48,575 research outputs found

    Hypotheses in Marketing Science: Literature Review and Publication Audit

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    We examined three approaches to research in marketing: exploratory hypotheses, dominant hypothesis, and competing hypotheses. Our review of empirical studies on scientific methodology suggests that the use of a single dominant hypothesis lacks objectivity relative to the use of exploratory and competing hypotheses approaches. We then conducted a publication audit of over 1,700 empirical papers in six leading marketing journals during 1984-1999. Of these, 74% used the dominant hypothesis approach, while 13 % used multiple competing hypotheses, and 13% were exploratory. Competing hypotheses were more commonly used for studying methods (25%) than models (17%) and phenomena (7%). Changes in the approach to hypotheses since 1984 have been modest; there was a slight decrease in the percentage of competing hypotheses to 11%, which is plained primarily by an increasing proportion of papers on phenomena. Of the studies based on hypothesis testing, only 11 % described the conditions under which the hypotheses would apply, and dominant hypotheses were below competing hypotheses in this regard. Marketing scientists differed substantially in their opinions about what types of studies should be published and what was published. On average, they did not think dominant hypotheses should be used as often as they were, and they underestimated their use

    Data mining as a tool for environmental scientists

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    Over recent years a huge library of data mining algorithms has been developed to tackle a variety of problems in fields such as medical imaging and network traffic analysis. Many of these techniques are far more flexible than more classical modelling approaches and could be usefully applied to data-rich environmental problems. Certain techniques such as Artificial Neural Networks, Clustering, Case-Based Reasoning and more recently Bayesian Decision Networks have found application in environmental modelling while other methods, for example classification and association rule extraction, have not yet been taken up on any wide scale. We propose that these and other data mining techniques could be usefully applied to difficult problems in the field. This paper introduces several data mining concepts and briefly discusses their application to environmental modelling, where data may be sparse, incomplete, or heterogenous

    APPLYING CHAID TO IDENTIFY THE ACCOUNTING-FINANCIAL CHARACTERISTICS OF THE MOST PROFITABLE REAL ESTATE COMPANIES IN SPAIN

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    The aim of this study is the determination, from an empirical perspective, of the accounting and financial features which could condition financial profitability of real estate companies, to identify the performances that guarantee its permanency in the current marketplace, characterized by the world economic crisis, specially in Spain, whose housing sector represents an important contributor to the economic growth. Although at a theoretical level the DuPont Model establishes the relationships between a group of accounting ratios and financial profitability. This paper uses a sample of 5,484 Spanish real estate companies to quantify these relationships and to extract the most relevant ones and to obtain the patterns of the most profitable companies. We use ROE to measure profitability and we analyze various independent variables about solvency, liquidity, activity, turnover, financial equilibrium and investment structure. The main contribution is of methodological nature, as we have applied statistics tools that do not require initial hypotheses on the distribution of the variables, by using a data mining technique of classification and regression tree based on rule induction algorithms known as CHAID. The study provides quantitatively success profiles by means of a set of rules describing the patterns of the most profitable companies.CHAID; financial profitability; classification trees; accounting ratios; Spain.

    Hypotheses in Marketing Science: Literature Review and Publication Audit

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    We examined three approaches to research in marketing: exploratory hypotheses, dominant hypothesis, and competing hypotheses. Our review of empirical studies on scientific methodology suggests that the use of a single dominant hypothesis lacks objectivity relative to the use of exploratory and competing hypotheses approaches. We then conducted a publication audit of over 1,700 empirical papers in six leading marketing journals during 1984-1999. Of these, 74% used the dominant hypothesis approach, while 13 % used multiple competing hypotheses, and 13% were exploratory. Competing hypotheses were more commonly used for studying methods (25%) than models (17%) and phenomena (7%). Changes in the approach to hypotheses since 1984 have been modest; there was a slight decrease in the percentage of competing hypotheses to 11%, which is explained primarily by an increasing proportion of papers on phenomena. Of the studies based on hypothesis testing, only 11 % described the conditions under which the hypotheses would apply, and dominant hypotheses were below competing hypotheses in this regard. Marketing scientists differed substantially in their opinions about what types of studies should be published and what was published. On average, they did not think dominant hypotheses should be used as often as they were, and they underestimated their use.marketing, marketing research, marketing science

    Towards technological rules for designing innovation networks: a dynamic capabilities view.

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    Inter-organizational innovation networks provide opportunities to exploit complementary resources that reside beyond the boundary of the firm. The shifting locus of innovation and value creation away from the “sole firm as innovator” poses important questions about the nature of these resources and the capabilities needed to leverage them for competitive advantage. The purpose of this paper is to describe research into producing design-oriented knowledge, for configuring inter-organizational networks as a means of accessing such resources for innovation

    Inference in classifier systems

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    Classifier systems (Css) provide a rich framework for learning and induction, and they have beenı successfully applied in the artificial intelligence literature for some time. In this paper, both theı architecture and the inferential mechanisms in general CSs are reviewed, and a number of limitations and extensions of the basic approach are summarized. A system based on the CS approach that is capable of quantitative data analysis is outlined and some of its peculiarities discussed

    Are foreign currency markets interdependent? evidence from data mining technologies

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    This study uses two data mining methodologies: Classification and Regression Trees (C&RT) and Generalized Rule Induction (GRI) to uncover patterns among daily cash closing prices of eight currency markets. Data from 2000 through 2009 is used, with the last year held out to test the robustness of the rules found in the previous nine years. Results from the two methodologies are contrasted. A number of rules which perform well in both the training and testing years are discussed as empirical evidence of interdependence among foreign currency markets. The mechanical rules identified in this paper can usefully supplement other types of financial modeling of foreign currencies.Foreign Currency Markets

    Unraveling the influence of domain knowledge during simulation-based inquiry learning

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    This study investigated whether the mere knowledge of the meaning of variables can facilitate inquiry learning processes and outcomes. Fifty-seven college freshmen were randomly allocated to one of three inquiry tasks. The concrete task had familiar variables from which hypotheses about their underlying relations could be inferred. The intermediate task used familiar variables that did not invoke underlying relations, whereas the abstract task contained unfamiliar variables that did not allow for inference of hypotheses about relations. Results showed that concrete participants performed more successfully and efficiently than intermediate participants, who in turn were equally successful and efficient as abstract participants. From these findings it was concluded that students learning by inquiry benefit little from knowledge of the meaning of variables per se. Some additional understanding of the way these variables are interrelated seems required to enhance inquiry learning processes and outcomes
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