selection at the amse conference-2017 valencia, spain ... · 1 lectures on modelling and...

Selection at the AMSE Conference-2017

Valencia, Spain, July 13-14, 2017

- Venture Capital Research: A Bibliometric Analysis

C.A. Cancino, J.M. Merigó, D. Díaz, J.P. Torres (Chile)…………………………………………………………....1

- Fuzzy Logic Measures and Non-Monotonic Distances Applied to Color Psychology

Flor Madrigal Moreno, Andreia Cristina Müller, Jaime Gil Lafuente, José M. Merigó Lindahl (Mexico, Spanish,

Chile)…………………………………………………………………………………………………………..…....11

- Linguistic Measures of Subjective and Objective Poverty

Maria Jose Fernandez (Argentine)………………………………………………………………..………………...23

1

Lectures on Modelling and Simulation; A selection from AMSE # 2017-N°1; pp 1-10

Selection at the AMSE Conference Valencia/Spain, July 13-14, 2017

Venture Capital Research: A Bibliometric Analysis

*C.A. Cancino, **J.M. Merigó, ***D. Díaz, ****J.P. Torres

Department of Management Control and Information Systems and Department of Business,

University of Chile

Av. Diagonal Paraguay 257, 8330015 Santiago, Chile

(*[email protected], **[email protected], ***[email protected],

****[email protected])

Abstract

Venture capital research is becoming very significant during the last decades. The aim of

this study is to present a general overview of the leading journals, articles and authors in

venture capital research between 1990 and 2014. Different analyses were performed, all of

them at a general level for the described period. In order to do so, as is usual in bibliometric

analysis, this work uses the Web of Science database. The article provides several

bibliometric indicators, that includes the total number of publications, the total number of

citations, and the h-index. The main contribution of this work is to develop a general

overview of the leading journals, authors, universities in venture capital research, which

leads to the development of a future research agenda for bibliometric analysis, such as the

review of the most productive and influential authors, universities, and countries in venture

capital research.

Keywords: Venture Capital; Bibliometrics; Journals; Authors, Universities; Web of

Science.

1. Introduction

Venture Capital (VC) is an instrument for supporting the development and growth of new

enterprises through the provision of financial resources and also offers business expertise,

customer networks and good management practices (Hochberg et al., 2010). According to

2

Cornelius and Persson (2006), venture capitalists are financial intermediaries who collect

excess capital from those who have it, and provide it to those who require it for the

development of a business venture. In general, in the decades, venture capital research has

grown considerably in proportion to other disciplines.

This article develops a way to analyze venture capital research over the last 25 years by

using bibliometric indicators. Bibliometric studies are becoming very popular in the

scientific literature (Merigó et al., 2016), strongly motivated by the access to bibliographic

information. Some studies have developed bibliometric analyses in a wide range of fields

including: entrepreneurship (Landström, 2012), innovation (Cancino et al., 2017a, 2017b),

health economics (Wagstaff and Culyer, 2012), among others.

The article develops a journal and authors analysis identifying the leading ones in the field.

In particular, this work describe that there are certain specialized journals that publish more

in venture capital research with respect to other journals, for example, Journal of Business

Venturing, Entrepreneurship Theory and Practice, and Small Business Economics. It also

highlights other journals for having a high number of citations, even if they publish a large

number of articles in VC research, such as the Journal of Finance, Journal of Financial

Economics, Research Policy, Strategic Management Journal, Academy of Management

Journal, Administrative Science Quarterly, among others. Moreover, a temporal analysis is

developed in order to see which journals have been the most influential ones throughout

time.

2. Literature Review

According to Gompers et al. (2008), Venture Capital (VC) research explores different

steps, which involve the pre-investment phase of VC, the management of VC, and the exit

strategies of VC. In the first step, pre-investment phase, VC research explores how changes

in public market signals affected VC, or the conditions to facilitate the creation of greater

firm value after receiving VC (Dushnitsky and Lenox, 2006). Research in this stage also

analyses the process of creating relationships between venture capitalists and entrepreneurs

(Hochberg et al., 2010). In the second step, research in the management stage focused its

attention on companies when they receive VC. For examples, researchers have explored the

3

links between the influence and control of VC firms (Bottazzi, Da Rin & Hellmann, 2008)

and the management skills and expertise of entrepreneurs and new ventures, such as

entrepreneurial orientation (Stam & Elfring, 2008). Finally, research in the exit step

reviews how firms can develop either their initial public offering (IPO) or their buyout.

Nahata (2008) suggests that companies backed by more reputable VCs by initial public

offering (IPO) capitalization share, are more likely to exit successfully, access public

markets faster, and have higher asset productivity at IPOs.

Even though VC research has three stages of analysis, VC research encompasses wide

range of academic areas, without a particular discipline leading scientific research in this

field. Academics from disciplines such as Finance, General Management, Innovation, Law,

Public Policy, Sociology and Economics present a wide range of research on venture

capital, which is very valuable because it brings different perspectives to analyze the

problem of financing new businesses.

The above shows that the analysis of VC research is varied and can derive from different

disciplines. It could be positive to have different perspectives to try to understand the

problem.

3. Methods

Bibliometric research is a field that quantitatively studies bibliographic material (Broadus,

1987) providing a general overview of a research field according to a wide range of

indicators. There are different ways of ranking material in a bibliometric analysis. The most

common approaches use the total number of articles or the total number of citations.

Another useful indicator is the h-index (Hirsch, 2005) that combines articles with cites

indicating the number of studies X that have received X or more citations. Normally, the

information about citations, total number of articles or h-index can be obtained from

academic databases as Web of Science (WoS), Scopus or Google Scholar. WoS is one of

the most popular databases for classifying scientific research worldwide. The assumption is

that it only includes those journals that are evaluated with the highest quality.

In order to search for articles that have focused on venture capital research, the study uses

the keywords “venture capital*” or “business venturing” or “corporate venturing” in the

4

title, abstract and keywords of any work available in WoS between 1990 and 2014, in order

to capture as many possible combinations of terms related to venture capital. This search

finds 2.086 articles that have become 1.820 studies by only considering articles, reviews,

letters and notes. The search was developed in October 2015 and January 2016.

4. Results

There are many journals in the scientific community that publishes material related to

venture capital research. Table 1 presents a list of the twenty journals with the highest h-

index in venture capital research.

R Journal Venture Capital

TPV TCV HV TP

1 Journal of Business Venturing 164 6976 48 836

2 Journal of Finance 23 2923 21 1972

3 Journal of Financial Economics 35 2884 21 1791

4 Entrepreneurship Theory and Practice 49 1070 21 515

5 Research Policy 37 1609 20 2059

6 Small Business Economics 67 833 16 1252

7 Strategic Management Journal 25 1477 15 1726

8 Journal of Management Studies 23 624 14 1252

9 Journal of Banking Finance 25 1024 13 3561

10 Journal of Corporate Finance 35 569 13 723

11 Technovation 30 396 13 1538

12 Academy of Management Journal 19 916 11 1490

13 Review of Financial Studies 26 763 10 1377

14 Harvard Business Review 26 634 10 4847

15 Management Science 14 966 9 3247

16 Entrepreneurship and Regional Development 18 300 9 381

17 Administrative Science Quarterly 8 1050 8 512

18 Organization Science 16 613 8 1301

19 Financial Management 14 494 8 832

20 Journal of International Business Studies 11 260 8 1162

Table 1: Most influential journals in venture capital research according to WoS

5

R means rank, HV means h-index in venture capital research, TPV means the total number

of publications in venture capital research, TCV means the total number of citations

in venture capital research, and TP means the total number of publication of the journal.

The first journal of the Table 1, Journal of Business Venturing, publishes about 20% of the

total articles on venture capital research,

Also, Table 1 show that scientific analysis on venture capital comes from many disciplines,

and it is not possible to identify a specific group of journals leading the discipline. This is

evident if the group of the twenty most cited papers in venture capital research is analyzed

(Table 2).

