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This study aims to provide an ontology based Baysian network for clinical specialty supporting. As a knowledge base, ontology plays an essential role in domain applications especially in expert systems. Interactive question answering systems are suitable for personal domain consulting and recommended for real-time usage. Clinical specialty supporting for dispatching patients can assist hospitals to locate desired treatment departments for individuals relevant to their syndromes and disease efficiently and effectively. By referring to interactive question answering systems, individuals can understand how to alleviate time and medical resource wasting according to recommendations from medical ontology-based systems.

This work presents an ontology based on clinical specialty supporting using an interactive question answering system to achieve this aim. The ontology incorporates close temporal associations between words in input query to represent word co-occurrence relationships in concept space. The patterns defined in lexicon chain mechanism are further extracted from the query words to infer related concepts for treatment departments to retrieve information.

The precision and recall rates are considered as the criteria for model optimization. Finally, the inference-based interactive question answering system using natural language interface is adopted for clinical specialty supporting, and indicates its superiority in information retrieval over traditional approaches.

From the observed experimental results, we find the proposed method is useful in practice especially in treatment department decision supporting using metrics precision and recall rates. The interactive interface using natural language dialogue attracts the users’ attention and obtains a good score in mean opinion score measure.

Logistics service providers (LSPs) may invest a lot of time in tenders unsuccessfully, as they do not meet the expectations of logistics service users (LSUs). In order to help them classify and target their customers more efficiently and effectively and make logistics outsourcing more successful for both LSUs and LSPs, this paper analyzes underlying dimensions of criteria German manufacturing and trading companies actually use in selecting an LSP and clusters of LSUs based on these dimensions.

A questionnaire survey with 110 manufacturing and 135 trading companies was conducted in Germany. Principal component analysis (PCA), cluster analysis, multivariate analysis of variance, analysis of variance and discriminant analysis were performed on the sample data.

PCA revealed eight dimensions of LSU criteria in selecting LSPs and that cost alone seems not decisive. Based on these dimensions, cluster analysis produced nine LSU groups. These groups differ the most in the selection criteria dimensions cost-performance ratio, operational collaboration, quality and locations. Recommendations for servicing these groups are given. The two largest groups, which make up 43.5%, seem not that demanding and price sensitive. The selection criteria dimensions and LSU groups enable LSPs to classify and target their customers more efficiently and effectively, to evaluate and develop their core competencies, and contribute to successful logistics-outsourcing relationships.

This research is the first to examine selection criteria dimensions and resulting clusters of German manufacturing and trading companies in order to make logistics outsourcing more successful.

This study constructs time series model, artificial neural networks (ANNs) and statistical topologies to examine the volatility and forecast foreign exchange rates. The Mauritian forex market has been utilized as a case study, and daily data for nominal spot rate (during a time period of five years spanning from 2014 to 2018) for EUR/MUR, GBP/MUR, CAD/MUR and AUD/MUR have been applied for the predictions.

Autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH) models are used as a basis for time series modelling for the analysis, along with the non-linear autoregressive network with exogenous inputs (NARX) neural network backpropagation algorithm utilizing different training functions, namely, Levenberg–Marquardt (LM), Bayesian regularization and scaled conjugate gradient (SCG) algorithms. The study also features a hybrid kernel principal component analysis (KPCA) using the support vector regression (SVR) algorithm as an additional statistical tool to conduct financial market forecasting modelling. Mean squared error (MSE) and root mean square error (RMSE) are employed as indicators for the performance of the models.

The results demonstrated that the GARCH model performed better in terms of volatility clustering and prediction compared to the ARIMA model. On the other hand, the NARX model indicated that LM and Bayesian regularization training algorithms are the most appropriate method of forecasting the different currency exchange rates as the MSE and RMSE seemed to be the lowest error compared to the other training functions. Meanwhile, the results reported that NARX and KPCA–SVR topologies outperformed the linear time series models due to the theory based on the structural risk minimization principle. Finally, the comparison between the NARX model and KPCA–SVR illustrated that the NARX model outperformed the statistical prediction model. Overall, the study deduced that the NARX topology achieves better prediction performance results compared to time series and statistical parameters.

