Impact of information and communication technology on mobility of urban and rural households: a comparative study from Nigeria

ABSTRACT

There has been a growing concern regarding the potential impact of technology on travel patterns, with limited attention given to its effects on both urban and rural households. To address this gap, our study focuses on the influence of Information and Communication Technologies (ICT) on rural-urban mobility. We conducted this research using primary data collected through a multi-stage sampling method, where 50% of the wards were selected randomly. Subsequently, a total of 510 questionnaires in urban areas and 492 questionnaires in rural areas was administered using a systematic sampling approach. The results of this investigation revealed significant variations in the socio-demographic and travel characteristics of respondents within the study areas. Notably, our findings indicate that ICT usage has not entirely replaced traditional trips in urban and rural settings. Instead, it has played a pivotal role in complementing travel activities. The implications of these findings are substantial. As tele-density improves and expands in rural areas, it is foreseeable that more cost-effective travel planning will be achievable.

KEYWORDS:

• Telecommunication
• mobility
• travel behavior
• Information and Communication technology

1. Introduction

Recent years have seen Information and Communication Technology (ICT) play a crucial role in urbanization, while also highlighting challenges like the digital divide (D. Wang et al., 2021). Among ICT devices, smartphones stand out, with China alone recording 817 million mobile internet users in 2018 (Nie et al., 2020; Yin et al., 2021). Smartphone adoption has profoundly influenced daily life, impacting activities like shopping, finance, and food ordering (Fang & Ma, 2021; Yin et al., 2021). This surge in ICT adoption has transformed cities, revolutionizing lifestyles and activities (Aguiléra et al., 2012; Lyons et al., 2018). B. Wang et al. (2015) noted ICT’s role in challenging traditional roles within households and disrupting established divisions of labor by enhancing flexibility.

The global urban-rural disparity in mobile telecommunications and internet access is well-documented, primarily due to the high investments needed for rural infrastructure (Adeel, 2018; Aderibigbe, 2021; Aderibigbe & Gumbo, 2022; Guldmann, 2020). This persistent digital divide hampers rural economic development while benefiting large cities, exacerbating economic inequality and posing political challenges (Graham & Marvin, 1996). Numerous studies have explored ICT’s impact on travel behavior, with some focusing on online time allocation and its correlation with trip generation (Hong & Thakuriah, 2016). Delbosc and Mokhtarian (2018) conducted a detailed analysis of ICT usage, including phone use, texting, email, and social media, revealing its positive influence on trip generation. However, the distinct impacts of online time and internet use frequency on trip generation remain unclear, especially in urban and rural contexts. This study aims to investigate how mobile phone usage affects the mobility patterns of urban and rural households, filling a gap in existing research. Our study investigated urban and rural households in Akure South Local Government (Urban) and Akure North Local Government Area (Rural), respectively, in Ondo State. These locations were chosen for their distinct characteristics, including population size, predominant activities, and infrastructure development. The urban areas primarily consist of modern, structurally sound residential properties built on approved layouts, whereas the rural areas in our study comprise small communities and villages lacking formal town planning approval.

In addition to this, the urban areas benefit from superior infrastructure such as piped water supply, electricity, drainage, roads, and waste management systems, unlike the rural communities like Ala-Igabatoro, Ilado, and Owode, which face challenges accessing essential services, especially during the rainy season when road networks become impassable due to flooding. According to the National Population Commission, a significant portion of the rural population lacks access to telecommunication facilities, with many relying on limited services from local business centres in emergencies. Based on the above, our study aimed to assess the impact of mobile phones as ICT tools on household mobility in both urban and rural areas, exploring differences in travel patterns and access to transportation and telecommunication facilities. The findings aim to inform policymakers and planners, providing insights into the role of telecommunication infrastructure in shaping household mobility across urban and rural contexts.

