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Transport Findings
December 23, 2021 AEST

Are Telecommunications and Travel Substitutes or Complements? An Empirical Analysis of a Developing City in Nigeria

Oluwayemi-Oniya Aderibigbe,
telecommunication travel behaviour mobility complementarity substitution
Copyright Logoccby-sa-4.0 • https://doi.org/10.32866/001c.30742
Findings
Aderibigbe, Oluwayemi-Oniya. 2021. “Are Telecommunications and Travel Substitutes or Complements? An Empirical Analysis of a Developing City in Nigeria.” Findings, December. https://doi.org/10.32866/001c.30742.
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  • Table 1. Socio-economic characteristics of Households
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  • Table 2. Average number of complemented and substituted trips by ICT means
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  • Figure 1. Study Area and Telecommunication Point Map for the Study areas.
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  • Table 3. Logit Model: Dependent Variable Capacity for Telecommunication to Complement (1) or Substitute (0)
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  • Supplementary Information: Survey Instrument
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Abstract

This study examines the relationship between household travel and telecommunication use with data collected from 498 participants in Akure metropolis, Nigeria and finds that a positive relationship exists between telecommunication and travel. While the substitution effect of telecommunication on the trip was not significant in the study, the result of the multinomial logistic regression revealed telecommunications complements travel more than substitute.

1. QUESTIONS

The possible relationships between information and communications technology (ICT) (or telecommunication) and physical travel include substitution, complementarity or modification, generation, and neutrality (Mokhtarian 1990; Mokhtarian and Salomon 2002; Nobis and Lenz 2003). Complementarity occurs where there is a modification or change of spatial and temporal characteristics of existing travel patterns by the use of telecommunications, while substitution refers to the number of trips being replaced by ICT (Senbil and Kitamura 2004; Oyesiku 1996). Unlike previous studies (Matous 2017; Olawole 2013) that focused on mobile phones, this study examines the relationship between household travel and telecommunication, which extends beyond call linkages to virtual activities such as e-shopping, e-banking, telework, to mention just a few. The research questions include:

  • What are the nature and characteristics of telecommunication in the study area?

  • Do telecommunications substitute for physical travel?

  • Is there clear evidence of the relationship between telecommunication and travel in the study?

Research Hypothesis

H0- There is no significant relationship between telecommunication usage and travel.

H1- There is a significant relationship between telecommunication usage and travel.

2. METHODS

This study uses primary data collected from a survey, which was conducted using trained research assistants and a questionnaire. A probability sampling technique was adopted, and a multi-stage sampling procedure was used. The first stage involved the stratification of residential areas and the selection of political wards. Of 11 political wards, 6 were sampled. Random sampling was adopted in selecting 38 registered streets from a total of 139, representing 20% of the streets in the selected wards.

Further information from the Area Town Planning Area office revealed that there are 5,123 registered buildings in the selected wards, of which 10% were systematically selected. Bookwalter, Fuller, and Dalenberg (2006) and Owoeye, Fadare, and Ojekunle (2018) justified surveying only the household head within the family on the basis that the response of household heads is mainly determined by factors shared by the entire household and not on those experienced primarily by the head. Where the respondent was not available, the next building was selected for the sample. A household head not below the age of 18 years (adult) on the first floor of each selected building was sampled. Based on this, a total of 512 respondents were surveyed. However, a total of 498 questionnaires (97%) were found usable for analysis.

The questionnaire designed for the research had three main sections. Section 1 comprised information relating to the socio-economic characteristics of the respondents. They included gender, age, education, income, marital status, employment status, occupation, and car ownership. Section 2 focuses on the travel characteristics of the people, information such as trip frequency (Average number of round trips), trip purpose (work, shopping, health, recreation), transport mode and travel cost were acquired. The last section comprised information on the telecommunication usage of respondents, questions relating to the type and number of telecommunication own (GSM or PC), the average number of trips complemented/substituted by telecommunication, trip activities/complemented or substituted by telecommunication among others.

