The effects of image and reputation on used car dealers in Singapore

Abstract

Automobiles have proven to be an indisputable means for effective traveling around the world. In a small country such as  Singapore, an automobile may go beyond the simple means of traveling and represent a certain status due to its high cost of ownership. With many factors involved, a car depreciates rapidly in Singapore and therefore many used car dealers attempted to sell a car in the fastest possible way. That would have included unscrupulous methods and while more are getting caught, the entire reputation of the used car industry has gone bad. This study aims to discover what are the reasons they would buy a used car and what can a dealer do to improve their image and reputation. An online self-administered survey is conducted to acquire primary data for this research with 193 respondents. The surveyed group consists of people from different age groups, education levels and working backgrounds to better gauge the opinion of the overall population. The hypotheses formulated in this research focuses on the 4 major factors namely the financial factors, company factors, media factors and consumer factors. They are being tested and analysed through IBM SPSS statistical tools for reliability and correlation. Based on the analysis in chapter 4, only 50% of the 8 hypotheses are accepted and another 50% are being rejected. The hypotheses are being analysed using Pearson correlation coefficient. Those which are accepted are being ranked using T-test from the most significant to the least significant namely consignment, large listing, listed dealer and highly exposed. The summary will draw the conclusion for all 4 research objectives as well as to discuss the recommendations for the used car dealers that were being drawn from the same survey. The survey questionnaires are being attached in the appendix.

Chapter 1. Introduction

1.1 Topic Introduction

It is since the beginning of time where consumers prefer to buy from a credible and trustworthy source (De Pelsmacker, et al., 2005). Cars, also known as automobiles, are an essential mode of transportation in larger countries (Schaeffer & Sclar, 2002) but are more of a luxury in Singapore as our transport system is one of the best in the world (May, 2004). With a small land mass and tight policies to control the quota of cars, Singapore’s new car is priced the highest among the world (Koh & Lee, 1994) and people tend to be more wary when it comes to buying a high commodity item (Di Muro & Noseworthy, 2013). Purchasing a new car from an agent or parallel importer (which will be explained later in this chapter) is often perceived as a safer choice and boosts the owner’s pride (Zhao & Zhao, 2015) as compared to buying used cars. Car pride has become a symbol of status in Asia (Zhao & Zhao, 2020) where it is no longer just as simplistic as a mode of transport any longer. Aside from pride, the bigger reason why consumers prefer new cars is because the used car dealers have been known for tampering mileage (Braithwaite, 1979), misrepresenting the car or not fulfilling the sales. There are many reported cases where the used car dealers disappeared after collecting the car deposit (The Straits Times, 2016). As such, this research is going to find out to what extent a better brand equity can improve the perception from the public and how do they define a better used car dealer’s brand.

1.2 Dissertation Overview

This report will start with an introduction to explain the various automobile terms and car dealer issues that other parts of this report will be discussing. The literature review will further identify the implications of the issues and discuss the various models that will be used in this report. 3rd chapter will then discuss the research methodology and its limitations, selecting a method to gather the data and critique on how effective this method of the used car industry is. To turn data into information (Madnick, et al., 2009), the surveyed data will be cleaned, analysed and have regression to be performed. All the results will be duly formatted, displayed and explained. With all the information, it will draw the conclusion on the hypotheses and throw plausible recommendations in the last chapter of this report. All externally gathered data will be attached in the appendix.

1.3 Introduction of Automobile Industry in Singapore

To understand the entire context of this report (Hinds, et al., 1992), it is important to understand what are the various ways a car can be transacted. The many concerns of why transacting a car can be bothersome is due to the many options available in the market and is known as decision fatigue (Pignatiello, et al., 2020). The car industry is very extensive so this research will only analyse the crucial factors that are relevant to the topic area. 

1.3.1 Certificate of Entitlement (COE)

Singapore is the first and only country that has implemented the COE (Meng, et al., 2015). COE is implemented to reduce the ownership of cars and it only lasts for 10 years. Any vehicle owner who wants to keep their vehicle after 10 years will have to renew its COE. Such policy also includes a bided premium and altogether it has caused the automobile price to rise in Singapore.

1.3.2 Lemon Law

Due to many errand used car dealers misrepresenting the vehicle’s actual condition, lemon law was implemented to protect the consumer where the consumer has the rights to claim or return the vehicle if it is not sold as described (Loke, 2014). This law is part of the Consumer Protection (Fair Trading) Act (Loo & Ong, 2016) and it safeguard the consumer in a blanket style besides the car industry. With such policy, many car dealers have favoured the consignment arrangement instead.

1.3.3 Purchasing From Agent

Agent is the direct representative of the car brand where either they are the brand itself or a third party has won the contract to deal with the brand in Singapore. This is the most preferred choice for consumers if pricing is not the factor (Sirdeshmukh, et al., 2002). Direct agents usually can afford better facilities and hence their showroom will be impressive and comprehensive. It is to no surprise the setup and services will lead to more trustworthiness and result in impulse purchase (Hausman, 2000) as compared to other purchase methods.

1.3.4 Purchasing From Parallel Importer

Parallel importers are car traders who are not related to the car manufacturers (Ahmadi & Yang, 2000). The reason why they exist is because they are able to get the same vehicle as sold by a direct agent cheaper and they can import any model that is not sold by the direct agent (Lee & Lim, 2002). The imported cars are not necessarily new cars, as long as it is less than 3 years from its first registration and the car is right-hand drive, it can be brought into Singapore.

1.3.5 Purchasing From Used Car Dealer

Figure 1. Example of an ad from SgCarMart with high car ownership.

Many people cannot afford a new car as car value depreciates highest in the first 2 years (Abeysinghe & Choy, 2007). As such, it is common to purchase used cars and there could be many owners beforehand. Naturally, the more owners will fetch a lower price as generally people do not take care of things especially when they know they are about to sell it (Dierdorff & Rubin, 2007).

1.3.6 Purchasing From Direct Seller

The Internet has enabled how consumers transact with one another without the need of a dealer (Hand, 2015). Consumers can find advertisements online or through referral to make a direct purchase with the seller. The process is very simple without much paperwork and the transfer fee is as low as $25. It would be more tedious when loan is involved or additional insurance underwriting is required. Buying directly from a consumer does not get covered under lemon law as the act only covers business to consumer transaction.

1.3.7 Selling to Dealer

Selling your car to the dealer is the fastest way to get your money especially when you are in a hurry. Car traders are able to apply for floor stock financing (TAN, 2009) from the financial institution to purchase vehicles at a reasonable interest rate as the purchased vehicles can be used as a collateral against the loan. Having said, car dealers will usually press down your vehicle especially when they know you are urgently selling your car (Shim & Von Peter, 2007).

1.3.8 Selling to Consumer

If a seller has the luxury to wait they can post an advertisement through various online means so as to fetch the best price (Olivola & Wang, 2016). They do not have to face the distress selling dilemma as towards the car dealers. It is not an issue to post the car advertisement however there is no pull factor (Riley & Van Doren, 1992) as a layman may not have so many connections or contacts as the car dealers. The seller also may not know what is the best advertisement content to attract customers.

1.3.9 Car Consignment

This method of dealing with cars is a relative new adoption where trust is highly valued. The seller will continue to own the car and viewing is fixed by appointment only where the seller and buyer will be arranged by the consignment party. The problem is the dishonest seller or buyer may circumvent the consignment party for a better price since they get to meet (Forganni & Reed, 2019). There is no easy fix for such an arrangement even when agreement is signed.

1.3.10 Scrapping Your Car

To scrap a car is to get its body value and the paper value that was paid in the beginning when the car is being registered. Cars that are sent to be scrapped can be turned into scrap metal, stripped for parts or exported overseas. It is not necessary to scrap a car only when the COE is due, there are scenarios where a bad accident that totalled the car or a car have major issues that the repair cost is worth it anymore.

1.4 Concerns in the Current Used Car Market

There are many news and reports on errant used car dealers where they abused the trust of a customer and committed fraud or other crimes (TNP, 2018; The Straits Times, 2016). Fraud is a very common practice among human society and it is more obvious in the automobile industry because cars are considered expensive commodities and that motivates the victim to seek redress (Murphy & Dacin, 2011). Another concern is the assessment from the workshop may not be fully thorough as there are many defects that cannot be easily detected (Akuh & Agyeman, 2019). Full diagnosis can be arranged however that would be financially impractical for both the seller and the buyer. An ideal car transaction for the dealer would be lesser touch up and quick sales so to minimise the operation overheads and maximise the capital used. A consumer on the other hand wants more for less (Felix, 2015) and expects the dealer to present them a car in perfect condition. This leads to tempering of mileage (Montag, 2017) and other frauds from the dealer to make a car sellable. To add salt to the wound, lemon law is unable to protect consumers from peer-to-peer vehicle transactions.

1.5 Research Objectives

The primary objective of this research is to understand how to improve consumer’s perception on the used car industry and how significant the improvement of the company’s image and reputation can affect their perception. Branding exercise can significantly change a consumer’s mindset through marketing communication (Martin, et al., 2005) while there is no empirical evidence on how much branding can help a commonly misunderstood industry like the used car industry. With a conclusion to this research, it is best the data can improve the overall used car industry for more ethicality and at the same time let consumers be better aware of what makes a worthy used car dealership. The research objectives are broken down into 4 goals as below.

1. To evaluate on what are the preferred choices of consumers on used car dealers based on the dealers’ image and reputation.
2. To derive a profile of a consumer who prefers used car dealers with better image and reputation.
3. To discover which element of the used car dealers is the most important factor that contributes to a good image and reputation.
4. To learn how a buyer’s circumstances will change his or her perception and acceptance of the used car industry. 

1.6 Reasons for Selecting This Topic

The general reason for selecting this topic is to improve how the public perceive the used car trade. There will always be black sheep in every trade (Taylor, 2017) therefore it is necessary to boost the branding for the ethical ones. The researcher is a car enthusiast who has owned many cars. He has been through the car buy and sell process many times and met many different car dealers. Further to that he invests with used car dealers, therefore he has substantial knowledge in this subject and be able to access information that is more confidential or quasi-private.

Chapter 2. Literature Review

2.1 General Buying Decision 

My topic area is on consumer perception, hence the models that will be reviewed are all marketing related. With the right amount of marketing effort, even the wrong product can become successful (Auh & Merlo, 2012). Knowing what are the various models and understanding the consumer behaviour can alter a buying decision which is why it is important to adhere to marketing ethics (Hunt & Vitell, 2006). Geographically, the behaviour of a consumer buying a vehicle varies (Bigne, et al., 2005). The factors that result in such decisions will be further analysed later in this chapter. In general, people desire for the things they cannot have (Ben-Ze'ev, 1992) and that have made owning a car in Singapore become a bucket list for many Singaporeans. Unlike other countries where cars are merely affordable transportation (Okulicz-Kozaryn, et al., 2015), it is expensive to own a car locally. Understanding how consumers from different geographic areas perceive the car industry will help to narrow down their purchasing motivation.

2.1.1 Brand Equity

Brand equity is intangible and it can generate a perception that would affect a consumer buying behaviour (Aaker, 2009). The positioning of a brand can influence how a consumer is willing to put extra effort to acquire it. A luxury brand gives consumers a trust in the product, paying extra for its perceived value, waiting for it patiently and as more relevant to the topic area - luxury branded companies are less likely to commit fraud (Romaniuk & Huang, 2020). There are many advantages of building a company towards the luxury route and one of such is to be acquired more easily even if their business is not doing well (Chung & Kim, 2020). Luxury brands speak for itself however it is practically impossible for every used car dealer to be positioned in that realm (Kapferer & Tabatoni, 2011).

