Exploring the Influence of Social Factors to Travel: A case study of

university workers and college students in Metro Manila, Philippines

Grace Padayhag Co-authored by: Dr. Daisuke FukudaGraduate student Associate Professor

Tokyo Institute of Technology

Japan Society for the Promotion of Science (JSPS) SymposiumUniversity of the Philippines Diliman

March 10, 2009

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Outline of presentation1. Introduction2. Conceptual framework3. Methodology4. Results and Discussion 5. Conclusion and recommendation6. Future works

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Motivation of the studyInformation and Communications Technology (ICT) is

gradually penetrating in the developing countries and have affected the daily life in so many ways.

e.g. do more social activities, social friends expand

The use of ICT also can induce, reduce or substitutetravel.

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Examined through social interaction, social activities and

social network

Examples of how ICT affected travelcell phone use – for quick and instant deals, may

create additional travels

online shopping – may substitute travel

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IntroductionUrry (2007)

German sociologist Georg Simmel stated two important points why people travel:

(1) they are attracted to others for ulterior reasons and (2) they enjoy engaging in “free-playing sociability,” namely forms of

social interactions that are free from content, substance, and ulterior end.

Axhausen (2003) conducted the initial research referring to social factors and travelthe core of the research hypothesis was that people’s travel pattern is

shaped by his network – a social network.

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IntroductionSocial network

Technically speaking, it is defined as a set of actors and the ties among them

In layman’s term, it is composed of a person’s relatives, colleagues from work (sports club or professional organization), friends, and acquaintances.

Figure 1 Social Network

You/actor

Your friends/ties

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IntroductionSocial interactionsArentze and Timmermans (2007) With social interactions, habitual or intermittent, people are able to exchange information (especially that ICT is rampant even in developing countries in which information can be delivered instantly)

Hibbitt et al., (2001)Social interaction create obligations and expectations of reciprocation.

Harvey and Taylor (2000)working in isolation at home (telecommuting) does not really diminish travel but will try to find social interaction elsewhere consequently generating travel.

Social activitiesLu and Pas (1999) who revealed that travel behavior is better explained when the activity participation, it incorporates social activities, is included in the analysis.

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Carrasco et al. (2006) suggested a method of collecting social network to study social activity-travel patterns which is the ego-centered approach

Ego-centered approachThe prevalent method to collect the members of ego’s social network.

This approach elicits the “ties” (your friends) of the “actor” (you) and their characteristics.

Each participant has to list down his friends and then characterize them according to gender, age and sometimes according to their roles (or relationship) to the ego.

Introduction

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Objectives

The purpose of this study is to investigate the activity-travel behavior of university students as related to their patterns of socialization.The study also examines the social factors that encompass the aspects of social interactions, social activities and the composition of social contacts and their effects on travel of the university workers in Metro Manila, Philippines

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Why university students?

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Source: Urban Travel Behaviour Characteristics Of 13 Cities Based On Household Interview Survey Data (Hyodo, 2005)

Figure 3 Age structure by trips

Younger people used to travel more often than the older people in the case of Manila (age 20y.o.)

Overall conceptual framework

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Social factors

Travel behavior

Trips/trip cost

Conceptual framework

Figure 1 Proposed exploratory factors influencing after-class side-trips on the way home by university students

First conceptual model

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−+

Text messaging

−−

−−

+

Socio-economic and socio-demographic characteristics

Travel behavior

Sending letters/cards

Landline calls

Cell phone calls

Face-to-face interaction Email

Online chat

Social network

+

Social factors

Methodology1. Paper and pen interview survey2. A questionnaire survey was develop

2 parts: a main survey sheet and a name generator sheet

Main survey sheet1. Socio demographic attributese.g. gender, age, school name and type, location, car ownership, cell phone ownership

2. Social interactioninformation about the usage of mobile phones and their communication patterns e.g. how many times in a day they socially interact? how many people they interact? whom they interact?

3. Social activitiesinformation about their social activity patternse.g. how many times they do social activities a week (common social activities were listed in the questionnaire)? to whom and how many people they socialize with? How long do they plan the social activity?

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Name generator sheet1. Social network

A customized name generator was created eliciting the respondent’s circle of friends. There were 4 types of friends were considered, namely:

1. friends for important matters (e.g. a friend where you can discuss important or serious matters)2. friends for social (e.g. a friend where you can socialize with in sports, parties, celebrations, etc.)3. friends for advice (e.g. a friend where you can seek advice for job opportunities, etc.)4. friends for small matters (e.g. a friend where you can borrow equipments, small amount of money, etc.)

