CUSTOMER SATISFACTION: DYNAMIC EFFECTS OF CONCESSION YEARS

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CUSTOMER SATISFACTION:

DYNAMIC EFFECTS OF

CONCESSION YEARS

Niklas Möttö

S2909367

Supervisor: Dr. Maarten, J, Gijsenberg

Second reader: Prof. Laurens, M, Sloot

Thesis mentor: Dr. Marc Stemerding

In cooperation with: Goudappel Coffeng

JUNE 15, 2020

UNIVERSITY OF GRONINGEN

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Abstract

In their efforts to increase sustainability, governmental agencies have attempted to increase the amount of people going from using private cars to using public transportation. Therefore, it is important to understand what makes people more satisfied with public transportation. In this paper, a random effect panel data model was created to investigate the relationship between changes in customer satisfaction and concession years. This relationship was moderated by a performance-based bonus. The research was conducted with longitudinal data from public transport concessions in the Netherlands. The results show a significant positive effect of first year on the change in customer satisfaction. Furthermore, performance-based bonus proved to have a significant positive effect on its own and as an interaction with the first year. The data was collected from OV-Klantenbarometer archives from 2006 to 2018.

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1 Introduction

Shifting as many people as possible towards using public transport is a common goal for many

governments. To encourage such behavior, it is important to make the usage of public transport as

enjoyable as possible. If the government wants to maximize enjoyment, it must understand what

makes a good service. Once they know the answer, they will be able to purchase a service which

delivers the highest quality with the lowest price (Mouwen, 2015).

Besides issues related to sustainability, public transportation is often operated by private

companies. Shareholders and managers want to know how their companies are performing.

Therefore, there has been a steady demand for reliable ways of measuring performance. As an

answer to this demand, several different key performance indicators have been identified by

scholars. One of the most important identified KPI’s is customer satisfaction (Bhattacharya,

2013; Mouwen, 2015). Due to the importance of customer satisfaction, it has become vital to be

able to manage it and to know what make increases and decreases it. Therefore, I will study the

dynamic effect of specific years of a concession on satisfaction in public transport.

To understand satisfaction, it is important to understand what drives satisfaction. Previous studies

have been able to identify several different significant influencers on satisfaction (Mouwen, 2013;

Gijsenberg, van Heerde & Verhoef, 2015; Mouwen, 2015; Verhoef, Heijnsbroek, & Bosma,

2017). Additionally, more studies indicate that the heterogeneity of consumers results in a

varying mix of explanatory powers which influence customer satisfaction (Eboli & Mazulla,

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satisfaction, it is time to investigate the effect of one fundamental factor, time. How will customer

satisfaction evolve throughout the first years of a concession?

When it comes to literature about concessions in public transport, a popular topic of the last two

decades has been proving the usefulness of competitive tendering versus non-tendering

procedures. As the superiority of competitive tendering has been established and most current

public transport concessions are resulting from a competitive tendering procedure (Mouwen &

Rietveld, 2013; Hensher & Wallis, 2005; Van Velde, Schipholt & Veeneman, 2008), it is time to

take a step further and see how the customer satisfaction is influenced throughout the concession.

The task of providing a high service quality in a considerable volume and simultaneously filling

the strict requirements of the governmental client is not easy, and this research will inspect this

challenge more deeply.

In continuation to the topic of competitive tendering, a possibly important aspect to look at is the

performance-based bonus. In an increasing amount of tendering procedures, other factors than

just price are gaining an increasing amount of attention due to the emergence of customer

oriented service contracts and performance based contracts (Mouwen & Rietveld, 2013; Hensher

& Wallis 2005; Hensher & Houghton, 2004). Such factors include qualitative aspects, such as

requirements regarding the equipment and personnel. Equipment related requirements are

commonly accessibility, capacity, and environmental requirements. The moderating effect of

such additions into concession contracts will be studied in this paper.

Once there is a decent overview of the source of satisfaction, it is necessary to make it

measurable. Some previous papers have been able to cut the overall satisfaction into several

subparts, which have later been used as a basis for customer satisfaction surveys (Eboli &

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satisfaction in transportation (Anderson, Klein-Pearo & Widener, 2008). This paper will serve as

an extension to the research in satisfaction in public transport that has been made in the

mentioned papers.

I will conduct an empirical research using archival data from 2006 to 2019. The area of focus is

on bus, tram, metro or regional train concessions in the Netherlands This study will focus on

Dutch public transport, more specifically on many different concessions happening in different

Dutch regions. The OV-Klantenbarometer, will be the main source of data. This study will

contain all transport modalities available in the survey data, including buses, trains, trams, ferries

and metros.

With this study, I will contribute towards academic fields of transportation and marketing. As it

has been pointed out before, the mixture of transportation and marketing has been lacking in

terms of academic research (Gijsenberg & Verhoef, 2019). This paper is a first step to fill this

existing gap. Additionally, like Gijsenberg and Verhoef, also this paper will add to the lacking

longitudinal sustainability related longitudinal research with time series data (Iyer & Reczek,

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2 Theory

2.1 Practical background

Public transport in the Netherlands is operated mostly by private companies which compete in

tendering procedures to obtain the rights to operate in a certain region for a given amount of

years. The tendering is conducted by the twelve different provinces and in two cases by regional

organizations - the Amsterdam transport region and the metropolitan region of Rotterdam and

The Hague.

