The authors declare that they have no conflicts of interest.
This study did not require formal ethics approval.
Data sharing is not applicable to this article.
The authors received no specific funding for this work.
Karachi, a megapolis city in specific, has seen a significant increase in urban growth and motorization over the last fifty years. The lack of effective public transportation is a consequence of incredibly reduced operational costs that are reasonable to many of Karachi's habitats, resulting in excessive car use.The study aims to figure out what psychological factors influence motorists'decisions to use sustainable alternative modes of choice. People's movements are strongly linked to their social demographic characteristics, such as age, gender, marital status, profession, education levels, and family activities. For research, data has been collected through a self-administered questionnaire. Partial Least Square Structural Equation Modelling (PLS-SEM) was used. Private Car users of Karachi's CBD were Focused. The descriptive analysis was adopted through SPSS. The "SEM" model was applied through the "PLS" Path Least Square Model. Resultantly, Intention is the crucial factor for the completion of the research.
The crucial aspects of people's daily routine without moving their activities cannot be performed without Transportation (Soomro et al., 2021). In the next few decades, the current global car population of one billion will almost double (Gordon & Sperling, 2009). Although becoming a chosen mode of transportation for a variety of reasons, the exponential increase in the number of automobiles has created serious problems. The potential impacts of heavy traffic are road accidents, emissions, and congestion(Qureshi et al., 2022). Because of increased urban sprawl and motorization, the scale of trouble will rise much faster than the city's population(Memon et al., 2020a). Both journeys in terms of space and time are now more distributed, and our knowledge of environmental problems has also changed(Kalwar et al., 2022; Shaikh et al., 2020; Talpur et al., 2016; Talpur et al., 2014). Elderly people are more concerned about pollution, while analysts speak about outlying gridlock, and edge cities are also connected to issues such as greenhouse gas emissions and social inequality (Ghaffar et al., 2021; Sahito et al., 2020; Shah et al., 2021a, 2021b). In most cities today, unaffordable problems, mostly in urban mobility networks, are being faced (Bulkeley & Tuts, 2013; Irfan Ahmed et al., 2021; Ki-moon, 2013; Memon, Kalwar, Sahito, & Napiah, 2021; Memon, Kalwar, Sahito, Talpur, et al., 2021; Memon, Napiah, et al., 2016a; Memon, Napiah, Talpur, et al., 2016). Generally, several transportation problems arise when transportation networks fail to meet the needs of urban mobility (Brohi, Memon, et al., 2021; Kalwar et al., 2019; MEMON, 2018a; Memon et al., 2022;
Transit, 2015). People's movements are strongly linked to their social demographic characteristics, such as age, gender, marital status, profession, education levels, and family activities. Job, school, shopping, outdoor sports, etc., all are part of the activities (Bowman et al., 2014). The massive expansion of urban areas has resulted in significant challenges such as increased use of the region, an increasing rate of vehicle dominance, and less efficient motorization (Pojani & Stead, 2015). Expansions in different cities have shown that population is the most important indicator for travel, with travel demand rising in lockstep with population growth (Alkhathlan & Javid, 2013; JAVID et al.). It's been noted that the future planning of Karachi's public transportation system should take into account the city's residents' cultural and social perceptions, as well as the need for protection and separate family carriages (Brohi, Kalwar, et al., 2021a; Gill et al., 2020; Kalwar et al., 2020; Memon et al., 2014; Memon et al., June 2014).
The importance of psychological variables in the modal split model is the subject of this initiative. These characteristics were measured using psychometric methods that were appropriate for discrete option models using a latent variables approach and path analysis (Galdames et al., 2011; MEMON, 2018b).
Problem Background
In classical choice models, selecting a mode of transportation is viewed as an operation involving straight observable variables such as the traveller's physical characteristics such as gender, age, and earnings, as well as aspects of the mode of travel choices such as trip length, trip cost, and so on. Current decision-making model efforts have highlighted the significance of precisely managing psychology-related factors that influence decision-making. (Antonini et al., 2004; Memon, 2010b; Memon et al., June 2014; Memon, Napiah, et al., 2016b; Memon, Napiah, Talpur, et al., 2016). Including psychology-related considerations leads to a more socially rational depiction of the options process and hence increased explanatory capacity (Antonini et al., 2004). According to studies, psychology-related aspects of choosing a mode of transportation are both rational and natural behaviour.
(MEMON, 2018b).