2. Literature review

2.1. The relationship between ICT and urban travel

Numerous studies have extensively explored the relationship between Information and Communication Technology (ICT) usage and travel behavior (Aderibigbe & Gumbo, 2022; Bert et al., 2013; Wojuade, 2014). While a wealth of evidence has been gathered on how ICT influences travel behavior, there remains ongoing debate regarding the findings. For instance, Ory and Mokhtarian (2005) have highlighted the insufficient understanding of telecommuting’s long-term effects on commute travel and residential choices. Nevertheless, existing evidence suggests that telecommuting may reduce overall travel. The emergence of cyberspace and the expansion of ICT have created a non-geographical realm within electronic networks, characterized by the absence of spatial constraints, with time prevailing over distance). O. Aderibigbe and Gumbo (2022) further argue that ICT significantly affects accessibility by reducing the need for physical proximity and travel. It allows individuals to navigate both physical and virtual realms simultaneously (Ghorbani et al., 2013), providing greater flexibility in travel timing and mode selection (Yuan et al., 2012), thus directly and indirectly alterin travel patterns and overall travel demand (Falch, 2012). However, due to the complex relationship between ICT use and travel behavior, reaching definitive conclusions about ICT’s precise influence on daily activities remains challenging (Aguiléra et al., 2012; Dadashpoor & Yousefi, 2018). Consequently, ongoing debate persists regarding how precisely ICT shapes travel behavior. The indirect impact of ICT on transportation becomes apparent through changes in land use patterns and activity organization. Telecommuting, e-commerce, internet-based services, email communications, and lifestyle changes all significantly influence transportation dynamics.

2.2. Impact of ICT on travel/trip making

Information and Communication Technology (ICT) exerts various impacts on urban travel patterns, with implications influenced by a complex interplay of socio-demographic, individual, spatial, and less tangible factors (O. Aderibigbe & Gumbo, 2022). Establishing a precise correlation between travel and diverse ICT-related activities poses a formidable challenge. Nevertheless, several studies (Dadashpoor & Yousefi, 2022; Yin et al., 2021; Yuan et al., 2012) have delineated primary interactions between ICT and travel, categorizing the implications into four key domains: substitution, complementarity, modification, and neutrality.

2.2.1. Substitution

Within the realm of substitution, ICT can replace physical activity through mechanisms like telecommuting, thereby obviating the necessity for corresponding travel (Elldér, 2020). Physical trips to conduct activities are no longer necessary, given the use of ICT to perform these activities. The best example here is telecommuting, where the commuter trip itself is skipped. For example, Shi et al. (2019) posit that e-shopping effectively substitutes traditional shopping trips, as the process of procuring and delivering goods online proves more efficient. The economic advantages associated with online shopping, stemming from savings in time and money incurred during travel, encourage its adoption. In the sphere of social trips, ICT may also manifest a substitutive role, while social outings assume complementary or neutral roles concerning ICT (Van Den Berg et al., 2013). Several studies suggest that mobile phone usage can curtail vehicle kilometers traveled (Jamal et al., 2017; Khan et al., 2020). Khan et al. (2020), for instance, argue that residents in mixed-use areas engage in fewer and shorter trips due to smartphone applications’ convenience. Nonetheless, contrasting findings exist (Kong et al., 2019), with some studies indicating that these effects remain insignificant (Konrad & Wittowsky, 2018) or that substitution does not result in a net reduction in total trips.

2.2.2. Modification/complementarity

The relationship between telecommuting and its impact on physical activity organization and travel behavior is significant. Lenz and Nobis (2007) opined that the complementing effect of telecommunication leads to a reorganization of activities in time and space or location. By modification, travel demand changes by using ICT. Travel is altered either by a shift in timing, routing, linking and trip chaining or travel mode. In the case of telecommuting people can shift the starting point of their trips to off-peak hours. Teleworking often serves as an effective alternative to lengthy and costly commutes, particularly for individuals residing in areas with limited accessibility (De Abreu Silva & Melo, 2018). This shift in work dynamics disrupts the conventional link between urban structure and travel patterns, fostering diverse mobility strategies and resulting in heterogeneous spatial travel behaviours driven largely by individual characteristics rather than traditional work-residence connections. As the number of telecommuters continues to rise, conventional location-based transportation models for forecasting and planning become increasingly inefficient (Elldér, 2020). In this context, the utilization of Information and Communication Technology (ICT) introduces transformative changes to travel dynamics, influencing factors such as timing, location, and other variables, hence, complementing trips.

2.2.3. Neutrality

In a non-involvement relationship, telecommuting exhibits negligible influence on individuals’ physical activity and traffic behavior, as evidenced by studies conducted by Hjortol (2002) and Gubins et al. (2019). Their findings converge in the conclusion that the utilization of Information and Communication Technology (ICT) fails to produce a substantial impact on daily commuting patterns. These researchers contend that while users of such technologies may exhibit a tendency to reduce work-related trips, the overall effect on the total number of trips remains statistically insignificant. Instead, the spatial and temporal flexibility afforded by ICT merely redistributes trips throughout the day. In fact, the long-term causal effect of ICT on commuting distance is so minuscule that it eludes recognition and might even be deemed non-existent.