As competitive tendering helped to reduce cost of subsidizing by 20% to 30%, another problem

emerged in terms of fears of decreasing quality due to the main target being minimization of

costs. This led to the growth in popularity of performance-based contracts (Hensher & Houghton,

2004; Hensher & Wallis, 2005). A performance-based contract is expected to encourage

companies to do better, which increases customer satisfaction. In the Netherlands, this clause in

the contract is known as a bonus/malus, where malus means a sort of a penalty for not reaching a

certain minimal level of service. Different measures are being used for determining the bonus, for

example, certain customer satisfaction elements or timeliness. As an example, in the

Groningen-Drenthe concession, the perceived friendliness of drivers as an individual factor is worth up to

10,000 euros per year (OV Bureau Groningen Drenthe, 2017). The province/municipality might

also use other methods, such as mystery shoppers, to determine whether bonus requirements are

being met. Malus requirements are usually quite loose or non-existent, for example, customer

satisfaction level of less than 5 is a quite common line for malus, but this is in practice

unrealistically low, as the lowest scores are usually around 7, except for price which is regularly a

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being assigned to solely supervise the fulfillment of bonus requirements, and companies reward

their employees for achieving the bonus. This indicates a considerable importance of the

bonus/malus system.

The business model is partially consumer (henceforth: B2C), partially

business-to-business (henceforth: B2B), and partially business-to-business-to-government (henceforth: B2G). Ticket sales

go to the operator. This creates an interesting setting for research focused on service. As

tendering procedures are highly competitive, the winning operator will end up doing it for such a

price which will not be profitable without a sufficient amount of ticket sales. Additionally, the

operator does not get to set the price, which is set by the public transport officials. This requires a

different kind of mindset than the traditional setting where ticket sales go to the client and the

operator merely focuses on minimizing cost. This setting requires finding a balance where cost

minimizing is still a highly important factor but there is potential in attracting as many passengers

as possible. As it has been discussed above, the concession operator gets to have a monopolistic

position in the market for the coming few years, releasing pressure from offering the highest

quality. However, as it has been discussed by Bhattacharya (2013), even in a oligopolistic

situation, it is important to put in effort to increase customer satisfaction as a happy customer is

more profitable than a customer who simply has no other choice.

2.2 Existing research and hypothesis

2.2.1 Customer satisfaction

As Eboli and Mazzulla (2011), conclude in their paper, it is ultimately the customers who give

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important factor when estimating the performance of the company. The relevance of satisfaction

as an important KPI has been established in terms of relationship to sales (Fornell, Rust, &

Dekimpe, 2010), determining customer behaviour (Mouwen, 2015), and as a frequently used

metric to satisfy the requirements of the governmental client (Verhoef, Heijnsbroek & Bosma,

2017). Even though the importance of satisfaction might be sometimes overstated, creating a

satisfaction trap (Reichheld, 1996), there is enough evidence for the importance of satisfaction.

Previous literature suggests an increase in customer satisfaction when a new concession starts

(Mouwen & Rietveld, 2013). However, that research had its main focus on the comparison

between competitive and non-competitive contracting procedures, neglecting the effect of other

important matters, such as a performance-based bonus or delayed effect. Interestingly, the same

study suggests that a new operator has a negative effect on satisfaction. This is particularly

interesting because a later research by Mouwen (2015), shows that new equipment has a

significant positive influence on customer satisfaction in some of the major segments in public

transportation (elderly and people from dense urban areas). Having a new operator would mean

even more new equipment so the positive effect should be even higher. Unfortunately, in the

dataset which was used for this research, there were not enough concessions with a new operator

to have any significant results.

2.2.2 A monopolistic market

There are some rather black and white competitive elements in public concessions. As an

example, when an operator operates in a certain region, it is not threatened by any competition,

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a transport provided can be regarded to be in a monopolistic position with its focus on

minimizing cost and fulfilling the contractual requirements. Customers have little possibility to

change companies, with the only competing option being a private car. This means that many

people do not have an alternative. Based on this, the natural marketing strategy for such operators

is defensive value recovery (Dube & Maute, 1998). Value recovery is focused on actions such as

service guarantees and complaint management. In some regions, there is low incentive to attract

new travellers as the capacities are already pushed to the limits and the real financial incentive to

keep the customer satisfaction on a high level is the performance based bonus (Hensher &

Houghton, 2004; Hensher & Wallis, 2005). However, the idea of oligopolistic companies being

able to hide behind high switching costs in stationary markets has been challenged in several

studies (Biglaiser, Cremer & Dobos, 2013; Jones, Mothersbaugh & Beatty, 2000; Shapiro &

Varian, 1998). Even if the customer was stripped of alternatives, the company would have an

incentive to keep the travelers satisfied due to, for example, lessened costs due to fewer

complaints (Bhattacharya, Morgan, & Rego, 2016).

2.2.3 Limited budget

Due to competitive tendering procedures, the operation itself is mostly executed with a limited

budget. In terms of customer satisfaction, this is an important aspect as budget restraints could

influence the way how customer satisfaction is managed. When an operator is dealing with a

limited budget, there are some studies which indicate opposing directions of action in order to

increase profitability. Gijsenberg and Verhoef (2019) were able to discover a significant positive

effect of advertising and promotions on travelled distance. Additionally, Rust, Moorman, and

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cost reduction or dual emphasis tend to be more successful. However, Steenkamp and Fang

(2011), argue in their research on business cycles that focus on internal process when dealing

with financial limitations is more effective than focus on advertisement. This financial limitation

is not the same as the one coming from working with a maximized efficiency, but it has some of

the same principles. Given that the study conducted by Gijsenberg and Verhoef is specifically

focused on public transport, and it is 8 years more recent, it does have a higher value in relation to

this research. It is still important to acknowledge existing schools of thought. Additionally, a

study on budget hotels shows that value for money and core products continues to play a critical

role on satisfaction even in a business with high efficiency (Rahimi & Kozak, 2017). Based on

this, whatever the approach is, the operator must first secure the fundamental service.

