Presenting traveller preference heterogeneity in the context of agency theory: understanding and minimising the agency problem

Figures & data

Table 1. Summary of extant research.

Issues/subjects	Methods used	Findings	Sources
Government support programs (GSPs) and academic entrepreneurship	Agency theory	The GSPs have an effect on reducing the agency problems in the relationship	Rasmussen and Gulbrandsen (2012)
Labour market	Agency theory and dichotomisation process	In the private sector, the principal is largely controlling the agents’ activities via contemporary HRM systems and by tying wages to performance. On the contrary, the principal’s lack of control over the agent’s activities is noticed in the public sector	Cohen and Baruch (2010)
Capital planning and financing	Interview and principal-agent model	Hospital planning process should necessarily be informed by an explicit understanding of powerful groups (e.g. central government) involved and their divergent preferences and utilities to avoid agency problems	Thompson and McKee (2011)
Supply chain management	Agency theory and outcome-based and behaviour-based approaches	Agent-based factors such as information asymmetry, goal conflict, risk aversion of suppliers, length of relationship, and task characteristics, can be expected to influence how firms design and manage their quality management systems for supply chains	Zu and Kaynak (2012)
Risk management	Framework of agency theory	Empirical results indicate that purchasing organisations address various sources of supply risk by implementing management techniques that reduce the likelihood that detrimental events will occur. Firm size, purchases as a percentage of sales, and industry characteristics have also been found to influence the manner in which supplier behaviours are managed	Zsidisin and Ellram (2003)
Public transportation	Structural equation model	Public transportation offers low utility due to low degree of congruence between user preferences and service provided. Increased utility may increase new customers and thus agency relationship may improve	Andreassen (1995)
Managerial ownership	Logistics regression approach	The risk at lower level of managerial is significant and positive relations with managerial ownership concentration. This supports the hypothesis that a higher level of managerial ownership can reduce the agency conflict between external equity claimholders and managers	Ahmed (2009)
Organisational management	Mixed logit model	Agency theory and stewardship theory are not necessarily in conflict with each other but can be combined into a more general governance framework for non-profit organisations. Consequently, incentive structures that incorporate different types of objectives can facilitate the recruitment and retention of employees in non-profit organisations	Puyvelde, Caers, Bois, and Jegers (2013)
Doctor and patients’ preferences	Logit model	GPs are not fully aware of patient preferences at an aggregate level. Even though GPs almost succeeded in predicting the rank order of preferences in the unforced choice, there is still room for improving the agency relationship in the organisation of general practice	Pedersen, Kaer, Kragstrup, and Hansen (2012), Scott and Vick (1999)

Table 2. LVs and their indicators.

Latent variables	Label	Explained by (indicators) Anwar et al. (2014)	Definitions
Comfort	y1	Enjoy time to read/relax on vehicle	Importance with 1, otherwise 0
	y2	Stressfulness on vehicle	Importance with 1, otherwise 0
	y3	Service slower	Importance with 1, otherwise 0
Convenience	y4	Alternative mode availability	Importance with 1, otherwise 0
	y5	Accessibility (does not go where required)	Importance with 1, otherwise 0
	y6	Timetable availability	Importance with 1, otherwise 0
Safety	y7	Safety response for mode used in 1st trip	perceived a safe trip with 1, otherwise 0
	y8	Safety response for mode used in 2nd trip	perceived a safe trip with 1, otherwise 0
	y9	Safety response for mode used in 3rd trip	perceived a safe with 1, otherwise 0
Flexibility	y10	Fixed start and finish times at office – each day can vary	Importance with 1, otherwise 0
	y11	Rotating shift (work flexibility)	Importance with 1, otherwise 0
	y12	Roster shift (work flexibility)	Importance with 1, otherwise 0
	y13	Variable hours (work flexibility)	Importance with 1, otherwise 0
Reliability	y14	Frequency (travel mode, e.g. bus)	Importance with 1, otherwise 0
	y15	Punctuality (for public transport)	Importance with 1, otherwise 0
	y16	Faster (for public transport)	Importance with 1, otherwise 0
Satisfaction	y17	Cleanliness (cleanliness inside vehicle)	Importance with 1, otherwise 0
	y18	Travel time	Travel time in minutes
	y19	Travel cost	Travel cost in Australian dollar
	y20	Waiting time	Waiting time in minutes

Figure 1. Structure of mode choice probability process.

Table 3. MIMIC model results: α vector matrix of structural equations (t-values in the parenthesis).

