerate upgrade of Chinese industry and gradually shorten gap of growth among Eastern, Middle and Western region.

FDI inflows in China not only stimulate the remarkable growth of bilateral trade between host country and home country, but also promote the growth of international trade between China and the rest of the world. Thus, policies of bilateral or multilateral free-trade and investment area should be encouraged, which will be also favorable to promote the growth and welfare in all the regions.

This paper demonstrates that spatial distributions of FDI firms in Chinese cities and provinces obey NB probability distribution pattern, and puts forward the methodology of model based on count data and continuous data. Besides, this paper quantitatively indicates trade characteristics of FDI inflows in China as well as the dynamic interaction between FDI inflows and China’s international trade.

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.

The purpose of this case study is to investigate the usefulness of altmetrics for measuring the broader impact of research.

This case study is based on a sample of 1,082 the Public Library of Science (PLOS) journal articles recommended in F1000. The data set includes altmetrics which were provided by PLOS. The F1000 data set contains tags on papers which were assigned by experts to characterise them.

The most relevant tag for altmetric research is “good for teaching”, as it is assigned to papers which could be of interest to a wider circle of readers than the peers in a specialised area. One could expect papers with this tag to be mentioned more often on Facebook and Twitter than those without this tag. The results from regression models were able to confirm these expectations: papers with this tag show significantly higher Facebook and Twitter counts than papers without this tag. This clear association could not be seen with Mendeley or Figshare counts (that is with counts from platforms which are chiefly of interest in a scientific context).

The results of the current study indicate that Facebook and Twitter, but not Figshare or Mendeley, might provide an indication of which papers are of interest to a broader circle of readers (and not only for the peers in a specialist area), and could therefore be useful for the measurement of the societal impact of research.

The purpose of this paper is to provide international data on the occurrence (and rates) of clinical errors, identified by type and consequence in the Lombardy region, and to assess empirically the association between hospital accreditation‐type measures and clinical error rates by merging hospital discharge records and medical malpractice claim data in the Lombardy region (Italy).

Data were drawn from the regional database collecting claims and demands for reimbursement declared by patients hospitalized in regional healthcare structures and regional archives collecting hospital discharge records. To model the variability of clinical errors rates, binomial negative regression models were applied. For improved interpretation of the results, a regression tree methodology was used.

The results demonstrated that the rate of readmission for the same major diagnostic category and the rate of discharges against medical advice significantly affect the incidence of errors causing patient death, whereas the rate of unscheduled surgical readmission in the operating room significantly affects the rate of surgical error.

The findings confirm that claims data is problematic in nature because of the limited number of claims generally emerging from administrative sources. The article proposes using proper regression models for count data, taking into account over‐dispersion and excess zeroes and classification tree methods for a better interpretation of empirical evidence.

Health structures where quality outcomes have a significant impact on clinical error rates should be monitored in depth, investigating the medical charts of involved patients to identify quality problems and problematic areas.

As a risk management strategy, the combined use of claims data and clinical administrative data is proposed to shed light on the more problematic, error‐prone areas, allowing regional stakeholders to receive relevant, highly cost‐effective and timely information and an in‐depth understanding of the problematic areas in the assessment of risk.

The purpose of this study is to investigate the relationship of the post-publication peer review (PPPR) polarity of a paper to that paper's citation count.

Papers with PPPRs from Publons.com as the experimental groups were manually matched 1:2 with the related papers without PPPR as the control group, by the same journal, the same issue (volume), the same access status (gold open access or not) and the same document type. None of the papers in the experimental group or control group received any comments or recommendations from ResearchGate, PubPeer or F1000. The polarity of the PPPRs was coded by using content analysis. A negative binomial regression analysis was conducted to examine the data by controlling the characteristics of papers.

The four experimental groups and their corresponding control groups were generated as follows: papers with neutral PPPRs, papers with both negative and positive PPPRs, papers with negative PPPRs and papers with positive PPPRs as well as four corresponding control groups (papers without PPPRs). The results are as follows: while holding the other variables (such as page count, number of authors, etc.) constant in the model, papers that received neutral PPPRs, those that received negative PPPRs and those that received both negative and positive PPPRs had no significant differences in citation count when compared to their corresponding control pairs (papers without PPPRs). Papers that received positive PPPRs had significantly greater citation count than their corresponding control pairs (papers without PPPRs) while holding the other variables (such as page count, number of authors, etc.) constant in the model.