2.2.4 Delayed customer satisfaction

Due to a possibly “undeserved” spike in customer satisfaction in the beginning of a concession, it is important to measure the delayed effects on customer satisfaction for the following couple of

years. Previous research shows that the longer the relationship between a customer and a

company, the less variance there will be in the satisfaction of that certain customer (Bolton,

1998).. This should indicate reduced fluctuation in satisfaction after the first year of the

concession, but not the direction of the effect. Additionally, a study from Gijsenberg, van Heerde

and Verhoef (2015), indicates a significant effect caused by past satisfaction. Other studies have

been able to show how active customer relationship management has significantly improved

customer satisfaction over time (Mithas, Krishnan & Fornell, 2005; Hassan, Nawaz, Lashari, &

Zafar, 2015). In the field of public transportation, Gijsenberg and Verhoef (2019), noticed a

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capacity issues (Anderson, Fornell, & Lehmann, 1994; Rego, Morgan, & Fornell, 2013).

Combining the findings from Bolton (1998), and Gijsenberg & Verhoef (2019), should give a

reason to expect at least a decrease in the growth of satisfaction after the first concession year.

By observing the summarized customer satisfaction results of the OV-Klantenbarometer from

previous years, a steady increase in overall satisfaction score is visible, with only price remaining

rather constant. A possible reason for this could be found in discussions held with some people

who work in the industry. What they have suggested is that companies put higher emphasis on

those elements which score low in customer surveys while high scoring elements remain stable,

which slowly increases their overall score. This type of a practice is not uncommon in the

professional environment across industries. Such a method is called management by exception

(Bittel, 1964). As the year in the reference shows, management by exception has been around for

more than 50 years already and is still regarded as a popular and effective way of quality

management. According to management by exception, management puts higher emphasis on

things that are varying most significantly from the planned course of action. Management by

exception would be in line with the assumption that customer satisfaction becomes more stable

after the initial shock from the first year.

2.2.5 Pricing cannot be used

An important marketing action which must be addressed is pricing. According to existing

research, pricing is an important tool when increasing customer satisfaction (Bhattacharya, 2013;

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monopoly pricing. In the Netherlands, public transport pricing is conducted by

Governmental authorities. This makes sense as the monopolistic pricing of necessary public

goods has been proven to be highly problematic (Havighurst & Richman, 2011). Independent

pricing would also create tremendous friction in the national public transport network as a whole

as intercity travelling would become more unpredictable.

2.2.6 Performance-based contract in a moderating role

Even though a public transport customer is faced with a great switching cost, it does not liberate

the operator from focusing on quality of the service. As it was pointed out by Bhattacharya

(2013), a customer who remains loyal for positive reasons is more profitable than a customer who

stays loyal for negative reasons, meaning that satisfaction is an important factor for the business

even in a monopolistic situation. As for the alternatives for the customer, it is a decision between

a few transport modalities. People who do not own a private car, might be left with no alternative

choice. Therefore, the role of the government as the supervisor is significantly important. To

reduce the risk of operators cutting costs at the expense of the overall travel experience,

governmental agencies have started to take a larger role in co-creating higher quality with the

operators (Verhoef, Heijnsbroek & Bosma, 2017). The most common form of governmental

interference in the service quality is a performance based contract (Hensher & Wallis 2005;

Hensher & Houghton, 2004) As performance based contracts have become increasingly common

they have also become rather specific, for example, in the Groningen-Drenthe concession, the

perceived friendliness of drivers as an individual factor is worth up to 10,000 euros per year (OV

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to improve the quality of the service, I expect that it will have a positive moderating effect

between the concession years and customer satisfaction.

2.2.7 Control Variables

Travel mode is one of the control variables of this study. The public transport modalities in this

research contain traditional core services of public transportation. These core services are bus,

train, subway, tram, and ferry. As bus is the most frequent mode of transport in the dataset, it will

be the one compared to the rest. Modality can have a significant effect on the results as it has

been confirmed before that in a multi-channel environment, customer perceptions can change

significantly even though the core service remains largely the same (Falk, Schepers,

Hammerschmidt, Bauer, 2007).

Another control variable is motivation to travel (commuters and students vs others). The reasons

to pick commuters and students as one group versus others is that they are mostly not paying for

their own tickets and they use public transport the most. As it was concluded before, there is a

negative relationship between travelled distance and satisfaction (Gijsenberg & Verhoef, 2019).

This might indicate a possible significant effect in the results of this study. As different elements

of the public transport market remain relatively stable throughout the years and the data is

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2.3 Hypotheses

Based on the discussion above, following hypotheses were constructed.

H1a: First year of the concession positively influences the change in customer satisfaction. H1b: The growth of customer satisfaction after the first year is an empirical question. H2: Performance-based bonus positively moderates the relationship between concession years and customer satisfaction.

Figure 1. Conceptual model.

3 Data

Most of the data was collected from the OV-Klantenbarometer archives held by Goudappel

Coffeng. Contract related data, such as performance-based bonus was collected by reading

concession contracts and asking from region representatives by email.