R Authors Year Journal

1 Stuart, TE; Hoang, H; Hybels, RC 1999 Administrative Science Quarterly

2 Zucker, LG; Darby, MR; Brewer, MB 1998 American Economic Review

3 Sahlman, WA 1990 Journal of Financial Economics

4 Megginson, WL; Weiss, KA 1991 Journal of Finance

5 Powell, WW; White, DR; Koput, KW; Owen-Smith, J 2005 American Journal of Sociology

6

Krueger, NF; Reilly, MD; Carsrud, AL

2000 Journal of Business Venturing

7 Berger, AN; Udell, GF 1998 Journal of Banking & Finance

8 Lee, C; Lee, K; Pennings, JM 2001 Strategic Management Journal

9 Sorenson, O; Stuart, TE 2001 American Journal of Sociology

10 Mcdougall, PP; Shane, S; Oviatt, BM 1994 Journal of Business Venturing

11

Shane, S; Stuart, T

2002 Management Science

12 Kaplan, SN; Stromberg, P 2003 Review of Economic Studies

13 Podolny, JM 2001 American Journal of Sociology

14 Hellmann, T; Puri, M 2002 Journal of Finance

15 Black, BS; Gilson, RJ 1998 Journal of Financial Economics

16

Kortum, S; Lerner, J

2000 Rand Journal of Economics

17 Lerner, J 1995 Journal of Finance

18 Di Gregorio, D; Shane, S 2003 Research Policy

19 Zucker, LG; Darby, MR; Armstrong, JS 2002 Management Science

20 Barry, CB; Muscarella, CJ; Peavy, JW; Vetsuypens, MR 1990 Journal of Financial Economics

Table 2: Most cited articles in venture capital research according to WoS

6

For this group it is possible to identify 12 different journals: Administrative Science

Quarterly, American Economic Review, American Journal of Sociology, Journal of

Banking & Finance, Journal of Business Venturing, Journal of Finance, Journal of

Financial Economics, Management Science, Rand Journal of Economics, Research Policy,

Review of Economic Studies and Strategic Management Journal. Among this group, three

journals (Journal of Financial Economics, Journal of Finance and American Journal of

Sociology) present three articles each on the list of the 20 most cited papers in venture

capital research.

Some leading authors in venture capital research stand out in this discipline, not only

because of the large number of publications which they develop but also because of their

high influence on the rest of the researchers of the world. Table 2 presents a ranking with

20 leading authors in venture capital research, which are classified according to their h-

index, which allows us to analyse their influence on other researchers.

R Name University Country TP TC H

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

Lerner J

Wright M

Shepherd DA

Cumming D

Lockett A

Sapienza HJ

Mason CM

Harrison RT

Gompers P

Busenitz LW

Manigart S

Hellmann T

Keil T

Schwienbacher A

Bruton GD

Dushnitsky G

Keuschnigg C

Pollock TG

Zahra SA

Dimov D

Harvard University

Imperial College Business School

Indiana University

York University

University of Nottingham

University of Minnesota

University of Strathclyde

University Belfast

Harvard University

University of Oklahoma

School and Ghent University

University of British Columbia

Aalto University

University of Amsterdam

Christian University

London Business School

University of St. Gallen

The Pennsylvania State University


Newcastle University Business School

USA

UK

USA

Canada

UK

USA

UK

UK

USA

USA

Belgium

Canada

Finland

Netherlands

USA

UK

Switzerland

USA

USA

UK

27

42

22

29

16

13

12

11

10

10

20

8

8

11

8

7

8

7

8

8

2821

1368

966

657

604

925

311

296

860

591

481

1003

266

180

411

383

358

344

296

158

21

20

17

16

13

12

12

11

10

10

10

8

8

8

7

7

7

7

7

7

Table 3: The most influential authors in venture capital research according to WoS

7

The results shown in Table 3 are that researchers from the USA and UK lead the ranking of

the most influential authors in venture capital research. Among the first 10 authors, 50%

works in USA universities and 40% works in UK universities. Also, from the total of 20

leading authors 60% works in USA and UK universities. Following the USA and UK,

researchers from Belgium, Canada, Finland, Netherlands and Switzerland are present in our

rankings. Another important highlight is that the most influential authors come from

different universities; the generation of the most influential knowledge on venture capital

research is not gathered in any particular university. In fact, among U.S. universities, only

Harvard University presents two authors in our rankings.

5.- Conclusions

This work presents a general overview of the leading journals, articles and authors in


them at a general level for the described period.

First, the analysis focused on studying a ranking of 20 leading journals that present a

greater h-index in the discipline. In this ranking, it is possible to observe an interesting

discussion that reveals that the most productive journals, i.e., those who have a greater

quantity of published work, are not necessarily the most influential, i.e. those who have a

greater number of citations by the scientific community. Only one case, Journal of Business

Venturing which is the most productive, is also the most influential journal. Evidently, this

is the only specialized journal in venture capital research. Interestingly, some cases, such as

Journal of Finance, Strategic Management Journal and Journal of Banking & Finance,

present an important number of citations (more than 1000) in fewer than 25 papers. These

three journals, despite not being specialized in venture capital research, publish very

influential papers. The work also develops ranking of the more cited articles in venture

capital research and a list with most influential authors in the discipline under study.

Clearly, venture capital research will continue growing and it is necessary to deepen the

analysis of the authors, countries and universities that lead research in this discipline, who

are not only the most productive players but also the most influential actors.

8

References

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Venture Capital in the Creation of Public Companies”, Journal of Financial

Economics, 27, 447-471.

Berger, A. and Udell, G. (1998), “The economics of small business finance: The roles of

private equity and debt markets in financial growth cycle”, Journal of Banking &

Finance, 22(6), 613-673.

Black, BS. and Gilson, RJ. (1998), “Venture capital and the structure of capital markets:

banks versus stock markets”, Journal of Financial Economics, 47, 243-277.

Bottazzi, L., Da Rin, M., Hellmann, T. (2008). “Who are the active investors?: Evidence

from venture capital”. Journal of Financial Economics, 89, 488-512.

Broadus, R.N. (1987), “Toward a definition of bibliometrics”, Scientometrics, 12(5), 373-

379.

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124.

Cancino, C., Merigó, J.M. & Coronado, F. (2017a). Big names in innovation research: A

bibliometric overview. Current Science, 13(8), 1507-1518.

Cornelius, B. and Persson, O. (2006), “Who's who in venture capital research”,

Technovation, 26(2), 142-150.

Di Gregorio, D. and Shane, S. (2003), “Why do some universities generate more start-ups

than others?”, Research Policy, 32(2), 209-227.

Dushnitsky, G., Lenox, M. J. (2006). “When does corporate venture capital investment

create firm value?” Journal of Business Venturing, 21, 753-772.

Gompers, P., Kovner, A., Lerner, J., Scharfstein, D. (2008). “Venture capital investment

cycles: The impact of public markets”. Journal of Financial Economics, 87, 1-23

Hellmann, T. and Puri, P. (2002), “Venture Capital and the Professionalization of Start-Up

Firms: Empirical Evidence”, The Journal of Finance, 57(1), 169–197.

Hirsch, J. (2005), “An index to quantify an individual’s scientific research output”,

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Hochberg, Y., Ljungqvist, A., Lu, Y. (2010). “Networking as a Barrier to Entry and the

Competitive Supply of Venture Capital”. Journal of Finance, 65, 829-859.

Kaplan, S. and Stromberg, P. (2003), “Financial contracting theory meets the real world:

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70(2), 281-315.

Kortum, S. and Lerner, J. (2000), “Assessing the Contribution of Venture Capital to

Innovation”, Rand Journal of Economics, 31(4), 674-692.

Krueger, N., Reilly, M. and Carsrud, A. (2000), “Competing models of entrepreneurial

intentions”, Journal of Business Venturing, 15(5–6), 411-432.

Landström, H., Harirchi, G. and Aström, F. (2012), “Entrepreneurship: Exploring the

knowledge base”, Research Policy, 41(7), 1154–1181.

Lee, C., Lee, K. and Pennings, J. (2001), “Internal capabilities, external networks, and

performance: a study on technology-based ventures”, Strategic Management

Journal, 22(6-7), 615-640.

Lerner, J. (1995), “Venture capitalists and the oversight of private firms”, The Journal of

Finance, 50(1), 301-318.

McDougall, P., Shane, S. and Oviatt, B. (1994), “Explaining the formation of international

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innovation: A country analysis”, Scientometrics, 108(2), 559-593.

Nahata, R. (2008). “Venture capital reputation and investment performance”. Journal of

Financial Economics, 90, 127-151.

Podolny, J.M. (2001), “Networks as the pipes and prisms of the market”, American Journal

of Sociology, 107(1), 33-60.

Powell, W., White, D., Koput, K. and Owen, J. (2005), “Network dynamics & field

evolution: The growth of international collaboration in life sciences”, American

Journal of Sociology, 110(4), 1132-1205.

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Sahlman, W. (1990), “The structure and governance of venture-capital organizations”,

Journal of Financial Economics, 27(2), 473-521.

Shane, S. and Stuart, T. (2002), “Organizational endowments and the performance of

university start-ups”, Management Science, 48(1), 154-170.

Sorenson, O. and Stuart, T.E. (2001), “Syndication networks and the spatial distribution of

venture capital investments”, American Journal of Sociology, 106(6), 1546-1588.

Stam, W., Elfring, T. (2008). Entrepreneurial orientation and new venture performance:

The moderating role of intra-and extraindustry social capital. Academy of

Management Journal, 51, 97-111.

Stuart, T., Hoang, H. and Hybels, R. (1999), “Interorganizational endorsements and the

performance of entrepreneurial ventures”, Administrative Science Quarterly, 44(2),

315-349.

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bibliometric lens”, Journal of Health Economics, 31(2), 406-439.

Zucker, L., Darby, M. and Brewer, M. (1998), “Intellectual human capital and the birth of

US biotechnology enterprises”, American Economic Review, 88(1), 290-306.

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Management Science, 48(1), 138-153.

