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With the progress of new technologies of information and communication, more and more producers of data exist. On the other hand, the web forms a huge support of all these kinds of data. Unfortunately, existing data is not proper due to the existence of the same information in different sources, as well as erroneous and incomplete data. The aim of data integration systems is to offer to a user a unique interface to query a number of sources. A key challenge of such systems is to deal with conflicting information from the same source or from different sources. We present, in this paper, the resolution of conflict at the instance level into two stages: references reconciliation and data fusion. The reference reconciliation methods seek to decide if two data descriptions are references to the same entity in reality. We define the principles of reconciliation method then we distinguish the methods of reference reconciliation, first on how to use the descriptions of references, then the way to acquire knowledge. We finish this section by discussing some current data reconciliation issues that are the subject of current research. Data fusion in turn, has the objective to merge duplicates into a single representation while resolving conflicts between the data. We define first the conflicts classification, the strategies for dealing with conflicts and the implementing conflict management strategies. We present then, the relational operators and data fusion techniques. Likewise, we finish this section by discussing some current data fusion issues that are the subject of current research.

This paper aims to propose a system for generating actionable knowledge from Big Data and use this system to construct a comprehensive knowledge base (KB), called GrandBase.

In particular, this study extracts new predicates from four types of data sources, namely, Web texts, Document Object Model (DOM) trees, existing KBs and query stream to augment the ontology of the existing KB (i.e. Freebase). In addition, a graph-based approach to conduct better truth discovery for multi-valued predicates is also proposed.

Empirical studies demonstrate the effectiveness of the approaches presented in this study and the potential of GrandBase. The future research directions regarding GrandBase construction and extension has also been discussed.

To revolutionize our modern society by using the wisdom of Big Data, considerable KBs have been constructed to feed the massive knowledge-driven applications with Resource Description Framework triples. The important challenges for KB construction include extracting information from large-scale, possibly conflicting and different-structured data sources (i.e. the knowledge extraction problem) and reconciling the conflicts that reside in the sources (i.e. the truth discovery problem). Tremendous research efforts have been contributed on both problems. However, the existing KBs are far from being comprehensive and accurate: first, existing knowledge extraction systems retrieve data from limited types of Web sources; second, existing truth discovery approaches commonly assume each predicate has only one true value. In this paper, the focus is on the problem of generating actionable knowledge from Big Data. A system is proposed, which consists of two phases, namely, knowledge extraction and truth discovery, to construct a broader KB, called GrandBase.

The study builds on studies in online shopping. Existing studies in online shopping proved that it is an attraction to shoppers. In Nigeria's emerging economy the increasing Internet penetration does not equate with intention to use online shopping because it is not really used by users for online shopping. Consumers are considering it unattractive because of serious concerns that border on product quality of online shops and poor know-how on e-tech. The study sought to explore factors that could mitigate challenges to successful online shopping in Nigeria's emerging economy.

Online survey method was used to sample 246 respondents. Measurement items were adapted from related literature. Confirmatory factor analysis and content validity were used to check the reliability and validity. A set of fit indices were used to check the goodness of fit. Data was analysed using structural equation model.

Results indicate direct effects of consumer attitude, perceived usefulness and social influence on intention to use online shopping with consumer attitude shown to have a greater degree of importance towards intention to use online shopping. Thus, consumers' attitude of browsing online and going offline for purchases is dependent on attitude of like or dislike. Perceived ease of use, social influence and perceived usefulness had an indirect positive effect on consumer attitude to intention to use online shopping. Social influence is indicated to have a direct positive effect on perceived ease of use. Also perceived ease of use had a positive and direct effect on perceived usefulness.

The sample size is not large enough and the use of snowball sampling limits representativeness.

The study indicated vital factors African emerging economies like Nigeria can use to improve consumer confidence towards intention to use online shopping and drive cashless policies. Several studies have missed the indirect effect of referents (social influence) on adoption of technology. The study proved that it can produce indirect effect as well as direct effect on intention to use online shopping.

Several studies have missed the indirect effect of referents (social influence) on adoption of technology. The study proved that it can produce indirect effect as well as direct effect on online shopping.

The purpose of this paper is to identify how project portfolio selection (PPS) methods have evolved and which approaches are more suitable for radical innovation projects. This paper addressed the following research question: how have the selection approaches evolved to better fit within radical innovation conditions? The current literature offers a number of selection approaches with different and, in some cases, conflicting nature. Therefore, there is a lack of understanding regarding when and how to use these approaches in order to select a specific type of innovation projects (from incremental to more radical ones).

Given the nature of the research question, the authors perform a systematic literature review method and analyze 48 portfolio selection approaches. The authors then classified and characterized these articles in order to identify techniques, tools, required data and types of examined projects, among other aspects.

The authors identify four key features related to the selection of radical innovation projects: dynamism, interdependency management, uncertainty treatment and required input data. Based on the content analysis, the authors identified that approaches based on different sources and nature of data are more appropriated for uncertain conditions, such as behavioral methods, information gap theory, real options and integrated approaches.

The research provides a comprehensive framework about PPS methods and how they have been evolving over time. This portfolio selection framework considers the particular aspects of incremental and radical innovation projects. The authors hope that the framework contributes to reinvigorating the literature on selection approaches for innovation projects.