The metropolitan city of Karachi, in specific, has seen a significant increase in urban growth and motorization over the last fifty years. The absence of adequate public transit in Karachi is due to the very low automobile running expenses that many Karachi residents can afford, resulting in excessive car use. This has been followed by a heavy dependence on private automobiles, resulting in serious car crashes, traffic congestion, and economic, social, and environmental consequences. The registered number of vehicles carrying rickshaws, according to excise and taxation, was 105,684, 6,506 buses, minibuses were 15,807, 104,097 vans/pickups, 47,165 taxis and motorcycles were 1,296,481 (Brohi, Kalwar, et al., 2021b; Irfan Ahmed et al., 2021; MEMON, 2018b; Memon, Kalwar, Sahito, Talpur, et al., 2021; Shah et al., 2021b; Shaharyar et al., 2021)
As stated above, in the context of transportation issues and sustainable alternative mode choices in the city of Karachi, the research poses the question: "What processes and approaches need to be dealt with to guarantee public transport uptake in Karachi?
1. Analysis of the measures that influence the modal choice pattern of private car users.
2. To develop a choice modal to definitive psychosomatic factors.
The four steps of the urban transportation planning system model are trip generation, Trip distribution, Mode-choice, and Route assignment.
Mode choice analysis is measured as the third step in the four-step transportation-forecasting model.
A continuing increase in road traffic congestion leading to driver frustration is disturbing many urban areas' Longer travel times, lower efficiency, more serious accident and car insurance rates, higher fuel consumption, higher cost of transport, and decreased air quality.
The Intention is a key factor affecting
behaviour, according to the Planned Behavior theory(Ajzen & Fishbein, 2005). Tpb assumes that deliberate behaviour captures and mediates all motivational factors that influence a person's behaviour. Behavioural values, subjective norms, perceived moral obligation and perceived behavioural regulation all influence the intent of behaviour. Top models have been adopted by numerous studies of travel mode choice Behavior (Hunecke et al., 2007)
The research design for this study begins with a probing investigation that reviews the literature to assess the research gap and describe the research questions. The research plan is regarded as a logical method as well as a master plan of the research effort that sheds light on research in order to give a solution to the research question(s) (Memon et al., 2020b; Stenson et al., 2003). It presents the researchers with knowledge for the data collection and analyzes in their research and also ensures them that the provided data applies to their effort and deals with the research requirements. This research is established on assembling quantitative facts regarding the upcoming recognition of the anticipated. Karachi inhabitants enforce public infrastructure and policies.
? CBD of Karachi I. I. Chandigarh Road
? Private Car users were focused.
? 100 Sample size questionnaires were collected.
? A self-administered questionnaire Survey was conducted among private transport users.
? Data was entered in “SPSS”.
? Descriptive analysis was done through “SPSS”.
? “SEM” Structural Equation model Through “PLS” Path Least Square Model.
SEM is a fairly generic statistical modelling tool that is commonly utilized in behavioral sciences. This is a combination of factor analysis and regression or path analysis. The importance of SEM is a lot on a hypothetical construct that is corresponded to the latent factors. The relations between the theoretical constructs are shown between the variables by path or regression coefficients. The structural equation model provides a structure for the covariance between the variables observed and offers modeling of the structure of covariance by another name. However, the model can be further expanded to include experimental means of variables or other things in the model, which allows the modeling of covariance structure a mere precise name. These models are mostly known by many researchers as 'Lisrelmodels' which is often less specific. (LISREL) is abbreviated as Linear Structural Relations is the name of one of Jöreskog's first and most well-known SEM algorithms. Structural equation models must now be nonlinear, and SEM's possibilities extend much beyond the actual Lisrel programme.Like Browne (1993), he presented the
possibility of fitting nonlinear curves.
Assessment of the Structural model with their steps is described in Fig. 2. Step 1 demonstrates collinearity. If any construct has a value greater than five, the construct is collinear, and the query must be rechecked or rewritten. The Variance Inflation Factor (VIF) must be less than five. Step 2, all p Values less than 0.05 are acceptable.
Fig.3 SEM is a univariate statistical analysis used to investigate structural relationships. Exogenous and endogenous variables are used in this model. In this model, the total variation of endogenic factors on exogenic factors was 42%.
In step 3 in step 3, we see access to the value of R2 the average variance extracted was established. Discriminant validity established. Endogenous variables had a cumulative variance of 42% on exogenous variables. Nonetheless, we should consider the factor and variable that account for 100% variance in the independent variable.
Assessment of Structural Model Structural Model Assessment Procedure
Assessment of the model for collinearity issues was tested as shown in Table 1. As shown in table 1 all factors all less than five.