The foreign exchange market is considered to be instable owing to uncertainties in the economic environment of any country and thus, accurate forecasting of foreign exchange rates is crucial for any foreign exchange activity. The study has an important economic implication as it will help researchers, investors, traders, speculators and financial analysts, users of financial news in banking and financial institutions, money changers, non-banking financial companies and stock exchange institutions in Mauritius to take investment decisions in terms of international portfolios. Moreover, currency rates instability might raise transaction costs and diminish the returns in terms of international trade. Exchange rate volatility raises the need to implement a highly organized risk management measures so as to disclose future trend and movement of the foreign currencies which could act as an essential guidance for foreign exchange participants. By this way, they will be more alert before conducting any forex transactions including hedging, asset pricing or any speculation activity, take corrective actions, thus preventing them from making any potential losses in the future and gain more profit.

This is one of the first studies applying artificial intelligence (AI) while making use of time series modelling, the NARX neural network backpropagation algorithm and hybrid KPCA–SVR to predict forex using multiple currencies in the foreign exchange market in Mauritius.

The purpose of this study is to propose a method of measuring service quality as well as suggesting to detect customer complaints through analysis of customer online reviews of mobile bank, which is unstructured data.

This study uses text mining approach for customer online reviews analysis. The research procedure includes: (1) extracting users' reviews for Kakao Mobile Bank, (2) pre-processing of the extracted review data, (3) analyzing the sentiment of each review, (4) measuring the service quality score of each dimension by analyzing keyword frequency and network for each polarity, (5) evaluating total score for mobile bank service quality, and (6) detecting customer complaints on online reviews.

There are some findings. First, from the customer's point of view, it was possible to see which factors are important among the various dimensions of service quality and which factors should be managed well in mobile banking setting. Second, by periodically finding customer complaints, service failures can be prevented early, and service quality and customer satisfaction can be improved.

From a practical point of view, mobile bank managers should pay more attention to the service quality dimensions of practicality and enjoyment. In addition, the results mean that the app design and aesthetics with the most negative reviews should be reviewed from the user's perspective rather than from the company's point of view. Second, it is possible for them to establish a systematic complaint management system that can prevent service failure in advance by detecting customer complaints early. Third, it is possible for them to make quick decisions regarding service quality with the help of real-time customer response through dashboard construction.

This paper is a pioneer study measuring service quality with sentiment analysis, one of the text mining applications, using customers' reviews of a mobile bank.

The aim of this paper is to investigate how a manufacturer's relationship with firms operating in different institutional logics can facilitate or hinder the outcomes of research partnerships.

The paper tests the study hypotheses based on a survey of 345 Chinese manufacturers.

Results reveal that a manufacturer's partnerships with private firms and government institutions are both directly related to the manufacturer's innovation performance. However, the effectiveness of the research partnerships depends on the different institutional logics within which these organizations operate.

This study used a binary variable to capture the existence or absence of the partnership types examined implying that this variable does not capture the quantity of R&D relationships the firm is engaged in or the time period of such engagements.

It is important for management to take into account the joint effect of both the firm and its partner's underlying institutional logics in establishing partnership relationships since the juxtaposition of different institutional logics can affect the outcomes of the relationship.

This research draws from institutional theory to contribute to knowledge in the area of innovation by emphasizing the importance of the overarching institutional logic on the effectiveness of different types of innovation‐driven research partnerships.

This paper tests whether Bayesian A/B testing yields better decisions that traditional Neyman-Pearson hypothesis testing. It proposes a model and tests it using a large, multiyear Google Analytics (GA) dataset.

This paper is an empirical study. Competing A/B testing models were used to analyze a large, multiyear dataset of GA dataset for a firm that relies entirely on their website and online transactions for customer engagement and sales.

Bayesian A/B tests of the data not only yielded a clear delineation of the timing and impact of the intellectual property fraud, but calculated the loss of sales dollars, traffic and time on the firm’s website, with precise confidence limits. Frequentist A/B testing identified fraud in bounce rate at 5% significance, and bounces at 10% significance, but was unable to ascertain fraud at the standard significance cutoffs for scientific studies.

None within the scope of the research plan.

Bayesian A/B tests of the data not only yielded a clear delineation of the timing and impact of the IP fraud, but calculated the loss of sales dollars, traffic and time on the firm’s website, with precise confidence limits.