11



Fuzzy Logic Measures and Non-Monotonic Distances Applied to

Color Psychology

* Flor Madrigal Moreno, **Andreia Cristina Müller, **Jaime Gil Lafuente, ***José M. Merigó

Lindahl

* Department of Accounting and Administrative Sciences, Universidad Michoacana de San

Nicolás de Hgo., Av. J. Mújica S/N, Felicitas del Rio 58030, Michoacán, México.

[email protected]

**Department of Economy and Business Organizations, Universidad de Barcelona

Av. Diagonal 690, 08034 Barcelona, España. [email protected] / [email protected]

***Department of Management, Control and Information Systems, Universidad de Chile

Diagonal Paraguay 257, Of. 1906, 833015, Santiago, Chile. [email protected]

Summary

The intention of this paper is to shape the profile of millennials by using fuzzy logic and color

psychology, with the purpose of having a communicative approach through the use of color red.

The data were collected from the literature review, and then a mathematical assessment was

given. The distances were measured to find the communication degree that the color red has with

the millennials, since it is linked with passion, consumption, practicality, and selectivity,

elements that reveal the attitude of this market segment. The Hamming distance and the ordered

weighted average (OWA) were used. The ordered weighted average distance operator (OWAD)

was also used; and finally, calculations were made with nonmonotonic operators of NOMOWA,

which has a negative value, and which exhibit non-monotonicity.

Keywords: Fuzzy logic, non-monotonic distances, color psychology, millennials.

1. Introduction

In recent years the study of generational groups has taken relevance, the millennial generation

stands out by its own, particularly for its buying behavior in addition to consumption in digital

12

environment. It is an attractive market for sensory and digital marketing since they are young

people who are accustomed to giving their opinion and being listened, guided not by established

formality, but by natural behaviors and providing credit to useful information from the interaction

between them through social networks.

Research related to the generation of communication links and the use of specific colors will

influence the millennials when making decisions. In specific, the red color is influencing them,

becoming then, an important part of their everyday decisions. Companies around the world use

signals such as colors and shapes to convey a brand image and to increase the possibility of

consumer purchase (Hess & Melnyk, 2016).

This research paper is based on the measurement of perceptions about the color red and what it

communicates, linked to the characteristics that define the millennials. When reference is made to

a subjective "sensation" or "perception" that is not possible or cannot be measured, another

concept is used: the valuation, using the theory of fuzzy numbers (Kaufmann & Gil Aluja, 1986).

Through the literature review, first the mathematization of the colors is carried out and then the

mathematization of the words that define millennials utilizing the fuzzy logic. The mathematical

framework allows modeling the uncertainty of the cognitive human processes that can be treated

by a computer (González, 2011), then the:

1.Weighted Hamming Distance (WHD) has been used to show the most definitive coincidences

between the characteristics of the millennials and the colors that communicate the values that

distinguish them, in such a way that the researchers of this generational group have more

information that allows them to approach to this group of people in specific.

2. Subsequently, the Ordered Weighted Average Distance OWAD was used;

3. After the Non-Monotonic Weighted Hamming Distance- NON-MONOTONIC-WHD;

4. The weighted average non-monotonic ordered distance is calculated. NON-MONOTONIC

OWAD. All the above tools will allow to observe that the use of the weights adjusted to the

individual characteristics, means that the degree of uncertainty in measuring is less. Therefore,

the distance is also smaller (WHD and NON- NOMOTONIC WHD). On the other hand, when

the weights are only ordered, the degree of uncertainty is more significant. Therefore, the

distance also increases (OWAD and NON-MONOTONIC OWAD).

2. Preliminaries

2.1 Color psychology

13

For centuries artists, philosophers, psychologists, and scientists have studied the effects of color,

developing many theories about the use of it. The number and variety of such approaches show

that universal rules cannot be applied: the perception of color depends on individual experiences.

To Goethe, it was really important to understand the human reactions to color, and his research is

a starting point of modern color psychology (Illusion Studio, 2016).

The study made by Kauppinen‐ Räisänen & Luomala, (2010), suggests that an essential function

played by colors is communication, and the evidence also shows the role of colors as a means of

communication. The color communication is related to the context, and there is a relationship

between the meaning of the packaging color and the type of product. Similarly, marketing

research suggests that consumers make product choices based on the meanings they associate

with colors and how the colors of the product fit their overall color preferences (Madden, Hewett,

& Roth, 2000).

2.2 Millennials

Millennials use consumption to define who they are and to distinguish themselves. In a research

made by Charters et al., (2011), it is evident that amongst millennial consumers the use of image,

color, and positioning vary from one country to another. On the other hand, a research made by

Credo, Lanier, Matherne, & Cox, (2016), shows that social and service-oriented activities are

increasingly important for young people.

In addition to a study made by Elliot & Barth, (2012), it was observed that in the design of wine

labels for millennial consumers, they want a more balanced mix between mind and heart (Harris

Interactive, 2001). This can explain their attempt to satisfy emotional needs through

consumption, often choosing brands of their choice in the same way they choose their friends

(Vrontis & Papasolomou, 2007).

Millennials are individualists, they do not want to be part of a mass of consumers, they are

selective, and they like personalized treatment. This includes products with a design, color, and

characteristics suitable for each buyer. In this line, colors often play a crucial role because they

are associated with a consumer culture or a consumer subculture. The notion of an association

between colors and cultures dates back to (Luckiesh, 1927), who proposed that race, customs and

the civilization type affect color preferences.

2.3 Fuzzy Logic implementation

The use of the Fuzzy model data analysis allows to have higher authenticity in the data collection,

maximizing the validity in the interpretation of results. This model reduces the uncertainty of the

14

information as it adapts to the consumer's performance and potentiates efficiency in decision-

making (Casabayó & Borja, 2010).

The complexity of the problems and the inaccuracy of the situations have made it necessary to

introduce mathematical schemes that are more flexible and adapted to reality. In this sense, the

theory of fuzzy sets, has allowed the birth of some techniques that will facilitate the solution of

those problems in which uncertainty appears (Kaufmann & Gil Aluja, 1986).

The theory of fuzzy sets is used to develop an evaluation procedure adjusted to reality. The

proposed approach makes it possible to treat impact dimensions as linguistic variables and, based

on them, formulate evaluative criteria in the form of fuzzy rules (García, Félix Benjamín, & Bello

Pérez, 2014).

2.4 Hamming Distance

The Hamming Distance is a useful technique to calculate the differences between two elements,

two sets, etc. For example, it can be useful in the fuzzy set theory to calculate the distances

between Fuzzy sets, Fuzzy value intervals, intuitionist fuzzy sets and interval intuitionist fuzzy

sets. The Hamming distance adapted from (Gil, 2012) can be described as follows:

Hamming between two fuzzy subsets and j:

Next:

To carry out this comparison, it is expected to use the so-called "Hamming relative distance." It is

obtained by dividing the absolute distance by the number of characteristics, qualities or

singularities, in this case, "n." It will be then:

D

˜

P

˜

15

2.5 OWA Operators

OWA operators are tools that allow adding information. That is, from a series of data, a single

representative value of the information can be obtained. As an additional characteristic of the

OWA operators, it can be said that the elected value obtained is an added value according to

predetermined optimism/pessimism parameters (Merigó, 2008).

The ordered weighted average distance operator (OWAD) is used as a data analysis tool since it

provides a parametrized family of distance aggregation operators between the maximum distance

and the minimum distance and can be further extended using other types of ranges such as the

Euclidean distance, the Minkowski distance, and the quasi-arithmetic distance (Merigo & Gil-

Lafuente, 2012).

1

n| ai -bi |

i=1

n

åæ

èç

ö

ø÷

2.6 Non-monotonic OWA and OWAD operators

It can be defined as follows for two sets X = {x1, x2, …, xn} and Y = {y1, y2, …, yn}.

Definition 1. A non-monotonic OWAD operator of dimension n is a NOM-OWAD mapping: [0,

1]n [0, 1]n → [0, 1] that has an associated weighting vector W with 11 nj jw and wj [-1, 1]

so that:

NOM-OWAD (x1, y1, x2, y2, …, xn, yn) =

n

jjj Dw

1

,

Where Dj is the j value of the longest individual distance from | xi - yi |

It should be noted that the main difference with the NOM-OWAD is that the weighting vector wj:

can be less than 0. In definition 1 the study considers between -1 and 1. But it is also possible to

consider more general cases, heavy OWA (Yager, 1999) (Merigo & Gil-Lafuente, 2012), where

weights can move between -∞ and ∞.

3. Applications

This is a transactional qualitative research, with primary and secondary data obtained from the

analysis of books, scientific articles, and specialized marketing magazines. A selection of

literature was carried out that in an extensive and detailed way to describe the importance of

color, its general aspects, its symbolism, what it communicates, as well as the characteristics and

concepts associated to it.