Table 1.Socio-economic characteristics of Households
Characteristics Variables Frequency Per cent
Gender Male 276 54.1
Female 234 45.9
Age Less than 30 years 127 33.6
30-39 114 30.1
40-49 80 21.2
50-59 38 10.1
60-69 13 3.4
70 years and above 6 1.6
Marital status Single 125 24.7
Married 369 72.8
Divorced 8 1.6
Widowed 5 0.9
Household size Less than 6 persons 217 51.5
6 - 9 persons 188 44.6
10 persons and above 15 3.9
Educational status No formal education 6 1.2
Primary school education 37 7.4
Secondary school education 90 18.0
Tertiary education 361 72.1
Other 7 1.4
Occupation of respondents Civil servant 174 40.4
Artisan/self-employed 51 11
Businessmen/women 129 8
NGO/Private organization 21 29.9
Health worker 13 3.0
Farming 2 .5
Others 41 9.5
Average monthly income <20,000 47 9.4
20,000 - 39,999 42 8.4
40,000 - 59,999 274 54.8
60,000 - 79,999 23 4.6
80,000 - 99,999 20 4.0
100,000 and above 94 18.8
Number of cars in the household None 30 9.8
1 151 49.2
2 100 32.5
3 12 3.9
4 13 4.2
5 1 0.3
Number of years spent in the pursuit of tertiary education 0 - 2 years 80 21.4
3 - 5 years 233 62.5
6 - 9 years 55 14.7
10 years and above 5 1.3
Travel Characteristics Variable Frequency Percent
Number of trips (per day) 1 94 20.1
2 75 16
3 41 8.8
4 237 50.8
5 6 1.3
6 and above 14 3.0s
Purpose of the trip Work 395 82.8
Shopping 4 .8
School 43 9.0
Recreation 1 .2
Farm 13 2.7
Place of worship 20 4.2
Others 1 .2
Dominant mode of transportation used Walking 38 8.6
Bicycle 11 2.5
Private car 162 36.8
Public transport 223 50.7
Others 6 1.4
Telecommunications usage of Respondents
Access to telecommunication facility Yes 498 100
No - -
Type of telecommunication facility frequently used Mobile Phone (GSM) 299 58.6
Personal computer/tablet 111 21.8
None 100 19.6
Social media as a means of communication WhatsApp 144 30
Instagram 10 2.0
Twitter 12 2.4
Facebook 206 41.3
None 126 25

Source: Author’s Field Work, 2020.

Information on the socio-economic characteristics of respondents revealed that the majority were male (54.1%), married (72.8%), had tertiary education (72.1%), 54.8% earned between ₦40,000 - ₦60,000. Hence the majority earned above the federal government minimum income wage of ₦20,000. Analysis of trip frequency showed that 50.8% made an average of 4 trips daily trips. Work trips comprised the majority (82.8%) of trips. A smaller majority (50.7%) made use of public transport. The telecommunication usage of households revealed that 98.2% owned a mobile phone while (55.2%) did not make use of virtual activities/platforms such as e-banking, email, and e-shopping. Facebook and WhatsApp were the most used smartphone applications, with 40.3% and 28.2% of the respondents, respectively.

The socio-economic characteristics of respondents revealed that the majority were educated and employed with the government as civil servants. The number of government-employed respondents is not surprising because an individual’s highest level of education determines the kind of occupation such a person can engage in and the income level (Ahn 2001; Badiora 2012; Stead and Marshall 2001).

The result of the average number of self-reported complemented and substituted trips by different ICT means is explained in Table 2. It is evident from the study that the use of a mobile phone (Global System of Mobile Communication (GSM)) for making calls was more predominant in the study area as a larger volume of trips were influenced through that platform, unlike emails, e-shopping, e-banking and social media platforms.

Table 2.Average number of complemented and substituted trips by ICT means
Telecommunication Means Number of Complemented Trips Number of Substituted Trips
Mobile phone (GSM calls) 932 (51.7%) 869 (48.2%)
Email 664 (64.2%) 369 (35.8%)
E-banking 462 (60.8%) 298 (39.2%)
E-shopping 402 (41.5) 568 (58.5%)
Social Media Platforms 525 (51.2%) 495 (48.3%)
Total 2985 (53.4%) 2599 (46.5%) 5584
Figure 1
Figure 1.Study Area and Telecommunication Point Map for the Study areas.

Source: Arc Map/Fieldwork 2020

3. FINDINGS

The result on trip frequency corroborates the findings of (Hine, Barnejee, and Kashyap 2012), which asserted that urban form influences one’s travel behaviour, as such, urban dwellers generate more trips than those in remote locations due to factors ranging from their socio-economic characteristics, access to public transport or private cars among others. As such, the large number of trips generated by respondents may be a function of some of these highlighted factors since most of them had access to either a private car or public transport.