Bayesian A/B testing can derive economically meaningful statistics, whereas frequentist A/B testing only provide p-value’s whose meaning may be hard to grasp, and where misuse is widespread and has been a major topic in metascience. While misuse of p-values in scholarly articles may simply be grist for academic debate, the uncertainty surrounding the meaning of p-values in business analytics actually can cost firms money.

There is very little empirical research in e-commerce that uses Bayesian A/B testing. Almost all corporate testing is done via frequentist Neyman-Pearson methods.

This paper gives a selective review on some recent developments of nonparametric methods in both continuous and discrete time finance, particularly in the areas of nonparametric estimation and testing of diffusion processes, nonparametric testing of parametric diffusion models, nonparametric pricing of derivatives, nonparametric estimation and hypothesis testing for nonlinear pricing kernel, and nonparametric predictability of asset returns. For each financial context, the paper discusses the suitable statistical concepts, models, and modeling procedures, as well as some of their applications to financial data. Their relative strengths and weaknesses are discussed. Much theoretical and empirical research is needed in this area, and more importantly, the paper points to several aspects that deserve further investigation.

The purpose of this paper is to identify the internationalisation patterns of small‐ and medium‐sized enterprises (SMEs) quantitatively, to describe SMEs as they follow different patterns over time and to discuss the determinants of these patterns through empirical study.

The authors conducted a questionnaire survey among mature German SMEs (n=674). To identify internationalisation patterns, a latent class clustering approach was applied. Because of the large sample, a multinomial logistic regression analysis could be used to analyse the factors influencing these patterns.

The authors empirically find three internationalisation patterns: traditionals, born globals and born‐again globals. Comparing modern SMEs with the same SMEs from ten years ago, it was found that firms may change their patterns. Moreover, the patterns are determined by international orientation, growth orientation, communication capability, intelligence generation capability and marketing‐mix standardisation.

Combining elements of the Uppsala model (countries and operation modes) and born global research (time lag and foreign sales ratio), three internationalisation patterns of established international SMEs from traditional sectors were identified empirically. Because of the multidimensional nature of internationalisation, the patterns may change over time. Different firm‐level factors determine the internationalisation patterns.

Instead of applying “arbitrary” thresholds, the paper provides a quantitative approach to identifying internationalisation patterns. These patterns confirm the three main internationalisation pathways discussed in the international marketing literature. The paper further advances the field by describing the patterns, showing evidence that the patterns may cross over time and providing information on the factors that influence the patterns.

Examines the ability of an expert computer system to evaluate uncertainty within a valuation context and thus emulate the professional skill of the valuer. Shows that because property valuation programs based on regression analysis require data input for each variable, they are unable to evaluate uncertainty and hence to apply the rational judgement which enables the human valuer to produce a valuation in the light of uncertain or incomplete information.

The purpose of this article is to explore and classify the pattern of themes and challenges in developing socially sustainable supply chains.

The methodology is based on a systematic review of the peer-reviewed literature to explore what major themes and challenges have been discussed and the significant gaps where opportunities for further research can be found.

In total, four categories of themes were identified, namely, human-centric, focal organization-centric, supply chain-centric and governance-centric. Challenges were classified into seven categories, namely, inadequate and asymmetric knowledge, difficulties of operationalization, shifting the values, subjectivity in evaluation, governance complexity, difficulties of small- and medium-sized enterprises and sustainability fade.

The focus of the article is on the social pillar of sustainable development in the context of supply chains. A more holistic systematic investigation of synergy of all the three pillars/bottom lines of sustainable development (economic, environmental and social) can be an opportunity for further research.

Taking a more holistic view of the pattern of currently discussed themes and challenges may be beneficial in increasing the absorptive capacity of industrial and business practitioners, by accumulating and assimilating external knowledge, when they design and operationalize innovative strategies in developing sustainable supply chains.

This article may increase awareness about the social responsibilities of supply chains actors and stakeholders in different scales. It may also guide managers, decision makers and practitioners to better understand the difficulties, obstacles or dilemmas that can hinder the sustainable development of supply chains. The results section presents a framework driven from the emerged themes, and the discussion section provides propositions for tackling the challenges and opportunities for further research.