16

As a second stage, some scientific articles were reviewed about millennials and their

consumption habits. The articles consulted to define the characteristics that define millennials

were the following: Engagement and talent management of gen Y(Weyland, 2011); Generation Y

values and lifestyle segments, (Valentine & Powers, 2013); Millennials (Gen Y) consumer

behavior, their shopping preferences and perceptual maps associated with brand loyalty, (Ordun,

2015); Consumer expectation from online retailers in developing e-commerce market: An

investigation of developing online market in Bangladesh, (Rahman, 2015a); Optimizing digital

marketing for generation Y: An investigation of developing online market in Bangladesh,

(Rahman, 2015b); Hip to be cool: A gen Y view of counterfeit luxury products, (Francis &

Burgess, 2015); Discovering the millennials’ personal values orientation: A comparison to two

managerial populations, (Weber, 2015); Effects of consumer embarrassment on shopping basket

size and value: A study of the millennials consumer, (Satinover N., Raska, & Flint, 2015);

Adaptative use of social networking applications in contemporary organizations: Examining the

motivations of gen Y cohorts, (Shirish, Boughzala, & Srivastava, 2016); Online purchase

behavior of generation Y in Malaysia, (Muda, Mohd, & Hassan, 2016); Acceptance of online

mass customization by generation Y, (Junker, Walcher, & Blazek, 2016); Gen Y: A study on

social media use and outcomes, (Omar, 2016); Creativity and cognitive skills among millennials:

Thinking too much and creating too little, (Corgnet, Espín, & Hernán-González, 2016); Gen Y

customer loyalty in online shopping: An integrated model of trust, user experience and branding,

(Bilgihan, 2016) y Generation X vs Generation Y: A decade of online shopping, (Lissitsa & Kol,

2016).

Later a matrix of the millennials’ profile was elaborated; the words that describe their personality

were identified, where these words that characterize them are mathematically defined, generating

then a pattern. An scale was established where the numerical interval from 0 to 1, according to

the highest occurrences of each word in the articles consulted, as well as the intensity of the

description to each construct. Later, a table is developed to establish a relationship between the

words associated with the profile of the millennials and the degree of association of each of these

words with the color red.

Table 1 shows the data using Weighted Hamming Distance (WHD) and the Ordered Weighted

Average Distance (OWAD).

Table 1. Hamming Distance, WHD, and OWAD to calculate distances.

Calculations Hamming Distance WHD OWAD

Characteristics HD W W*

Results

W Ŵ Ŵ* Results Ŵ

17

W1 Hope 1 0.35 0.026 0.026 0.85 0.064 0.064

W2 Technology 1 0.65 0.049 0.049 0.65 0.049 0.049

W3 Freedom 1 0.30 0.022 0.022 0.65 0.049 0.044

W4 Innovation 0.9 0.21 0.016 0.014 0.55 0.042 0.037

W5 Balance 0.9 0.21 0.016 0.014 0.35 0.026 0.024

W6 Friendship 0.9 0.21 0.016 0.014 0.35 0.026 0.024

W7 Communication 0.9 0.55 0.041 0.037 0.30 0.023 0.018

W8 Perception (interaction) 0.8 0.21 0.016 0.013 0.25 0.019 0.015

W9 Education 0.8 0.21 0.016 0.013 0.25 0.019 0.015

W10 Cooperativism 0.8 0.21 0.016 0.013 0.21 0.016 0.013

W11 Dynamism - multitasking 0.8 0.25 0.019 0.015 0.21 0.016 0.011

W12 Strong intellect /intelligence 0.7 0.21 0.016 0.011 0.21 0.016 0.011

W13 Connectivity 0.7 0.35 0.026 0.018 0.21 0.016 0.011

W14 Leadership 0.7 0.21 0.016 0.011 0.21 0.016 0.011

W15 Emotional sensitivy 0.7 0.21 0.016 0.011 0.21 0.016 0.011

W16 Attention 0.7 0.21 0.016 0.011 0.21 0.016 0.011

W17 Ethics 0.7 0.21 0.016 0.011 0.21 0.016 0.011

W18 Egocentrism 0.7 0.35 0.026 0.018 0.21 0.016 0.010

W19 Trust 0.6 0.21 0.016 0.009 0.21 0.016 0.010

W20 Human values 0.6 0.21 0.016 0.009 0.21 0.016 0.010

W21 Entertain 0.6 0.21 0.016 0.009 0.21 0.016 0.010

W22 Personal growth 0.6 0.21 0.016 0.009 0.21 0.016 0.010

W23 Changes 0.6 0.21 0.016 0.009 0.21 0.016 0.008

W24 Joy 0.5 0.21 0.016 0.008 0.21 0.016 0.008

W25 Impersonality 0.5 0.21 0.016 0.008 0.21 0.016 0.008

W26 Pleasure 0.5 0.25 0.019 0.009 0.21 0.016 0.006

W27 Beauty 0.4 0.21 0.016 0.006 0.21 0.016 0.006

W28 Health 0.4 0.21 0.016 0.006 0.21 0.016 0.006

W29 Depression 0.4 0.21 0.016 0.006 0.21 0.016 0.006

W30 Protection 0.4 0.21 0.016 0.006 0.21 0.016 0.006

W31 Positive energy 0.4 0.21 0.016 0.006 0.21 0.016 0.006

W32 Easy 0.4 0.21 0.016 0.006 0.21 0.016 0.006

W33 Solvency 0.4 0.21 0.016 0.006 0.21 0.016 0.005

W34 Experience 0.3 0.65 0.049 0.015 0.21 0.016 0.005

W35 Stability 0.3 0.21 0.016 0.005 0.21 0.016 0.003

W36 Luxury/sophistication 0.2 0.21 0.016 0.003 0.21 0.016 0.003

W37 Fidelity 0.2 0.21 0.016 0.003 0.21 0.016 0.003

W38 Self-confidence 0.2 0.21 0.016 0.003 0.21 0.016 0.003

W39 Activity / Anxiety 0.2 0.21 0.016 0.003 0.21 0.016 0.003

W40 Love 0.2 0.21 0.016 0.003 0.21 0.016 0.003

W41 Coherence 0.2 0.21 0.016 0.003 0.21 0.016 0.002

W42 Passion 0.1 0.21 0.016 0.002 0.21 0.016 0.002

W43 Power 0.1 0.21 0.016 0.002 0.21 0.016 0.002

W44 Work /Physical activity 0.1 0.21 0.016 0.002 0.21 0.016 0.002

W45 Selectivity 0.1 0.21 0.016 0.002 0.21 0.016 0.002

W46 Maturity 0.1 0.21 0.016 0.002 0.21 0.016 0.002

W47 Competitiveness 0.1 0.21 0.016 0.002 0.21 0.016 0.002

18

W48 Consumption 0.1 0.85 0.064 0.006 0.21 0.016 0.002

W49 Commitment 0.1 0.21 0.016 0.002 0.21 0.016 0.002

W50 Nutrition /feeding 0.1 0.21 0.016 0.002 0.21 0.016 0.000

W51 Praticity 0 0.21 0.016 0.000 0.21 0.016 0.000

W52 Bored 0 0.21 0.016 0.000 0.21 0.016 0.016

TOTAL 0.475 13.37 1 0.494 13.23 1 0.556

Source: Original from the author.

Table 1 shows how the distances are different using each method, with Hamming distance the

value is 0.475, while using weighted Hamming Distance (OWA), the result is 0.494, and with

Distance (from Hamming) ordered weighted average (OWAD), the value is 0.556. When the

weights are ordered, the degree of uncertainty is more significant therefore the distance increases.

4. Non-monotonic applications

NOMOWA operators have negative weights and exhibit non-monotonicity. While monotonicity

is undoubtedly a useful property in aggregation, there are situations in which nonmonotonicity

may be helpful. A potential application of these non-monotonic operators of OWA is in the

multi-criterion aggregation domain guided by quantizer in which the guide quantifier is not

monotonic (Yager, 1999).

The author Ovchinnikov, (1998) introduced an extension of the OWA operators that allow the

possibility of having a non-monotonicity in the aggregation process.

The following is shown in Table 2. The distinctive feature of these operators that allow negative

weights to be used in the OWA weighting vector, the application of non-monotonic tools: Non-

monotonic weighted Hamming Distance (WHD) and the weighted average distance ordered non

monotonic (OWAD), applied to color red, where the weighting was made based on the literature

review of color psychology to relate the reactions that could be extreme by red.