Based on a broader range of PPPR sentiments, by controlling many of the confounding factors (including the characteristics of the papers and the effects of the other PPPR platforms), this study analyzed the relationship of various polarities of PPPRs to citation count.

The emergence of mobile health (mHealth) products has created a capability of monitoring and managing the health of patients with chronic diseases. These mHealth technologies would not be beneficial unless they are adopted and used by their target users. This study identifies key factors affecting the usage of mHealth apps based on user usage data collected from an mHealth app.

Using a dataset collected from an mHealth app named mPower, developed for patients with Parkinson's disease (PD), this paper investigated the effects of disease diagnosis, disease progression and mHealth app difficulty level on app usage, while controlling for user information. App usage is measured by five different activity counts of the app.

The results across five measures of mHealth app usage vary slightly. On average, previous professional diagnosis and high user performance scores encourage user participation and engagement, while disease progression hinders app usage.

The findings potentially provide insights into better design and promotion of mHealth products and improve the capability of health management of patients with chronic diseases.

Studies on the mHealth app usage are critical but sparse because large-scale and reliable mHealth app usage data are limited. Unlike earlier works based solely on survey data, this research used a large user usage data collected from an mHealth app to study key factors affecting app usage. The methods presented in this study can serve as a pioneering work for the design and promotion of mHealth technologies.

The World Health Organization issued its global action plan on physical activities 2018–2030, emphasizing the importance of context-specific evidence on the subject. Accordingly, this study aims to provide unique and important policy insights on trends and drivers of participation in physical exercises by academic community in Sri Lankan universities.

For this purpose, we collected cross-sectional data (n = 456) in 2020 using a survey, and first, estimated a double-hurdle model to uncover covariates influencing likelihood and intensity of physical exercises overall. Second, count-data models are estimated to capture regularity of key exercises.

The results reveal that about 50% of members do not participate in any general physical exercise. Older members (marginal effect (ME) = 3.764, p < 0.01), non-Buddhists (ME = 54.889, p < 0.01) and alcohol consumers (ME = 32.178, p < 0.05) exhibit a higher intensity of participating in exercises overall. The intensity is lower for rural members (ME = −63.807, p < 0.01) and those with health insurance covers (ME = −31.447, p < 0.05). Individuals diagnosed for chronic illnesses show a higher likelihood of exercising but, their time devotion is limited. The number of children the academic staff members have as parents reduces the likelihood, but for those who choose to exercise have higher time devotion with increased number of children. The covariates play a similar role in determining regularity of key exercises: walking, jogging and exercising on workout machines.

The results imply a need to promote exercising in general and particularly among younger, healthy, insured and female individuals living in rural sector.

The study covers an under-researched professional sub-group in an under-researched developing context, examining both the likelihood and regularity of exercising as both dimensions are equally important for individuals to maintain healthy lives.

To provide a brief survey of the literature directed towards the analysis of warranty claim data.

For convenience, this survey of the analysis of warranty claims data is somewhat arbitrarily be classified by topics as follows: age‐based claims analysis, aggregated warranty claims analysis, marginal counts of claims analysis, warranty claims analysis by using covariates, estimation of lifetime distribution using supplementary data, two‐dimensional warranty, warranty costs analysis, sales lag and reporting lag analysis, and forecasts of warranty claims.

Emphasis is placed on a discussion of different kinds of warranty claims data selected from reviews and on a comparison of the statistical models and methods used to analyze such data.

Since the literature on product warranty data is vast, more work on this problem is needed.

This review points out why warranty claims data is important and gives a survey of the literature pertaining to the analysis of such data. The emphasis is on the analysis of minimal databases of real warranty data, constructed by combining information from different sources, which can be collected economically and efficiently through service networks. The research is applicable for those responsible for product reliability, product design decisions and warranty management in manufacturing industries.

The paper reviews different statistical models and methods used to analyze warranty claims data. The statistical models and methods presented are be valuable and meaningful tools for product reliability and warranty management and analysis.

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.