2.2.4. Trip generation

The synergy between Information and Communication Technology (ICT) and physical activity significantly influences trip generation. ICT amplifies travel demand, stimulating telecommunications-induced trips. Previous predictions by Graham and Marvin (1997) foresaw an increase in urban trips due to ICT’s interaction with automobile-centric urban structures, potentially leading to traffic congestion. Budnitz et al. (2020) note that while teleworking reduces certain trip types, it fosters other trip activities. Zhu (2012) observes that teleworking shapes individual travel patterns, complementing both business and non-business trips, fostering non-vehicular trips and active lifestyles (Chakrabarti, 2018). Studies suggest online shopping supplements rather than replaces in-store shopping (Van Den Berg et al., 2013; Zhou & Wang, 2014), augmenting overall trip numbers (Etminani-Ghasrodashti & Hamidi, 2020; Mokhtarian, 2004). Tonn and Hemrick (2004) find ICT usage yields a complex dynamic, reducing and generating trips simultaneously, emphasizing the intricate relationship between ICT, travel behavior, and its broader implications.

Before using any telecommunication service, individuals must subscribe to a mobile telephone or internet network, contingent upon various factors. Verkasalo (2008) suggests subscription decisions hinge on perceived benefits versus costs. If perceived benefits outweigh subscription costs, individuals subscribe; otherwise, they abstain. Cost factors include line acquisition, mobile phone price, and usage costs. Kyeremeh and Fiagborlo (2016) find subscription probability related to factors like access price, income, education, and employment level. Ultimately, ICT’s impact on travel and mobility hinges on socio-economic characteristics, travel habits, location, subscription costs, and other factors, crucial for effective transportation planning to leverage telecommunication benefits on people’s mobility.

3. Methodology

This study employed a meticulous approach to data collection, involving trained research assistants and a multi-stage sampling method. Primary data were gathered through face-to-face surveys using questionnaires administered by trained research assistants over a period of four months. In urban areas, we began by stratifying the study areas, identifying 11 political wards, from which 50% were randomly chosen. Subsequently, a total of 5,123 registered buildings were identified within these selected wards. Then, 10% of these buildings were randomly selected, resulting in 512 surveyed buildings representing households. Respondents were selected based on the household head using systematic random sampling, following the method outlined by Owoeye et al. (2018). Ultimately, 498 usable questionnaires were collected and analyzed, achieving a 97% response rate.

For rural areas, a similar stratification process identified 12 political wards, from which 50% were randomly selected, resulting in six wards for sampling. Data from the National Population Commission indicated 12,365 registered buildings in these wards, from which 4% were randomly selected for questionnaire administration, aligning with established practices. Using systematic random sampling, respondents aged 18 years or older were selected, resulting in surveys of 495 respondents in rural areas and 512 in urban areas.

These meticulous sampling procedures ensured the robustness and representativeness of the data collection process.

The formula for the sample size in the study location:

$\mathit{K}=\mathit{\hspace{0.17em}}\mathit{N}/\mathit{n}\dots \mathit{i}$

Where K = sample size

N = Number of registered building/dwelling unit/household

n = represents 10% and 4% of all households per settlement in the urban and rural areas, respectively.

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), 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, among other questions were asked.

Table 1 provides a detailed description of the data types and variables.

Table 1. Data types and variables description.

Variables	Data Types and Description
Gender	Male, Female
Age	Age in Years
Household size	Number of people living in the same house and feeding from the same pot
Income	Monthly Income earned by respondent
Occupation	What is the nature of your Job e.g Farming, Civil servant
Education	What is your highest level of education? Primary school, Secondary/High School/or Tertiary education
Trip Frequency	Average number of daily round trips (completed)
Number of Gsm/Internet Subscribers	How many people in your household subscribe or make monthly/weekly/daily subscription for internet services to browse or access the internet and use services such as facebook, Instagram, whatsapp, e-banking, e-shopping among others
Call Recharge Frequency/Internet/Data Subscription Frequency	How many times do you recharge or subscribe to the internet or subscribe for data services
Cost of recharge/mobile subscription	How much do you spend daily to recharge your phone or subscribe for data to make calls and access other internet services
What is the most frequently used ICT used	Mobile Phones, Desktop, Laptop
What type of services do you access or use frequently on these devices	Sending emails, social media (facebook, Instagram etc), WhatsApp, e-banking services, e-shopping services/applications,
Complemented Trips	What is the average number of trips that are modified for instance change in time, mode of transport or location as a result of using your mobile device or other ICT tools for trip making
Substituted Trips	What is the average number of trips replaces as a result of using mobile phone or other ICT device (for instance, when you make calls, send emails, e-shopping, e-banking or Whatsapp messages or use any of the social media platforms, does it automatically replace physical movement or cancel trip making)
Trip Generation	What is the average number of trips induced or generated as a result of using mobile phones (for instance, when you make calls or send emails or shop online, does it necessitate making additional trips which should have been avoided)