Efforts to quantify the level of satisfaction towards public transport in the Netherlands have been

taken in many forms, one of them being the OV-Klantenbarometer. OV-Klantenbarometer is a

national customer survey which measures several different aspects of customer satisfaction, such

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annually since 2006 in the same set-up. This survey will serve as an important source of

information for this research.

The surveys were distributed in varying public transport vehicles on an annual basis until 2017,

after which it became a quarterly procedure. People who received the survey were asked to rate

their experience on a scale from 1 to 10. These questions are related to a wide array of topics, all

believed to be an important factor in overall satisfaction. For this research, the most important

question is the one asking about overall satisfaction, since this gives the overview of how the

traveler felt about the trip.

The survey consists of 33 questions which are used to measure 25 different parameters. The

measurable parameters are provided in the appendix. The survey provides plenty of data on

individual level. However, the data will be aggregated on a yearly level. Analysis on individual

level is simply unnecessarily much for the sake of this research especially considering other

variables such as performance-based bonus, which does not seem logical for the individual level.

4 Methodology

4.1 Cross-sectional panel data model, fixed, random, and mixed effects

A cross-sectional panel data model is a model where the dataset consists of data from different

periods in different sectors. In this case, different years and different concessions. Panel data

models are approached in one of three ways, fixed effects, random effects, or mixed effects.

The main difference between fixed effects and random effects models is the source of variation.

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variation within a certain sector, in this case, a certain concession. It will not take variation

between different sectors into account, making it stricter than the random effects model and less

generalizable (Hedges & Vevea, 1998). Therefore, a random effects model is seen as more lenient

and more efficient model, which is why it is usually preferred if it can be used. Some researchers

take an even stronger stance, saying that in most research scenarios, random effects provides

everything fixed effects provide and more (Bell, Fairbrother & Jones, 2019). Whether it can be

concluded that a dataset contains random effects, there are some assumptions. One assumption is

that when a dataset represents a larger population, it should be considered to contain random

effects, which in this case would suggest using random effects. To confirm the correct decision, a

Hausman test is conducted to determine whether random effects model can be chosen over a

fixed effects model (Baltagi, Presson & Pirotte, 2003).

Besides random and fixed effects, a mixed effects model will also be tested. A mixed effects

model is a mix between fixed effects and random effects (Bates, Mächler, Bolker, Walker, 2014).

If a mixed effects model is used, there needs to be a decision made about which variables are

generating random effects and which are generating fixed effects. This could make sense as

concession years could be a representor of a larger population, whereas more concession specific

variables, such as modality, could be a fixed effect variable.

Apart from the Hausman test, there does not seem to be a widely accepted consensus about the

selection criterion between these three methods. Therefore, the best thing to do is to create all

three models and see which method performs the best. Previous literature on the topic of

choosing between fixed and random effects shows that the are no great differences and the main

argument for the decision should be what kind of inferences the researcher wants to make

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4.2 Model specification

As the variables of the final model are known, a model can be estimated. Whether it will be a

fixed, random, or mixed effects model, it will be fully pooled so that the parameters of the model

can be used to estimate all concessions. A complementary model with overall satisfaction as the

dependent variable was also produced for a more complete overview of the market.

∆𝑆𝑎𝑡𝑖𝑗= 𝛽0+ 𝛽1𝐹𝑖𝑟𝑠𝑡+ 𝛽2𝑆𝑒𝑐𝑜𝑛𝑑+ 𝛽3𝑇ℎ𝑖𝑟𝑑+ 𝛽4𝑓𝐹𝑖𝑟𝑠𝑡∗𝐵𝑜𝑛+ 𝛽5𝑆𝑒𝑐𝑜𝑛𝑑∗𝐵𝑜𝑛+ 𝛽6𝑇ℎ𝑖𝑟𝑑∗𝐵𝑜𝑛+ 𝛽7𝐵𝑜𝑛+ 𝛽8𝐵𝑢𝑠+ 𝛽9𝐶𝑜𝑚+ 𝜀

Where:

∆𝑆𝑎𝑡𝑖𝑗 = Change in satisfaction between t and t-1 during a certain year in a certain concession 𝛽0 = Intercept

𝛽1𝐹𝑖𝑟𝑠𝑡 = First year of the concession 𝛽2𝑆𝑒𝑐𝑜𝑛𝑑 = Second year of the concession 𝛽3𝑇ℎ𝑖𝑟𝑑 = Third year of the concession

𝛽4𝑓𝐹𝑖𝑟𝑠𝑡∗𝐵𝑜𝑛 = Interaction between first year and performance bonus 𝛽5𝑆𝑒𝑐𝑜𝑛𝑑∗𝐵𝑜𝑛 = Interaction between second year and performance bonus 𝛽6𝑇ℎ𝑖𝑟𝑑∗𝐵𝑜𝑛 = Interaction between third year and performance bonus 𝛽7𝐵𝑜𝑛 = Performance bonus

𝛽8𝐵𝑢𝑠 = Bus

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While the model above is the basic version describing the relationship, there are some aspects to

take into consideration depending on whether it is a fixed or random effects model. If it is a

random effects model, the following equation shows what happens within the statistical software

while creating the estimations. The following discussion is largely inspired by the work from

Bell, Fairbrother, and Jones (2019).

The time varying variable receives two parameters, one for within effects, 𝛽1𝑊, and one for

between effects, 𝛽2𝐵. The separation between the within and between effects can be left out

when the assumption of them being equal holds. The time invariant variable receives a parameter

for between effects (𝛽3). In the case of this research, this model would look longer due to a

higher number of variables, but the idea remains the same. The random effects are represented as

𝑣_𝑖0 and 𝑣_𝑖1. The former is attached to the intercept while the latter is attached to the within slope.

