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Thinking Out of the Box. Non-typical Research Methods in Business

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Modernizing the Academic Teaching and Research Environment

Part of the book series: Progress in IS ((PROIS))

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Abstract

After discussing statistical techniques for data selection, collection, coding, manipulation, summarizing and presentation, this chapter describes one of some relatively new research methods in business, which are non-typical, non-statistical in nature. Artificial Neural Networks (ANNs), case-based reasoning, fuzzy logic and genetic algorithms are advanced techniques that show promises as enablers to solve some difficulties that may lie in analyzing and synthesizing complex systems, which include large quantities of data from several different sources into a coherent research model. Raising the idea up of discovering un-noticed observations or data in front of a researcher is for a purpose. One of the new techniques proposed in this chapter, like data mining, rely on discovering unobserved or unnoticed patterns in the already available data and data sources. This chapter will focus on using ANN method, what is it, who will use it, why and how to use it. The chapter ends by presenting the future trend in using this method, which is the combination among typical and non-typical methods.

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Notes

  1. 1.

    Exclusive OR̛ (XOR): means (either A or B but not both). In neural networks, it is a classification problem. Where A and B are groups, and x1, x2 are explanatory variables. When both x1 and x2 are either large (1, 1) or small (0, 0), the resulting group is B (0). When the same variables go in opposite directions (0, 1) or (1, 0), the group is A (1). [20, p. 106, 21].

  2. 2.

    Type 2 errors are very important because that directly affect the quality and effectiveness of an audit. Such errors could easily result in an audit failure. If an auditor fails to identify a material fraud and gives a client a clean-audit report, then there is little doubt that an audit failure has occurred. On the other hand, a type 1 error has a direct impact on audit efficiency as it forces the auditor to increase substantive testing and over consume organizational resources. Persistent type 1 errors could also affect trust in the audit and risk assessment process [36, p. 207].

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Authors and Affiliations

  1. Department of Management Information Technology, Arab International University (AIU), Damascus, Syria

    Kinaz Al Aytouni

  2. Department of Accounting and Finance, Arab International University (AIU), Damascus, Syria

    Kinan M. Naddeh

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  1. Kinaz Al Aytouni
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Correspondence to Kinaz Al Aytouni .

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Editors and Affiliations

  1. Department of Computing Science, University of Oldenburg, Oldenburg, Germany

    Jorge Marx Gómez

  2. Department of Banking and Finance, Arab International University, Damascus, Syrian Arab Republic

    Sulaiman Mouselli

Appendix

Appendix

Neural network terminology

Statistical modelling terminology

Neural network

Model

Synapses, weights, connectivity, etc.

Coefficients of the model

Inputs

Independent variables

Outputs

Dependent variables

Outcome or target

Expected value

Node

Logistic regression

Hidden layer

Intermediate set of logistic regressions

Learning

Coefficient estimation

Supervised learning

Regression, discriminant analysis, etc.

Unsupervised learning

Principal components and cluster analyses

Architecture

Model description (e.g., number of nodes and layers)

Convergence

In-sample performance

Generalisation

Out-of-sample performance

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Al Aytouni, K., Naddeh, K.M. (2018). Thinking Out of the Box. Non-typical Research Methods in Business. In: Marx Gómez, J., Mouselli, S. (eds) Modernizing the Academic Teaching and Research Environment. Progress in IS. Springer, Cham. https://doi.org/10.1007/978-3-319-74173-4_8

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