Table 2. Application of non-monotonic tools WHD and non-monotonic OWAD

Non-Monotonic-WHD Non-Monotonic-OWAD

Characteristic Non-Mon-WHD Non-Mon-WHD*

Results Non- Mon -

OWAD

Non- Mon -

OWAD*

Results

Non-Mon-WHD Non- Mon -

OWAD

19

W8 -0.3 -0.019 -0.015 0.9 0.057 0.046

W11 -0.2 -0.013 -0.01 0.8 0.051 0.035

W15 -0.1 -0.006 -0.004 0.5 0.032 0.022

W17 -0.4 -0.025 -0.018 0.45 0.029 0.02

W19 -0.3 -0.019 -0.011 0.4 0.025 0.015

W20 -0.3 -0.019 -0.011 0.4 0.025 0.015

W28 -0.4 -0.025 -0.01 0.2 0.013 0.005

W29 -0.5 -0.032 -0.013 0.2 0.013 0.005

W34 -0.1 -0.006 -0.002 0.1 0.006 0.002

W37 -0.1 -0.006 -0.001 0.1 0.006 0.001

W41 0 0 0 -0.1 -0.006 -0.001

W42 1 0.063 0.006 -0.1 -0.006 -0.001

W43 0.9 0.057 0.006 -0.1 -0.006 -0.001

W44 0.8 0.051 0.005 -0.2 -0.013 -0.001

W45 0.2 0.013 0.001 -0.2 -0.013 -0.001

W46 0.2 0.013 0.001 0.3 0.019 0.002

W47 0.4 0.025 0.003 -0.3 -0.019 -0.002

W48 0.1 0.006 0.001 -0.3 -0.019 -0.002

W49 0 0 0 -0.3 -0.019 -0.002

W50 -0.2 -0.013 -0.001 -0.4 -0.025 0

W51 0.3 0.019 0 -0.4 -0.025 0

W52 0 0 0 -0.5 -0.032 -0.032

TOTAL 15.78 1 0.501 15.78 1 0.811


5. Conclusions

This research allows to know the demands of the millennial generation, and to discover the

perception they have over certain products and services. By using the fuzzy logic, the Hamming

distance, the ordered weighted average (OWA), the ordered weighted average distance operator

(OWAD) and the nonmonotonic operators of NOMOWA, it has been possible to value the

millennial's perceptions. The millennial generation has an imprecise conduct. Therefore, the

behavioral phenomenon must be contextualized to a geographical place and specific conditions.

In this context, the theory of fuzzy sets allows reducing the uncertainty when communicating

with the millennials. Fuzzy logic does not increase the difficulty of traditional mathematics and is

closer to human thinking (Canós, 2013).

It is important to clarify that shorter distances are the ideal ones since they accurately show a

slight gap between the ideal and what is sought, before this it can be established that the red color

20

is not exactly the one that generates more anchoring with the millennials according to the

mathematical results. However, the color red is linked to consumption, passion, practicality and

selectivity, elements that reveal to a large extent the tastes and preferences of this market

segment, as well as its link with the brands, generating a passionate defense of the brands that

define them. And with those that they identify with, as well as those which they do not respect.

The selectivity of millennials includes friends, brands and experiences. Millennials like

distinctive brands.

This research focuses on the mathematical evaluation of the words that define the millennial and

the communication through colors, distinguishing the importance of the use of psychology color

and the analysis of phenomena related to perception through fuzzy logic as well as new non-

monotonic combination tools WHD and OWAD. Since these combinations use negative weights

for very radical aggregation cases, this analysis represents an innovation in the study of color

theory.

Finally, this document can be strengthened with future research where other colors are analyzed

and their link with millennials, as well as the integration of new combination tools.

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23



Linguistic Measures of Subjective and Objective Poverty

Maria Jose Fernandez

Universidad de Buenos Aires, CIMBAGE, IADCOM / CONICET

Av. Córdoba 2122, Ciudad de Buenos Aires, C1120AAQ, Argentina

[email protected]

Abstract

Last decades several studies of subjective welfare with a great diversity of approaches have

appeared. Some have focused on finding variables that determine it. Others stress the importance

on quantifying it. Some investigates the determinants that make some people declare high levels

of welfare and others do not. One of crucial social problems in the study of welfare is the

recognition, measurement and analysis of the causes of poverty.

It is necessary to complement the objective poverty analysis with subjective indicators of

welfare in order to assess the correspondence between the objective improvements and the

subjective perceptions.

In this paper, we propose an evaluation model of Economic Welfare which includes

subjective and objective poverty indicators with the use of mathematical tools for the treatment of

uncertainty, in particular, linguistic models.

Keywords

Linguistic models, poverty measures, objective poverty, subjective poverty.

1. Introduction

Welfare Economics can be defined as a branch of Economics that explains the satisfaction of the

agents and the mechanisms that generate their increase and decrease. Its study is part of

disciplines as diverse as psychology, politics, sociology, philosophy and economics.

Utilitarianism posed by Bentham [1] and Mill [2] laid the foundations from which modern

Economic Science began the study of welfare. Welfare is also an abstract concept with subjective

24

connotations, but correlated with objective economic factors and individual welfare refers to each

person's perception of having covered their needs [3].

During recent years, subjective assessments of quality of life are considered when measuring

development. It is often said that subjective welfare is a necessary and sufficient condition for

human development. Subjective welfare refers to an assessment of the welfare of an individual

obtained through a survey. Subjective happiness or welfare is a global assessment of the quality

of life by each individual [4]. The employment of subjective welfare measures is based on the

basic assumption the governments needs to evaluate the improvements in the quality of life of

their citizens [5]. Traditionally, study, measurement and design of public policies have paid

attention to economic indicators. Recent studies highlighted the importance of incorporating

subjective indicators, such as care or perception of happiness [6]. During last decades several

studies of subjective welfare that present a great diversity in the type of approach that they realize

have appeared. Some have focused on finding the variables that determine welfare. Others stress

the importance on quantifying happiness, and finally a group investigates the determinants that

make some people declare high levels of welfare and others do not. Dimensions that agents

consider important regarding their welfare and quality of life are the benchmark in the study of

subjective welfare. The self-evaluation that the subjects make regarding the satisfaction and

happiness they perceive in relation to the different dimensions is taken into account [7]. Main

social problems in the study of welfare are the recognition, measurement and analysis of the

causes of poverty. It is also fundamental to be able to design effective measures to reduce it.

However, this concept presents significant difficulties when it’s intended to measuring it

accurately. Evaluation of economic welfare can be based on two types of indicators: objective-

quantitative indicators (poverty lines, unsatisfied basic needs, human development index,

anthropometric indicators, etc.) and subjective-qualitative indicators (based on surveys that reveal

perceptions of individuals or households) [8].

Subjective poverty approach defines as poor those who are not satisfied with their situation,

because it is considered excluded from the normal way of life, regardless of the economic

situation of the agent. Studying poverty from a subjective point of view means considering that

any person or family can give their judgment about the degree to which it satisfies their basic

needs. To determine whether a person or family considers themselves poor or not, two forms are

generally used. It is possible to ask directly about certain perceptions about their condition or

observe their behavior. Subjective poverty can be understood as the perception of poverty that

has a sector of the population that feels and defines itself as poor because they cannot access a set

of goods and services that they consider of great importance [9]. Recognizing what causes

25

subjective poverty can be a good mechanism to improve public policies based on a better

understanding of the needs and expectations of the population and the redefinition of priorities

[10]. Measuring welfare is a frequent task, and there is a high consensus on the need to quantify

it, but there are some divergences as to which indicators use to reflect a society's welfare situation

and little information on how population evaluates what happens with their own welfare [11].

Subjective measurements are based on population surveys where they are asked to define their

situation, usually located at a point on a qualitative scale. In general, information of welfare level

of each person is obtained through simple questionnaires with simple and direct questions that

capture people's perception of their satisfaction with access to certain goods or services.

The existence of qualitative variables, inherent to human behavior, or elements of the external

environment of difficult objective quantification, makes it difficult for individuals to represent

with an exact numerical value the valuation of the different aspects related to the welfare to be

assessed. Under such circumstances, it is more appropriate to express their responses by means of

linguistic values rather than exact numerical values. This approach is based on fuzzy sets theory

and is called linguistic approach. It is applied when the variables involved are of a qualitative

nature [12, 13, 14]. It is possible to model in a more appropriate way a great number of real

situations, since they allow representing the information of the individuals, that it is not always

precise, in a more appropriate way. A linguistic variable differs from a numerical one in that its

values are not numbers, but words or sentences of the natural language, or of an artificial

language [12]. The use of the diffuse linguistic approach implies the need to operate with words

[15].

It is necessary to complement the analysis of the population living conditions with the subjective

indicators of welfare in order to assess the correspondence between the objective improvements

and the subjective perceptions that the agents perform on them. Given that the type of questions

used to define a subjective welfare indicators are qualitative in nature, the use of a linguistic

model that operates with words directly will allow aggregating opinions of the individuals

adequately without losing information. The proposed approach will let to capture the nuances and

degrees of welfare present in human perceptions, whereas the classical models allow only binary

contrasts between positive and negative perceptions, not including the variations of intensities

between them. Application of linguistic models to evaluate the population's perceptions of their

welfare makes it possible to analyse individuals’ life quality under the use of linguistic variables

belonging to the habitual language. In addition, it will allow studying and processing individual

and aggregated opinions operating with words directly without losing information nor rigorous.

26

This paper is structured as follows. In first place, a Linguistic Combined Model for Economic

Welfare is presented. A Welfare Linguistic Subjective Indicator and a Linguistic Poverty Line are

combined to measure a household multidimensional poverty. Next section develops an example

and finally some conclusions are presented.