Friends listed in the name generator were then characterized by the ego’s relationship to them and by their age.

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Example of customized Name generator used

in the survey

age

relationship

Descriptive results of university studentsTable 1 Descriptive statistics of the respondents (N = 287)

Age M =19.96, SD =1.328Social network composition M = 23.45, SD = 13.03M: mean, SD: standard deviation

Genderuniversity

Type of universityLiving with whom

DLSU: 83, 29%

PUP: 70, 24%Male: 204, 71%

FEU-EAC: 63, 22%

UPD: 71, 25%Female:

83, 29%

State univ: 141, 49%

Private univ: 146, 51%

Not living with parents: 134, 47%

Living with parents: 152, 53%

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Social interaction per day

Average side-trips on the way home = 2.71 trips

Path analysis results

Chi-square(d.f.) = 106.47 (6) p < 0.01 Goodness of fit index (GFI) 0.933 Adjusted goodness of fit index (AGFI) 0.833Comparative fit index (CFI) 0.934 Normed fit index (NFI) 0.928 Non-normed fit index (NNFI) 0.8910

Legend:

** significant at the 0.001 level * significant at the 0.01 level ( ) t values

Figure 3 Estimated causal relationship model of socialization and number of side-trips taken on the way home for the university students

Socialization factors

Number of side-trips on the way home

Frequency of text messaging per day

Frequency of online chat per day

Frequency of Side-trips going home

Size of social networkSize of the people interacted face-to-face

β31 = 0.188 ∗∗

(5.56)

λ11= 0.730∗∗

(16.2)

β32 = 0.0610∗

(2.96)

λ31=0.775∗∗

(22.8)

λ21 = 0.508∗∗

(10.2) λ22 = 0.143∗

(2.90)

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Results and discussionThe number of side-trips made while heading home have direct and positive

effects for the number of people with whom one interacts face to face per day, the frequency of text messaging, and the size of social networks.

The number of people with whom one interacts face to face and social network size mediated the relationship among text messaging, chatting online, and side-trips on the way home.

Implications to transportation planning:Overall, the results imply that the opportunity to socialize is a sound

motivation for trip generation even in developing countries and should be considered when constructing transportation planning policies.

To better understand activity-travel behavior and motivation, the incorporation of variables related to socializing is worthwhile as part of transportation planning and research.

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Why university workers?

The survey samples was extended to university workers it will be easy to

collect the social network data of the participants (ego) as well as the data of his friends (ties) and the analysis

was deepen …

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Conceptual framework

Social factors

Travel factor

Figure 2 Conceptual model of the study for the university workers in Metro Manila, Philippines

Frequency of Social activities

Frequency of Social interaction

Social network

Degree of travel

++

+

+

Second conceptual model

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Methodology1. Paper and pen interview survey2. A questionnaire survey was develop

2 parts: a main survey sheet and a name generator sheetMain survey sheet1. Socio demographic attributese.g. gender, age, civil status, school name and type, location, household size, car ownership, cell phone ownership, monthly income

2. Social interactioninformation about the social interaction patterns for every type of contacts were emphasized and recorded. e.g. how many times in a day they socially interact with family members, with close friends, with not so close friends? how many people they interact? whom they interact?

3. Social activitiesinformation about their social activity patternse.g. how many times they do social activities a week (common social activities were listed in the questionnaire)? to whom and how many people they socialize with? How long do they plan the social activity?

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Name generator sheet1. Social network

A customized name generator was created eliciting the respondent’s circle of friends. There were 4 types of friends were considered, namely:

1. friends for important matters (e.g. a friend where you can discuss important or serious matters)2. friends for social (e.g. a friend where you can socialize with in sports, parties, celebrations, etc.)3. friends for advice (e.g. a friend where you can seek advice for job opportunities, etc.)4. friends for small matters (e.g. a friend where you can borrow equipments, small amount of money, etc.)

Friends listed in the name generator were then characterized by the ego’s relationship to them and by their age.

An additional attribute was added in the name generator, that is the estimated spatial distance of the ego to his friends.