2.1.2 Brand Trust 

Brand trust is how to secure loyal customers and it further reinforces the brand equity (Delgado‐Ballester & Munuera‐Alemán, 2005). It is ideal to have customer devotion however it is not as feasible in the automobile industry (Devaraj, et al., 2001) because the general consumer does not change their vehicle often. Having good brand trust is like sowing for the future where the consumer will return despite it being many years later and they might also provide quality referral along the way. Building a long haul customer base in such a manner will result in a snowball effect (Markovich, 2008) where until a point in time customers will flow in automatically.

2.1.3 Customer Lifetime Value (CLV)

CLV calculates how much will the customers worth entirely (Gupta, et al., 2006) and from such reading the business owner would be able to plan the resource and effort to engage them. Unfortunately it is a clear case that the consumer loyalty continues to decline (Dawes, et al., 2015) and the CLV may be depicting the potential value instead of actual value. Even when the machines are getting more sophisticated there are too many sampling noises and unpredictable behaviour that affect the customer churn rate prediction (Vafeiadis, et al., 2015). Having inaccurate CLV prediction may cause the used car dealers to overleveraged their resources and make bad business decisions.

2.1.4 Customer acquisition cost (CLC)

CLC is how much a firm has to spend to obtain a new customer and it is often linked to customer retention strategy (Min, et al., 2016). Acquiring a customer is much higher than retaining a customer (King, et al., 2016) and many firms have adopted extraordinary methods to retain their customers. Retaining customers has been a dilemma till now as reckless retention of abusive customers may not only produce negative sales and will also create disharmony between the servicing staff and the firm (Gaucher & Chebat, 2019). Measuring the cost of CLC in the used car industry is very straightforward due the limited marketing channels available to the dealers. As most dealers strongly believe most transactions are one-off therefore they only focus on new customer acquisition.

2.2 Word-of-mouth Model 

WOM model is a form of referral marketing where it is conducted in a peer-to-peer fashion (Anderson, 1998). Having incentive to refer would be ideal however some products are so well sold or the service is so commendable that a consumer promotes it willingly (Wirtz & Chew, 2002). The WOM model, as shown in Figure 2, can be broken into 6 different parts on how effective the marketing message is (Asada & Ko, 2016). Most consumers are more receptive to who is sending the message and even though these experts are not always correct, it is still a safer bet to the public (Grundmann, 2017). The strength of delivering the marketing message is crucial to push the recipient in its adoption however it should not sound too marketing oriented or the consumer can detect it and then detest it (Goldman, 2006). This happens when the referral is being rewarded and they are too eager to promote which may become counterproductive. The consumer will also be more receptive towards the message they are closely knitted or share common traits similarly to birds of a feather flock together (Kossinets & Watts, 2009).

Figure 2. Key characteristics of WOM model (Asada & Ko, 2016)

As cars are expensive in Singapore, it requires a higher level of trust to commit into such a long-term depreciating asset. The WOM model poses as an essential part in the decision equation on whether to buy from the recommended dealer or not. Consumers are more receptive when they get the recommendation from their peers, especially if the recommender is already a customer (Kurt, et al., 2011). Unfortunately, research shows that people tend to trust wealthier people even when it is a common misjudgement (Qi, et al., 2018). This states that when a peer who has purchased a vehicle of a higher value and his recommendation will be much stronger and valid. 

2.2.1 The Benefits of WOM

With all the discussed trust issues, buying a car will require a high level of trust and the WOM model is one of the more credible sources. Besides the key characteristic of a WOM model, it is also important that the consumer have someone to rely on when things go south (Malle, et al., 2014). The referrer has indirectly and unknowingly become the “guarantor” for the vehicle on its smooth transaction in terms of the sales process and trouble-free from mechanical issues in the next 6 months. This psychological blame effect reduces the toil from the car dealers and puts it onto the referrer. Another benefit of the WOM is that the marketing budget is reduced or better managed. Freelancing has been known to improve the profitability of a company because they are being paid only when something is done (Burke & Cowling, 2015). In fact, their referral fee can be very attractive as there is no overhead on them until a successful transaction is orchestrated. There may be some referrals that the referrer does not require any monetary incentives as they are being rewarded in an intrinsic way (Toker-Yildiz, et al., 2017). This will help to minimise the cost and build an invisible selling network.

2.2.2 The Limitations of WOM

As the referrers are not the trained salesperson and they thought they have no legal liabilities, their communication to the recipients may go awry. The problem is most people lack self-control and tend to oversell when they believe a negative result is inconsequential to them (Leotti, et al., 2010). It could become worse when the referrer is motivated by greed and the more they will misrepresent the actual sales message (Wells, 2001). This is an issue that is difficult to resolve as the used car dealers have no control or are unable to regulate such errant behaviour. Another issue is that most sales will still require a trained salesperson to perform closing and that will diminish the salesperson commission creating an imbalance equation to their effort. With the issues altogether, it provides impractical data for sales forecasts (Jordan & Messner, 2020) which may create an uncertainty in the firm’s projection and planning.

2.3 Multi-attributes Model 

Multi-attributes model uses linear compensatory attitude modelling (Wilkie & Pessemier, 1973) to examine consumer attitudes and enhance the products or brands of the firm. It is somewhat similar and is an extension of the Fishbein model (Ryan & Bonfield, 1975) on how a consumer behaves when it comes to making choices. This model rationalised through various behaviours and logical measurements on how a consumer adopts the brand. The multi-attributes model’s attributes are being evaluated mainly through the multiplication of beliefs and weight index (Yang, et al., 2016). Beliefs are being considered as how a consumer trusts the product or brand to deliver the promised features (Srinivasan, 1979). For instance, dealer ABC does a better marketing communication and the consumer believes that getting a car from them will be relatively a safer choice and more trustworthy than its competitors. The dealer ABC however just made a claim with superior marketing work and does not actually prove their credibility. The weights reflect on how important are the features (Srinivasan, 1979). In this other instance, dealer ABC provides extended warranty and free car servicing up to a certain mileage for all their used cars. With a convincing yet baseless claim, together with actual action justifying the claim, dealer ABC’s attributes will be significantly improved. The multi-attributes models therefore can be portrayed in a simpler form such as:

Attribute as Differentiator = Beliefs x Weights (Felli, et al., 2009)

Originally, the full formula for determining multi-attributes would be as follows:

Figure 3. Multi-attribute Model Formula (Holbrook & Hulbert, 1975)

2.3.1 Brand Attribute Value

Due to the varying beliefs and weights, the attributes or the features of the same brand can be somewhat similar but one’s brand can be downplayed as compared to another’s. By having unequal brand attributes, used car dealers will also be subjected to very different market shares (Fetscherin & Toncar, 2009). There will be cases where several dealers are listing the similar car and receive different responses. There are also cases where several dealers offer the same consignment arrangement and the consignee took side. Sellers will also skew towards certain dealers even when the selling price of the car is the same. Humans are often perceived as judgemental (O'Connor, 1989) and that is one other reason a consumer would choose to buy from a dealer that will attract less negativity from the peers. Buying from bad dealers means the car could be sold undervalue and the person who bought it may be judged as a cheapskate. Aside from that, the peers may give a wordy lecture on why the consumer should not have bought from that dealer. As people tend to avoid unpleasantness (Case, et al., 2005), the peers’ comments have become problems and worries that a consumer may want to steer clear at all cost.

2.3.2 Brand-specific Effect

To have an overview of a multi-attribute model approach, the construction of customer decisions will be aligned to the market standing between the brand and its competitors (Srinivasan, 1979). It will show the path on how a consumer made their choice and what are the variables that affect it. This is a model where many attributes have to be tested in order to know what are the better courses of action to improve. Once the tested data are acquired and processed, it can be used to design a better brand that offers a higher standard of service and improves the overall revenue (Shocker & Srinivasan, 1979). There are tangible and intangible perspectives involved with this model that are both important in determining the brand effect. Such attributes can alter the consumer penchant on their decision to make the purchase (Myers, 2003). In the good aspect, reviewing and embracing these data can help to portray the brand's actual standing. Brands with a poor image due to misunderstanding from the consumer’s perspective can get a correction to improve its position. It is however if the data is being misused, the dealer is able to create a good brand that masks their actual misdoings.

2.4 Self-congruity Model 

This model manifests a consumer already having an image in mind before selecting a brand that aligns with that image (Hughes, 1971). This behaviour does not stop at determining the brand preference before making the purchase while it also extends to buying first and decides liking the brand later (Sirgy, 1986), which explains the self-congruity. To justify one’s purchase of the automobile which is an expensive commodity, many would convince themselves it is a good buy and they have made the right choice. Opposing that will generate a buyer’s remorse effect which would cost the buyer a lot of money as cars depreciate the money you made the purchase and that is why most buyers would rather cheat themselves in believing the purchase is value for money (Bell, 1967). This has contributed to the self-delusional behaviour that the consumer has in viewing a particular brand (Landon Jr, 1974). Such behaviour can extend to the consumer personality and affect how they view the brand in terms of angle, fondness, solution, loyalty and so on (Boksberger, et al., 2001). Aside from personality, this phenomenon will make a consumer align the marketing message from the brand owner to his own imagination (Kassarjian, 1971). This means the consumer will actually think the marketing message is meant for them like the stars were all lined up. In extreme cases, the consumer will create a matching symptom that resonates with everything and everyone to their decision even when there are clear evidence they have made the wrong choice (Rosenberg, 1981).

Figure 4. Antecedents and repercussion of self-congruity (Hohenstein, et al., 2007)

2.4.1 Preconceived Notion 

A person could derive an affirmation or fabrication of a hypothesis to give an impression of something they have not experienced before based on their own assumption (Pöppel, 1990). It is like jumping to conclusions based on non-factual means and it could be unfair to the subject because such feelings are just presuppositions. Preconceived notions can be unacceptable when it comes to social judgement (Pöppel, 1990) due to its prejudicial nature. Consumers who receive negative news about the used car industry will develop a cognitive effect in relation to the credibility of the used car industry as people are more reactive towards cynical news (Soroka, et al., 2019). It could be a psychological effect where the consumer wants to be more careful when it comes to huge spending (Lusardi & Mitchell, 2014) that is why the consumer would only go for the reliable car dealers even if it may cost more for the same vehicle. The impact for used car dealers will be pre-judged even when they have done nothing wrong and have no intention to. Even though preconceived notions are classified as a type of unhealthy stereotype (Hilton & McCleary, 2019), it protects the consumer in a form of self-preservation which is a natural ability embedded in humans since the beginning of time (Northcraft & Ashford, 1990).

2.4.2 Halo Effect

Another popular stereotype is the halo effect where a consumer will already have a positive impression of a brand before experiencing it (Nisbett & Wilson, 1977). As opposed to the preconceived notion which produces negative thoughts of a brand, consumers with halo effect appreciate the brand in a misleading manner. It is not necessarily good that a consumer likes a brand even before knowing it thoroughly because there are chances where the marketers are unable to replicate the effect or measure it (Leuthesser, et al., 1995). Marketers are trying to eliminate such effects (Wetzel, et al., 1981) and want to grow it in a more measurable approach. This is perfectly reasonable and especially applicable in the used car trade because of the volume of sales they are dealing with. If the effect is not replicable, the used car dealers may enjoy a few months of good business, utilise more resources to cope with the sales and get themselves into trouble after the effect has ended. This has 2 sides of thought as Rosenzweig argued it is being well-received by many other trades including pharmaceutical, lean manufacturing and so on (Rosenzweig, 2014).