This is done to account for heterogeneity in the within effect of 𝑥𝑖𝑡 across concessions.

Random effects formula

𝑦

_{𝑖𝑡= 𝜇+ 𝛽1𝑊(𝑥}

𝑖𝑡− 𝑥_̅𝑖) + 𝛽2𝐵𝑥̅𝑖 + 𝛽3𝑧𝑖+𝑣𝑖0 + 𝑣𝑖1 (𝑥𝑖𝑡− 𝑥_̅𝑖)+ 𝜀𝑖𝑡0

As for fixed effects, again giving credit to Bell, Fairbrother, and Jones (2019), the between effects

would not be considered as a fixed effects model is only interested in the within effects. Like the

random effects model, also the fixed effects model makes use of mean-centering in the within

effects, making the estimations based on deviations of the mean over time, which makes it less

useful compared to random effects as it cannot describe relationships with independent variables

which do not change over time. Therefore, using fixed effects leads to a loss of important

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Fixed effects formula

(𝑦

_𝑖𝑡−

𝑦̅

_𝑖

) = 𝛽1(𝑥

_{𝑖𝑡− 𝑥̅}_𝑖

) + (𝜖

_𝑖𝑡

)

4.3 Measurement and definition of the variables

4.3.1 Customer satisfaction

Yearly level aggregation of the self-reported overall satisfaction scores. The average score of all

respondents in a certain concession area in a certain year. Customer satisfaction ratings are taken

from the OV-Klantenbarometer. The OV-Klantenbarometer report contains several different

aspects of customer satisfaction, one of them being the overall satisfaction of the traveler. In this

research, the overall satisfaction will be the one that represents the variable. In some concession

contracts it can be seen from the performance-based contract measures that it is other factors than

the pure overall satisfaction which is seen to be more important. However, overall satisfaction is

the only one which can be reasonably argued above all else, without the need of evaluating the

parameter of choice against all other parameters.

The variable will be computed to represent the difference between the current and the previous

year, in other words, creating a first difference variable. The first difference variable is used

because it represents better whether there is an increase in satisfaction in a certain year.

Hypotheses 1a and 1b are focused on the growth of customer satisfaction. These questions could

not be answered with overall satisfaction as the dependent variable as it does not take into

account the growth or decline in relation to the previous year. Therefore, first difference gives a

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As the dependent variable will be the increase in customer satisfaction in a specific year of the

concession, I will not be able to measure 2006 or the first years of some concessions which

entered the system during the 2006-2018 period. The loss of one possible year of data is

unfortunate but necessary.

4.3.2 Main independent variable

The independent variable will be the sequential year of a concession. This is a series of dummy

variables which takes the value of 1 for a specific year of a concession. This will allow me to

have an estimate for several individual years of the concession. In this research, I will focus on

the first three years.

4.3.3 Moderator

The moderator, performance-based contract, will be a dummy variable. The dummy will

represent the existence of a performance-based clause in the contract. This data will be collected

from differing sources, for example, going through concession contracts and emailing regional

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Several different control variables have been identified. As there are different transport modalities

present, it is important to control for that. Therefore, I will include a dummy variable to indicate

whether it is a bus or not. The reason to single out only bus is that it is the most common

modality in the dataset, especially because the national train network was only introduced rather

recently to the OV-Klantenbarometer.

Motivation for travelling will represent the percentage of commuters and students in the dataset.

Therefore, the value will be between 0 and 1.

4.4 Statistical validity

Besides the models, the dataset will be tested for many issues regarding statistical validity, such

as heteroscedasticity, multicollinearity, and non-normality. Problems arising from these issues

might require some changes to what has been written above.

4.5 Preliminary statistics

Table 1 shows the descriptive statistics and correlations between variables. None of the

correlations are too high so multicollinearity is not an issue. The dataset contains 1051

observations from a total of 106 different concessions. This table contains two version of the

dependent variable. First is satisfaction first difference and the second is satisfaction.

The dependent variable, satisfaction first difference, was tested for normality with a Shapiro-Wilk

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variable can be assumed to have a normal distribution. Therefore, it will not be necessary to

adjust for nonnormality.

A simple model without interactions was created to run a Breusch-Pagan test for

heteroscedasticity, which was significant with a test statistic of BP=27.45 and a p-value of < 0.00.

This means that there are no issues with heteroscedasticity in the dataset. The variance of the

dependent variable remains stable throughout the dataset.

Table 1. Descriptive statistics.

Figures 2 and 3 show how satisfaction evolves throughout the years. In figure 2, the plot shows

that overall average satisfaction increases with time. In figure 3, the average satisfaction in

relation to the last year of the previous concession is plotted. Figure 3 shows the effect of first

Mean Std. Deviation Satisfaction, First Difference Satisfaction First Year Second Year Third

Year Bus Commuters

Performance Bonus Satisfaction, First Difference 0.05 0.19 1 Satisfaction 7.43 0.31 0.320** 1 First Year 0.1 0.30 0.124** -0.041 1 Second Year 0.11 0.31 0.012 -0.013 -0.11** 1 Third Year 0.1 0.30 0.002 -0.017 -0.11** -.115** 1 Bus 0.6 0.49 -0.046 0.107** 0.045 0.044 .075* 1 Commuters 0.38 0.11 -0.023 -0.024 0.017 0.036 -0.042 0.019 1 Performance Bonus 0.42 0.49 0.068 .174** .102** .083** 0.054 .180** 0.048 1

** Correlation is significant at the 0.01 level (2-tailed).

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year having the strongest increase in satisfaction while it keeps increasing after the beginning.

