Innovative Research Methodologies in Management: Volume II: Futures, Biometrics and Neuroscience Research

245 Pages·2018·4.328 MB·English

by  Luiz Moutinho,Mladen Sokele (eds.)

EDITED BY
LUIZ MOUTINHO
MLADEN SOKELE
INNOVATIVE
RESEARCH
METHODOLOGIES
IN MANAGEMENT

VOLUME II: Futures,
Biometrics and
Neuroscience
Research
Innovative Research Methodologies in
Management
Luiz Moutinho • Mladen Sokele
Editors
Innovative Research
Methodologies in
Management
Volume II: Futures, Biometrics and
Neuroscience Research
Editors
Luiz Moutinho Mladen Sokele
University of Suffolk, Suffolk, England, UK Faculty of Electrical Engineering and
The University of the South Pacific, Suva, Fiji Computing
University of Zagreb
Zagreb, Croatia
ISBN 978-3-319-64399-1 ISBN 978-3-319-64400-4 (eBook)
DOI 10.1007/978-3-319-64400-4
Library of Congress Control Number: 2017954805
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To everyone in the world that kindly resonates with my thoughts….
Luiz Moutinho
To my dear son Leo
Mladen Sokele
Preface
I am very grateful to Palgrave for the fact that they were enthusiastic
about this project. It has been one of my scholarly “pet” projects for some
time. I was also extremely happy that I was able to secure the incredible
collaboration of my friend Mladen Sokele as my co-editor of this book.
Methodologies are the basis for scientific research. However, scholars
often focus on a very limited set of methodologies, partly due to a lack of
knowledge about innovative methodologies outside their area. This is
especially true in the area of management science. Providing manage-
ment scholars with an education about methodologies outside their pri-
mary area of expertise is the goal of the proposal made to Palgrave
Macmillan, global academic publisher, for a book providing a compre-
hensive presentation of innovative research methodologies: Palgrave’s
Innovative Research Methodologies in Management.
This book is to be positioned as a seminal collection of mind-stretching
and thought-provoking research methodology essays. Hopefully, these
research methods and topics will greatly enhance the research methodol-
ogy “armoury” of management scholars and alter the research “modus
operandi” of academic research output and published work.
One of the aims of the Innovative Research Methodologies in Management
text is to identify and foster methodological research innovation in the
academic management field. This book project seeks out research prac-
tices that are not highlighted through the majority of academic research
vii
viii Preface
outlets and journals, that are not highlighted typical research method
courses or and to have an impact on the research process in novel ways.
These innovative methodologies usually entail a combination of (i) tech-
nological innovation, (ii) the use of existing theory and methods in new
ways, and (iii) interdisciplinary approaches. The project’s focus on inno-
vative research practices will range across new philosophical insights into
academic research, new experimental designs to new technological
research contexts, and new analytical techniques. Departing from the
somewhat perennial situation of academic research methodolatry and sci-
entism, this tome will be focusing on a series of rigorous methodological
advances in many areas. The primary focus relies on emerging and
bleeding- edge areas of academic research methodologies in management,
making the contents of this book an authoritative source on the applica-
tions of these methodologies to management science.
Volume 1 is dedicated to the coverage of innovative research method-
ologies within the realms of research philosophy, research measurement,
and research modeling.
Volume 2 is focused on chapters dealing with Futures Research,
Biometrics Research, and Neuroscience Research in Management.
Chapter 1 (Zarkada, Panigyrakis, and Tsoumaka) introduces a panoply
of metamodern mixed methods in management research dealing with
Web 2.0+. It discusses metamodern socioeconomic phenomena, mixed
methods designs, and sampling integrity, among many other topics. It
follows an interesting light analogy and is very robust in terms of theo-
retical content. Interesting reflections are also included.
In Chap. 2, Hackett and Foxall tackle the topic of neurophilosophy.
This is a chapter with a rich theoretical background. There is an interest-
ing section on psychological constructs and neurophysiological events.
There is also a challenging exploration into mereological
understandings.
Kip Jones (Chap. 3) deals with emotivity and ephemera research. The
content is focused on arts-led and biographical research as well as rela-
tional aesthetics. There are some interesting insights into neo emotivism.
There is a challenging section on performative social science. There are
also interesting comments on experimentation and the experimental
redux.
Prefac e ix
Chapter 4 presents the novel approach—Abductive Thematic Network
Analysis (ATNA) using ATLAS-ti written by Komalsingh Rambaree.
This chapter introduces ATNA as a methodological approach for qualita-
tive data analysis. It starts by providing a brief description on abductive
theory of method and thematic analysis method. Then, it highlights how
the two methods are combined to create ATNA. Using a qualitative data-
set, this chapter demonstrates the steps in undertaking ATNA with a
computer-aided qualitative data analysis software—ATLAS-ti v.7.5. The
chapter concludes that ATNA provides to researchers a much-needed
pragmatic and logical way of reasoning, organizing, and presenting quali-
tative data analysis.
Sullivan, Lao, and Templin (Chap. 5) deal with diagnostic measure-
ment. With diagnostic measurement, the aim is to identify causes or
underlying properties of a problem or characteristic for the purposes of
making classification-based decisions. The decisions are based on a
nuanced profile of attributes or skills obtained from observable character-
istics of an individual. In this chapter, the authors discuss psychometric
methodologies involved in engaging in diagnostic measurement. They
define basic terms in measurement, describe diagnostic classification
