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This paper is an overview of reconstructability analysis (RA), an approach to discrete multivariate modeling developed in the systems community. RA includes set‐theoretic modeling of relations and information‐theoretic modeling of frequency and probability distribution. It thus encompasses both statistical and nonstatistical problems. It overlaps with logic design and machine learning in engineering and with log‐linear modeling in the social sciences. Its generality gives it considerable potential for knowledge representation and data mining.

The purpose of this paper is to develop a geographic information system (GIS)‐based risk assessment tool for visualising climate change impacts in agricultural industries and evaluating eventual adaptation strategies.

A climate change adaptation strategy tool (CCAST) with built‐in GIS capability has been developed for agricultural industries. Development of the GIS functionality within CCAST includes the implementation of map projection, boundary allocation, interpolation and a graphical display of spatial data. In total, 20 climatic and crop indices are computed alongside basic climate variables (rainfall and temperature) from downscaled global climate models at 1,062 sites across the state of New South Wales (NSW) located in eastern Australia.

A case study in Australia is used to demonstrate use of this tool. This shows selecting suitable genotypes of wheat is a key adaptation strategy to mitigate the impacts of climate change on wheat cropping. It shows that spring wheat genotypes will become predominate, while the winter genotypes will only be viable in clearly defined areas where sufficient days of cool temperature exist for completion of vernalisation in a future warmer climate.

CCAST integrates knowledge relevant to climate impact management in a stand‐alone environment. It benefits from statistical analysis and GIS functionalities and provides many user‐friendly GIS features to make it suitable for practitioners on the ground.

The problem of measurement errors in the national accounts has been recognized for a long time. The error chiefly arises from various source data and the timing of the flow of data received from providers. This chapter first discusses the type of measurement errors confronted by statistical agencies. Second, it presents a model of their behavior that illustrates the trade-offs that must be made in dealing with such errors. Third, the chapter discusses how the quality of the estimates can be gauged given measurement error and the inability to conduct standard statistical tests. Although the focus is on the production of U.S. Gross Domestic Product, the principles are applicable to all national statistical agencies.

The purpose of this paper is to design and validate an electronic nose (E-nose) prototype using commercially available metal oxide gas sensors (MOX). This prototype has a sensor array board that integrates eight different MOX gas sensors to handle multi-purpose applications. The number of sensors can be adapted to match different requirements and classification cases. The paper presents the validation of this E-nose prototype when used to identify three gas samples, namely, alcohol, butane and cigarette smoke. At the same time, it discusses the discriminative abilities of the prototype for the identification of alcohol, acetone and a mixture of them. In this respect, the selection of the appropriate type and number of gas sensors, as well as obtaining excellent discriminative abilities with a miniaturized design and minimal computation time, are all drivers for such implementation.

The suggested prototype contains two main parts: hardware (low-cost components) and software (Machine Learning). An interconnection printed circuit board, a Raspberry Pi and a sensor chamber with the sensor array board make up the first part. Eight sensors were put to the test to see how effective and feasible they were for the classification task at hand, and then the bare minimum of sensors was chosen. The second part consists of machine learning algorithms designed to ensure data acquisition and processing. These algorithms include feature extraction, dimensionality reduction and classification. To perform the classification task, two features taken from the sensors’ transient response were used.

Results reveal that the system presents high discriminative ability. The K-nearest neighbor (KNN) and support vector machine radial basis function based (SVM-RBF) classifiers both achieved 97.81% and 98.44% mean accuracy, respectively. These results were obtained after data dimensionality reduction using linear discriminant analysis, which is more effective in terms of discrimination power than principal component analysis. A repeated stratified K-cross validation was used to train and test five different machine learning classifiers. The classifiers were each tested on sets of data to determine their accuracy. The SVM-RBF model had high, stable and consistent accuracy over many repeats and different data splits. The total execution time for detection and identification is about 10 s.