2.5 Media Mix Model (MMM) 

MMM is also known as a marketing mix model, however unlike the conventional marketing mix where it revolves around the 4Ps namely product, price, promotion and place (Borden, 1964). MMM being a framework operates on hard indices to measure the marketing performance and use the time series data to make predictions on the media type (Thomas, 2006). With such information the used car dealer is able to optimise the advertising effort or use it to run promotions or advertising related strategy. MMM is preferred to use various statistical models to accurately analyse the success of marketing initiatives or promotional campaigns (Wang, et al., 2017). One such method is to use the Bayesian hierarchical model which will amass the precursory or associated MMMs (Jin, et al., 2017).

Figure 5. Schematic of a Bayesian hierarchical model (Kwok & Lewis, 2011)

With MMM, all the aggregated marketing campaigns and activities’ data are broken down to be more readable and run through a series of regression or other statistical processes. MMM is not as straightforward as it sounds and comprises many working parts, often the return on marketing effect (RoME) should be considered before adopting this model (van Gils & Proper, 2018). RoME requires huge resources and coordination to make it work, besides it is expensive to have a comprehensive plan, the data should be handled meticulously or it will end up with inaccurate information (Kilkenny & Robinson, 2018). Marketing plans should be simple to understand and executive (Mohr & Nevin, 1990), and not every used car dealer has the ability to afford the MMM.

There are too many different types of media to choose from and let alone measuring their effectiveness. Selecting the wrong media to promote a used car dealer will not only deteriorate its resources and also potentially create a negative impact (Liu, et al., 2014). The wrong media will target the wrong crowd while the competitors have already won the heart of the potential customers through the correct media. Consumers are like they are wired to shift whenever the competitors have a better branding (Gong, et al., 2020), the consumer will not hesitate to make negative assumptions when they do not see certain dealers in the general media. Having understood that, if the right media combination is being utilised correctly it will produce sales maximisation effect and increase of market share (Tellis, 2006). Nevertheless, choosing the media is not that much of a headache for the used car dealers as there are not many media choices for this trade.

2.5.1 Limitations of MMM

The major downside of MMM is that it requires huge resources to execute it where different media are to be tested even if they may not be effective. There are also execution factors that may hinder used car dealers from adopting such a model, especially the acquisition and integrity of the data. It is a prolonged period to operate such models where the variables are to be adjusted accordingly before the results are to be examined (Chan & Perry, 2017). Due to the many available media, as many trial and errors are to be performed by the marketer to ensure accurate and satisfactory results. As far as the modern society is advancing and the digital age is progressing, to acquire usable data is not as easy as it seems (Sivarajah, et al., 2017). The basic usable data should be clean, not correlated to the process, have a vast range of samples and should be impartial (Shi, et al., 2019). It is not easy to achieve that as compared to a survey due to the testing cost for each media. To have so much clean data, an used car dealer will have to spend a humongous amount of advertising dollars to test the major media with a targeted communication style. Used car industry is highly competitive and it is not economically viable to apply such a model until the business direction is aligned with such spending (Noo-urai & Jaroenwisan, 2016).

2.6 Multinomial Logit (MNL) Choice Model 

The final model in this literature review will be the MNL choice model which analyses the choice probabilities through irrelevant alternatives (Gensch & Recker, 1979). Unlike the other discussed models, this model measures other irrelevant variables that will affect the choice of the decision maker (Elshiewy, et al., 2017) and it could be an invaluable information for the used car dealers to set the competition aside. MNL model is derived from the discrete choice model (McFadden, 1973) which will be discussed later in this section. MNL is rather equitable as it factors in unknown variables that will yield unbiased estimates for the choice surmise. Measuring the unknown does give marketers who embody explanatory variables a chance to explore the possibility of brand loyalty (Guadagni & Little, 1983). An example for the explanatory variable can be ‘sticker shock’ where the consumer has a mannerism effect on unbelievable price (Winer, 1986). In the used car trade, if a price is too high or low it will also have a questionable consequence to the market. An automobile can be sold above market rate when they are getting a higher loan quantum (Kahn, et al., 2005) while if it is below market rate, it is often perceived as problematic and scares off customers (Farm, 2020). All this data will help the marketers to improve their marketing mix (Shah, et al., 2015) and greatly assist them to analyse consumer behaviour in the choice aspects (Fader, et al., 1992). The equation for MNL model is as follows, where the possibilities to choice will sum up to 1:

Figure 6. The transformed MNL model equation (Engel, 1988).

2.6.1 Nested MNL model

The complication with MNL is that choices can be made from independence of irrelevant alternatives (IIA) where the selections are wrongly grouped and no correlation altogether (Hendrickx, 2000). IIA assumption will often provide misleading results as the test subjects are of two entirely different categories (Gensch, 1997). To resolve such issues, a nested MNL model is used to detach the choices into K nests (Koppelman & Wen, 1998). This is important as it will clearly segmentise which competitor the used car dealer is facing and let them adopt the right strategy for counter-measure. To illustrate the nested MNL model, this research will be using an example of two dealers selling different cars while at the same price. Dealer A is selling a Toyota Corolla at $100,000 while dealer B is selling a BMW X6 at $100,000. Multiple nests are needed to correctly classify (Paredes-García & Castaño-Tostado, 2019) these dealers. Dealer A is a direct agent selling a new car while dealer B is selling a used car. Even so, dealer B is selling a continental car while dealer A represents a Japanese car. In such a case, dealer A should only be compared to new car dealers selling regional cars while dealer B should only be tested against used continental cars.

2.6.2 Discrete Choice (DC) Model

As mentioned in the above section DC is the predecessor of MNL where it predicts the choice a person has with the alternatives that are associated with them (Small, 1987). In a simple term, if a person only has limited alternatives, the choice they will make is highly predictable and vice versa for a person with huge alternatives. Unlike the MNL where it incorporates random factors (Horowitz, 1991), the DC model relies more on linear regression. With the constant evolution of consumerism (Morewedge, et al., 2020), it is important for the used car trade to add in random variables (Horowitz, 1991) when it comes to choosing cars. As the consumers are no longer as simple to predict in modern days, using the MNL choice model could produce a more accurate prediction. Another concern is the amount of alternatives available to a consumer have been overloaded by the Internet (Huff & Johnson, 2014) and such overchoice effect actually makes the consumer more unpredictable (Vieira, 2017). However, there is no best model for choice prediction (Louviere, et al., 2011) while it is possible to find the most suitable one. It could even be more than one or have other applied framework within a choice model (Ben-Akiva & Boccara, 1995). 

2.7 Hypothesis Formation 

With the above researched models, the right hypotheses could be formulated to revolve around the question on whether brand equity will impact the used car dealers in Singapore. In that case, the dependent variable is the consumer will be more receptive to purchase from used car dealers who have a better branding. There are four main categories of better branding which is the financial factors, company factors, media factors and consumer factors.

Figure 7. Conceptual model of consumer being receptive to dealers with a better branding.

H1A: Used car dealers who are listed in the stock exchange board will give a more positive branding effect to the consumers. Stock exchange boards will be the local exchange and regardless of being the main board or secondary board such as SGX and catalyst.

H1B: Used car dealers who only sell high end will give a more positive branding effect to the consumers. The category of high end car includes exotic brands like Lamborghini, Ferrari, Pagani, Aston Martin and so on. There are semi-exotic brands with higher range such as the BMW Alpina, Mercedes Maybach, Audi R series and so on.

H1C: Used car dealers who are already more than 15 years in the market will give a more positive branding effect to the consumers. Many consumers prefer brands with a long history (Ebrahim, 2016) and 15 years have been selected as the test variable. Car trades come and go so it will create high constraints if they were to test beyond 15 years.

H1D: Used car dealers with a lot of cars listed under them will give a more positive branding effect to the consumers. The most common gauge will be using SgCarMart which is the most popular choice to sell or buy a car in Singapore.

H1E: Used car dealers who are more exposed in the media will give a more positive branding effect to the consumers. There are media that have dedicated automobile industry sections such as our mainstream newspaper The Straits Times. Other media includes online advertisement such as Google advertisement and Facebook advertisement.

H1F: Used car dealers with more reviews from social media will give a more positive branding effect to the consumers. Consumers currently depend more on reviews to make a buying decision (von Helversen, et al., 2018), where both rating and reviews are important (Moen, et al., 2017). This excludes the consideration of how authentic the ratings and reviews are as it is easy to create fake reviews and a considerable amount of unethical dealers actually fake them (Wang, et al., 2018).

H1G: Consumers will buy a car with a good service record or serviced with a renowned workshop from the used car dealer than one without. There has been an ongoing reputation of used cars being in substandard condition and reputable workshops will not do appalling servicing due to their stature. Even though many times such a reputation is a marketing gimmick that misled the consumer (Nuseir, 2018), it will still give more confidence to the consumer. 

H1H: Consumers prefer to meet up and transact with the car seller in an car consignment arrangement over direct purchase from the dealer. Even consignment can be done in a peer to peer manner, selling a car through a dealer is still preferred mainly because of how they can negotiate better with the buyer (Abraham, et al., 2016). It is assumed that buyers would like to converse with the seller and gain some actual insight with any sales elements like how the dealers usually adopt. 

Chapter 3. Research Design and Methodology

3.1 Research methodology 

It is common for students to misunderstand the theoretical concept between ontology and epistemology (Al-Saadi, 2014) however it is important to clearly understand these research methods to know why and what are being researched (Kothari, 2004). Ontology studies the meaning of existence and its construction in its instinctual form (Crotty, 1998) and in what ways such information is being understood (Ormston, et al., 2014). Ontology is often associated with interpretivism and constructionism (Chen, et al., 2011) where perceptions were used to deduce the outcome of what their feelings say (Bryman, 2008). The ontology part of this research makes rise to the perception that used car dealers in Singapore do not have a positive branding due to what people see or hear from around us and the way they handle the transaction. It also brought the perception that by shifting the brand image of the used car dealers around, it would create a better impression for them.

Epistemology on the other hand wants to understand the world and find coherence in it (Richards, 2003). Unlike ontology where it accepts what has happened, epistemology wants to find out why something happened based on the available knowledge (Cohen, et al., 2017). Positivism and objectivism, being the need of evidence to support a research is associated with epistemology (Hiller, 2016), this research method values facts and will reject findings that are not sufficiently backed (Wellington, 2015). The epistemology part of this research has derived that the local used car dealers become unethical due to the short lifespan of the vehicle and its high depreciation. Ultimately, the majority of the root cause for unethical actions are linked back to dollars and cents (Kiyotaki & Moore, 2002). Through evidence, reputable used car dealers generate better revenue as the consumer would like to safeguard their investment.

With the ‘what’ and the ‘why’, the ‘perception’ and ‘assumption’ it is important to use methodological means to confirm the findings with a conclusion (Watts & Stenner, 2012). The outcome may not be supporting the hypotheses but it will be made known if the research may need further continuation. In summary, quantitative research will be performed with a structured close-ended survey targeting a sample group from a population.

3.1.1 Research Design 

The research will be conducted using a popular online survey tool called Google Forms (Mallette & Barone, 2013). Google Forms is selected based on its several incomparable attributes namely being interactive, easy to use hence is time effective, have all the complete features necessary for this survey and most importantly it is free to use (Hettrick, et al., 2014). All the results will be conveniently stored in a separate spreadsheet file that can be easily exported to multiple file formats that is suitable for most data analytical tools. Google Forms also assist in psychological advantage where it is a well-known brand and people will tend to be more open to participate or provide factual information on sources they are familiar with (Reis, et al., 2011). Online survey is able to gain massive outreach on wide variety and sample targeting through very cost effective methods (Wright, 2005). To have workable data, interviewer bias needs to be eliminated (Boyd Jr & Westfall, 1970) and an environment where people taking the survey do not get judged (Ambady, et al., 1995). With these in place, this research will be able to get genuine survey results that are of high quality and factual.