Variance Inflation Factor (VIF)
ATTITUDE3
ATTITUDE 4
ATTITUDE 5
ATTITUDE 7
ENVIROMENT5
IntToPublTrans1
IntToPublTrans2
IntentionpWalking
PercivedNBP2
PercivedNBP3
PercivedNBP1
PercivedWalkingE10
PercivedWalkingE6
PercivedWalkingE7
PercivedWalkingE8
PercivedWalkingE9
Subjectnorm1
Subjectnorm 2
Subjectnorm 3
Trip-Charctere1
Trip-Character 2
Partial Least Square Structural Equation Model
Assessment of Model for Path Coefficient is shown in Table II. Size of Path-coefficients shows the strength of relationship and importance among constructs
? Attitudes_Towards_PS has a weak relationship with Intentions and is not significant)
? Environmental_Awarness has a strong relationship with Attitudes_Towards_PS and it is significant
? Environmental_Awarness has a weak relationship with Percieved_Behevior_C not significant
? Environmental_Awarness has a weak relationship with Behavior not-significants.
? Moderating Effect 1 has a weak relationship with Behavior not-significants.
? P_Walking -> has a weak relationship with Behavior not-significants.
? Percieved_Behevior_C has a weak relationship with Intentions not-significants
Social Norms have a weak relationship with intentions and are not significant
| | | | | | |
| ATTI TO PS > INTEN | 0.15 | 0.14 | 0.26 | 0.58 | 0.55 |
| ENV AWAR > ATTI TO PS | 0.39 | 0.39 | 0.18 | 2.10 | 0.03 |
| ENV AWAR > PERCIV BEVR | 0.19 | 0.25 | 0.23 | 0.83 | 0.40 |
| ENV AWAR > SOCI NRM | 0.39 | 0.37 | 0.23 | 1.67 | 0.09 |
| INTEN > BEVR | 0.04 | 0.12 | 0.23 | 0.17 | 0.86 |
| MODERT EFCT-1 > BEVR | 0.03 | 0.04 | 0.25 | 0.11 | 0.90 |
| PERCIV WALK ENV > BEVR | 0.36 | 0.26 | 0.45 | 0.79 | 0.42 |
| PERCIV-BEVR-CON > INTENTION | 0.19 | 0.20 | 0.20 | 0.96 | 0.33 |
| SOCI NRM > INTENTIONS | 0.43 | 0.47 | 0.25 | 1.71 | 0.08 |
Shows Assessment of R2
Assessing Predictive Relevance (Q2) is presented in Table III. In which it is presented that Intention to use private transport is greater than 0 so (0.206) it is Established.
Blindfolding Results in Predictive Relevance
ATTI TO PS
132
126.28
0.05
BEHAVIOR
66
72.35
0.08
ENV AWAR
33
33
INTENTS
66
52.39
0.20
MODERT EFCT-1 >
33
33
PERCIV WALK ENV
165
165
PERCIV BEVR
99
98.08
0.00
SOCI NRM
99
96.93
0.01
Bootstrapping Results are Hypothesis Testing
Bootstrapping: Hypothetical Testing
ATTI TO PS > INTEN
0.156
0.150
0.260
0.590
0.560
ENV AWAR > ATTI TO PS
0.394
0.399
0.188
2.101
0.036
ENV AWAR > PERCIV BEVR
0.196
0.255
0.235
0.832
0.406
ENV AWAR > SOCI NRM
0.393
0.375
0.235
1.674
0.094
INTEN > BEVR
-0.041
-0.124
0.236
0.173
0.862
MODERT EFCT-1 > BEVR
0.030
-0.041
0.255
0.117
0.907
PERCIV WALK ENV > BEVR
-0.363
-0.262
0.455
0.797
0.425
PERCIV-BEVR-CON > INTENTION
0.197
0.206
0.205
0.961
0.337
SOCI NRM > INTENTIONS
0.437
0.473
0.254
1.718
0.086
? Attitudes_Towards_PS has no significant relationship with Intentions)
? Environmental_Awarness positive and significant relationship with Attitudes_Towards_Private Transport use
? Environmental_Awarness has no significant relationship with Percieved_Behevior_C
? Environmental_Awarness has no significant relationship with Behaviour
? Moderating Effect 1 has no significant relationship with Behaviour
? Percived_Walking -> has no significant relationship with Behaviour
? Percieved_Behevior_C has no significant relationship with the Intention to use private transport
? Social Norms have no significant relationship with the Intention to use private transport Effect Size f2
The guidelines for assessing f2 values
Effect Size
LOW EFFECT = 0.02
MEDIUM EFFECT = 0.15
HIGH EFFECT = 0.35
(Cohen, 1988)
Assessing Effect Size of F2 (Predictive) is expressed in Table V.