Using information extracted from transient response of the sensors, the system proved to be able to accurately classify the gas types only in three out of the eight MQ-X gas sensors. The training and validation results of the SVM-RBF classifier show a good bias-variance trade-off. This proves that the two transient features are sufficiently efficient for this classification purpose. Moreover, all data processing tasks are performed by the Raspberry Pi, which shows real-time data processing with miniaturized architecture and low prices.

This paper aims to examine the prospect of utilising corporate social responsibility and entrepreneurship (CSRE) as antidotes for mitigating the incidences of poverty, insecurity and underdevelopment in Nigeria. The paper derives its theoretical foundation from the stakeholder, instrumental and legitimacy theories, which all justify the use of CSRE for actualisation of Triple Bottom Line (i.e. the social, economic and environmental concerns of business organisations).

The study used the quantitative research method relying on the use of secondary data published by institutional bodies. The quantitative method entail a systematic extraction of reliable data on corporate social responsibility (CSR), insecurity, poverty and development from the publications of Office of the Millennium Development Goals in Nigeria, CLEEN Foundation, National Bureau of Statistics and Central Bank of Nigeria, respectively. For missing years, the authors improvised using projections as well as proxies. The extracted data, which spanned a period of 13 years, were subjected to econometric tests using SPSS, on the basis of which informed conclusions were drawn.

The first econometric result indicates a negative relationship between gross domestic product and poverty. The second result indicates that there is a positive significant relationship between gross domestic product and total crime rate. The third result indicates that there exists a positive relationship between gross domestic product and unemployment rate. The fourth result indicates that there is a negative relationship between gross domestic product and industrial growth rate. The last result indicates that there is a significant positive relationship between gross domestic product and CSR.

The results of this research have macro-level application, hence the outcomes cannot be narrowed to any particular sector of the economy. A micro-level analysis across diverse sectors of the economy is recommended in future studies. The implication of this empirical research is that policymakers in the Nigerian private sector need to reinvent their CSR programmes as mechanisms for poverty eradication, entrepreneurship development (CSRE), dousing tension of restive youth, empowerment/support for security agencies for better crime prevention and for impacting on sustainable development.

In the face of dwindling financial resources in the treasury of governments, the reinvention of CSRE by private sector organisations as complementary mechanisms for combating social problems is becoming acceptable in both developed and developing nations. This paper therefore boldly recommends that policymakers reinvent CSRE as development mechanisms through a sound partnership between government, advocacy groups and business corporations in Nigeria.

The paper explicates that CSR can indeed be reinvented by corporations as part of their social concerns to their operating environment instead of leaving all social problems to governments.

The research lends credence to stakeholder, instrumental and legitimacy theories of CSR. It also justifies the plausibility of CSRE, a novel concept being promoted in this research.

The purpose of this paper is to show that despite the critical importance of using accurate data when identifying geographic patterns and studying hotspots, few have explored the data quality issues introduced by Geographic Information Systems (GIS) software applications. While software manufacturers provide some information about the address matching process, critical details are left out or are buried in technical, and sometimes proprietary, jargon. The purpose of this paper is to address these issues.

The paper demonstrates, with three datasets of 100 cases each, how the assumptions built into popular GIS software produce systematically missing data during the data importing process commonly referred to as address matching.

Inclusion of directional indicators and zip codes are more important than previously thought. The results highlight the critical need to provide complete descriptions of research methodology. All geographic analyses must be accompanied with: information about the hit rate (percent of cases plotted), details about the software and process used to import tabular crime data, information about the software parameters set for the importation process (geocoding preferences), reference information about the street file used; and, an examination of the missing cases to identify some of the sampling error. When forecasting crime issues or identifying hot spots, analysts must be cognizant of the differential impact this bias will have on the generalizability of the results.

The paper explores previously neglected issues in data quality introduced by GIS software applications.

Big data is one of the most demanding topics in contemporary marketing research. Despite its importance, the big data-based strategic orientation in international marketing is yet to be formed conceptually. Thus, the purpose of this study is to systematically review and propose a holistic framework on big data-based strategic orientation for firms in international markets to attain a sustained firm performance.