3.1.2 Questionnaire Design 

My research survey consisted of 36 questions and it was derived from the literature review and its hypotheses. It is important to ask good questions that are concise and be able to achieve the purpose in the shortest possible methods (Fowler Jr & Fowler, 1995). To prevent respondent fatigue that will give poor bias answers (Porter, et al., 2004), the survey is estimated to be able to finish within 7 minutes and is separated into 4 different sections. Another strategy to ensure the survey is enjoyable is to adopt closed-ended questions entirely (Desai & Reimers, 2019) and furthermore it is easier to analyse closed-ended questions. The first section is to collect general information of the respondents without personal particulars as people prefer to stay anonymous in surveys (Gnambs & Kaspar, 2015) and this would aid if they fall into the preferred samples. The second section is to collect basic statistics of the respondent’s car usage and reasons with both car owners and not car owners. This would give us an insightful opportunity to correlate (Curtis, et al., 2016) with the third section on why certain used car dealers are a more preferred choice. The third section is conducted in Likert scale to measure the strength (Albaum, 1997) of how much the respondent agrees or disagrees with each statement. Using the Likert scale can give a highly quantifiable outcome with the freedom to express (Joshi, et al., 2015), however respondents may not like to select the extreme answer that will affect the true result of the survey (Pimentel, 2019). The last 2 questions in this section (Q.27 and Q.28) are asked in a reverse scoring construction to cross check if the respondent took the survey seriously (Malhotra & Peterson, 2001). The last section finds out what an ideal used car dealer should behave and some of its response will be used in the recommendation section in the later part of this report. These are the 4 sections and by deliberately segmenting them will provide a better flow for the respondent (Kelley, et al., 2003).

3.1.3 Research Design Limitations

As much as how flawless a methodology may seem, conducting an online survey has its limitations too. Though the population is identified and samples are pulled out from it, there will always be the gap of insufficient results (Biemer, 2009). As this survey will be sent to many SMEs bosses, most of them can be considered as high net worth and may only buy new cars from the direct agent or premium auto reseller. They may have personal experience with the common used car dealers. This also happens to respondents who never experience the services of high-end used car dealers and may set the wrong standard in taking this survey. The lack of experience will create respondent burden (Sharp & Frankel, 1983) and the outcome may not be accurate or fair.

Another limitation is the lack of complete answers and depth to the questions. Respondents may have a deeper reason or unspoken behaviour that lead them to such a choice and open-ended questions will be able to mitigate the inconsistencies (Singer & Couper, 2017). It is difficult to control the variations in a closed-ended question (Kieruj & Moors, 2010) where there cannot be too many options or too few to get the correct response. Therefore the right balance of closed-ended questions would require time and effort to calibrate and multiple pilot runs may be necessary to the optimum result.

One last limitation to be discussed is the downside of Likert scale. There are too many controversies of the Likert scale (Joshi, et al., 2015) however not many feasible alternatives have been found. The most common controversy of the Likert scale is the inefficiency of the neutral option (Komorita, 1963). By eliminating the neutral point will force respondents to pick a side however it will again create room for biasness (Guy & Norvell, 1977). A 4 or 6 Likert scale has its potential (Chang, 1994) but also creates the same problem in cases where the respondents pick an equal amount of agree or disagree.

Even though with all such flaws being presented, it would not create a huge hindrance to the integrity of the survey results. There might be room for further study and qualitative study can also be performed for extra quality.

3.2 Research Strategies

3.2.1 Primary Versus Secondary Research 

Primary research is an investigation performed by the researcher who amasses the data himself (Driscoll, 2011) while secondary research is analysing intelligence published by other researchers or recognised bodies (Stewart & Kamins, 1993). Even though secondary research may pose to be more resource effective (Glass, 1976), the data required for the area of research is not readily available and will have to be acquired. As this research has adopted primary research, the researcher will be gathering data through a survey on consumers’ perception of the local used car dealers. Besides addressing the research needs, primary research also allows the researcher to have access to first-hand information. Though both primary and secondary research has their benefits, they too have their downside and it is recommended to have primary and secondary dualism (Bishop, 2007).

3.2.2 Quantitative Versus Qualitative Research 

Quantitative research utilises heavily on numbers with formulated analysis (Apuke, 2017) while qualitative research employs free text literature content and derives a qualified assumption (Chesebro & Borisoff, 2007). Quantitative research has its questions structured in a control manner and will not go out of topic (Hanson, 2007), which is why this is the selected method for this report. As such, the test results will be in accordance and adhering to the hypotheses formed in section 2.7. If time permits, it is ideal to combine both quantitative and qualitative for the best result (Onwuegbuzie & Leech, 2005). To have a better understanding, the minorities who have chosen otherwise should get an interview to find out if there is a gap in the survey or further research should be performed.

3.2.3 Deductive Versus Inductive Research 

Deductive reasoning is more logical and it begins with a hypothesis or statement and takes a process to prove it (Woiceshyn & Daellenbach, 2018). Inductive reasoning draws conclusion from observation and will form a statement based on what the information leads (Azungah, 2018). As is a firm research topic, the practice is going to be the deductive process. To add on, quantitative research is commonly associated with the deductive process (Hyde, 2000). Though a research has to be specific, it is fuzzy to know what method is truly adopted. For example, in order to create a hypothesis, inductive reasoning will be used through observation to assume the used car dealers are being affected because of their reputation.

3.2.4 Research Ethics

Most research that involves humans will contain strands of ethical issues, whether it is as huge as it implicates legality matters or petty issues (Gregory, 2003). As much as to keep the research authentic, there may be several ethical issues that will be overlooked or performed out of desperation. However, if the potential ethical breach has been explored, there is a higher chance of preventing it from happening (Festinger, 1962). One common ethical issue is the fabrication of the research data (Jenn, 2006), it involves massaging the end result or filling it up with the desired outcome. It happens when there is insufficient data due to limited response or the researcher is biased toward a certain outcome. This could be audited by adding a non-editable timestamp and the Internet protocol of the respondent but the cost of running such a survey will become more expensive.

3.3 Respondent Samples

3.3.1 Population Definition 

Population refers to the subjects that are of interest in a research topic where they should be the most ideal respondents to take feedback from (Banerjee & Chaudhury, 2010). It should consist of the eligible criteria with the inclusion and exclusion criteria to select only the more relevant data for examination (Majid, 2018). In this case, the most ideal population can be genderless, people residing in Singapore, middle income class and possess or want to possess a car. In order to rate the used car dealer industry, the respondents should have personal experience dealing with them and ideally tried both the extreme ends of the dealers. The population can include a tiny portion of respondents who are out of the criteria to have a more balanced and optimised data (Erba, et al., 2018).

3.3.2 Sampling Definition 

The best case scenario would be getting the entire population as defined in section 3.3.1 to be involved in the survey however it is virtually impossible to achieve that. In a realistic case, samples are to be chosen from the subset of a population as a representation to provide sufficient data for the research (Martínez-Mesa, et al., 2016). There are disputes about the sufficiency of samples (Raudenbush & Liu, 2000) and there is no formal depiction on how much data is required for what type of research (Palinkas, et al., 2015). Even though there are various formulas in calculating the sample size (Taherdoost, 2016), it is not a one size fits all technique. The sample size that will be used is between 150 to 200 respondents. This was being considered based on the estimated number of used car dealers in Singapore, the number of consumers who will buy from them, the accessibility of such crowds and the resources involved in this research.

3.3.3 Sampling Methods And Strategies 

To have a prime research result, selecting the sampling method between probability and non-probability method is important (Berndt, 2020). Probability sampling gives every population an equal chance of participating while non-probability does not have every member of the population being selected (Turner, 2020). Out of the different methods that exist in non-probability, convenience sampling is the fastest, most economical and labour-saving (Elfil, 2017). These are the core reasons why convenience sampling has been selected for this research. Most online platforms such as Google Forms which will be used are classified as convenience sampling because once they are blasted out there will be no control over the recipients. Even though there is a slight element of purposive sampling where the respondents can be selected to participate (Campbell, et al., 2020), it can be shared around and totally deviated from the original intention. As convenience samplings will land on any available respondent like an accident, this research should accept and be prepared for the data to be unprecedented (Etikan, et al., 2016).

There are strategies that can ensure the sample does not deviate too much from the original intention. A strict filter can be added into the survey and make it out of bound to respondents that are not in the criteria. By doing this, more respondents need to be added in case many of the current list does not fulfil that criteria. Another strategy is to look for past customers in social media and send them this survey. Many used car dealers have social media pages and there are happy and unhappy customers giving reviews. Both customer groups could be reached out and be asked to participate as they are the closest to the criteria the researcher have set.

3.3.4 Sampling Methods And Strategies Limitations 

Non-probability sampling is unable to generalise the entire population as there is no control over people outside the criteria from participating (Sharma, 2017). There is an issue of under- or over-representation (Jager, et al., 2017) where a huge group of respondents falls within the same category and does not create an average result (Bornstein, et al., 2013). The imbalance from convenience will therefore create bias results (Kriska, et al., 2013) that render poor quality data. Getting participants from the dealers’ social media page is also tedious to perform and is laborious. As people always associate cold calls as sales pitch and reject them (Walsh, 2004), connecting with unknown contacts in social media may not be that effective. Social media also has a high tendency to filter non-connected members’ messages to the spam box.

3.3.5 Statistical Tools

To analyse the results, several statistical procedures may be used to test the data. Mean and standard deviation will be used to measure how much disparity or scattering the sets values are (Livingston, 2004). Correlation coefficient will determine the relationship between two variables if they are similar and if it is usable (Schober, et al., 2018). By associating the data, it will find out which answers are linked and if a conclusion can be drawn to it. Regression will estimate the relationship between dependent variable and independent variable, it can also be used for prediction based on the presented data (Draper & Smith, 1998). Chi-squared test will perform relevancy matching to the null hypothesis by identifying the similarity or differences between the observed frequencies (McHugh, 2013). The frequency will determine if the hypothesis should be independent. ANOVA analysis group means to further estimate the alliance between samples (Iversen, et al., 1987) and this is the final tool that will be expected to perform with the results.

Chapter 4. Results And Analysis

4.1 Analysis Overview

The data from the survey taken by the respondents will be analysed and presented in this chapter. To ensure the usability of the data, a reliability test will be performed on selected questions. It will be followed by an analysis of the consumer profile, an analysis of the car usage, an analysis of the factors determining the image and reputation of an used car dealer, and finally an analysis of the benefits for a used car dealer with good image and reputation. 

4.1.1 Data Reliability

Table 1. Combined results of Cronbach’s Alpha for reliability testing from SPSS.

Data reliability and validity is important to ensure the research result is being carefully considered and usable for its subsequent decisions (Rothman, 2007). As the data does not contain direct information about the research topic, it is practically useless without the correct analytics (Harris, 2012). Only when the data is reliable, all the analysis based on it will be truthful and functional. Data reliability can be tested by Cronbach’s alpha which is the regular testing way (Bell & Bryman, 2007). In this analysis, multiple factors are being tested against each other for its reliability. The factors being tested have similar attitudes and therefore can be tested against each other.