2. Linguistic Combined Model of Economic Welfare Evaluation

Poverty is a multidimensional reality that is not usually completely measured because of its

nature. Population’s living conditions are characterized by subjective and objective aspects. Thus,

poverty measures sometimes are in the need to combine subjective and objective features.

Since subjective well-being indicators are built through surveys that reveal the individual's

perception in certain areas considered using qualitative scales, is very relevant the use

of linguistic variables in their formulation [16]. Then, since poverty is a matter of degrees, a

linguistic poverty line is used to evaluate objective welfare of each household [17].

First, households' perceptions in five chosen areas will be collected using a multiple choice

survey (Appendix). With the information obtained, ILBE index will be calculated for each

household [16]. Then, the fuzzy poverty line will be calculated for that household [17]. Finally,

the subjective evaluation of the household will be contrasted with the degree of poverty for

identifying different situations on multidimensional poverty.

2.1. Welfare Linguistic Subjective Indicator

Welfare Linguistic Subjective Indicator (ILBE) takes into account five areas of welfare: 1. health,

2. education, 3. housing, 4. income and 5.employment [16]. Economic welfare is determined by

their perception about these aspects. These perceptions are taken from direct surveys of heads of

households. The questionnaire induces households to assess their access to the five areas

considered using linguistic labels. The assessment of each area will provide a component of ILBE

and aggregation enable an indicator for each household.

According to the domain of the variables involved, it is assumed the use of one of the following

sets of linguistic terms, in order to the head of household express their views on each question

made:

excellentgood,very good, mean,bad, bad,very dreadful, 65432101 sssssssS

poor}non poor,non almost

poor,somewhat poor,notorpoornor poor,rather poor, very poor,absolutely {

65

432102

ss

sssssS

important} absolutely important, very important,pretty

careless, important, little t,uninportanrather t,unimportan {

654

32103

sss

ssssS

27

With the information obtained in surveys the indicator value for a family is obtained. This value

is calculated for each area of satisfaction for the selected household. The assessment of the degree

of satisfaction of each home for each area that integrates the ILBEh is obtained by

using the aggregation operator of linguistic information with the information gathered from

surveys (Appendix).

- Health Area: Evaluation will be based on household responses to questions 1 (access to

healthcare) and 2 (access to medicines and vaccination): heqq sssEAA 21

,1.

Where 11

Ssq is the response of question 1, 12Ssq is the response of question 2 and is the

evaluation of the health area of this household.

- Education Area: Evaluation will be based on household responses to questions 3 (conformity

to the educational level of the head) and 4 (access to the education system): edqq sssEAA 43

, .

Where 13

Ssq is the response of question 3, 14

Ssq is the response of question 4 and is the

evaluation of the education área of this household.

- Housing Area: Evaluation will be based on household responses to questions 5 (housing

conditions) and 6 (neighborhood general conditions): hoqq sssEAA 65

, .

Where 15



evaluation of the housing área of this household.

- Income Area: Evaluation will be based on household responses to questions 7 (income) and 8

(household income need not to feel poor): iqq sssEAA 87

, .

Where 11



evaluation of the income área of this household.

- Employment Area: Evaluation will be based on household responses to questions 9 (number of

hours worked) and 10 (working conditions): emqq sssEAA 109

, .

Where 11



evaluation of the employment área of this household.

Since not all areas that compose the indicator are equally important for all households, the survey

includes a question which asks the degree of importance assigned to each family to each of them

(Question 13, appendix) corresponding to a linguistic label in the set 3S .

If 3Ss

jh

is the linguistic label that shows the importance allocated by the household h the area

j

1 EAA: extended arithmetic mean [16].

28

( 5,...,1j ); the weighting, hjw , correspondent must verify that 1,0jhw

5

1

1j

jhw , and is

obtained by applying

5

1j

jjjh hhw nh ...,,1 .

Having assessed the components of the index and obtained their respective weights, the ILBEh is

obtained by the use of aggregation operator of linguistic

information hemihoedhehh ssssssEWAAILBE ,,,, 2.

If it is wanted to express the degree of aggregate welfare of each household by a term of the set

1S , the sub index of the virtual label hs is approximated to an integer

value through the usual rounding operation (round h ) and it is got a linguistic original label.

Questions 11 and 12 will be used to compare the consistency of the households’ responses,

211Ssq and

112Ssq .

2.2. Objective Poverty Evaluation

It is needed to classify the household according to their income, because it is important to

analyze how poor households perceive their welfare as how they do the non-poor ones. For

classifying the household, we will employ the Fuzzy Poverty Lines and the Poverty Degrees

developed by Fernandez [17]. In this model, poverty is considered as a matter of degree.

First, a Fuzzy Basic Food Basket (CBAF) is determined to calculate the Indigence Line to the

Equivalent Adult. To assess the CBAF for an adult fuzzy triangular numbers are expressed by its

confidence intervals and are operated with them [18]. Given nCCC ,...,1 , its cardinal is

nC monthly valuation of CBAF is given by:

n

i

ii

CBAF PQV1

/ niRPQ ii ,...,1, .

Being each n component of the basket, the quantity of component i of the basket, the

price of that good [17]. Then, a fuzzy scale is constructed to determine the units of equivalent

adults of each household, obtained from a fuzzy energy needs table [17]. Being hU the units of

equivalent adults of h-th household and CBAFV the valuation of CBAF for an equivalent adult unit,

the valuation of the CBAF for the h-th household is: CBAF

hh

CBAF VUV .

In order to obtain the fuzzy poverty line, it is indispensable to establish the fuzzy inverse of

Engel's coefficient e~ 3. Fuzzy poverty line for the equivalent adult will be determined

by: eVLP CBAFf~. .

2 EWAA: weighted arithmetic mean [16]. 3 Fuzzy inverse Engel's coefficient relates food expenditures with non-food ones using triangular fuzzy numbers [17].

29

Being fLP the fuzzy poverty line for the equivalent adult and hU the units of equivalent adults

of h-th household, the valuation of the Poverty Line for the h-th household is: f

hh

f LPULP .

Once calculatedh

fLP , household's monthly effective total income is compared and it is

determined whether it is completely poor, not poor, or whether it is in the gray area. For

classifying households within the gray zone, it is possible to associate the degree of belonging to

the set of poor households with a set of labels. It is possible to construct a set of labels to classify

households with respect to the concept of poverty (Table 1 and Figure 1).

The use of this approach allows capturing the different degrees present when valuing a measure

that represents welfare that is intended to measure. The use of fuzzy sets theory helps to

understand the phenomenon dimensions more comprehensively.

2.3. Joint Poverty Analysis

In order to analyze the determinants of the non-poor / feeling poor and the poor / non feeling poor

families, households will be separated into four groups.

The objective poorness or non-poorness will be assessed with the fuzzy poverty lines method. A

household will be considered poor with a qualification lower than “High” (Left branch and

) and will be considered non poor in any other situation.

The subjective feeling of poverty will be determined with ILBE index. Once is approximated

to a label of the set , it will be considered feeling poor households those who gets a valuation

lower than “bad” ( , and ), and feeling non poor in any other situation.

Households will be categorized into 4 groups.

Group 1. Poor / Feeling Poor.

Objective Poverty degree: “High”, “Very High” and

“Absolute”

ILBE: “Bad”, “Very Bad” and “Dreadful”.

Group 2. Poor / Non feeling poor.


“Absolute”

ILBE: “Mean”, “Good”, “Very Good” and “Excellent”.

Group 3. Non poor / Feeling poor. Group 4. Non poor / Non feeling poor.

30

Objective Poverty degree: “Medium”, “Low”, “Very

Low” and “Null”





Special attention will be paid to Group 2 and 4 in order to make deeper analysis of the

determinants of perceptions of poverty.

3. Application

A household will be evaluated in order to classify it into one of the four groups outlined above.

In first place, it will be calculated the Fuzzy poverty line for that family, and then it will be

compared with its income. For that period, the amount needed to buy the Fuzzy Basic Food

Basket for the equivalent adult will be an approximate triangular fuzzy number:

82.2031,62.1766,50.1501CBAFV [17, 19]. Fuzzy inverse of Engel's coefficient will be

50.2,41.2,30.2~ e , and the poverty line for the equivalent adult will be an approximate

triangular fuzzy number 55.5079,4.4257,45.3453~. eVLP CBAFf.

Household is structured as follows: Head of household, a female 35 years old, her son 18 years

old and her mother 61 years old. This household represents 59.2,44.2,17.2hU units of

equivalent adults. The fuzzy poverty line will be calculated and approximated to a triangular

fuzzy number 03.1315605.1038898.7493 ,,LPULP f

hh

f [17, 19].

The family declares to the interviewer that its monthly effective total income is $9500. Thus, its

income belongs to the poverty set in a 0.69 degree; household presents a high level of objective

poverty.