Source: Author’s Field work.

A stepwise regression analysis was adopted to determine the factors influencing telecommunication usage of respondents in both the urban and rural areas. For instance, Verkasalo (2008); Kyeremeh and Fiagborlo (2016), has identified some factors such as age, income, cost of subscription, education among others factors at influencing telecommunication. Hence, these factors were employed and tested using the regression analysis in our study. The formula is given as:

$\mathit{Y}\mathit{\hspace{0.17em}}=\mathit{\hspace{0.17em}}\mathit{a}\mathit{\hspace{0.17em}}+\mathit{\hspace{0.17em}}{b}_1{x}_1+\mathit{\hspace{0.17em}}{b}_2{x}_2+\dots \dots \dots +\mathit{\hspace{0.17em}}{b}_n{x}_n+\mathit{\hspace{0.17em}}\mathit{e}$

Where Y represents the dependent variable. The dependent variables in this case represents Y= frequency or Average number of Recharge/Subscription, x₁, x₂, x₃…….x_n represent the independent variables (Distance of call, Age, Occupation of household head, Household Size, Educational Status, number of mobile phones owned/internet subscribed to, Average monthly income of household head, average number of daily calls, cost of recharge card/mobile subscription, Marital Status)

a, b are constants.

e is the error term

This represents the relationship between the volume of trips (Y) and socio-economic variables or other factors as x₁, x₂,…….x_n.

However, the unstandardized coefficients was utilized in the model to explain the influence of each independent variables on the dependent variables.

3.1. Study area

Figures 1 and 2 illustrate the study locations: Akure North Local Government Area (Rural) and Akure South Local Government Area (Urban). These locations were selected based on criteria such as population size, occupation patterns, administrative functions, infrastructural provision, and overall development. Akure South Local Government Area, established as the capital of Ondo State in 1976, serves as a hub for diverse socio-economic activities, attracting individuals from across the state. The dynamic activities within the area have prompted improvements in transportation infrastructure, including the construction of new roads and the refurbishment of existing ones by the state government. Conversely, Akure North Local Government Area (Rural) was established on 1 October 1996. The region boasts fertile land conducive to agriculture, with farming being the predominant occupation. This agricultural focus characterizes the area as rural, with the majority of its inhabitants engaged in agricultural pursuits. Despite accessibility by road, certain communities within Akure North, such as Ilado and Moferere, face challenges during the rainy season due to flooded footbridges, hindering transportation.

Figure 1. Study location (Akure north local government area–rural area).

Source: ARCGIS (2022).

Figure 2. Study location (Akure south local government area–urban area).

Source: Author’s Field Work (2022)

Data from the Ondo State Bureau of Statistics (2012) indicates that in urban areas, 61.7% of households reside within 1 km of the nearest public transport, while in rural areas, 61.6% are situated 2–3 km away. Additionally, 71.3% of urban households and 62.8% of rural households own mobile phones, while computer ownership is reported at 8.5% in urban areas and 0% in rural areas. However, consistent access to internet services is limited, with only 7.4% of urban households and none in rural areas due to fluctuating internet connectivity.

4. Results

This section presents results on the socio-economic characteristics of respondents, travel characteristics of respondents and telecommunication usage of households in the urban and rural areas of the study.

4.1. Socio-economic characteristics of respondents

The study reveals notable differences between urban and rural households, particularly regarding age distribution, education levels, occupations, and income. In urban areas, a significant majority (63.7%) of respondents were aged 18–40, while in rural regions, a substantial portion (43.7%) fell into the 60–69 age group. However, the median age was higher in rural areas (51) compared to urban areas (45), indicating demographic disparities. Regarding education, a considerable 72.1% of urban respondents had tertiary education, contrasting sharply with rural areas where only 33.3% attained this level, with a majority (54.4%) having completed secondary education. Occupation-wise, civil servants dominated urban employment (42.5%), while farming was prevalent in rural areas (40.3%).