These figures are important to justify the usage of a first difference variable as the dependent

variable to control for the trend of increasing satisfaction.

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Figure 3.Satisfaction during concession years in relation to last year of previous concession

5 Results

5.1 Method Selection

The first step was to decide upon the most suitable model. The three options are fixed effects,

random effects and mixed effects models. The mixed effects model was not suitable for this

dataset, due to singularity issues arising from the complexity of the model. Therefore, the

statistical software was not able to estimate the confidence intervals and reported some of the

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Both fixed effects and random effects could be created. Theoretical reasoning suggested that

random effects would be more suitable as this dataset is an aggregated compilation from a larger

population. Furthermore, a Hausman test was conducted to have statistical evidence for the

decision between the two. The Hausman test resulted in a p-value of 0.78, which means that

random effects could be used. The decision to use random effects is also supported by a higher

adjusted r-squared, which the statistical software reported as a negative value in a fixed effects

model.

5.2 Testing hypotheses

To test the hypotheses, six different random effect models were created. Models 1, 2, and 3 are

the main models as they represent the first difference satisfaction. These models are found in

table 2. Models 4, 5, and 6 are complementary models with overall satisfaction being the

independent variable. The latter models perform better in terms of a higher r squared, meaning

that changes in the dependent variable are more explained by the independent variables than in

table 2. However, models 4, 5, and 6 do not exactly measure what needs to be measured, which is

the change in satisfaction in relation to previous year, giving it a supporting role. First and fourth

models are without interaction effects. Second and fifth are with an interaction between first year

and performance bonus. Third and sixth models with an interaction between first, second, and

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24 5.2.1 Models 1, 2, and 3

Table 2. Output of random effects models, Satisfaction First Difference as dependent variable.

model 1 model 2 model 3

Intercept 0.061** 0.064** 0.064**

First Year 0.081*** 0.011 0.013

Second Year 0.016 0.016 -0.001

Third Year 0.013 0.013 0.042

Interaction First Year and Bonus 0.113* 0.109*

Interaction Second Year and Bonus 0.031

Interaction Third Year and Bonus -0.061

Bus -0.021 -0.021 -0.021 Commuters -0.053 -0.044 -0.05 Performance Bonus 0.022* 0.012 0.016 R-Squared 0.023 0.030 0.034 Adjusted R-Squared 0.015 0.021 0.023 Chi Squared 18.428 24.268 27.307 P-value 0.005 0.001 0.001 Signif. codes: 0.01 *** 0.05 ** 0.1 *

First year of the concession has a significant positive effect on the dependent variable, indicating

that customer satisfaction does increase during the first year of the concession. When the

interaction effect is introduced, the first year variable loses its significance and only has a

significant influence when it interacts with performance based bonus. The same thing happens to

the performance bonus variable. However, the combined effect of first year and performance

bonus is considerably higher than any other parameter, explaining most of the effect when the

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Second and third year do not have a significant effect on the change in satisfaction. The

parameters are considerably closer to 0 than first year, which gives a slight indication of a

slowing increase in satisfaction, but this cannot be concluded since the significance is on a low

level. Furthermore, the p-value for second year increases from 0.4 to 0.9 when the interaction

effect is introduced. Both control variables, bus and commuters, are insignificant. Both have a

negative sign, which in the case of commuters is against the reasoning of why it was introduced

to the model.

Even though all the models are overall significant, their explanatory power is low. The adjusted

R-squared implies that the independent variables only explain few percentages of the dependent

variable. This means that interpretations should be taken with some precaution, but they will

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26 5.2.2 Models 4, 5, and 6

Table 3. Random effects with Overall satisfaction as dependent variable.

Model 4 Model 5 Model 6

Intercept 7.11*** 7.12*** 7.12***

First Year -0.08*** -0.08** -0.08**

Second Year -0.05** -0.05** -0.07**

Third Year -0.04* -0.04* -0.05

Interaction First Year and Bonus -0.00 -0.00

Interaction Second Year and Bonus 0.04

Interaction Third Year and Bonus -0.04

Bus 0.09** 0.09** 0.09** Commuters 0.33*** 0.33*** 0.33*** Performance Bonus 0.27*** 0.27*** 0.27*** R-Squared 0.848 0.848 0.874 Adjusted R-Squared 0.847 0.847 0.845 Chi Squared 4902.79 4882.03 4844.02 P-value <0.00 <0.00 <0.00 Significance codes: 0.01***, 0.05**, 0.1*

As mentioned earlier, due to the weak performance of models 1, 2, and 3, models 4, 5, and 6 were

created. In table 3, first year dummy has a significant negative effect on overall satisfaction. This

does indicate a dip in satisfaction when entering a new concession. However, the following years

show that the negative parameter climbs closer towards 0, indicating an instant growth in

satisfaction after the dip. Furthermore, the difference between first and second year is greater than

the difference between second and third year, indicating that the growth does in fact start slowing

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The combined inspection of table 2 and table 3 shows that the importance of performance bonus

is significant. In both tables, performance bonus has a significant positive influence, making it the

only variable besides the intercept which has the same sign in both tables. Table 3 was not able to

support the interaction effect which was seen in table 2. This could be because the year dummies

became negative in table 3, giving them the opposite effect than performance bonus. Therefore, it

seems that the satisfaction level as such does not depend on a performance bonus but the speed of

the increase after the dip in satisfaction does strongly depend on the bonus being present in the

contract.