As unfortunate and dissociated as the results in Table 1 seems, Cronbach’s alpha acceptable value is ranging from 0.70 to 0.95 (Taber, 2018). This means that only the consignment factor with an alpha of 0.754 is barely acceptable. The image and reputation factor with the alpha of 0.562 if the only other variable exceeds 0.5 while the rest of the factors fall below 0.5. Having such a poor result could be due to limited questionnaires, poor construct of relevant questions or extensive topic area (Tavakol & Dennick, 2011). However Tavakol and Dennick 2011 also state that a low alpha can occur with high respondents due to huge variation. Besides that, the demographic such as age, sex, education level or etc can cause low alpha even though the questions is being created with relatedness (Ursachi, et al., 2015). As of this point, the researchers have decided to continue with the collected data and carry on with the rest of the analysis.

4.1.2 Analysis of Dependent Variable 

Figure 8. Chart display of respondents on dependent variable from Google Form

Table 2. Mean, skewness and kurtosis of Q14 from SPSS

With 1 being strongly disagree and 5 being strongly agree, almost half of the respondents in figure  (47.2%) agree that they will buy a car from the used car dealer. Just 1 respondent that contributes to 0.5% of the result would strongly agree to accept the used car dealer while there is none that would strongly disagree to buy from the used car dealer. In total, only 4.7% held an opposing attitude and almost another half of the respondents (47.7%) held a neutral ground. Table 2 shows a negative kurtosis value of -0.576 meaning the top of the graph is narrowly flat and the choices are quite equal. With the negative skewness value of -0.353, it describes there is a long tail at the left of the graph and therefore the acceptance is plotted towards the agreed attitude. The acceptance is however to a low extent that could be confirmed with a mean score of 3.44.

4.2 Analysis of Consumer Behaviour

As per Figure 9, out of the 193 respondents there were 129 males making 66.8% and 57 females making 29.5%. Men are naturally attracted to automobiles (Deaner, et al., 2016) which is why more men would take this survey instead. To be diplomatic, a gender neutral option is included which garnered 7 respondents at 3.6%.

In Figure 10 and out of expectation, there are 2 respondents below 21 making the 1%. 9 respondents (4.7%) are between 21 to 30 and 78 respondents (40.4%) are between 41 to 50. Majority of the respondents of 104 (53.9%)are between 31 to 40 years old. There is no respondent beyond 50.

Figure 11 depicts the majority of the respondents are Singapore with 161 counts and 83.4%. There are 32 permanent residents at 16.6% and no other variations.

From Figure 12, 8 respondents (4.1%) are from ITE, 28 respondents (14.5%) are from polytechnic and 47 respondents (24.4%) are postgraduate and above. The bigger half consist of 110 undergraduate at 57% and there is no other variation.

Figure 13 exhibits 2 are students and out of job at 1% each. 29 (15%) are investors, 33 (17.1%) are blue-collars, 50 (25.9%) are business owners and 77 (39.9%) are PMETs. The respondent's profession is quite evenly distributed.

In Figure 14, only 1 (0.5%) have income above $300k, 2 (1%) is below $20k and 21 (10.9%) range from $120k to $149k. 50 (25.9%) are between $200k to $300k, 54 are between $50k to $79k and the most common salary range with 65 respondents (33.7%) are between $80k to $120k.

4.2.1 Consumer Age 

To analyse if the respondent’s age group is correlated to the their acceptance of used car dealers, 2 hypotheses are formed:

H0: There is no interconnection between respondent acceptance and age

HA: There is interconnection between respondent acceptance and age

Table 3. Crosstab of acceptance versus age from SPSS

The main age group is 31 to 40 and 41 to 50. It seems that both groups have selected largely neutral and agree, while 31 to 40 group favours agree (60 versus 44) and 41 to 50 group favours neutral (46 versus 31). The younger group of 21 to 30 have unanimously disagreed to accept the dealer while those below 21 stay neutral.

Table 4. Chi-Square test of acceptance versus age from SPSS

Asymptotic Significance is the P value that is used for hypothesis testing. When P is <0.05 the variables are dependent and if P is >0.05 the variables are independent (Dahiru, 2008). As such, Table 4 has confirmed the rejection of H0 and the acceptance of HA.

Table 5. Contingency coefficient of acceptance versus age from SPSS

Since the alternate hypothesis is accepted and it is a dependent variable, the strength of this relationship can be further tested through contingency coefficient. The further the value from zero signifies the stronger the dependency of both variables (Mirkin, 2001) and in this case the C value is 0.715 showing a strong relationship.

4.2.2 Consumer Education 

The next analysis based on respondent’s profile is whether the education level does affect how one perceive the used car industry and hypotheses are:

H0: There is no interconnection between respondent acceptance and education level

HA: There is an interconnection between respondent acceptance and education level

Table 6. Crosstab of acceptance versus education level from SPSS

As the lowest level of education from the respondents is ITE, there might not be enough samples to confirm the test of relatedness. In this education level test, every group’s decision varies among agree and neutral while only the undergraduate have selections on disagree and strongly agree.

Table 7. Chi-Square test of acceptance versus education level from SPSS

As mentioned in 4.2.1, if the P value (asymptotic significance) is not lesser than 0.05, the variables are independent and hence proving there is no relationship between the acceptance and educational level. This is further confirmed with the contingency coefficient in Table 8 that has a low C value of 0.329.

Table 8. Contingency coefficient of acceptance versus education level from SPSS

Education improves one’s analytical skill (Hegelund, et al., 2020) to better understand news and messages on the used car trades. Even with variations on the undergraduate respondents, it is surprising that Chi-square does not confirm their connectedness. In this scenario, H0 is accepted and HA is rejected.

4.2.3 Consumer Profession 

This section will test if a person working in a different trade will affect how well they receive the used car dealers. The same hypotheses will be formed as: 

H0: There is no interconnection between respondent acceptance and profession

HA: There is an interconnection between respondent acceptance and profession

Table 9. Crosstab of acceptance versus profession from SPSS

In Table 9, there is an obvious shift of trend between the different professions. There are only two jobless individuals and they have  selected and strongly agree. Since they are out of job, they would not be able to afford a car at the moment and therefore they have no qualms with this industry. The group that has the only 9 disagree attitude is the PMET and this should display a relationship between this pair

Table 10. Chi-Square test of acceptance versus profession from SPSS

The P value in Table 10 has confirmed the dependency between these two variables and therefore it is safe to accept the HA and discard the H0.

Table 11. Contingency coefficient of acceptance versus profession from SPSS

Even though the strength of this relation shown in Table 11 is just 0.646, which is moderately strong, it is evident that people of different professions may have different spending power. Since money changes how a person thinks and acts (Vohs, 2015), different professions give us different levels of income and naturally certain groups will be more cautious when it comes to spending.

4.2.4 Consumer Annual Income

The last test on the consumer behaviour will be to confirm if income level does affect their view on the used car industry. The final set of hypotheses from this section:

H0: There is no interconnection between respondent acceptance and income level

HA: There is an interconnection between respondent acceptance and income level

Table 12. Crosstab of acceptance versus annual income from SPSS

The assumption in section 4.2.3 is the income level will affect how a person perceives a rugged industry, besides how an individual's profession mould their mindset. In Table 12, generally people with more than $120k annual salary tend to accept this industry while the income with the most variation is between $20k to $50k. Another theory that people are more cautious on their spending when they have limited funds can also be confirmed over this result based on the disagreements from the $20k to $50k group. Respondents with income below $20k stay neutral could be due to they have never considered getting a personal vehicle.

Table 13. Chi-Square test of acceptance versus annual income from SPSS

The Chi-square in Table 13 have confirmed that annual income is related to the acceptance used car dealer. HA is therefore accepted and H0 is being rejected.

Table 14. Contingency coefficient of acceptance versus annual income from SPSS

The final strength test shows a weak relationship with just a C value of 0.438 and this has concluded the analysis of the consumer behaviour section.

4.3 Vehicle Ownership And Usage Behaviour

After analysing how consumer profiles have affected the way they accept the used car dealers, the car usage will be tested if it affects the way the consumer thinks. The same Chi-Square tests will be performed without hypothesis and crosstabulation.

4.3.1 Driver And Non-driver 

Figure 15. Respondents driving status (Chart from Google Form)

This survey was designed for drivers who possess valid local driving licenses on the account that people will be more concerned over matters that are directly related to them (Eastham, et al., 1970). In the survey response, 192 respondents at 99.5% possess a valid license while only 1 respondent at 0.05% did not have a driving license. It shows that the survey has attained a certain degree of success however there may not be enough non-drivers to share their views on the acceptance of used car dealers and therefore the dependency may not be accurate.

Table 15. Chi-Square test of acceptance versus driving status from SPSS

As mentioned in the earlier paragraph, the limited data did hindered the accuracy of the dependency. Table 15 shows a C value of 0.776 which is strongly independent and this test was just a formality.

4.3.2 Consumer With Direct Experience 

Figure 16. Respondents as a car consumer (Chart from Google Form)

2 respondents who made up 1% did not purchase any car before while 29 (15%) had purchased a new car and 58 (30.1%) had purchased a used car. The majority of 104 (53.9%) have purchased both new and used cars which can be a better gauge to compare both kinds of dealership. This question does not include whether do they still own the car and how long ago was it being purchased.

Table 16. Chi-Square test of acceptance versus car consumer from SPSS

Based on Table 16, it seems like there is no relationship between respondents who had purchased a car before and respondents who will buy a car from a used car dealer. As for respondents who had chosen the option “used car” and “both”, it would be contradicting if they have expressed they do not trust the used car dealers.

4.3.3 Car Owner and Non-car Owner 

Figure 17. Respondents who are car owner (Chart from Google Form)

As those who have purchased cars may not be owning a car or those who own a car because it was given by their company, parents or others may give a different view on their acceptance. In Figure 17 2 (1%) did not own any car, 84 (43.5%) own 1 car, 74 (38.3%) own 2 cars and 33 (17.1%) own more than 3 cars currently.

Table 17. Chi-Square test of acceptance versus car owner from SPSS

Table 17 has confirmed the relationship between multiple car owners and acceptance of used car dealers. There are scenarios where a consumer still would make a purchase even when they do not trust the seller with sufficient due diligence (Jamaludin & Ahmad, 2013). Avid car owners who have done many transactions could tell the condition and market value of the car.

4.3.4 Car Usage Analysis 

Figure 18. Respondents car usage reasons (Chart from Google Form)

Typically the kind of usage of a car will also affect the perception of a used car dealer as certain usage is thought to be essential to the car owners and they can only go for the cheaper choice. Based on Figure 18 which is a multi-select box, less than 50% of the respondents have selected work related options in terms of sales, private-hire and others. 152 (78.8%) is using the car for family commitment while more than 97% of the respondents have selected leisure driving and transportation.

Table 18. Chi-Square test of acceptance versus usage reasons  from SPSS

Table 18 shows a highly relevant dependency between these two variables which casts a doubt as it is out of the expectation that so many people bought a car for leisure reasons. One possibility is lockdown from COVID-19 pandemic has encouraged many people to drive around their permissible regions (Butu, et al., 2020).

4.3.5 Purchase Mindset Analysis 

Figure 19. Respondents’ reasons to purchase a car (Chart from Google Form)

To further understand the usage, explicit questions on car ownership were asked. Figure 19 showed two schools of thoughts where less than 27% of the respondents have enough financial capability or be able to maintain the car. Another group of 53.9% agrees that cars are reasonably priced. Less than 8% of the respondents buy because of the dealer’s reputation or fair law. More 81% need it for family, fulfilling their dream and work related.

Table 19. Chi-Square test of acceptance versus purchase reasons from SPSS

Table 19, as expected shows a relationship between these variables. As suspected in section 4.3.3 and 4.3.4, many buyers are motivated under harsh circumstances. This has confirmed that even with less trust on the dealers, consumers will continue to buy from them as long as their requirements persist.