Effect Size of F
ENV AWAR
0.180 Modest
INTENTS
0.002 Less Effect
MODERT EFCT-1 >
0.002 Less Effect
PERCIV WALK ENV
0.130 Modest
PERCIV-BEVR-CON
0.041 Less Effect
SOCIAL Norms
0.176 Modest
The goodness of the Fit index
The goodness of Fit (GoF)
G0F=?average R2 x average communalityG0F=?(0.183 x) 0.702 =0.35
Analysis of GoF is finest considered 0, 0.35 proposes good GoF. As per results, if it is 0.7 >then it indicates poor GoF.
The Correlation Coefficient of Latent Variables is shown in Table VI.
Correlation Coefficient of Latent Variables
ATTITUDES TOWARDS PS
1.00
BEHAVIOR
-0.01
1.00
ENVIRONMENT AWARENESS
0.39
0.14
1.00
INTENTIONS
0.50
0.16
0.44
1.00
MODERATING EFFECT 1
-0.24
0.02
0.32
0.56
1.00
P_WALKING
0.18
-0.37
0.23
0.27
0.08
1.00
PERCEIVED BEHAVIOR C
0.62
0.01
0.19
0.51
0.26
0.27
1.00
SOCIAL NORMS
0.71
0.06
0.39
0.64
0.49
0.13
0.51
1.0
Table 5 shows a strong correlation between the latent exogenous constructs and the latent endogenous construct.
Correlation Coefficient of Latent Variables
Attitudes Towards PS Behaviour
0.150
0.150
0.260
0.590
0.560
Positive
Not Significant
Rejected
Environmental Awareness Attitudes towards PS
0.394
0.399
0.188
2.101
0.036
Positive
Significant
Accepted
Environmental Awareness Perceived Behavior
0.196
0.255
0.235
0.832
0.406
Positive
Not Significant
Rejected
Environmental Awareness Social Norms
0.393
0.375
0.235
1.674
0.094
Positive
Not Significant
Rejected
Intentions Behavior
-0.041
-0.124
0.236
0.173
0.862
Negative
Not Significant
Rejected
Moderating Effect
0.04
-0.040
0.255
0.118
0.910
Positive
Not Significant
Rejected
Walking
-0.360
-0.260
0.460
0.780
0.430
Negative
Not Significant
Rejected
Perceived Behavior Intentions
0.197
0.206
0.205
0.961
0.337
Positive
Not Significant
Rejected
Social Norms
0.440
0.480
0.250
1.719
0.090
Positive
Not Significant
Rejected
The measurement model was assessed through Correlation Coefficient. The composite reliability of all values was established. The average variance extracted was established. Discriminant validity established. Endogenous variables had a cumulative variance of 42% on exogenous variables.
The Structural Model was evaluated using the F2 effect size, which revealed that all independent variables have a very small effect size, except for the Intention to use private transportation. Except for Environmental-Awareness, none of the independent variables had a significant size or importance. The R2 impact of all independent variables on the dependent variable was extremely high at 42 per cent.
The Q2 value was higher than 0 (0.206), indicating a predictive value.
The goodness of fit index was 0.35, which shows that empirical data fits the model satisfactorily. Except for Environmental-Awareness, all hypotheses are dismissed using the bootstrapping method. The part of the Intention is very important for the accomplishment of the research. Such as the Intention to use private transport is satisfactory.
According to the study's results, Intention is the most important factor.
This study was performed on factors influencing private transport users to shift towards public transport. The result shows that Environmental_Awarness has a strong relationship with an attitude toward public transportation (Attitudes_Towards _PS) and its significance.
The interpersonal model was assessed through composite reliability in which average variance was applied. Discriminant validity is satisfactory according to the Fornel – Lacher cetteiorn.
The average variance extracted was established. Discriminant validity established. Endogenous variables had a cumulative variance of 42% on exogenous variables.
The Structural Model was evaluated using the F2 effect size, which revealed that all independent variables have a very small effect size, except for the Intention to use private transportation. Except for Environmental-Awareness, none of the independent variables had a significant size or importance. The R2 impact of all independent variables on the dependent variable was extremely high at 42 per cent.
The Q2 value was higher than 0 (0.206), indicating a predictive value.
The goodness of fit index was 0.35, which shows that empirical data fits the model satisfactorily. Except for Environmental-Awareness, all hypotheses are dismissed using the bootstrapping method. The part of the Intention is very important for the accomplishment of the research. Such as the Intention to use private transport is satisfactory.
According to the study's results, Intention is the most important factor.
The authors declare that they have no conflicts of interest.
This study did not require formal ethics approval.
Data sharing is not applicable to this article.