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Descriptive results of university workersTable 2 Descriptive statistics of the university worker participants in Metro Manila (N = 235)

Age M = 29.23 years old, S.D. = 8.427Household size M = 4.510, S.D. = 2.157Monthly Income in Php M = 15,219.15, S.D. = 6731.72Number of years working M = 4.99, S.D. = 6.255Social network composition M = 24.8, S.D. = 18.8M:mean, SD: standard deviation

Gender Civil status University workers

Male: 92, 39%

University professors: 98, 42%

University staffs: 137, 58%

Single: 156, 66%

Married: 79, 34%Female:

143, 61%

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Type of university Education

level

Location of residence

State university: 156, 66%

Private university: 79, 34%

Graduate: 74, 31%

Undergraduate: 161, 69%

Outside Metro Manila: 39, 17% Within Metro

Manila: 196, 83%

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Social interaction per day

Social activities per week

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Social Network of the respondents

Descriptive resultsAverage travel time from home to work placeCar ownership

Trav

el ti

me

in m

inut

es

Num

ber o

f par

ticip

ants

Number of cars in household Mode of travel

Average total trips traveled per day 3.81 (SD 1.52)

Average travel cost from home to work place 51.24 Php

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Structural Equation Model result

Figure 3 The estimation results for social factors and travel factors

δ3

δ4

δ5

δ2

δ1

ε8

ε5

ε12

ε9

ε3

ε13

ε1

ε2

ε4

ε10

ε11

ζ2

ζ3

ζ1

Social activities, η2

Social network,η1

Social interaction, ξ

Degree of travel, η3

y8

y9

y10

y11

x1

x2

x3

x4

x5

y13y12

y1

y2

y3

y4

y5

x6δ6 ε6y6

ε7y7

1

1

0.24 *** (3.49)

0.17 *** (3.42)

0.22 ***(3.51)

0.20***(3.55)

0.23*** (3.65)

0.06***(2.9)1

1.37* (2.54)4.63**(2.90)

2.93** (2.79)

6.24** (2.85)

5.99*

* (2.09

)

0.51* (2.11)

0.14 ** (2.55) 0.074

* (2.49

)

0.29** (3.12)

1.01*** (4.32)

10.82 *** (7.15)0.47 **(2.99)

2.52** (3.00)*** significance level at 0.001** significance level at 0.005* significance level at 0.01

Chi-square = 432.6 d.f. 148 p< 0.001GFI = 0.85AGFI = 0.80SRMR = 0.90

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Results and discussionThe result of the structural model using the survey data collected from

university workers in Metro Manila indicates statistically positive and significant in all estimated parameters.

From the perspective of the university workers within Metro Manila, the structural model reveals that social interaction has a substantial causal effect on social network as well as on social activities. Moreover, social network could be a causal factor to social activities. There is also a significant effect of social activities to the degree of travel.

In addition, the strong significant effect comes from the path of social interaction via social activities then finally to the degree of travel.

Implication to transport planningThough the result is only for a small population (university workers in the

Philippines), it implies that the inclusion of social factors in transport planning should be treated with significance and should be recognized as part of the consideration of transport policies, even in the developing countries.

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Conclusion and recommendationsWith respect to the analysis of the university students data, it was found that

certain types of socialization had significant effects on trip frequencies among university students in Metro Manila, which indicate that various forms of socialization play important roles in trip generation.

From the perspective of the university workers within Metro Manila, the hypothesis that social factors, such as social interaction, social activities and social network, would have a significant effect on travel factors, i.e. total travel cost per day as well as the total traveled trip per day as considered in the study was confirmed and supported by the result of the structural model.

Though the both results (university students and university workers in the Philippines) is just a small population, it calls for an attention that transportation planning should also take into consideration in incorporating the social aspects.

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Future worksA broader survey for general population is recommended for the future

works.

Although the findings of the current study is enriching and useful, there are also new areas to explore more on the ICT interaction, since these new technologies are built for social interaction purposes but did not reflect on the effects to transport.

The ongoing research study is also considering how travel is affected by the capability of ICTs to reduce the planning time horizon of some social activities.

To explore on including the spatial distance of the friends in the social network subject, which is actually the current state of the research progress.

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Maraming salamat sa pakikinig! ☺

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Methodology

where y = p × l vector of observed dependent variables measured without error, b = m × m matrix of coefficients relating p dependent variables to one another, x = q × l vector of observed independent variables measured without error, l = m × n matrix of coefficients relating q independent variables to p dependent variables, and z = p × l vector of errors in the equation.

y = βy + λx + ζ

η = Βη+ Γξ + ζ. (2)

(1)

y = Λyη + ε, x = Λxξ + δ, (3)

1. Path analysis

2. Structural equation model (SEM)

B, Γ, Λy, Λx : unknown parameter array, ξ : endogenous or latent dependent variable vector,

ζ,ε ,δ : error term vector following a multivariable normal distribution,x : vector of observed exogenous or independent variables,y : vector of observed endogenous or dependent variables.

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