Income levels varied significantly, with urban dwellers earning an average monthly income of N51,686.8k, compared to N35,215.9k in rural areas. The majority of urban earners fell within the N60,000–N79,999 income range (54.8%), whereas a significant portion of rural respondents (34.8%) earned below the federally mandated minimum wage of N20,000. Car ownership mirrored income disparities, with nearly half (49.6%) of rural respondents lacking a car, while almost half (49.3%) of urban households owned one. These findings underscore the influence of income on mobility patterns and access to resources such as telecommunication/ICT usage.

4.2. Travel characteristics of households in the urban and rural areas

From the study, variations exist in the travel characteristics of respondents in the urban and rural areas, while 50.8% of households in the urban areas make four (4) trips in a day and 48.9% of them make an average of twenty (20) trips in a week, 71.9% of their counterparts in the rural areas make 1 trip in a day. This is sharp contrast from what was observed in the urban areas as only 20.8% of them make only 1 trip on a daily basis. Also, the mean trip for households in both the urban and rural areas also differs, while the mean trip frequency for households in the urban area were 2.29 that of the rural areas were 1.36. This implies that household in the urban areas make more trips than their counterparts in the rural areas. Information on trip purpose and transport mode of households revealed that 82.8% and 65.3% of households in the urban and rural areas made more of non-discretionary trips (trip to work). However variation exist in the transport mode of respondents in both areas. While majority (43.2%) of households in the rural areas make use of non-motorized transport (walk), 50.7% of their counterparts in the urban areas make use of public transport. It was however discovered that only 8.6% of households in the urban areas walk against the 43.2% found in the rural areas.

4.3. Analysis of telecommunication usage of respondents

Information from the respondents revealed that the most frequently used telecommunication facility is the Global system of mobile telecommunication (GSM). From our study, 58.6% and 64.3% of the respondents in the urban and rural areas use the GSM respectively compared to their counterparts who made use of other ICT devices such as computers, tablets among others. Also, 21.8% and 14.5% frequently use the internet such as emails in the urban and rural areas respectively. The remaining 19.6% and 9.1% indicated the social media such as WhatsApp, Facebook as the most commonly used type of telecommunication media for communication in the urban and rural area respectively. However, findings from our study showed that 14.2% of households in the rural areas against the 0% of their counterparts in the urban areas do not use telecommunication facilities for carrying out their activities.

4.4. Internet facilities as a form of telecommunication in the urban and rural areas

Information and Communication Technologies (ICT) encompass a wide array of tools and services that extend beyond the ubiquitous mobile phone, which is the most widely recognized and commonly used among the general population. This spectrum includes mobile phones as well as a myriad of internet-based services such as e-shopping, e-banking, e-business, email communication, and various others. Base on this, respondents were asked to provide information on the majorly used type of internet facilities which could also have profound effect on their travel behaviour. The information provided revealed that majority 45.5% and 85.5% of respondents in the urban and rural areas do not make use of these available platforms such as e-banking, e-business, emails among others.

4.5. Social media as a form of telecommunication

The social media such as Facebook, whatsapp, Instagram among others have been seen as a form of telecommunication which enables social interaction thus having impact on the trip making behaviour and mobility pattern of people. Information from this study revealed that 40.3% and 28.2% of the respondents make use of the facebook and whatsapp for communicating with relatives, friends, colleagues and workers in the urban area, while 2.4% and 2% make use of the twitter and Instagram respectively. In the rural areas, almost all the sampled respondents (92.9%) do not use any of the social media platforms.

4.6. Factors influencing telecommunication usage of respondents in the urban and rural areas

A study by Kyeremeh and Fiagborlo (2016) on factors influencing telecommunication usage, discovered that socio demographic characteristics such as age, income, household size, level of education were significant factors influencing the use of telecommunication. In view of this, the study carried out a survey to determine the factors influencing telecommunication usage among the urban and rural respondents. A total of Ten (10) predictors were utilized; overall, it was discovered from the study that factors influencing telecommunication usage in the urban area were household size, years spent in the pursuit of education, household head income, occupation, average number of daily calls and amount spent on phone recharge while three (3) significant factors which included number of phones, average number of daily calls and distance of call destination (see Table 2)were the influencing factors determining telecommunication usage of in the rural area. The coefficient of multiple determination (R²) in Table 3 explains the percentage contribution of the three significant variables at determining the telecommunication usage of respondents in the rural area was 80.3%. It thus explains that the three (3) significant variables jointly accounts for 80.3% of all the variables at influencing telecommunication usage in the rural area. The variables were significant at p < 0.05. It was thereafter used to generate a model : For the rural area, the model is given below.

