NJB Vol. 69, No. 1 (Spring 2020)
Lyudmila Gadasina, Sergey Voitenko and Pasi Luukka – The Digital Diversity in Russian Regional Dynamics: Analysis by Machine Learning Methods
Jan Stoklasa, Azzurra Morreale, Tomáš Talášek and Mikael Collan – The Mean & Range Effect – A Sweet Spot Stimulating Risk-Seeking in Managerial Decision Support
Jani Kinnunen and Irina Georgescu – Fuzzy Real Options Analysis based on Interval-Valued Scenarios with a Corporate Acquisition Application
Mikael Collan and Jyrki Savolainen – To Phase or Not to Phase: Quantifying the Effect of Phasing Construction Projects
The whole issue as a PDF here.
Guest Editor’s Letter
This special issue is born of the papers presented at the joint meeting of the North-European Society for Adaptive and Intelligent systems (NSAIS) and the Finnish Real Options Society (FROS) organized in August 2019 in Lappeenranta, Finland. The main context of the joint meeting was that of discussing adaptive and intelligent systems, today better known as “analytics”, and real option analysis in the light of digitalization and modern manufacturing. This is a topical area that seems to bring together analytics and business in more than one way and seemed to also resonate with the participants, as many interesting and fresh contributions were presented. In this vein, this special issue is a collection of four selected papers from the meeting that resonate with the theme in different ways.
In the first paper, Lyudmila Gadasina, Sergey Voitenko, and Pasi Luukka explore and analyze the level of digitalization in different regions of Russia. In a sense, the research belongs to the field of economic geography, but as it touches the issue of digitalization the main points to be drawn are very business-oriented that is, where the regional readiness for digitalization is strong, there businesses based on digitalization can thrive. Also, as most businesses today are digitalizing their processes and in vein with Manufacturing 4.0 also physical production processes are being digitalized and networked, digitalization may become a point of competitive advantage and a pull factor for regions that are strong in this sense.
The authors use a combination of analytics methods based on statistical data – first they identify clusters of the Russian data that represent similar regions in terms of their digital “competence” and second, they classify each region to a cluster. The resulting classification shows that the digitalization in Russia can be characterized as “digital inequality”, as there are considerable large differences between regions. Regions that have been trailing have had a fast development in the recent years, however, large differences remain.
The second paper by Jan Stoklasa, Azzurra Morreale, Tomas Talasek, and Mikael Collan studies human behavior in the context of financial information affecting decision-making. Their work can be said to belong in the stream of literature on behavioral finance, where the study of cognitive biases to decision-making has received a lot of attention – however, their fo- cus is on how different presentation formats used to present information about uncertain out- comes affect decision-making. This focal area has received only limited attention in the past. The authors study how different amounts of information and different formats of information presentation, ranging from simple single-numbers to histograms, and further to continuous distributions, are used to represent the same or similar decision-making situations.
The main finding in the paper is that changing the presentation format of information about uncertain outcomes from presenting only a simple expected value to presenting an expected value and a range of possible values increases the decision-makers ́ propensity to take risk while adding even more information about the nature of the risk, such as presenting histograms or continuous distributions (with or without a mean value highlighted) seems to again reduce decision-makers ́ risk-taking. This indicates that there is a “sweet spot” in terms of what type of presentation format for information induces the higher propensity to take risks. The authors call the effect the “mean & range effect”. Further confirmatory research is presented to validate the obtained results with populations from altogether three countries – what is found is that the effect is manifested in roughly twenty percent of decision-makers. The twenty-percent of decision-makers who manifest the highlighted behavior are further analyzed. The results indicate that the assumption that the perception of risk is invariant may not hold and further- more that we must first understand what is perceived from information before we can study the rationality of decision-making. The implications of the findings presented in the paper are especially important from the point of view of the financial industry that typically and almost invariably deals with information about uncertain future outcomes.
The third paper by Jani Kinnunen and Irina Georgescu presents a new variant for fuzzy real option valuation that is based on using trapezoidal fuzzy numbers with interval-tails referred to as interval valued fuzzy sets. Real option valuation is the latest addition to profitability analysis of real-investments and attacks the problem of understanding the value of potential provided by managerial flexibility. The first fuzzy variants of the traditional option pricing methods arrived in the 1990s, but specific methods designed for fuzzy real option valuation only after the year 2000. The new variant that the authors present is based on the fuzzy Pay- Off Method for real option valuation and more specifically a later version of the method that has been replaced using the possibilistic mean with the center of gravity in the calculation of the single value representative centroid for the positive side of the underlying asset pay-off distribution. The focus is on the variants that use trapezoidal fuzzy numbers and the authors present comprehensively and completely with code (for R) the models on which their new con- tribution is based on.
Fuzzy logic is a precise way of modeling imprecision and in the context of real option valuation imprecision plays a great role, as the analysis is forward-looking and most often based on expert judgment, the new variant presented is designed to perform real option analysis in cases, where the available subjectively perceived information about cash-flows used as the ba- sis of the evaluation is in the form of trapezoids, where the tails of the trapezoids are interval to reflect and to capture the uncertainty about the minimum possible and the maximum possible values. The context of the paper is that of mergers and acquisitions and the authors illustrate the new method-variant with a case from within that context.
The fourth and last paper of this special issue is by Mikael Collan and Jyrki Savolainen and takes the reader to the world of the construction industry and specifically looks at the problem of phasing construction. Phasing is a real option often used in the real world and under- standing it better is highly relevant from the practitioner point of view. The research presented is focused on comparing and analyzing the different types of decision-support that can be reached for phasing of construction with two very different methods available for the job – the first method they present, test, and discuss is perhaps the simplest to use real option analysis method available the fuzzy pay-off method that is based on using two, three, or more managerially estimated cash-flow scenarios to construct a net present value distribution for an asset. They show that the method is usable in producing fast intuitive decision-support for construction-phasing decisions. The method is described as “quick and dirty” and the results given by it are direction-giving, on the other hand, the method does not require precise information and can handle a lot of imprecision with regards to the project. Furthermore, the method does not rely on any specific processes to work, this is beneficial from the point of view of the context of the construction industry, as the processes that govern the prices of real estate are typically very complex and most often unknown.
The second methodology for analyzing and supporting the decision to phase in the construction industry context presented in the paper is system dynamic simulation modeling. The method is a much more “deep going” method and requires much more time and planning, compared to the pay-off method. The methodology requires that a model that depicts the analyzed reality is constructed and in such a way that the dependencies (temporal and otherwise) between variables and parts of the model are taken into consideration. Once a model is built, in this case of a construction investment with the real option to construct in phases, Monte Carlo simulation is performed on the model. In the simulation, input variable values are randomly drawn from distributions for each (input) variable and the simulation continues by drawing values the “environmental variables” for each time-step until the end of the analysis horizon. The model reacts to the drawn environmental variable values according to the pre-set and pre-modeled rules. In the paper, five different construction strategies, or sets of rules, are analyzed and one thousand scenarios for each strategy is run. The novelty of the paper is in that it compares the usability of two methods in the context of the construction industry with numerical cases and presents a critical analysis of the suitability of the methods for the task.
All in all, the four papers of this special issue all present interesting, relevant, and new contributions within the broad topical areas of analytics and real option analysis. From the presented papers one thing emerges – in the study of economic and business phenomena the use of sophisticated analytics methods is on the rise and typically the results gained by using them offer deeper insight into the studied phenomena than what can be reached with only simple and more traditional methods. This is also an indication of the fact that the context of business is very lucrative from the point of view of analytics and that there are great synergies to be found by combining these two disciplines.
LUT-University, School of Business and Management