4.3.6 Non-purchase Mindset Analysis

Figure 20. Respondents' reasons to not purchase a car (Chart from Google Form)

Since most respondents are car owners, this question makes them wear a different hat on why they do not want to own a car. Less than 34% of the respondents find public transport is convenient and they have no use for a car. More than 59% of them thought that cars are too expensive and maintenance is not affordable. At least 67% of them agree that the law does not protect the consumer and the dealers are not trustworthy.

Table 20. Chi-Square test of acceptance versus non-purchase reasons from SPSS

The P value of this last analysis on the usage behaviour shows a strong relationship between the tested variables. Figure 20 confirmed the car is not affordable locally while they generally do not trust the dealers and this section is concluded.

4.4 Analysis Of Factors That Influence Dealers Acceptance

This section will be testing and analysing all the data taken from the survey that are formed through the hypotheses. These data will also be tested with Pearson correlation to confirm the connection (Obilor & Amadi, 2018) between the assuming relevant factors and if better reputation will gain more consumer’s trust. The final test will be using multivariate regression to approximate the significance of each factor (Nimon & Oswald, 2013).

4.4.1 Listed Dealers 

Figure 21. Chart display of respondents on listed dealers from Google Form

Table 21. Mean, standard deviation, skewness and kurtosis of Q21 from SPSS

In Figure 21, the majority of the respondents prefer to buy their used car from listed dealers. 70 of them at 36.3% and 107 at 55.4% agree and strongly agree respectively to this factor. This has exhibited the respondents are highly in favour of dealers who are being regulated by the stock exchange. 15 (7.8%) of them stay neutral to this factor and 1 (0.5%) disagree that being listed is important. In Table 21, the mean is between agree and strongly agree which has further confirmed the importance of this factor. The standard deviation is 0.662 which is relatively low as long as it is below 1 (Barde & Barde, 2012). It has a -0.966 skewness which shows a gradual slope towards right with a long left tail, there is no anomaly presented in such a presentation. The low positive Kurtosis of 0.272 implies there is a sign of low acuteness.

4.4.2 Selling Premium Cars 

Figure 22. Chart display of respondents on premium cars from Google Form

Table 22. Mean, standard deviation, skewness and kurtosis of Q23 from SPSS

If a dealer is able to sell premium cars it will mean they have a good financial backing and less likely they will default or risk their reputation. Figure 22 shows 122 (63.2%) respondents which consist of more than half of them are strongly agreed in this factor. Another 69 respondents agree that made up of 35.8% while only 1 (0.5%) stays neutral and another 1 (0.5%) disagree. In this factor, 99% of the respondents prefer a dealer who deals with premium cars. Table 22 shows a high mean of 4.62 telling us averagely the respondents strongly agree. This standard deviation of 0.528 says the respondents are united in their choice and it has a high long left tail with -1.121 skewness. However the Kurtosis of 1.52 means the results have high anomaly.

4.4.3 Long History In Market 

Figure 23. Chart display of respondents on long history from Google Form

Table 23. Mean, standard deviation, skewness and kurtosis of Q25 from SPSS

Local used car dealers come and go on a regular basis and there are not many used car dealers that can survive the market for more than 10 years. With 15 years of business, a dealer can be quite stable and that is why in Figure 23 high number of 108 (56%) respondents agree to this factor. While not as important, only 82 (42.5%) strongly agree that history plays a large part in trust. 2 (1%) respondents stay neutral while only 1 (0.5%) disagree with this factor. The mean of this factor from Table 23 confirms the factor is leaning towards only agreeing. Standard deviation is low showing their general decision is generally close to the mean. The skewness of -0.304 shows a gentle slope which may not be the case as it could be averaged down with a declining right. Kurtosis of 0.331 is normal. 

4.4.4 Listing Large Number Of Cars

Figure 24. Chart display of respondents on large listing from Google Form

Table 24. Mean, standard deviation, skewness and kurtosis of Q27 from SPSS

Commonly used car dealers have less than 20 cars under their purview and those with above 50 cars are considered a sizable dealer. Figure 24 shows the respondents are in high consensus with 85 (44%) agree and 107 (55.4%) strongly agree that a dealer should have a huge car listing. Only 1 (0.5%) remain neutral which is insignificant. Having a large listing represents the consumer trust and financial capability. The mean in Table 24 shows an average of strongly agreed is the preferred choice. With low standard deviation of 0.509, the respondents have a very similar consideration about the importance of this factor. There is a low negative skewness of 0.319 which spawn a slow short left tail because the Kurtosis is -1.616 which depicts relatively flat chart results.

4.4.5 Highly Exposed In Media

Figure 25. Chart display of respondents on highly exposed from Google Form

Table 25. Mean, standard deviation, skewness and kurtosis of Q29 from SPSS

High exposure in the media does not necessarily reflect a positive reputation as any dealer can pay for advertisement fees to be exhibited online. This could be the reason why 9 respondents who made up to 4.7% chose neutral for this factor. It may still be important to the respondents as there are 77 (39.9%) who agree and 105 (55.4%) opt for strongly agree. Interestingly no one disagrees that being exposed in the media is not a factor. Even with more respondents selected neutral in this question, the mean as shown in Table 25 gives an average of strongly agreed. The standard deviation of 0.587 reflects low variation and denotes most respondents are aligned. The mid-high negative skewness of -0.732 draws a long left tail as there is 0 disagreement. A negative Kurtosis of -0.433 explains there is no drastic selection.

4.4.6 Highly Rated By Reviewers 

Figure 26. Chart display of respondents on highly rated from Google Form

Figure 26. Chart display of respondents on highly rated from Google Form

Ratings are usually given by direct customers who have gone through the entire sales process and therefore qualified to give a review, however there are unscrupulous dealers creating fake accounts to boost their rating. Figure 26 shows there is 0 disagreement and only 2 respondents selected neutral. 60 (31.1%) of the respondents agree to this factor and more than half respondents in the number of 131 (67.9%) strongly agreed. Out of the all 8 factors, being rated by reviewers received the highest numbers of “strongly agree” which could tell us a certain modern personality. The mean in Table 26 shifts toward strongly agree and a low standard deviation of 0.494 confirmed most results are close to the mean. This factor has a high negative skewness due to the high agreement versus disagreement. The surprisingly low negative Kurtosis of -0.417 states there is no anomaly in the response.

4.4.7 Car Servicing 

Figure 27. Chart display of respondents on car servicing from Google Form

Table 27. Mean, standard deviation, skewness and kurtosis of Q17 from SPSS

With price not in the picture, the condition of the car is one of the most crucial factors when it comes to functionality. It totally defeats the purpose to purchase a car and it cannot really be used because of the constant breaking down. Most respondents agree to this factor except 1 (0.5%) respondents disagree and 11 (5.75%) of them remain impartial. 86 (44.6%) of the respondents agree while almost half of the respondents of 95 (49.2%) strongly agree with this. A mean of 4.42 in Table 27 shows most respondents agree to this factor and the middle low standard deviation of 0.626 tells us all the respondents are not too far from the mean. The skewness measured at -0.744 showing gradual build up on the agreements while positive low Kurtosis of 0.218 signifies a little extreme sign for this data.

4.4.8 Consignment

Figure 28. Chart display of respondents on consignment from Google Form

Table 28. Mean, standard deviation, skewness and kurtosis of Q20 from SPSS

The last factor to be analysed in the section is whether respondents prefer consignment arrangement. Consignment has less dealership elements as it is a peer-to-peer deal, however the consumer may want a dealer to buy their car at a lower amount with the fact that the sales can be immediate. This theory is derived as Figure 28 has the highest selection of neutral with 28 (14.5%) response as compared to other factors. 1 (0.5%) disagree and 71 (36.8%) strongly agree. 93 (48.2%) of the respondents, which made up to almost half of the sample voted for agreement. The mean in Table 28 also favours a low agree and the results revolve around it with a low standard deviation of 0.701. Again the skewness is -0.412 which leans towards the agreeing chunk and the low negative Kurtosis shows a stable sample data.

4.4.9 Pearson Correlation Analysis 

Pearson correlation coefficient is considered the best statistical tool that tests against the relatedness or connections between a couple of constants (Frey, 2018). There is only +1 and -1 variation with Pearson correlation coefficient (Ratner, 2009). A negative value of Pearson correlation value R signifies the tested variables are not related while a positive 0.3 to 0.7 measurement spells a weak to medium relationship and anything above 0.7 is considered a strong interrelation (Myers & Sirois, 2004). Besides the value R, the Sig (2-tailed) is to test if between the null and alternative hypothesis where if the p-value is smaller than 0.05 it is safe to reject the null and accept the alternative (Greenland, et al., 2016). The mean scores that are tested (Table 21 to Table 28) for all 8 factors are between agree and strongly agree which could have confirmed the hypotheses however it could not be an adequate deduction. Therefore a correlation test will be done to all the factors against the acceptance of used car dealers with an additional null and alternate hypothesis.

Table 29. Pearson correlation of the 8 factors from SPSS

Listed dealer on H1A:

H0: There is no interconnection between respondent acceptance and listed dealer

HA: There is an interconnection between respondent acceptance and listed dealer

Table 29 shows a low correlation of 0.224 and has very low sampling error or 0.002 p-value (Sig. 2-tailed). Even the values are not significant, H0 is therefore rejected, HA is accepted and H1A is also accepted.

Premium cars on H1B:

        H0: There is no interconnection between respondent acceptance and premium cars

HA: There is an interconnection between respondent acceptance and premium cars

The R value for this factor is -0.030 and has a p-value of 0.681. The negative R value together with high p-value mark the acceptance of H0, rejection of HA and also the rejection of H1B.

Long history on H1C:

        H0: There is no interconnection between respondent acceptance and long history

HA: There is an interconnection between respondent acceptance and long history

Interesting the R value and p-value for long history are both 0.114. Even with a low positive correlation, the high p-value made H0 being accepted, HA being rejected and H1C also being rejected.

Large listing on H1D:

        H0: There is no interconnection between respondent acceptance and large listing

HA: There is an interconnection between respondent acceptance and large listing

It has a R value of 0.257 which is the highest amount of all the factors and a perfect 0 p-value. This has gladly been identified as the most relevant factor and therefore H0 is rejected, HA is accepted and H1D is also accepted.

Highly exposed on H1E:

        H0: There is no interconnection between respondent acceptance and highly exposed

HA: There is an interconnection between respondent acceptance and highly exposed

This is an unfortunate case where the R value is a positive 0.140 while the p-value is 0.052 which is slightly over the accepted range. Due to the tiny gap, impact of this factor and the small amount of accepted hypotheses, the researcher decided to reject H0, accept HA and accept H1E.

Highly rated on H1F:

        H0: There is no interconnection between respondent acceptance and highly rated

HA: There is an interconnection between respondent acceptance and highly rated

It has the most irrelevant R value of -0.216 as compared to other factors and a low p-value of 0.003. Hence, H0 is accepted, HA is rejected and H1F is also rejected.

Car servicing on H1G:

        H0: There is no interconnection between respondent acceptance and car servicing

HA: There is an interconnection between respondent acceptance and car servicing

Similarly to H1B where the R value of this factor is -0.52 and a high p-value of 0.474. The double irrelevancy cause the H0 to be accepted, HA to be rejected and H1G also to be rejected.

Consignment on H1H:

        H0: There is no interconnection between respondent acceptance and consignment

HA: There is an interconnection between respondent acceptance and consignment

The last factor has a weak R value of 0.215 and a tiny p-value of 0.003. With such scoring, H0 is rejected, HA is accepted and H1G is also accepted.