Y = 1.523–0.287(Number of phones) +0.254(distance of call) +0.190 (number of daily calls).

Table 2. Factors influencing telecommunication usage of respondents in the rural areas.

Coefficients^a
Model		Unstandardized Coefficients		Standardized Coefficients	T	Sig.
		B	Std. Error	Beta
(Constant)		1.523	.239		6.373	.000
Age		5.628E–005	.004	.001	.014	.989
Marital Status		−.029	.076	−.041	−.385	.702
Household Size		−.008	.015	−.031	−.509	.613
Educational Status		−008	.031	.014	.252	.802
Average monthly income of household head		1.391E–006	.000	.043	.669	.506
Occupation of household head		.004	.009	.023	.402	.689
Number of GSM Owned/Internet Network subscribed to		−.287	.057	−.347	−5.085	.000
Cost of recharge card/subscription		.009	.031	.027	.295	.769
average number of daily calls		.190	.048	.398	3.933	.000
Distance of call		.254	.046	.685	5.530	.000

^aDependent Variable: Call Recharge Frequency/Internet/Data Subscription Frequency.

Source: Author’s Field Work.

Table 3. Model summary (rural area).

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.896^a	.803	.775	.19456

^aPredictors: (Constant), Distance of call, Age, Occupation of household head, Household Size, Educational Status, number of mobile phones, Average monthly income of household head, average number of daily calls, cost of recharge card/mobile subscription, Marital Status.

Source: Author’s Field Work.

Highlight of the model is that a unit increase in frequency of recharge or mobile subscription and distance of call will increase telecommunication use by 0.190 and 0.254 respectively. Likewise, the negative unstandardized coefficient for number of phones implies that a unit increase in the number of phones will reduce telecommunication usage by 0.287. The combined influence of the three significant variables at influencing telecommunication usage of respondents in the rural area accounted for 80.3%, this implies that the coefficient of determination (R²) is 80.3%.

In the urban area, six (6) variables were significant at influencing telecommunication usage and, this contributes a total of 72.4% at influencing the dependent variable. The result of the regression analysis in Table 4 were thus used to formulate a model which explains the influence of the independent variables at influencing telecommunication usage. The model for the urban area is given below:

Y =5.629+0.072(HHS)+0.250(EDUS)+0.931(INC)+0.075(OCCU)+0.758(FRERE)+0.545(RE/SUB).

Table 4. Factors influencing telecommunication usage of respondents in the urban area.

	Pooled Data
	Coeff.	Std. Error	t-value	p-value
Constant	5.629	1.010	5.575	0.00
Age (core)	−0.004	0.008	−0.540	1.47
Marital status	−0.128	0.171	−0.746	1.36
Household size	0.072	0.022	3.273	0.00
Educational status	0.250	0.125	1.998	0.05
Household head income	0.931	0.416	2.238	0.02
Occupation	0.075	0.027	2.809	0.02
Number of GSM lines owned/Internet network subscribed to	−0.197	0.113	−1.737	1.20
Average number of daily calls	0.758	0.196	3.867	0.02
Amount spent on recharge cards	0.545	0.086	6.337	0.00
Distance of call destination	−0.024	0.067	−0.357	1.75
R-Squared (R^2)	0.724

Dependent variable: Call Recharge Frequency/Internet/Data Subscription Frequency.

Source: Author’s Field Work.

From the model, it was discovered that the positive standardized coefficient for household size indicated that a unit increase in the household size by 0.072 will increase telecommunication usage. Likewise, an increase in the number of years spent in the pursuit of formal education by 0.250 will increase telecommunication usage for households in the urban area. The percentage contribution of the independent variables at influencing the dependent variable as shown in Table 5 in the urban area was 72.4%, this implies that the coefficient of multiple determinations (R²) is 72.4%.

Table 5. Model summary (urban area).