Bus and Commuters variables became significant in table 3 with a positive sign. Combining this

with the knowledge that they have no significant contribution towards the change in satisfaction,

the most likely reason is that concessions which already had a high satisfaction level were

operated by buses and had a high level of commuters and students on board.

5.3 Robustness check

The VIF scores resulted in all values being below 3. The VIF test was conducted for model 3 as it

included all variables. This means that there are no issues with multicollinearity. The same

models were ran using a fixed effects method. This resulted in similar signs of individual

variables, except for “Year Two”. This variable has a negative sign in random effects models when interaction is present but stays positive in three first models in fixed effects. For models 4,

5, and 6, fixed effects gave similar signs but with a lower significance. Furthermore, the

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6 Discussion

6.1 Overview

Pollution and congestion are some of the major issues facing most countries in the coming future.

Therefore, it is vitally important to accelerate the switch from private vehicles to public

transportation. For this to be possible, a deeper understanding is required of what makes people

prefer public transportation. Previous findings show that satisfaction of public transportation

users has been an important indicator for guiding people to leave their private cars. In this paper,

random effects panel data models were created to further investigate the dynamic effects of

concession years on satisfaction. The resulting model show that customer satisfaction increases

most rapidly in the very beginning of a new concession. Furthermore, when the concession makes

use of a performance-based bonus, the slope of the increase in satisfaction becomes even steeper.

In the following is a more detailed discussion about the results and implications.

6.2 Analysis of results

Hypotheses 1a is confirmed while there is some strong indication about the truthfulness of 1b.

Performance-based bonus seems to help boost satisfaction, especially in the beginning. This

seems to be in line with previous research (Hensher & Houghton 2004; Hensher & Wallis,

2005). This might not come as news to transport officials since performance bonuses have been

increasing in popularity in public transport concessions in previous years. This could explain the

relationship between performance bonus and overall satisfaction as later years have a higher rate

of satisfaction, but it would not explain the positive significant influence towards the first

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First year does seem to boost customer satisfaction the most. However, entering a new concession

does seem to cause a slight dip before it starts increasing. This is partially in line with research

conducted by Mouwen (2015), who did not report a dip in customer satisfaction when entering a

new concession but also concluded that the beginning of a concession does increase satisfaction.

This additional boost to satisfaction could come from updated equipment, but it does not explain

why satisfaction continues growing after the beginning. Despite constant growth, there is a visible

decrease in the intensity of the growth after the first year. This is in line with previous research

from Bolton (1998), showing how the variance in customer satisfaction decreases the longer the

customer relationship lasts. In the context of public transport, it could be argued that the

beginning of a new concession is not a beginning of a customer relationship as the same person

has already been using the same service before the concession started. However, as the new

concession comes with updated equipment and sometimes with an updated scheduling scheme, it

can be seen as a partial rebirth for the customer relationship. This would especially be the case if

the operator is not the same as in the previous concession.

As the two major differences for customers at the start of a new concession is new equipment

(sometimes new operator), and sometimes a new scheduling scheme, the underlying reasons

could be searched from there. First, customers get used to these changes, which is why their

satisfaction will not keep on rising at the same pace. Same can be also said about employees who

might also experience a period of more energetic approach towards customer service as they are

operating with new equipment. Previous research shows a significant link between employee

wellbeing and customer satisfaction (Bulgarella, 2005). Working with updated equipment could

be a part of the reason. A practical explanation to what might influence the continued increase in

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punctuality and tidiness. These functions can always be improved while they are universally

common reasons for complaints in the public transport industry.

Commuters seem to have a weak effect on growth of satisfaction while it simultaneously seems to

have a strong positive effect on overall satisfaction. Same goes for buses. This would indicate that

concessions where satisfaction was already on a high level before the start of measurements had a

high degree of commuters and/or operated by buses. Therefore, it would be wrong to say that

commuters are more dissatisfied than others or buses are a less preferred modality. The positive

relationship between commuters and satisfaction provides some interesting insights. First,

commuters and students travel in general at least 10 times per week, accumulating a substantial

number of travelled kilometers. This could be a conflicting result to what Gijsenberg and Verhoef

(2019), noticed about the negative effect of travelled distance to customer satisfaction. However,

a unique element about commuters and students is that in most cases they travel with a discount

or their trips are paid by the employer. Combining this to the fact that price is, by a substantial

margin, the element which consistently has the lowest satisfaction score, bringing down the

overall satisfaction, people who do not need to pay the full price may report a higher overall

satisfaction.

6.3 Limitations and future research

This research only contains concession from the Netherlands. Therefore, these results might not

be fully generalizable globally. This might be especially the case in countries where the public

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Performance based bonuses were measured as a dummy variable based on the sole existence of

the bonus. As there are different types of performance bonuses which might differ in the strength

of effect, some important insights might be lost.

OV-Klantenbarometer surveys were handed out in differing external conditions. Such variance in

external conditions can be seen in exceptionally busy days or a day when there was a unusual

problem, for example, air conditioning of a bus was broken. In such cases, much of the data does

not represent the overall situation in that concession. The size of the sample should control for

that, but full control of external conditions is not possible.

This research has raised some important questions which should be further investigated. The first

question is the following: What drives the continuous growth of customer satisfaction? Transport

providers in the Netherlands seem to have found an answer to a question which is regularly in

minds of marketeers in companies across most industries. They have managed to increase

customer satisfaction annually for over a decade. Much of the reasoning in this paper is focused

on the first years of the concession, which was rightfully expected to have the strongest effect,

but these explanations are not valid to explain how customer satisfaction keeps on growing after

the very beginning of the concession. Along with this topic, future research could investigate the

relationship between employee satisfaction and customer satisfaction in public transport and how

employee satisfaction could be most efficiently increased.