LVs	Travel time	Travel cost		Waiting time	Age	Income	Family size	Gender	Car ownership	No. child	Full time	Trip rate	Distance travelled	Trip purpose
Comfort	−.045 (–3.16)	−.212 (–3.86)		−.165 (–5.71)	−.011 (–2.91)	.121 (2.87)	−.002 (–3.01)	.061 (4.1)	.301 (6.12)	.202 (3.89)	.006 (2.01)	.038 (2.21)	.123 (3.81)	.021 (1.90)
Convenience	−.211 (–7.27)	−.102 (–1.71)		−.216 (–5.13)	−.125 (–2.21)	.156 (2.53)	−.002 (–2.76)	.126 (2.63)	.275 (5.48)	.189 (4.51)	.002 (1.67)	.117 (2.51)	.11 (2.63)	.131 (2.01)
Flexibility	−.092 (–3.47)	−.003 (–1.99)		−.066 (–1.89)	−.088 (–3.41)	.031 (1.90)	.022 (3.01)	−.102 (–2.13)	−.117 (–5.15)	−.131 (–5.31)	−.007 (–2.85)	.001 (2.13)	.013 (4.11)	.126 (4.20)
Safety	−.091 (–4.22)	−.012 (–3.04)		−.132 (–3.91)	−.21 (–4.67)	−.088 (–2.89)	.005 (3.64)	−.098 (–4.12)	−.219 (–7.72)	−.166 (–6.61)	−.008 (–2.44)	.112 (3.01)	.171 (3.69)	.041 (2.58)
Reliability	−.514 (–6.21)	−.011 −2.01		−.107 (–6.11)	−.042 (–1.89)	.031 (2.12)	−.005 (–2.11)	.012 (3.07)	.414 (4.56)	.003 (4.11)	.007 (2.12)	.016 (3.19)	.112 (3.12)	.009 (2.51)
Satisfaction	−.192 (–3.91)	−.166 (–6.21)		−.121 (–3.71)	−.142 (–5.11)	.032 (3.90)	−.008 (–2.12)	−.087 (–3.21)	.139 (5.11)	.092 (6.15)	.007 (5.16)	.097 (6.91)	.062 (5.33)	.068 (3.01)
Model fit criteria
GFI			.963
AGFI			.945
NFI			.901
CFI			.950
RMSEA			.033
Lower bound			.013 (90% CI of RMSEA)
Upper bound			.048 (90% CI of RMSEA)

Anwar et al. (2015).

Figure 2. Latent variables model relationships.

Table

yy1,ij = γy1,j × Comfortij + ζy1,ij	yy11,ij = γy11,j × Flexibilityij + ζy11,ij
yy2,ij = γy2,j × Comfortij + ζy2,ij	yy12,ij = γy12,j × Flexibilityij + ζy12,ij
yy3,ij = γy3,j × Comfortij + ζy3,iq	yy13,ij = γy13,j × Flexibilityij + ζy13,ij
yy4,ij = γy4,j × Convenienceij + ζy4,ij	yy14,ij = γy14,j × Reliabilityij + ζy14,ij
yy5,ij = γy5,j × Convenienceij + ζy5,ij	yy15,ij = γy15,j × Reliabilityij + ζy15,ij
yy6,ij = γy6,j × Convenienceij + ζy6,ij	yy16,ij = γy16,j × Reliabilityij + ζy16,ij
yy7ij = γy7,j × Safetyij + ζy7,ij	yy17,ij = γy17,j × Satisfactionij + ζy17,ij
yy8,iq = γy8,j × Safetyij + ζy8,ij	yy18,ij = γy18,j × Satisfactionij + ζy18,ij
yy9,ij = γy9,j × Safetyij + ζy9,ij	yy19,ij = γy19,j × Satisfactionij + ζy19,ij
yy10,ij = γy10,j × Flexibilityij + ζy10,ij	yy20,ij = γy20,j × Satisfactionij + ζy20,ij

Table 4. The γ vector matrix of standardised regression weights of indicators (t-statistics in parenthesis).

Indicators	Regression weights
	Group 1		Group 2		Group 3
	Comf.	Conv.	Flex.	Safety	Reliab.	Satis.
Enjoy time to read/relax in the vehicle	.136 (5.59)	.007 (.141)
Stressfulness in the vehicle	1.178 (4.89)	.141 (1.11)
Slower service (in-vehicle longer travel time due to many stops during the travel)	−.056 (–3.84)	−.002 (–.04)
Availability of alternative mode of transport	.201 (.152)	.239 (5.61)
Accessibility (go where required)	.834 (1.21)	1.060 (7.12)
Public transport timetable availability	.090 (.89)	.195 (4.44)
Fixed start and finish times at work – each day can vary			−.201 (–7.10)	−.021 (–.98)
Rotating shift at work			.992 (4.51)	−.717 (–.06)
Roster shift at work			.331 (5.36)	−.215 (–1.01)
Variable hours at work			.916 (6.12)	−.202 (–1.20)
Safety response for mode used in 1st trip			.071 (.21)	1.250 (10.41)
Safety response for mode used in 2nd trip			.010 (.11)	.970 (7.84)
Safety response for mode used in 3rd trip			.069 (.09)	.870 (7.11)
Frequency of bus or train					1.415 (7.11)	.213 (.12)
Punctuality of bus or train					1.517 (6.10)	.201 (.10)
Faster service (in-vehicle shorter travel time due to fewer or no stops during travel					1.211 (4.14)	.415 (.09)
Cleanliness on public transport and stops and platform					.450 (.05)	.568 (3.84)
Travel time					−1.015 (–.10)	−1.212 (–6.74)
Travel cost					−.008 (–.84)	−.024 (–4.15)
Waiting time					−1.111 (–.76)	−1.512 (–6.14)
Goodness-of-fit
GFI	.985		.989		.984
AGFI	.978		.981		.971
NFI	.911		.975		.901
CFI	.931		.981		.990
RMSEA	.036		.017		.039
Lower bound	.000 (90% CI of RMSEA)		.000 (90% CI of RMSEA)		.017 (90% CI of RMSEA)
Upper bound	.076 (90% CI of RMSEA)		.067 (90% CI of RMSEA)		.057 (90% CI of RMSEA)