4.4.10 Multivariate Regression Analysis 

As per section 4.4.9, only the hypotheses H1A (listed dealer), H1D (large listing), H1E (highly exposed) and H1H (consignment) are accepted. Since only 4 out of 8 factors are accepted, it is important to find out how significant each of the factors is to the dependent variable. To ensure there is no gap in the accepted factors, a collective significance test will be conducted using a one-way analysis of variance or ANOVA (Douglas & Michael, 1991). The higher the F-value from the regression model shows a higher significance amongst the constants (Glantz & Slinker, 2001). T-test will then be performed on each individual factor to determine their ranking of significance. The higher the t-value, the more significant it is (Bishara & Hittner, 2012).

Table 30. Regression analysis on ANOVA for the accepted hypotheses

The p-value (Sig.) of 0.000 from Table 30 has shown zero sampling fault while the F value is only 6.709. Even though this is not a large number, the collective factors do show a relevancy to the dependent variable. Since the F value and p-value are both validated, the data henceforth is deemed usable.

Table 31. Regression analysis on T-test for the accepted hypotheses

Consignment is ranked number 1. The t-value of this factor from Table 31 stands the highest at 2.567 and a lowest  p-value (Sig.) among the rest of the factors that stands at 0.011. As the highest t-value and the lowest p-value this has become the most significant factor.

Large listing is ranked number 2. This factor has a second highest t-value of 2.130 and a second lowest p-value of 0.034. This is the other factor that has a t-value of more than 2 and a p-value of less than 0.1.

Listed dealer is ranked number 3. With a 1.445 t-value and a p-value of 0.150, this factor is placed at the second last place of importance.

Highly exposed is ranked number 4. Even if it is ranked last, at least this factor falls into the accepted group. Based on Table 31, its t-value is a low 1.154 and the p-value is a high of 0.250.

With these, it concluded the analysis that will influence the acceptance of the used car dealers.

4.5 Analysis Of Improvements Outcome 

This final section will analyse the consumer choice on what are their expectations for an ideal used car dealer. It will provide recommendations and insights for the next chapter. As these questions are expected to mimic an exemplary dealer, the data here should not be related to any of the above analysis. Each of the subsections will explain the importance of the question.

4.5.1 Sales Commitment Analysis 

Figure 29. Respondents buying trigger (Chart from Google Form)

The question related to this analysis is the final trigger factor that will entice a consumer to make their sale commitment by placing a deposit with the dealer. Direct questions like this will either get the immediate truth or it forces the respondent to give a presume answer (Burns & Kho, 2015) which may not necessarily reflect the truth. This question however if done correctly can understand what is the core motivation factor and reduces the company’s overhead by cutting unnecessary sales activities (Cugini, et al., 2007). From Figure 29, almost half of the respondents voted the stronger sales trigger to be able to send the vehicle for inspection standing at 91 (47.2%) counts. It shows that the important factor about buying a car is its functionality and therefore to ensure the car is working is more crucial than any other matters. 41 (21.2%) choose low mileage and 39 (20.2%) choose not to pay a deposit which is out of the market norm. 22 (11.4%) selected if the dealer provides in-house loans which is common for consumers who cannot get financial institution facilities. None picked pristine conditions and traded in options.

4.5.2 Sales Process Analysis

Figure 30. Respondents ideal transaction (Chart from Google Form)

Besides the primary factors of committing to a sale, Q34’s question pertains to the secondary process of accepting the vehicle. With the current social issues, many individuals like to brag about how busy they are and in due course they believe they actually are (Vercruyssen, et al., 2014). Bringing the ease of convenience to this group of consumers will become a huge advantage. As it may not seem to be the most important factor, a process hiccup can disgust the consumer and make the eventual sale unsuccessful (Guido, et al., 2018). In Figure 30, the majority of the respondents standing at 106 (72.1%) prefer a speedy sales process. This answer speaks for the fact that many people lose motivation or enthusiasm fast (Bargh, 2002) and therefore they would be excited to want to quickly receive their stuff before it happens. The second biggest chunk with 27 (18.4%) of them choose transparency during a consignment deal. The lesser significance are salesperson to handhold at 9 (6.1%), car being delivered at 3 (2%) and money held by escrow at 2 (1.4%). None has chosen the option of washed and full tank of gas.

4.5.3 Sales Deposit Analysis 

Figure 32. Respondents deposit percentage (Chart from Google Form)

Figure 33. Respondents deposit amount (Chart from Google Form)

Cash flow in most cases is more important than profit for the survivability of a company (Stice, et al., 2017). Getting deposit as a form of commitment is a healthy method of generating cash flow therefore Q35 and Q36 addresses the willingness of how much more deposit a consumer is willing to pay when they encounter an ideal dealer. Besides being the heartbeat of a firm, higher deposit will discourage the buyer from backing out due to frivolous or invalid reasons (Donnely Jr & Ivancevich, 1970).  Even when a dealer has done their part to fulfil all of the customer’s requirements, from Figure 32 and Figure 33 it shows that consumers would still not be willing to increase their deposit amount. Ridiculously 21 (10.9%) of the respondents actually picked 0% down payment in Figure 32. Almost half of them at 81 (42%) picked 1% while 40 (20.7%) picked 3% and 50 (25.9%) of them are willing to pay a 5% deposit. Only 1 (0.5%) respondent is willing to pay beyond 5% of the deposit. It is intriguing with such a response because the money will have to be paid sooner or later. Anyway, Figure 33 speaks a different scenario as the percentage is converted into dollars. Suddenly more than half of the respondents of 112 (58%) would only pay between $1,000 and $2,000 of deposit. Another huge chunk of 71 (36.8%) want to pay less than $1,000 and only 9 (4.7%) willing to pay between $2,000 and $5,000. 1 (0.5%) hero expressed he or she is willing to pay more than $25,000 which believes to be the same person who selected >5% in Figure 32. None have selected between $5,000 and $25,000. 

Chapter 5. Conclusion And Recommendation

5.1 Summary Of The Acceptance Of Used Car Dealership 

As road transportation is the most common infrastructure in the world (Berg, et al., 2015), it will continue to be with better technology such as electrical vehicles. Cars will continue to be a huge necessity in Singapore even if the transport infrastructure here is among the best in the world (May, 2004). Used cars business on the other hand will also continue to thrive because of its affordability. The reputation of the used car dealers may have improved along the way but through this research there are more surprising results. Such results will be summarised in the following research objectives, followed by some recommendations for this industry to improve themselves and this paper will end with its research limitation. The information in this research aims to provide the dealers a grasp on how to provide a sustainable and ethical business while also to educate the consumers on what are the key areas to be concerned of when buying a used car.

5.2 Research Objectives Summary

5.2.1 Research Objective 1

To evaluate on what are the preferred choices of consumers on used car dealers based on the dealers’ image and reputation.

The 8 hypotheses are formed to learn if the consumers prefer the used car dealers to be in a certain category. As consumers are swayed by perceived reputation and image (Jung, 2016), they might not really know which category is the best for them. It is considered that this research objective has been met as the survey result and the analysis has proven it to be relevant. Even though only 4 out of 8 hypotheses have been accepted, analysing the survey result alone have proven high agreement from the consumers. Listed dealers have achieved 91.7% agreement, premium cars have achieved 99% agreement, long history have received 98.5% agreement, large listing have received 99.4%, highly exposed have received 95.3% agreement, highly rated have received 99% agreement, car servicing have received 93.8% agreement and consignment have received 85% agreement. Every factor has shown to achieve high agreement which reflects on the consumers’ preferred choices. It does raise some suspicion as there are minimal to none disagreements and therefore additional cross checking using Cronbach's alpha is performed. The results are quite unfortunate as most alpha are below the acceptance range. These factors will eventually be a guideline for the used car dealers to know how they can shape up their business accordingly. However, even with these facts humans can be subjected to confirmation bias where they choose to only believe in what they want to know . Besides to the used car dealers, consumers can also refer to such information to better gauge who they want to buy their car from.

5.2.2 Research Objective 2

To derive a profile of a consumer who prefers used car dealers with better image and reputation.

Knowing the market is important as a company needs to plan and predict their strategy to suit the market’s needs. Market segmentation however is more important (Fonseca, 2011) as it will help to identify the tappable market instead of spending resources to do a blanket cover with low efficiency. Respondents from different profiles have reacted differently to the reputation and image of the used car dealers. The reaction also depends highly on whether they need or be able to afford the car at the time of the survey. With clear identification of the profiles, this research objective is also considered as met. The data from this objective are being cross tabulated to associate the results to their segment and performed a Chi-square test to calculate their dependency. To further confirm, the right respondents have been surveyed for the fitness of defining consumer profile, following are several car ownership results. 99.5% of the respondents are drivers, 84% have purchased a used car before and 98.9% of them currently own a car. With such high numbers of qualified respondents, 3 out of 4 profiles namely the consumer’s age, consumer’s profession and consumer’s annual income are being accepted. Consumer’s education level is being rejected though. Especially with the hit of COVID-19 pandemic, school does not have much impact on the buying decision (Mehta, et al., 2020). It is how the society and retailers who shaped the buying behaviour (Stankevich, 2017). When a used car dealer understands the range of their customer for example, using the annual income as a measurement to determine what the customer wants, the dealers will be able to more effectively build up their image and reputation in that area. 

5.2.3 Research Objective 3

To discover which element of the used car dealers is the most important factor that contributes to a good image and reputation.

Of all the 8 factors discussed in section 5.2.1, only 4 are accepted. Out of the 4, a T-test is done using regression analysis to rank the 4 factors. As important as every factor, there ought to be some that are more important than the others. In a perfect scenario, a dealer can focus to work on every factor however since it would be unrealistic to implement so many changes at a go (Kotter & Schlesinger, 1979). Realistically, dealers can work on the most important factor or the most achievable factor first. How achievable that factor is can be determined by measuring the effectiveness of resources spent over the returns generated (Kasim, et al., 2018). This objective also has been met and has explained how attainable are the four factors. Consignment being the most important factor can be easily achieved by not buying in the car and enabling a peer-to-peer way of transaction. Large listing being the second can be achieved by lowering the commission to introduce more sellers to list with the dealer. Being listed in the stock market is the third most important however it is very difficult to achieve as the stock exchange is a highly regulated platform. Highly exposed is the least important and can be easily accomplished with more advertising budget. It seems like converting a traditional used car dealership business to consignment will help to improve the dealer’s reputation and budget. Consignment will ease the tension between buyer and seller as the middleman has little to lose (Valentini & Zavanella, 2003). As compared to a dealer purchasing a car, his main goal is to reduce the depreciation. The buyer in this case knows the car is with the seller and usually a layman would not know how to buff up a car unethically. By having more consignment cars for sales exhibited the transparency of the dealer and therefore helps improve their reputation and image.

5.2.4 Research Objective 4

To learn how a buyer’s circumstances will change his or her perception and acceptance of the used car industry.

This will be the most interesting objective as when people with different car usage and ownership reasons will react to used car dealers differently. Even when the reputation and image is as bad, the necessity of a car or the financial situation will encourage certain people to still patronise the used car dealers. This last objective is only considered to be somewhat met as there are assumptions being considered instead of mathematical tests. The only question that was not being analysed from the survey in chapter 4 is question number 13 as that was a straightforward answer. In appendix 7.2, only 47.2% agrees that the used car dealers are trustworthy while 25.4% finds the used car dealers are trustworthy. However, when testing the dependent variable it shows that only 4.7% of the respondents would not want to buy a used car from the dealer. This shows a contradicting point where desperate consumers with few alternatives have to go for choices they do not like (Bellezza, et al., 2017). High number of respondents also selected the reasons they would buy a car is due to work and family commitment while the reason not to buy a car is due to its affordability and maintenance. Further to that in section 4.2.4, respondents with lower income tend to not buy or trust the used car dealers. It will have something to do with their financial situation as they only have the budget to purchase an item, if that item is not what it promises to be, additional costs will be needed to repair it. The lack of confidence and uncertainty of a consumer will result in them avoiding the risks altogether (Locander & Hermann, 1979). On the other hand, the dealers may exploit the desperate customers after knowing their circumstances. This is why there are some recommendations being put together in the next sections to improve the confidence of a consumer.