Concerning telecommunication usage, the use of virtual platforms such as e-banking, e-shopping, telework, and e-business to replace physical movement were not significant as respondents rely more on telecommunications for a reorganization of activities or modifications with respect to time and space. It, thus, stipulates that a larger percentage of the population still embarks on physical trips against virtual activities. Overall, 53.4% of trips were complemented by telecommunication. A close reflection showed that 58.5% of total shopping trips were replaced by telecommunication, thus, in contrast with the findings of Farag et al. (2007) and Douma et al. (2004), which concluded that e-shopping is used as an additional shopping method which does not change trip making behaviour nor replaces in-store shopping but does change shopping behaviour.

The hypothesis testing result revealed that a positive relationship exists between the average number of trips complemented and telecommunication usage (call volume). This implies that the null (H0) hypothesis is rejected, and the alternative (H1) hypothesis is accepted. The correlation coefficient for the relationship between telecommunication usage and complemented trips in the study is 0.535, significant at 0.00.

Multinomial logistic regression (logit) analysis was further carried out using SPSS to determine the impact of telecommunication on travel of households. The dependent variables, in this case, represents self-reported capacity for telecommunication to either substitute [0] or complement [1] overall travel. The independent variables represent the gender (male [1], female[0]), Age, and household size of respondents which was categorized into small [1,0], medium [1,0], and large [1,0].

The model fitting information contains a likelihood ratio chi-square test comparing the full model (predictors) against a null or intercept only model. The statistical significance (0.032) indicates that the full model represents a significant improvement in fit over the null model. [ X2 (9) 18.315, P=0.032]. The Pearson and deviance chi-square test indicates that the model is a good fit, [ X2 (293) =267.047, P= 0.859], [X2 (293)=218.370, P= 1.000] respectively. The result of the likelihood ratio test which explains the overall contribution of each independent variable revealed that household size and gender with P = 0.00 and P = 0.04 respectively were significant variables influencing the complementarity ability of telecommunication on travel. From the model equation, even though the complementarity effect of telecommunication on travel was significant in this study, male household heads were more likely to complement their trips (P = 0.035) than females. For the parameter table, the coefficient represents comparison between telecommunication capacity to complement (1) and telecommunication capacity to substitute (0). In this case, gender (male) and household size were significant. This implies that telecommunication was more likely to complement trips rather than substitute.

Although the study found a substitution effect of ICT on shopping trips, it, however, supports the perspective that the overall trend of the relationship is shifting from substitution to complementarity, which has been widely discussed in previous studies (De Graaff and Rietveld 2007; Wojuade 2014). Findings from the study corroborate the studies of Oyesiku (1996) and Choi, Choo, and Kim (2020) that telecommunication cannot fully compensate for face-to-face contact, for instance, the conveyance of complex, non-structured, or potentially ambiguous information. Also, the social and cultural background of people in Nigeria, a developing country, is such that the physical presence of friends and relatives is often appreciated; hence telecommunication may not be able to fully substitute travel. Overall, the impact of telecommunication on travel depends largely on individuals’ socio-economic attributes such as gender and household size, and activity patterns (shopping, work, banking, etc.) of an individual or household.

Table 3.Logit Model: Dependent Variable Capacity for Telecommunication to Complement (1) or Substitute (0)
B Std. Error Wald Sig. Exp(B)
Intercept 5.213 .288 6.087 .001
Age .008 .010 .671 .413 .992
HH Size = Small [1,0] 3.830 .586 6.342 .000 4.693
HH Size = Medium [1,0] 2.380 .319 4.380 .000 3.459
Male [1,0] .574 .344 2.790 .035 1.776

Notes: Pseudo-R2: Cox and Snell: 0.251, Nagelkerke: 0.323, McFadden: 0.150.


Acknowledgement

The author wishes to acknowledge the support of Management and staff of Akure South Local Government Authorities, National Population Commission, Ondo state, Ministry of Physical Planning and Urban Development, Federal University of Technology Akure, Ondo state Nigeria and University of Johannesburg South Africa, for providing relevant information and support for the successful completion of the work.