In a second phase, the household answer the subjective survey. The responses where:

Question Valuation Associated Linguistic Label

1 Good 141Sssq

2 Good 142Sssq 421

, sssEAAs qqhe

3 Mean 133Sssq

4 Good 144Sssq 5.343

, sssEAAs qqed

5 Bad 125Sssq

6 Bad 126Sssq 265

, sssEAAs qqho

7 Very bad 117Sssq

8 Bad 128Sssq 5.187

, sssEAAs qqi

9 Mean 139Sssq

31

10 Mean 1310Sssq 3109

, sssEAAs qqem

11 Rather

poor 2211

Sssq

12 Bad 1212Sssq

Question 13: 1. Health Very important 351Sss

25.01 w

2. Education Very important 352Sss 25.02 w

3. Housing Careless 333Sss 15.03 w

4. Income Pretty Important 344Sss 20.04 w

5. Employment Careless 335Sss 15.05 w

1392.2,,,, SssssssssEWAAILBE hemihoedhehh → presents a mean feeling of poverty.

When classifying into one of the groups, this household belongs to Group 2 “Poor / Non feeling

poor”. Further analysis will be needed to understand why this family doesn’t perceive poverty or

maybe it weigh mostly access to certain goods or services than its own income.

4. Conclusions

Subjective welfare measures are a complementary tool of objective indicators. It is important to

understand the connection between objective and subjective improvements.

The proposed approach allows showing different groups considering objective and subjective

poverty. This disaggregated analysis will help the analysts to understand the determinants of

poverty perceptions in relation to objective well-being.

The implementation of linguistic models will help to understand the degrees inherent in the

analysis of human welfare.

In future researches it will be interesting to go deeper into the structure of the survey, the

determinants of welfare perceptions versus the objective situation of poverty, and it will be

possible to make contributions regarding the construction of a single index combining both

approaches.

Acknowledgement

32

The authors thanks to the Secretaría de Ciencia y Técnica of the Universidad de Buenos Aires

(UBACyT Project 2016 20020150100090BA), to the Facultad de Ciencias Económicas of that

University where the Project belongs to and to the Consejo Nacional de Investigaciones

Científicas y Técnicas (CONICET).

References

[1] Bentham, J. (1948). An Introduction to the principles of morals and Legislation. Oxford

Blackwell.

[2] Mill, J.S. (1971). El Utilitarismo. Ediciones Aguilar.

[3] Iglesias Vázquez, Emma; Pena López, José Atilano; Sánchez Santos, José Manuel (2013).

“Bienestar Subjetivo, Renta Y Bienes Relacionales. Los determinantes de la felicidad en

España”. Revista Internacional de Sociología (RIS), Vol.71, nº 3. pp. 567-592.

[4] Veenhoven, R. (2009). «Measures of Gross National Happiness». Intervención Psicosocial

18(3), pp.279-299.

[5] Vargas Pérez, Andrés Mauricio (2013). “Bienestar subjetivo y políticas públicas de los

gobiernos locales”. Revista de Economía del Caribe n°12, pp. 106-129

[6] Carrasco-Campos, Ángel; Martínez, Luis Carlos; Moreno Mínguez, Almudena (2013).

“Revisión crítica de la medición del bienestar desde una perspectiva interdisciplinar”. Prisma

Social - Revista de Ciencias Sociales nº 11. Pp. 91-122.

[7] Yasuko Arita, B., Romano, S., García, N., del Refugio Félix, M. (2005). “Indicadores

objetivos y subjetivos de la calidad de vida”. Enseñanza E Investigación En Psicología

vol.10, num. 1, pp. 93-102.

[8] Aguado Quintero, L.F.; Osorio Mejía, A.M. (2006). ”Percepción subjetiva de los pobres:

Una alternativa a la medición de la pobreza”. Reflexión Política Año 8 Nº 15, pp. 26-40.

[9] Ravallion, M. (2010). “On multidimensional indices of poverty” Journal of Economic

Inequality.

[10] Giarrizo, V. (2007). Pobreza Subjetiva en Argentina. Construcción de indicadores de

Bienestar Económico. Tesis Doctoral. Facultad de Ciencias Económicas. Universidad de

Buenos Aires.

[11] Bradshaw, J., Finch, N. (2003). “Overlaps in Dimensions of Poverty”. Journal of Social

Policy 32, pp. 513-525.

[12] Zadeh, L.A. (1975). “The concept of a linguistic variable and its applications to approximate

reasoning”. Part I, Information Sciences, Vol. 8, pp.199-249. Part II, Information Sciences,

Vol. 8, pp.301-357. Part III, Information Sciences, Vol. 9, pp.43-80.

33

[13] Herrera, F.; Herrera-Viedma, E. (2000). “Linguistic decision analysis: steps for solving

decision problems under linguistic information”. Fuzzy Sets and Systems, vol. 115, pp.67-

82.

[14] Lazzari, L.L. (2010). El comportamiento del consumidor desde una perspectiva fuzzy.

Editorial Edicon.

[15] Xu, Z. (2008). “Linguistic aggregation operators: An overview” en: Bustince, H. et al. (eds.),

Fuzzy Sets and Their Extensions: Representation, Aggregation and Models. Berlin: Springer-

Verlag, pp.163-181.

[16] Lazzari, L.L., Fernandez M.J., Mouliá, P.I. (2013). “Welfare Linguistic Subjective

Indicator” en Anna M. Gil Lafuente; José M. Merigó, Luciano Barcellos-Paula, F. Silva-

Marins, C. Azevedo-Ritto (editors) Decision Making Systems in Business Administration.

World Scientific, Singapore. ISBN 978-981-4452-04-5.

[17] Fernandez, M.J. (2012). Medidas de pobreza. Un enfoque alternativo. Tesis Doctoral.

Facultad de Ciencias Económicas. Universidad de Buenos Aires.

[18] Kaufmann A., Gil Aluja J., Terceño Gómez A. (1994). Matemática para la Economía y la

Gestión de Empresas. Ediciones Foro Científico. Barcelona.

[19] INDEC (2017). Condiciones de vida. Vol. 1, nº 4 Incidencia de la pobreza y la indigencia en

31 aglomerados urbanos. Segundo semestre de 2016. Instituto Nacional de Estadística y

Censos (INDEC), Buenos Aires.

Appendix. Survey form

1. You consider that your access to health care is:

Dreadful – very bad – bad – mean – good- very good – excellent.

2. You consider that your access to medication and vaccination, if needed, is:


3. You consider your education level is:


4. You consider that access to the education system is:


5. You consider that your housing conditions are:

Dreadful – very bad – bad – mean – good- very good – excellent

6. You think that the general conditions of their neighborhood (asphalt, lighting, sewers,etc.) are:


34

7. You think your income is:


8. How much extra home income your household need not to feel poor?

Income amount Linguistic

label

Income

amount

Linguistic

label

More than double Dreadful 40 – 50%

more Good

double Very bad 10 -30 %

more Very good

80 – 90% more Bad nothing excellent

60 – 70% more Mean

9. Considering the number of the amount of hours that you work, you find it:


10. You think that your working conditions are:


11. Do you feel poor?

Absolutely poor – very poor – rather poor – nor poor or not poor – somewhat poor – almost non

poor – non poor.

12. How do you evaluate your level of economic welfare?


13. Indicate the importance of each area for your welfare.

area Absolut.

important

Very

important

Pretty

important Careless

Little

important

Rather

unimportant Unimportant

1. health

2. Education

3. Housing

4. Income

5. Employment

1



Venture Capital Research: A Bibliometric Analysis

*C.A. Cancino, **J.M. Merigó, ***D. Díaz, ****J.P. Torres

Department of Management Control and Information Systems and Department of Business,

University of Chile

Av. Diagonal Paraguay 257, 8330015 Santiago, Chile

(*[email protected], **[email protected], ***[email protected],

****[email protected])

Abstract

Venture capital research is becoming very significant during the last decades. The aim of

this study is to present a general overview of the leading journals, articles and authors in


them at a general level for the described period. In order to do so, as is usual in bibliometric

analysis, this work uses the Web of Science database. The article provides several

bibliometric indicators, that includes the total number of publications, the total number of

citations, and the h-index. The main contribution of this work is to develop a general

overview of the leading journals, authors, universities in venture capital research, which

leads to the development of a future research agenda for bibliometric analysis, such as the

review of the most productive and influential authors, universities, and countries in venture

capital research.

Keywords: Venture Capital; Bibliometrics; Journals; Authors, Universities; Web of

Science.

1. Introduction

Venture Capital (VC) is an instrument for supporting the development and growth of new

enterprises through the provision of financial resources and also offers business expertise,

customer networks and good management practices (Hochberg et al., 2010). According to

2

Cornelius and Persson (2006), venture capitalists are financial intermediaries who collect

excess capital from those who have it, and provide it to those who require it for the

development of a business venture. In general, in the decades, venture capital research has

grown considerably in proportion to other disciplines.