models in the context of latent variable models, demonstrate an empirical
example, and express the broad purpose of how diagnostic assessment can
be useful in management and related fields.
Yang and Fong (Chap. 6) explore the issues of incremental optimiza-
tion mechanism for constructing a balanced, very fast decision tree for
big data. Big data is a popular topic that highly attracts attentions of
researchers from all over the world. How to mine valuable information
from such huge volumes of data remains an open problem. As the most
widely used technology of decision tree, imperfect data stream leads to
tree size explosion and detrimental accuracy problems. Over-fitting prob-
lem and the imbalanced class distribution reduce the performance of the
original decision tree algorithm for stream mining. In this chapter, the
authors propose an Optimized Very Fast Decision Tree (OVFDT) that
possesses an optimized node-splitting control mechanism using Hoeffding
bound. Accuracy, tree size, and the learning time are the significant fac-
tors influencing the algorithm’s performance. Naturally, a bigger tree size
takes longer computation time. OVFDT is a pioneer model equipped
x Preface
with an incremental optimization mechanism that seeks for a balance
between accuracy and tree size for data stream mining. OVFDT operates
incrementally by a test-then-train approach. Two new methods of func-
tional tree leaves are proposed to improve the accuracy with which the
tree model makes a prediction for a new data stream in the testing phase.
The optimized node-splitting mechanism controls the tree model growth
in the training phase. The experiment shows that OVFDT obtains an
optimal tree structure in numeric and nominal datasets.
Sokele and Moutinho (Chap. 7) introduce the Bass model with
explanatory parameters. Over the 45 years, the Bass model is widely
used in the forecasting of new technology diffusion and growth of new
products/services. The Bass model has four parameters: market capac-
ity; time when product/service is introduced; coefficient of innovation;
and coefficient of imitation. Although values of coefficient of innova-
tion and coefficient of imitation describe the process of how new prod-
uct/service gets adopted as an interaction between users and potential
users, their explanatory meaning is not perceptible. These authors
explore this important gap.
Chapter 8 by Volker Nissen is titled “A Brief Introduction to
Evolutionary Algorithms from the Perspective of Management Science.”
Summarizing, it is useful to differentiate between several perspectives.
From a methodological point of view, the myriad of nature-inspired heu-
ristics is rather confusing for casual users and definitely not helpful in
creating more acceptance for metaheuristics in practical applications.
Moreover, there is evidence (e.g., Weyland 2015; Sörensen 2015) that at
least some of the nature-inspired concepts published recently (such as
Harmony Search) are rather old wine in new skins. One should better
look for over-arching and bearable concepts within evolutionary algo-
rithms (EA) and related metaheuristics, putting together a common
framework (or toolbox) that integrates different options of solution rep-
resentation, search operators, selection mechanisms, constraint-handling
techniques, termination criteria, and so on. Fortunately, such frameworks
are available today. Then, properly choosing the components for a hybrid
heuristic from such a framework requires a deep understanding of which
components actually fit together and work well for certain classes of
problems or search spaces.
Prefac e xi
Moreover, following the No-Free-Lunch Theorem (Wolpert and
Macready 1997), problem-specific fine-tuning of heuristics remains
important to achieve truly good results. Today, much of this is still more
an art than a science, despite helpful textbooks like Rothlauf (2011). As
a consequence, there are lots of interesting research issues to be solved
along these lines. This process is indeed ongoing for several years now,
but, as Sörensen et al. (2016) point out, also bypassed by useless initia-
tives to invent ever “new” nature-inspired heuristics.
For more than 20 years now, EA have become integrated in business
software products (e.g., for production planning), so that, as a result, the
end user is often unaware that an evolutionary approach to problem solv-
ing is employed (Nissen 1995). Today, large software companies like SAP
use EA in their enterprise software. However, since customizing options
are limited in these systems, it appears fair to say that the full power of EA
is frequently not unleashed by such standardized approaches. This con-
fronts us with a dilemma. Simple forms of EA that can be fairly easily
understood and applied widely are of only limited power. If we want to
use metaheuristics like EA to full extent, then this requires knowledge
and experience in their design and application. Most users have neither
the qualification nor the time to dive that deep into the matter. According
to my own observations as an IT consultant, this unfortunately also holds
for most consultants that could potentially help customers in applying
modern heuristics. Thus, creating really powerful applications of EA
today is frequently an issue for only a small number of highly specialized
IT companies. This situation is unsatisfactory and could only be changed
if the design and application of modern heuristics becomes an important
topic in general management studies at universities and related higher
learning institutions. The author argues that this should indeed be the
case, because in today’s digital era, there is strong evidence that we are
entering an age of knowledge-based competition where a qualified work-
force that is able to creatively use modern tools for data mining, ad-hoc
reporting, heuristic optimization, artificial intelligence, and so on will
make the difference in many branches of industry.
Volker Nissen also kindly contributes to Chap. 9 on applications of EA
to management problems. EA (or Evolutionary Computation) represent
as nature-inspired metaheuristics a branch of computational intelligence