What are the most important aspects of a performance bonus in the context of public transport?

This information is important for transport officials who oversee providing quality transportation

efficiently. It is important to understand which factors can be influenced by the transportation

providers and which cannot. As an example, in the case of Dutch concessions, some performance

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Klantenbarometer, while some bonuses are about punctuality. Some of these

OV-Klantenbarometer elements are out of control for transport providers, such as chance to find a

seat. If the transport provider receives a penalty because of such a factor, it can have a

demotivating effect and can be generally perceived as unfair. Therefore, this is an important

question for future research.

6.4 Managerial Implications

Since performance-based bonuses are significantly important, managers should understand them

well. By having more extensive data on performance-based bonuses from previous concessions,

transportation companies could have an overview on which bonuses are more likely attainable

and which conditions require a higher pay-out. Furthermore, as it was mentioned earlier, transport

providers who operate in the Netherlands seem to have a process in which they annually check

the weakest performing elements of their score from the OV-Klantenbarometer survey and focus

on those elements more in the coming year. This could be an important part of the reason why

satisfaction keeps marginally rising in general. Managers in other markets could perhaps

investigate the possibility of adding elements of management by exception into their own

processes.

For policy makers, it seems that a performance-based bonus acts as a powerful tool to increase

customer satisfaction. Therefore, it is worth giving the bonus clause more attention during a

tendering procedure than some policy makers might have done until now. Some policy makers

could perhaps even consider paying higher bonuses to see if it boosts satisfaction even more.

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reaching the upper limits in satisfaction in about a decade. This will call for modifications in

bonus conditions as increases in satisfaction become increasingly difficult and penalizing minor

decreases would become rather unfair.

7 Conclusion

In conclusion, in a continuously urbanizing environment, public transportation is vitally

important to create a sustainable future where pollution and congestions are minimized. To

maximize the benefits of public transport, governments need to find means to maximize the usage

of public transport while the need for private vehicles lessens. For this to happen, people must be

satisfied with the service.

This study contributed towards an increased understanding of which factors drive customer

satisfaction and how it can be increased. The results show that satisfaction increases more rapidly

during the beginning of the concession, while it continues increasing throughout the concession.

Furthermore, performance-based bonus has a significant positive effect on satisfaction, which

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Appendix

Ov-Klantenbarometer elements

- Chance of finding a seat

- station at beginning end of the trip

- punctuality

- ease of getting in

- friendliness of personnel

- driving style of the chauffeur

- quickness of the trip

- cleanliness

- noice

- atmosphere

- design of the vehicle

- information at the stop/station

- travel information in the vehicle

- ease of purchasing a ticket or loading saldo

- price

- other passengers

- level of stress/relaxation

- frequency

- transfer time

- information about delays

- ease of use of OV-chip card

- safety overall

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39 - safety at the station

- overall score of the trip

Model 1

estimate Std. Error z-value P-value (>|z|)

Intercept 0.061926 0.025673 2.4121 0,015861* First Year 0.081448 0.023485 3.4681 0,0005241*** Second Year 0.015631 0.021309 0.7336 0.4632208 Third Year 0.012897 0.021172 0.6092 0.5424146 Bus -0.020722 0.014656 -1.4139 0.1573926 Commuters -0.052607 0.058559 -0.8984 0.3689929 PerBon 0.022894 0.013567 1.6874 0.0915187

Total Sum of Squares 28.344

Residual Sum of

Squares: 27.692

R-Squared 0.023024 Adj. R-Squared: 0.015528

Chi Squared 18.4287 DF 6

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Model 2

Intercept 0.064138 0.025613 2.5042 0,01227*

First Year 0.011014 0.037608 0.2929 0.76962

Second Year 0.016545 0.021248 0.7787 0.43617

Third Year 0.013308 0.021109 0.6304 0.5284

Interaction First Year And

Bonus 0.113787 0.047546 2.3932 0,0167*

Bus -0.021758 0.014618 -1.4884 0.13664

Commuters -0.044455 0.058482 -0.7601 0.44717

Performance Bonus 0.012808 0.014168 0.904 0.36599

Total Sum of Squares: 28.344

Residual Sum of

Squares: 27.49

R-Squared: 0.030136 Adj. R-Squared: 0.021443

Chisq: 24.2676 DF 7

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Model 3

Intercept 0.064368 0.025793 2.4956 0,01257*

First Year 0.013008 0.037781 0.3443 0.73062

Second Year -0.001181 0.031289 -0.0377 0.96989

Third Year 0.042674 0.029229 1.46 0.14429

Interaction First Year And

Bonus 0.109511 0.048263 2.2691 0,02327*

Interaction Second Year and

Bonus 0.031355 0.042596 0.7361 0.46166

Interaction Third Year and

Bonus -0.061056 0.042289 -1.4438 0.1488

Bus -0.02154 0.01461 -1.4743 0.14039

Commuters -0.050368 0.058562 -0.8601 0.38975

Performance Bonus 0.016888 0.016449 1.0267 0.30456

Total Sum of Squares: 28.344

Residual Sum of

Squares: 27.384 R-Squared: 0.033867 Adj. R-Squared: 0.022705

Chisq: 27.3073 DF 9

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CUSTOMER SATISFACTION: DYNAMIC EFFECTS OF CONCESSION YEARS