This article develops a way to analyze venture capital research over the last 25 years by

using bibliometric indicators. Bibliometric studies are becoming very popular in the

scientific literature (Merigó et al., 2016), strongly motivated by the access to bibliographic

information. Some studies have developed bibliometric analyses in a wide range of fields

including: entrepreneurship (Landström, 2012), innovation (Cancino et al., 2017a, 2017b),

health economics (Wagstaff and Culyer, 2012), among others.

The article develops a journal and authors analysis identifying the leading ones in the field.

In particular, this work describe that there are certain specialized journals that publish more

in venture capital research with respect to other journals, for example, Journal of Business

Venturing, Entrepreneurship Theory and Practice, and Small Business Economics. It also

highlights other journals for having a high number of citations, even if they publish a large

number of articles in VC research, such as the Journal of Finance, Journal of Financial

Economics, Research Policy, Strategic Management Journal, Academy of Management

Journal, Administrative Science Quarterly, among others. Moreover, a temporal analysis is

developed in order to see which journals have been the most influential ones throughout

time.

2. Literature Review

According to Gompers et al. (2008), Venture Capital (VC) research explores different

steps, which involve the pre-investment phase of VC, the management of VC, and the exit

strategies of VC. In the first step, pre-investment phase, VC research explores how changes

in public market signals affected VC, or the conditions to facilitate the creation of greater

firm value after receiving VC (Dushnitsky and Lenox, 2006). Research in this stage also

analyses the process of creating relationships between venture capitalists and entrepreneurs

(Hochberg et al., 2010). In the second step, research in the management stage focused its

attention on companies when they receive VC. For examples, researchers have explored the

3

links between the influence and control of VC firms (Bottazzi, Da Rin & Hellmann, 2008)

and the management skills and expertise of entrepreneurs and new ventures, such as

entrepreneurial orientation (Stam & Elfring, 2008). Finally, research in the exit step

reviews how firms can develop either their initial public offering (IPO) or their buyout.

Nahata (2008) suggests that companies backed by more reputable VCs by initial public

offering (IPO) capitalization share, are more likely to exit successfully, access public

markets faster, and have higher asset productivity at IPOs.

Even though VC research has three stages of analysis, VC research encompasses wide

range of academic areas, without a particular discipline leading scientific research in this

field. Academics from disciplines such as Finance, General Management, Innovation, Law,

Public Policy, Sociology and Economics present a wide range of research on venture

capital, which is very valuable because it brings different perspectives to analyze the

problem of financing new businesses.

The above shows that the analysis of VC research is varied and can derive from different

disciplines. It could be positive to have different perspectives to try to understand the

problem.

3. Methods

Bibliometric research is a field that quantitatively studies bibliographic material (Broadus,

1987) providing a general overview of a research field according to a wide range of

indicators. There are different ways of ranking material in a bibliometric analysis. The most

common approaches use the total number of articles or the total number of citations.

Another useful indicator is the h-index (Hirsch, 2005) that combines articles with cites

indicating the number of studies X that have received X or more citations. Normally, the

information about citations, total number of articles or h-index can be obtained from

academic databases as Web of Science (WoS), Scopus or Google Scholar. WoS is one of

the most popular databases for classifying scientific research worldwide. The assumption is

that it only includes those journals that are evaluated with the highest quality.

In order to search for articles that have focused on venture capital research, the study uses

the keywords “venture capital*” or “business venturing” or “corporate venturing” in the

4

title, abstract and keywords of any work available in WoS between 1990 and 2014, in order

to capture as many possible combinations of terms related to venture capital. This search

finds 2.086 articles that have become 1.820 studies by only considering articles, reviews,

letters and notes. The search was developed in October 2015 and January 2016.

4. Results

There are many journals in the scientific community that publishes material related to

venture capital research. Table 1 presents a list of the twenty journals with the highest h-

index in venture capital research.

R Journal Venture Capital

TPV TCV HV TP

1 Journal of Business Venturing 164 6976 48 836

2 Journal of Finance 23 2923 21 1972

3 Journal of Financial Economics 35 2884 21 1791

4 Entrepreneurship Theory and Practice 49 1070 21 515

5 Research Policy 37 1609 20 2059

6 Small Business Economics 67 833 16 1252

7 Strategic Management Journal 25 1477 15 1726

8 Journal of Management Studies 23 624 14 1252

9 Journal of Banking Finance 25 1024 13 3561

10 Journal of Corporate Finance 35 569 13 723

11 Technovation 30 396 13 1538

12 Academy of Management Journal 19 916 11 1490

13 Review of Financial Studies 26 763 10 1377

14 Harvard Business Review 26 634 10 4847

15 Management Science 14 966 9 3247

16 Entrepreneurship and Regional Development 18 300 9 381

17 Administrative Science Quarterly 8 1050 8 512

18 Organization Science 16 613 8 1301

19 Financial Management 14 494 8 832

20 Journal of International Business Studies 11 260 8 1162

Table 1: Most influential journals in venture capital research according to WoS

5

R means rank, HV means h-index in venture capital research, TPV means the total number

of publications in venture capital research, TCV means the total number of citations

in venture capital research, and TP means the total number of publication of the journal.

The first journal of the Table 1, Journal of Business Venturing, publishes about 20% of the

total articles on venture capital research,

Also, Table 1 show that scientific analysis on venture capital comes from many disciplines,

and it is not possible to identify a specific group of journals leading the discipline. This is

evident if the group of the twenty most cited papers in venture capital research is analyzed

(Table 2).

R Authors Year Journal

1 Stuart, TE; Hoang, H; Hybels, RC 1999 Administrative Science Quarterly

2 Zucker, LG; Darby, MR; Brewer, MB 1998 American Economic Review

3 Sahlman, WA 1990 Journal of Financial Economics

4 Megginson, WL; Weiss, KA 1991 Journal of Finance

5 Powell, WW; White, DR; Koput, KW; Owen-Smith, J 2005 American Journal of Sociology

6

Krueger, NF; Reilly, MD; Carsrud, AL

2000 Journal of Business Venturing

7 Berger, AN; Udell, GF 1998 Journal of Banking & Finance

8 Lee, C; Lee, K; Pennings, JM 2001 Strategic Management Journal

9 Sorenson, O; Stuart, TE 2001 American Journal of Sociology

10 Mcdougall, PP; Shane, S; Oviatt, BM 1994 Journal of Business Venturing

11

Shane, S; Stuart, T

2002 Management Science

12 Kaplan, SN; Stromberg, P 2003 Review of Economic Studies

13 Podolny, JM 2001 American Journal of Sociology

14 Hellmann, T; Puri, M 2002 Journal of Finance

15 Black, BS; Gilson, RJ 1998 Journal of Financial Economics

16

Kortum, S; Lerner, J

2000 Rand Journal of Economics

17 Lerner, J 1995 Journal of Finance

18 Di Gregorio, D; Shane, S 2003 Research Policy

19 Zucker, LG; Darby, MR; Armstrong, JS 2002 Management Science

20 Barry, CB; Muscarella, CJ; Peavy, JW; Vetsuypens, MR 1990 Journal of Financial Economics

Table 2: Most cited articles in venture capital research according to WoS

6

For this group it is possible to identify 12 different journals: Administrative Science

Quarterly, American Economic Review, American Journal of Sociology, Journal of

Banking & Finance, Journal of Business Venturing, Journal of Finance, Journal of

Financial Economics, Management Science, Rand Journal of Economics, Research Policy,

Review of Economic Studies and Strategic Management Journal. Among this group, three

journals (Journal of Financial Economics, Journal of Finance and American Journal of

Sociology) present three articles each on the list of the 20 most cited papers in venture

capital research.

Some leading authors in venture capital research stand out in this discipline, not only

because of the large number of publications which they develop but also because of their

high influence on the rest of the researchers of the world. Table 2 presents a ranking with

20 leading authors in venture capital research, which are classified according to their h-

index, which allows us to analyse their influence on other researchers.

R Name University Country TP TC H

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

Lerner J

Wright M

Shepherd DA

Cumming D

Lockett A

Sapienza HJ

Mason CM

Harrison RT

Gompers P

Busenitz LW

Manigart S

Hellmann T

Keil T

Schwienbacher A

Bruton GD

Dushnitsky G

Keuschnigg C

Pollock TG

Zahra SA

Dimov D

Harvard University

Imperial College Business School

Indiana University

York University

University of Nottingham


University of Strathclyde

University Belfast

Harvard University

University of Oklahoma

School and Ghent University

University of British Columbia

Aalto University

University of Amsterdam

Christian University

London Business School

University of St. Gallen

The Pennsylvania State University


Newcastle University Business School

USA

UK

USA

Canada

UK

USA

UK

UK

USA

USA

Belgium

Canada

Finland

Netherlands

USA

UK

Switzerland

USA

USA

UK

27

42

22

29

16

13

12

11

10

10

20

8

8

11

8

7

8

7

8

8

2821

1368

966

657

604

925

311

296

860

591

481

1003

266

180

411

383

358

344

296

158

21

20

17

16

13

12

12

11

10

10

10

8

8

8

7

7

7

7

7

7

Table 3: The most influential authors in venture capital research according to WoS