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.801^a	.724	.694	.18753

^aPredictors: (Constant), Distance of call, Age, Occupation of household head, Household Size, Educational Status, number of mobile phones, Average monthly income of household head, average number of daily calls, cost of recharge card/mobile subscription, Marital Status.

Source: Author’s Field Work.

4.7. Impact of telecommunication usage on mobility of respondents

The specific focus of this section is the impact of telecommunication on trip making behaviour of respondents in the urban and rural areas. It focused on the complementing, substitution and inducement effect of telecommunication on trip making.

4.7.1. Trip types complemented, induced and substituted by the different telecommunication means in the urban and rural areas

In Table 6, we present the outcomes pertaining to the average number of trips influenced by telecommunication, categorized as complemented, substituted, or induced. Our analysis highlights that telephone calls played a prominent role in both urban and rural areas, affecting a substantial number of trips. Specifically, in the urban area, telecommunication, notably phone calls, influenced a total of 2,233 trips, while in the rural area, a comparable figure of 1,654 trips was influenced by the same means. Additionally, email usage contributed to 1,248 urban trips and 217 rural trips. Furthermore, the utilization of e-banking as a communication tool impacted 958 urban trips and 361 rural trips by either complementing, substituting, or inducing them. Notably, the dominance of phone calls, facilitated by the GSM platform, is evident from the analysis of the data.

Table 6. Average Trips Complemented, substituted and induced by telecommunication in the study areas.

Telecommunication Means	Number of Complemented Trips		Number of Induced Trips		Number of Substituted Trips		Total
	Urban	Rural	Urban	Rural	Urban	Rural	Urban	Rural
GSM (CALLS)	932	652	432	311	869	691	2233	1654
Email	664	51	215	54	369	112	1248	217
E-banking	462	182	198	78	298	101	958	361
E-shopping	402	165	218	101	568	89	1188	355
Social Media Platforms	525	201	102	48	495	256	1122	505
Total	2985	1251	1165	592	2599	1249

Source: Author’s Field Work.

4.7.2. Trip activities complemented, substituted and induced by telecommunication in the urban and rural areas

The findings on Table 7 revealed that the complementarity effect of telecommunication were significant for the different trip purpose in both the urban and rural areas. As established from the table, a total of 825 (42%) and 546 (49%) work/business trips were complemented via the use of telecommunication in the urban and rural areas respectively. This brings this trip activity to the highest being complemented by the use of telecommunication in the study areas. However, the use of telecommunication as a substitute for telecommunication were significant on shopping trips in the urban areas as 573 (40%) of their trips to shopping is being substituted through the use of telecommunication. This is a reflection of the findings on Table 6 where larger number of shopping trips were being substituted by conducting e- shopping.

Table 7. Average daily trip activities complemented, substituted and induced by the use of telecommunication in the urban and rural areas.

Trip purpose	Number of trips Complemented		Number of Induced Trips		Number of Substituted Trips
	Urban	Rural	Urban	Rural	Urban	Rural
Work/business	825 (42%)	546 (49%)	648 (51%)	350 (40%)	269 (19%)	519 (48.3%)
Shopping	460 (24%)	212 (19%)	221 (17%)	102 (12%)	573 (40%)	153 (14.2%)
Financial activities(banking)	170 (9%)	92 (8%)	103 (8%)	50 (6%)	329 (23%)	29 (2.7%)
Religious	101 (5%)	50 (5)	98 (8%)	0 (0%)	50 (3%)	35 (3.3%)
Health	102 (5%)	104 (9%)	78 (6%)	123 (14%)	35 (2%)	217 (20.2%)
Recreational/social	292 (15%)	112 (10%)	125 (10%)	252 (28%)	192 (13%)	120 (11.2%)
Total	1950 (100%)	1116 (100%)	1273 (100%)	877(100%)	1448 (100%)	1073 (100%)

Source: Author’s Field Work.

5. Discussions

Recent studies by O. Aderibigbe (2021) and Gomez et al. (2022) have highlighted a significant correlation between socio-economic factors and various aspects of Internet usage and travel behaviour among households. Our research, consistent with previous findings by World Bank (2008), reveals that individuals aged 60 and above resides in the rural areas. Consequently, there’s a pressing need for policies catering to sustainable infrastructure to accommodate the mobility requirements of this demographic, particularly telecommunication facilities that mitigate physical mobility challenges associated with health decline.