Submitted: August 06, 2021 AEST

Accepted: December 13, 2021 AEST

References

Ahn, Hyungtaik A. 2001. “Nonparametric Method of Estimating the Demand for Mobile Telephone Network: An Application to the Korean Mobile Telephone Market.” Information Economics and Policy 13 (March): 95–106. https://doi.org/10.1016/s0167-6245(00)00035-4.
Google Scholar
Badiora, O. 2012. “Spatial Pattern of Crime and Delinquency in Ile-Ife.” An M.Sc Thesis Submitted to the Department of Urban and Regional Planning Ile Ife.
Bookwalter, Jeffrey T., Brandon S. Fuller, and Douglas R. Dalenberg. 2006. “Do Household Head Speak for the Household: A Research Note.” Social Indicators Research 79 (3): 405–19. https://doi.org/10.1007/s11205-005-4925-9.
Google Scholar
Choi, Sungtaek, Sangho Choo, and Sujae Kim. 2020. “Is the Relationship between Transportation and Communication Industries Complementary or Substitutional? An Asian Countries-Based Empirical Analysis Using Input-Output Account.” Sustainability 12 (April). https://doi.org/10.3390/su12083085.
Google Scholar
De Graaff, T., and P. Rietveld. 2007. “Substitution between Working at Home and Out-of-Home: The Role of ICT and Commuting coTRDAS5Dst.” Transportation Research Part A 41 (2): 142–60.
Google Scholar
Douma, F., K. Wells, T. Horan, and K. Krizek. 2004. “CT and Travel in the Twin Cities Metropolitan Area: Enacted Patterns between Internet Use and Working and Shopping Trips.” Paper presented at the Transportation Board 83rd Annual Meeting, 2004, Washington, D.C. January 11-15.
Farag, Sendy, Tim Schwanen, Martin Dijst, and Jan Faber. 2007. “Shopping Online and/or in-store? A Structural Equation Model of the Relationships between E-Shopping and in-store Shopping.” Transportation Research Part A 41 (2): 125–41. https://doi.org/10.1016/j.tra.2006.02.003.
Google Scholar
Hine, J., U. Barnejee, and A. Kashyap. 2012. “Effect of Urban Form on Travel Behaviour – A Review of the Literature.” In Proceedings of the ITRN Conference Held on 29th-30th of August. University of Ulster 2012.
Google Scholar
Matous, Petr. 2017. “Complementarity and Substitution between Physical and Virtual Travel for Instrumental Information Sharing in Remote Rural Regions: A Social Network Approach.” Transportation Research Part A: Policy and Practice 99 (May): 61–79. https://doi.org/10.1016/j.tra.2017.02.010.
Google Scholar
Mokhtarian, Patricia Lyon. 1990. “A Typology of Relationships between Telecommunications and Transportation.” Transportation Research Part A: General 24a (3): 231–42. https://doi.org/10.1016/0191-2607(90)90060-j.
Google Scholar
Mokhtarian, Patricia Lyon, and I. Salomon. 2002. “Emerging Travel Patterns: Do Telecommunications Make a Difference?” In Perpetual Motion: Travel Behavior Research Opportunities and Application Challenges, edited by H. S. Mahamassani, 592. Amsterdam: Pergamon.
Google Scholar
Nobis, C., and B. Lenz. 2003. “Changes in Transport Behavior by the Fragmentation of Activities.” Paper presented at the Transportation Board 83rd Annual Meeting, Washington, D.C. January 11-15.
Owoeye, A.S., S.O. Fadare, and J.A. Ojekunle. 2018. “Households’ Socio-Economic Characteristics and Urban Travel Behaviours in Minna Metropolis, Nigeria.” International Journal of Research Publication 9 (1): 1009172018291.
Google Scholar
Oyesiku, K. 1996. “Inter-City Travels and Telecommunications Relationship: An Exploratory Study in Nigeria.” Ife Social Sciences Review 3 (1 & 2): 37–49.
Google Scholar
Senbil, Metin, and Ryuichi Kitamura. 2004. “Reference Points in Commuter Departure Time Choice: A Test of Alternative Decision Frames.” Journal of Intelligent Transport Systems 8 (1): 19–31. https://doi.org/10.1080/15472450490437726.
Google Scholar
Stead, D., and S. Marshall. 2001. “The Relationship between Urban Form and Travel Patterns. An International Review and Evaluation.” European Journal of Transport and Infrastructure 1 (2): 113–41.
Google Scholar
Wojuade, C.A. 2014. “Telephone Usage and Travel Behaviour in Nigeria.” Developing Country Studies 4 (20): 202–14.
Google Scholar

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