Table 5. Modelling results.

Attributes	SDRPL (t-values)	HRPL (t-values)
Random parameter in utility functions
Travel cost (mean)	−3.14 (–4.15)	−2.09 (–3.00)
Travel cost (st.dev.)	.41 (3.11)	.70 (2.22)
Waiting time (mean)	−1.76 (–3.19)	−1.70 (–4.00)
Waiting time (st.dev.)	.03 (5.00)	.09 (3.94)
Age (mean)	−.111 (–.05)	−.091(–1.60)
Age (st.dev.)	.25 (1.891)	.49 (1.70)
Car ownership (mean)	1.86 (5.11)	1.94 (5.55)
Car ownership (st.dev.)	.01 (4.51)	.05 (3.55)
Having children (mean)	−1.77 (–4.11)	−1.81 (–5.01)
Having child (st.dev.)	.06 (4.00)	.09 (5.19)
Trip purpose (mean)	.071(3.01)	.062 (3.00)
Trip purpose (st.dev.)	.04 (3.12)	.02 2.72)
Comfort (mean)		3.51 (8.79)
Comfort (st.dev.)		.11 (6.66)
Convenience (mean)		3.25 (5.46)
Convenience (st.dev.)		.02 (4.36)
Safety (mean)		5.51 (10.22)
Safety (st.dev.)		.09 (7.01)
Flexibility (mean)		.72 (.80)
Flexibility (st.dev.)		.03 (1.21)
Reliability (mean)		5.71 (9.01)
Reliability (st.dev.)		.01 (5.15)
Satisfaction (mean)		1.25 (3.00)
Satisfaction (st.dev.)		.10 (3.25)
Non-random parameter in utility functions
Travel time	−1.20 (–4.10)	−1.13 (–4.64)
Gender	.40 (1.89)	−.214 (–2.01)
Income	1.99 (2.11)	1.46 (1.99)
Family size	.90 (1.12)	.89 (1.00)
Full time workers of HH	.94 (.56)	.93 (.07)
Trip rate	.89 (2.55)	.85 (2.70)
Distance travelled	−.81 (–2.22)	−.26 (–1.90)
Mode constant
Car as a passenger (base)	0	0
Car as a driver	−2.09 (–3.00)	−2.56 (–10.0)
Train	−2.21 (–4.41	−2.41 (–4.15)
Bus	−.15 (–4.89)	−.103 (–3.11)
Heterogeneity around the mean
Travel cost: income	−.129 (–3.51)	−.011 (–4.11)
Waiting time: income	−.48 (–5.01)	−.033 (–4.15)
Age: Income	−.07 (–.98)	−.11 (–1.96)
Car ownership: income	.011 (2.91)	.61 (4.15)
Having child: income	−.1 (–3.16)	−.19 (–4.07)
Purpose: income	.001 (3.01)	.052 (3.11)
Comfort: income		.101 (4.21)
Convenience: income		.112 (3.80)
Safety: income		.51 (10.51)
Flexibility: income		.052 (1.80)
Reliability: income		.35 (9.10)
Satisfaction: income		.089 (4.11)
Model statistics
Log likelihood function	−696.80	−576.53
McFadden pseudo R-squared	.28	.38
AIC	.0165	.0136
Modal choice probability
Car as a driver	.720	.770
Car as a passenger	.204	.211
Train	.049	.020
Bus	.053	.033

Table 6. Forecasting changes in traveller mode choice.

Mode	Base year mode share in %		Predicted changes^a
			Scenario 1 (S1)		Scenario 2 (S2)		Scenario 3 (S3)
	SDRPL	HRPL	SDRPL	HRPL	SDRPL^3	HRPL	SDRPL	HRPL
Car as a driver	72.0	77.0	−.07	.21	−1.00	−.85	−.54	−.32
Car as a passenger	20.4	21.1	.33	.17	.95	.52	.64	.35
Train	4.9	2.0	−.04	.08	−.08	−.01	−.06	.04
Bus	5.3	3.3	−.08	−.04	.51	.48	.22	.22

^a Changes are the differences in the probabilities between changed and unchanged conditions.

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