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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 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.

From the standpoint of a novice researcher, the author has examined how blogging as a literacy practice enables one to explore and understand self in relation to analysing data and making meaning. The purpose of this paper is to discuss how blogging can be used as a reflective and critical tool and explore the notion of blogging as transgressive, or stumble data.

The theoretical underpinnings of blogging as a literate critical practice are congruent with the work of Freire. Thinking about research through this paradigm thus becomes transformative because it enables one to do data differently. This paper also considers and interrogates different approaches to the analysis of data, and frames blogging as a way to inquire more subjectively.

In this paper, blogging is represented as uncoded data; a concurrent and seemingly unstructured method of analysis. To this end, the definition of data is questioned in terms of what is legitimate, and what counts. Subjectivity puts blogging into a relevant context, as one’s positioning and experiences become a layered foundation upon which to understand one’s research more closely and meaningfully. Self-representation is deeply entwined with self-documentation, which can enable the identity of the writer/researcher to attain greater definition.

Flirting with data in unconventional ways and inhabiting a space of unknowing enabled original thinking and creative processes to enter the research space. Reading and writing are framed as methods of inquiry and analysis that are separate, or an alternative, to coding data.

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 study found a significant correlation between the number of external links and the journal impact factor for LIS journals. Journals with higher journal impact factor scores tend to attract more links to their Web sites. The study also investigated issues pertaining to data collection methods for webometrics research. It showed that the choice of search engine for data collection could affect the conclusion of a study. Data collected at different time periods were found to be fairly stable. The use of multiple rounds of data collection was shown to be beneficial, especially when the result from a single round of data is borderline significant or inconclusive.

The purpose of this paper is to examine the determinants of operational losses in insurance companies.

By using most common estimates of frequency and severity of losses that affected business‐lines during 2009, the paper integrates a quantitative aspect that reflects the mode of organization in the insurance company. In this paper, it would be more appropriate to focus on the frequency and severity of losses estimated by insurers and which are related to each category of operational risk events that took place in 2009.

The paper finds that the frequency of operational losses is positively related to the Market Share (MARKSHARE) and the Rate of Geographic Location (RAGELOC). However, the occurrence of loss is negatively related to the Variety of Insurance Activities (VARIACT). The paper also found a decrease in the frequency of losses associated with a large number of employees. Therefore, there is a significant relationship between the Human Factor (HF) and the occurrence of operational losses. In terms of severity, the empirical study has shown that the probability of zero intensity of operational losses is negatively influenced by the Market Share (MARKSHARE) and the Rate of Geographic Location (RAGELOC). In the same framework, the Variety of Insurance Activities (VARIACT) has a negative effect on the probability of high operational loss severity.

Despite the absence of the quantitative data of operational risk, this article will discover a new research perspective to estimate the frequency and severity of operational losses in the insurance sector in Tunisia.

The paper aims to study the role of local institutions in the establishment of fast-food outlets in urban districts of Peru. In most urban districts, there are no fast-food outlets. The authors, therefore, study the effect of institutional quality on the presence or absence of these outlets and the number of outlets if these are present.

The theoretical framework in which this paper is based on is the theory of agglomeration, which establishes that firms benefit from being close to each other. In particular, the paper builds on a model of market entry and competition in geographically independent local markets. An explicit expression was found for the equilibrium number of outlets (including zero) as a function of exogenous determinants of the demand for fast-food in each market, available infrastructure and institutional quality of the district’s government. Principal component analysis was used to construct measures of institutional quality based on administrative and organizational characteristics of district’s municipalities. These measures were incorporated as explanatory variables in a zero-inflated Poisson model, which is appropriate to handle count data and to accommodate excess zeros and which also allows the specification of different models for the zero part and the positive part.

Institutional quality mainly affects the presence of fast-food outlets in a district. The quality of urban development management and use of information systems are relevant. An institutional variable particularly relevant in explaining the number of outlets is the presence of an investment programming office in the municipality. The authors confirm the general hypothesis of the paper: institutions have a role in explaining both the presence and number of fast-food outlets in a district. Overall, the results of this paper suggest that institutional quality of a municipal district is related to better infrastructure, which lowers the costs of establishing outlets.

Limitations in the availability of data at the regional and urban district level did not allow the authors to analyze other factors that affect entry decisions in the fast-food industry in Peru, such as controls to prevent corruption, legal uncertainty or crime. Another limitation was the lack of data on entry costs for each franchisee in each urban district. This forced the authors to use public infrastructure characteristics of the district as (imperfect) proxies of the entry costs.

The instruments of urban development management and information systems can be effective at attracting investment to a district. These tools operate partly through an indirect effect, namely, the improvement of district infrastructure, which is necessary to reduce the costs of establishing companies. There is also synergy between national government’s programs to attract investment and the good institutional quality in local governments. On the contrary, poor local institutions can be an obstacle to the successful implementation of those national programs.

Foreign direct investment has a positive impact on the economic development of a country through knowledge spillovers. Therefore, any administrative reform to make local government practices more efficient can have an indirect impact on development.

Principal component analysis is a statistical tool that can be important in building good measures of institutional quality by allowing the combination of different observable characteristics into one component that can be interpreted as an operational restriction. The count model allows the use of the primary, easily observable, dependent variable, namely, the number of outlets. Finally, the two-part model makes it possible to discern the effect of institutional quality on the presence or absence of outlets and the number of outlets if these are present.

Value‐at‐Risk (VaR) has become a mainstream risk management technique employed by a large proportion of financial institutions. There exists a substantial amount of research dealing with this task, most commonly referred to as VaR backtesting. A new generation of “self‐learning” VaR models (Conditional Autoregressive Value‐at‐Risk or CAViaR) combine backtesting results with ex ante VaR estimates in an ARIMA framework in order to forecast P/L distributions more accurately. In this commentary, the authors present a systematic overview of several classes of applied statistical techniques that can make VaR backtesting more comprehensive and provide valuable insights into the analytical properties of VaR models in various market environments. In addition, they discuss the challenges associated with extending traditional backtesting approaches for VaR horizons longer than one day and propose solutions to this important problem.

This paper aims to estimate the determinants of the intensity of use of financial inclusion by households in Ghana.

Due to the reality of a household using one or more financial products or services, this study uses the generalised Poisson model applied to GLSS6 and GLSS7 data collected in 2012/2013 and 2016/2017 respectively, to estimate the determinants of the intensity of use of financial inclusion. To deepen the analysis, a multinomial probit model is also applied.

Results show that infrastructural variables such as roads, public transport and banks stimulate the intensity of financial inclusion. In addition, agricultural development characteristics such as markets and cooperatives are essential for the intensity of inclusion.

There is a need to incorporate how many services or depth of services that people use as part of the conceptualisation of financial inclusion, as this can provide more policy-relevant evidence to enhance priority setting in financial inclusion policies. Also, micro-level financial inclusion studies in agrarian economies should consider exploring agricultural development and infrastructure variables in the modelling framework. As lead to further studies, count models of financial inclusion should consider exploring cross-country analysis, the use of panel data, or other methodological approaches to provide more robust evidence.

Previous studies have not modelled financial inclusion based on a count model as a means of measuring intensity though conceptualisations highlight the fact that people use varied financial products or services. Following from this angle, to the best of the authors’ knowledge, this study provides the first attempt at analysing the underlying determinants of the number of financial products or services used by households.