This study contributes to the scant research on dealcoholised wine from a consumer behaviour perspective by providing insight and reporting on the attributes that South African Generation Y consumers prefer when purchasing dealcoholised wine.

A two-phased research approach was adopted, involving a main quantitative phase, preceded by a qualitative phase. Data were gathered from 626 South African Generation Y respondents by means of a questionnaire. The best–worst scaling method was applied to 13 selected dealcoholised wine attributes, to measure the level of importance of each attribute. To gain more insight on the data, the best-worst scaling scores were further standardised to a probabilistic ratio scale.

“Taste”, “price” and “I have tried it before” were the most important attributes that respondents considered when purchasing dealcoholised wine. Furthermore, “taste” was by far the most important of all the attributes. The attributes of “back label”, “attractive front label” and “brand name” were identified as the least important by the respondents, suggesting that they did not consider the visual elements of a bottle of dealcoholised wine as particularly important in their purchasing decision.

The findings of this pioneering study contribute to the lack of knowledge about dealcoholised wine from a consumer behaviour and marketing perspective, and provide insights and strategies that can be used by stakeholders to enhance the dealcoholised wine market in South Africa.

The purpose of this research is to contribute to knowledge-building on microenterprises in emerging economies, by assessing the determinants that drive their use of accounting systems.

A probabilistic model was developed to determine the likelihood that a micro-firm would adopt an accounting registry system as a function of a series of contingencies and personal characteristics of their owners/managers. Data from the Microentrepreneurship Survey (EME), from the National Institute of Statistics of Chile for 2017 was used.

The findings suggest that access to external funds, the size and the use of technology strongly influence micro-firms' adoption of accounting systems.

Despite the richness and scope of the data, direct measurements of entrepreneurial orientation and environmental uncertainty, both central variables of the contingency theory, were missing. Hence, duly justified proxies were applied. It is also likely that there would be other variables that also influence the probability of using accounting tools.

The study contributes to a better understanding of microenterprises, and the factors that determine the use of accounting systems. The results highlight that public policies aimed at fostering microenterprises should facilitate access to technology and external funds. Consistent with previous studies, the authors’ findings highlight the importance of training owner/managers on issues related to their business.

This paper contributes to theory by arguably being the first study to confirm that contingency theory does explain the adoption of accounting systems in microenterprises in emerging countries.

The literature on economic forecasting, is showing an increase in criticism, of the inaccuracy of forecasts, with major implications for economic, and fiscal policymaking. Forecasts are subject to the systemic uncertainty of human systems, considerable event-driven uncertainty, and show biases towards optimistic growth paths. The purpose of this study is to consider approaches to improve economic foresight.

This study describes the practice of economic foresight as evolving in two separate, non-overlapping branches, short-term economic forecasting, and long-term scenario analysis of development, the latter found in studies of climate change and sustainability. The unique case of Ireland is considered, a country that has experienced both steep growth and deep troughs, with uncertainty that has confounded forecasting. The challenges facing forecasts are discussed, with brief review of the drivers of growth, and of long-term economic scenarios in the global literature.

Economic forecasting seeks to manage uncertainty by improving the accuracy of quantitative point forecasts, and related models. Yet, systematic forecast failures remain, and the economy defies prediction, even in the near-term. In contrast, long-term scenario analysis eschews forecasts in favour of a set of plausible or possible alternative scenarios. Using alternative scenarios is a response to the irreducible uncertainty of complex systems, with sophisticated approaches employed to integrate qualitative and quantitative insights.

To support economic and fiscal policymaking, it is necessary support advancement in approaches to economic foresight, to improve handling of uncertainty and related risk.

While European Union Regulation (EC) 1466/97 mandates pursuit of improved accuracy, in short-term economic forecasts, there is now a case for implementing advanced foresight approaches, for improved analysis, and more robust decision-making.

Building economic resilience and adaptability, as part of a sustainable future, requires both long-term strategic planning, and short-term policy. A 21st century policymaking process can be better supported by analysis of alternative scenarios.

To the best of the authors’ knowledge, the article is original in considering the application of scenario foresight approaches, in economic forecasting. The study has value in improving the baseline forecast methods, that are fundamental to contemporary economics, and in bringing the field of economics into the heart of foresight.

Breast cancer is a global public health dilemma and the most prevalent cancer in the world. Effective treatment plans improve patient survival rates and well-being. The five-year survival rate is often used to develop treatment selection and survival prediction models. However, unlike other types of cancer, breast cancer patients can have long survival rates. Therefore, the authors propose a novel two-level framework to provide clinical decision support for treatment selection contingent on survival prediction.

The first level classifies patients into different survival periods using machine learning algorithms. The second level has two models with different survival rates (five-year and ten-year). Thus, based on the classification results of the first level, the authors employed Bayesian networks (BNs) to infer the effect of treatment on survival in the second level.

The authors validated the proposed approach with electronic health record data from the TriNetX Research Network. For the first level, the authors obtained 85% accuracy in survival classification. For the second level, the authors found that the topology of BNs using Causal Minimum Message Length had the highest accuracy and area under the ROC curve for both models. Notably, treatment selection substantially impacted survival rates, implying the two-level approach better aided clinical decision support on treatment selection.

The authors have developed a reference tool for medical practitioners that supports treatment decisions and patient education to identify patient treatment preferences and to enhance patient healthcare.