The study employed a systematic literature review to synthesize research rigorously. Initially, 2,242 articles were identified from the selective databases, and 45 papers were finally reported as most relevant to propose an integrative conceptual framework.

The findings of the systematic literature review revealed data-evolving, and data-driven strategic orientations are essential for performing international marketing activities that contain three primary orientations such as (1) international digital platform orientation, (2) international market orientation and (3) international innovation and entrepreneurial orientation. Eleven distinct sub-dimensions reflect these three primary orientations. These strategic orientations of international firms may lead to advanced analytics orientation to attain sustained firm performance by generating and capturing value from the marketplace.

The study minimizes the literature gap by forming knowledge on big data-based strategic orientation and framing a multidimensional framework for guiding managers in the context of strategic orientation for international business and international marketing activities. The current study was conducted by following only a systematic literature review exclusively in firms' overall big data-based strategic orientation concept in international marketing. Future research may extend the domain by introducing firms' category wise systematic literature review.

The study has proposed a holistic conceptual framework for big data-driven strategic orientation in international marketing literature through a systematic review for the first time. It has also illuminated a future research agenda that raises questions for the scholars to develop or extend theory in this area or other related disciplines.

This paper aims to propose a structured model based on a data mining algorithm that can calculate, based on business association (BA) attributes, the probability of micro and small enterprises (MSEs) becoming a new member of a BA. Another goal is the probability of a BA attracting new members.

As a methodological procedure, the authors used the Naive Bayes data mining algorithm. The collected data were analyzed both quantitatively and qualitatively and then used to define the model, which was tested randomly, while allowing for the possibility of future validation.

The findings suggest a structured model based on a data mining algorithm. The model can certainly be used as a management tool for BAs concentrating their efforts on those businesses that are certainly potential new recruits. Further, for an MSE, it serves as a means of evaluating a BA, indicating the possible advantages in becoming a member of a particular association.

This paper is not intended to be generalized, considering that it only analyzes the BAs of Rio Grande do Sul, Brazil. In this way, when applying this model to other situations, the attributes listed here can be revised and even modified to adapt to the situation in focus.

The use of the proposed model will make it possible to optimize the time of BA managers. It also gives MSE greater reliability in choosing BA.

Using this model will provide better decision-making and better targeting, thus benefiting both the BAs and the MSEs, which can improve their management and keep jobs.

This paper contributes to the literature because it is the first to connect BAs, MSEs and Naive Bayes. Also, this study helps in better management for BA managers in their daily activities and provides a better choice of BA for MSE managers. Also, this study contextualizes BAs, MSEs and data mining in an objective way.

The purpose of this paper is to investigate publicly traded restaurant companies and food & beverage companies from 2014 to 2019 in Taiwan to explore their human capital efficiency.

According to the theoretical framework of human capital in micro and macro perspectives, the empirical model is built with two stages; the first stage is to examine the perspective of micro-level human capital theory through determining whether knowledge ability and working experience (proxies for micro-level human capital) can efficiently convert to employee-level output such as salary. The second stage is to test macro-level human capital theory through checking whether company inputs such as salary expenses and benefits expenditures can be efficiently transferred into enterprise annual revenues.

The results of this research reveal that the average efficiency score of stage 1 is 73.6% while that of stage 2 is 75.1%; this indicates that micro-level human capital has more room to improve than macro-level human capital. Meanwhile, the findings also demonstrate that there is negative relationship between efficiency score from stage 1 and turnover rate; this implies that companies with higher micro-human capital have lower turnover rates. Furthermore, there is significantly positive relationship between a company's efficiency score from stage 2 and its return on equity (ROE).

This study contributes to both academia and industry. From a theoretical perspective, the theory of strategic human resources management is applied through the methodology of production theory to examine human capital management efficiency in the restaurant and food and beverage industry. From a practical perspective, this study identifies the factors that assist the restaurant or food and beverage industry retain employees and gain a solid workforce, because manpower is the core resource for an industry and a country to grow sustainably.