Similarly, our investigation into the educational status of rural and urban households mirrors Gardiner’s (2017) observations, indicating higher educational attainment among urban residents aged 25–34. Additionally, our study underscores the disparity in tertiary education attainment between urban and rural populations, reflecting the broader educational and income gap highlighted by Kyeremeh and Fiagborlo (2016). Additionally, our study demonstrated that more than twice the number of urban residents had achieved tertiary or university education compared to their rural counterparts. Furthermore, our findings concerning occupation disparities align with Nwachukwu (2016) observations, particularly regarding the prevalence of farming as a primary economic activity in rural households, leading to lower incomes. This contributes to the lower rates of car ownership among rural residents, as also noted by O. Aderibigbe and Gumbo (2022), emphasizing the link between income levels and car ownership. Regarding travel behaviour, our research reveals disparities in trip frequency between urban and rural households, with urban dwellers making more trips, consistent with national travel surveys. Mode choice also varies, influenced by income levels and access to private vehicles, with urban residents favouring private transport, contrasting with rural areas where non-motorized transport is more common, hence, corroborating the findings of Starkey et al. (2002).

Telecommunication usage disparities between urban and rural areas reflect the evolving nature of the digital divide, shifting focus from Internet access to usage patterns. These differences, influenced by socio-economic factors such as age, income, and education, present challenges to bridging the digital gap, as highlighted by scholars like Van Dijk (2005) and Ragnedda et al. (2019). From our study, a larger percentage of those in the rural areas are not aware that the use of telecommunication extends beyond call linkages to other uses such as online shopping and teleworking, thus, impacting their mobility and trip making pattern. The rural respondents attributed the low level of usage of these internet facilities to inadequate awareness, lack of accessibility to internet facilities within the neighbourhood, and the inability to utilize the platforms for carrying out the identified activities. In their respective studies, Dutton and Blank (2015) as well as Gómez et al., (2022) emphasized the correlation between digital exclusion and insufficient access to the Internet, particularly highlighting its strong connection to economic factors. This alignment with our own research findings reinforces the significance of this relationship. It is important to note that recognizing that unique digital practices and subsequent digital disparities emerge from the interplay of socio-economic and techno-social factors.

Moreover, our findings underscore the influence of socio-economic factors on telecommunication usage, with education level and expenditure on subscriptions being significant determinants. The influence of some of the significant socio-economic characteristics of households such as number of years spent in the pursuit of formal education at influencing telecommunication usage corroborates the findings of Muriiti et al. (2016) which asserted that usage of telecommunication facilities such as internet facilities were prominent among those with PhD academic qualification. Apart from the significant socio demographic variables, it was also discovered that amount spent on Recharging or subscription also influences telecommunication usage in the urban area. Our findings underscore the significant complementary effect of telecommunication on trips, which is particularly pronounced in both urban and rural contexts, where a substantial number of trips (2,985 in urban and 1,251 in rural areas) were complemented by telecommunication. However, telecommunication also substitutes for physical trips, notably in urban shopping activities, consistent with trends observed by Shi et al. (2019). Further to this, more social/recreational trips were induced as a result of telecommunication use in the rural areas. This corroborates the findings of Olawole (2013) which asserted that the physical presence of friends and relatives is much valued among rural dweller. Overall, our research endeavours to categorize ICT users based on the degree of influence these factors exert on telecommunication usage, hence, impacting the mobility pattern of households either through substitution, complementarity or trip induction.

Overall, our research categorizes ICT users based on socio-economic influences, impacting household mobility patterns through substitution, complementarity, or trip induction, underscoring the intricate relationship between socio-economic factors and telecommunication usage in shaping travel behaviour.

6. Conclusion

Conclusively, the study has investigated the impact of telecommunications on mobility of households in the urban and rural areas. The importance of telecommunication at shaping people’s travel behaviour as well as reducing some of the problems associated with transport; one of which includes traffic congestion is highly imperative. It is therefore necessary that measures aimed at encouraging the use of telecommunication in both urban and rural areas is encouraged and enforced by stakeholders, government and transport personnel. This will have practical implications for mode choice modelling, traffic management measures and traffic congestion alleviation measures. It is also imperative that relevant stakeholder understand that disparities exist in the urban and rural areas which will necessitate a need for stakeholders and government to come up with a policy that gives attention to the rural dwellers especially when making provision for an affordable, accessible and stable transport and telecommunication infrastructures which will facilitate and ease their mobility.

Disclosure statement

No potential conflict of interest was reported by the author(s).