5.3 Recommendations

Even though the used car dealers may have been practising what they did for a prolonged period, it is still possible to change for the better through infusion of the right knowledge (Emberson, 2016) which would be shared from the survey results. Before going into the 3 recommendations for the process, it is wise to recap on the realistic reputation and image recommendation. A used car dealer can have more car consignment arrangement, enlarge their car listings and create more media presence through online or offline means. The first recommendation is to enable the buyer to inspect the vehicle using their own trusted workshop. This will grant immense confidence to the seller for showing them there is nothing to hide and open for transparency. To further enhance that experience and if the result is acceptance, the car should be remained at the workshop till collection to ensure nothing could go wrong in between. The second recommendation is to speedily receive the car. There are scenarios where a car transaction can take place within the day itself as for those who require car loans can get same day approval and the insurance generally does not require additional underwriting if the driver has a clean history. This however conflicts with the first point as inspection will usually take a few days and therefore the second recommendation will go well with warranty from a reputable workshop. The last recommendation is to collect as little deposit as possible as a gesture of goodwill. By collecting lesser deposits and enabling full refund it will also boost the buyer’s confidence by telling them this car can be sold easily so it is not necessary for an obligatory commitment.

5.4 Research Limitations

The relationship between the reason owning a car and their likelihood to buying from a used car dealer based on the reputation and image still remain a little vague. The questionnaires analysed on section 4.3.4, 4.3.5 and 4.3.6 did show a link on how usage behaviour and affordability can affect their buying decision. However, the scale of sacrifice and acceptance remains unclear. A consumer could know the used car dealer is only a little unethical and he may urgently require a car so will go ahead with the purchase. On the other hand a consumer who knows the used car dealer is gravely unethical and only needs a car in a leisure manner will not go ahead with the purchase. To find the balance the neutral party should know the ethicality of the used car dealer without bias and perhaps need but not desperately require a car. The difference in level will result in different research results therefore this survey may be unable to adequately dig into the precise answers.

Another problem is the timeline of when was the last time the respondents made a transaction. A person’s perception can be easily influenced (Brown, 1995) be it through friends, news or when an incident happened to him a long time ago. There could be people who last bought a car decades ago and that experience may be different at that time. It is either additional questions to be asked or to impose more filters on the sample group.

The last issue that has been identified is the choice of disagreeing to agree. There are way too many debates on optimising or disagreeing the Likert scale (Hodge & Gillespie, 2003) and this research shall skip that discussion altogether. Instead, people often have no idea what they want (Rassin, 2003) and therefore choose an answer based on convenience. As most people want to give a perfect or model answer (Bergen & Labonté, 2020), they would be more diplomatic in answering the survey questions. This will defeat the true purpose of the survey and generate irrelevant data.

All in all there are still gaps in this research and there are many areas that could be improved. The depth of analysis is too shallow and many considerations are still missing from it. 

Bilblography

1. Aaker, D., 2009. Managing brand equity. s.l.:Simon and schuster.
2. Abeysinghe, T. & Choy, K., 2007. The Singapore economy: An econometric perspective. Volume 70.
3. Abraham, H., McAnulty, H., Mehler, B. & Reimer, B., 2016. Case study of today’s automotive dealerships: Introduction and delivery of advanced driver assistance systems. Transportation research record, 2660(1), pp. 7-14.
4. Agnihotri, R. & Krush, M., 2015. Salesperson empathy, ethical behaviors, and sales performance: The moderating role of trust in one's manager. Journal of Personal Selling & Sales Management, 35(2), pp. 164-174.
5. Ahmadi, R. & Yang, B., 2000. Parallel imports: Challenges from unauthorized distribution channels. Marketing Science, 19(3), pp. 279-294.
6. Akuh, R. & Agyeman, P., 2019. Assessing the Knowledge and Impact ofAdvanced Automobile Technology on Repair Business of Informal Automobile Mechanics in Ghana. International Journal of Science, Engineering and Technology Research (IJSETR), 8(3), pp. 2278-7798.
7. Al-Saadi, H., 2014. Demystifying Ontology and Epistemology in research methods.. Research Gate, 1(1), pp. 1-10.
8. Albaum, G., 1997. The Likert scale revisited. Market Research Society Journal, 39(2), pp. 1-21.
9. Allen, I. & Seaman, C., 2007. Likert scales and data analyses. Quality progress, 40(7), pp. 64-65.
10. Ambady, N., Hallahan, M. & Rosenthal, R., 1995. On judging and being judged accurately in zero-acquaintance situations. Journal of Personality and Social Psychology, 69(3), p. 518.
11. Anderson, E., 1998. Customer satisfaction and word of mouth. Journal of service research, 1(1), pp. 5-17.
12. Appelbaum, D., Kogan, A., Vasarhelyi, M. & Yan, Z., 2017. Impact of business analytics and enterprise systems on managerial accounting. International Journal of Accounting Information Systems, Volume 25, pp. 29-44.
13. Apuke, O., 2017. Quantitative research methods: A synopsis approach. Kuwait Chapter of Arabian Journal of Business and Management Review, 33(5471), pp. 1-8.
14. Asada, A. & Ko, Y., 2016. Determinants of word-of-mouth influence in sport viewership. Journal of Sport Management, 30(2), pp. 192-206.
15. Auh, S. & Merlo, O., 2012. The power of marketing within the firm: Its contribution to business performance and the effect of power asymmetry. Industrial Marketing Management, 41(5), pp. 861-873.
16. Ayanwale, A., Alimi, T. & Ayanbimipe, M., 2005. The influence of advertising on consumer brand preference. Journal of social sciences, 10(1), pp. 9-16.
17. Azungah, T., 2018. Qualitative research: deductive and inductive approaches to data analysis. Qualitative Research Journal, 18(4), pp. 383-400.
18. Balla-Elliott, D. C. Z. G. E. L. M. a. S. C., 2020. Business reopening decisions and demand forecasts during the COVID-19 pandemic (No. w27362). National Bureau of Economic Research..
19. Banerjee, A. & Chaudhury, S., 2010. Statistics without tears: Populations and samples. Industrial psychiatry journal, 19(1), p. 60.
20. Barde, M. & Barde, P., 2012. What to use to express the variability of data: standard deviation or standard error of mean?. Perspectives in clinical research, 3(3), p. 113.
21. Bargh, J., 2002. Losing consciousness: Automatic influences on consumer judgment, behavior, and motivation. Journal of consumer research, 29(2), pp. 280-285.
22. Beck, J., Scholer, A. & Schmidt, A., 2017. Workload, risks, and goal framing as antecedents of shortcut behaviors. Journal of Business and Psychology, 32(4), pp. 421-440.
23. Bell, E. & Bryman, A., 2007. The ethics of management research: an exploratory content analysis. British journal of management, 18(1), pp. 63-77.
24. Bell, G., 1967. Self-confidence and persuasion in car buying. Journal of Marketing Research, 4(1), pp. 46-52.
25. Bell, G., 1967. The automobile buyer after the purchase.. Journal of Marketing, 31(3), pp. 12-16.
26. Bellezza, S., Paharia, N. & Keinan, A., 2017. Conspicuous consumption of time: When busyness and lack of leisure time become a status symbol. Journal of Consumer Research, 44(1), pp. 118-138.
27. Ben-Akiva, M. & Boccara, B., 1995. Discrete choice models with latent choice sets. International journal of Research in Marketing, 12(1), pp. 9-24.
28. Ben-Ze'ev, A., 1992. Envy and inequality. The Journal of Philosophy, 89(11), pp. 551-581.
29. Berg, C., Deichmann, U., Liu, Y. & Selod, H., 2015. Transport policies and development. The World Bank.
30. Bergen, N. & Labonté, R., 2020. “Everything Is Perfect, and We Have No Problems”: Detecting and Limiting Social Desirability Bias in Qualitative Research. Qualitative Health Research, 30(5), pp. 783-792.
31. Berndt, A., 2020. Sampling Methods. Journal of Human Lactation, 36(2), pp. 224-226.
32. Biemer, P., 2009. Measurement errors in sample surveys. In Handbook of Statistics, Volume 29, pp. 281-315.
33. Bigne, E., Ruiz, C. & Sanz, S., 2005. The impact of internet user shopping patterns and demographics on consumer mobile buying behaviour. Journal of Electronic Commerce Research,, 6(3), p. 193.
34. Bishara, A. & Hittner, J., 2012. Testing the significance of a correlation with nonnormal data: comparison of Pearson, Spearman, transformation, and resampling approaches. Psychological methods, 17(3), p. 399.
35. Bishop, L., 2007. A reflexive account of reusing qualitative data: Beyond primary/secondary dualism. Sociological Research Online, 12(3), pp. 43-56.
36. Bliss, C. a. T. R., 1997. Does competition kill corruption?. Journal of political economy, 105(5), pp. 1001-1023.
37. Boksberger, P., Dolnicar, S., Laesser, C. & Randle, M., 2001. Self-congruity theory: To what extent does it hold in tourism?. Journal of Travel Research, 50(4), pp. 454-464.
38. Borden, N., 1964. The concept of the marketing mix.. Journal of advertising research, 4(2), pp. 2-7.
39. Bornstein, M., Jager, J. & Putnick, D., 2013. Sampling in developmental science: Situations, shortcomings, solutions, and standards. Developmental Review, 33(4), pp. 357-370.
40. Boyd Jr, H. & Westfall, R., 1970. Interviewer bias once more revisited. Journal of Marketing Research, 7(2), pp. 249-253.
41. Braithwaite, J., 1979. An exploratory study of used car fraud. 
42. Brown, A. & Rahhal, T., 1994. A questionnaire study of mnemonically risky behavior.. Applied cognitive psychology, 8(2), pp. 141-154.
43. Brown, S., 1995. Time, change, and motion: The effects of stimulus movement on temporal perception. Perception & psychophysics, Volume 57, pp. 105-116.
44. Bryman, A., 2008. Of methods and methodology. Qualitative Research in Organizations and Management: An International Journal, 3(2), pp. 159-168.
45. Burke, A. & Cowling, M., 2015. The use and value of freelancers: The perspective of managers. International Review of Entrepreneurship, 1(3), pp. 1-14.
46. Burns, K. & Kho, M., 2015. How to assess a survey report: a guide for readers and peer reviewers. Cmaj, 187(6), pp. 198-205.
47. Butu, A. et al., 2020. The impact of COVID-19 crisis upon the consumer buying behavior of fresh vegetables directly from local producers. Case study: the quarantined area of suceava county, Romania. International journal of environmental research and public health, 17(5), p. 5485.
48. Campbell, S. et al., 2020. Purposive sampling: complex or simple? Research case examples. Journal of Research in Nursing, 25(8), pp. 652-661.
49. Case, D., Andrews, J., Johnson, J. & Allard, S., 2005. Avoiding versus seeking: the relationship of information seeking to avoidance, blunting, coping, dissonance, and related concepts.. Journal of the Medical Library Association, 93(3), p. 353.
50. Chan, D. & Perry, M., 2017. Challenges And Opportunities In Media Mix Modeling. , s.l.: s.n.
51. Chang, L., 1994. A psychometric evaluation of 4-point and 6-point Likert-type scales in relation to reliability and validity. Applied psychological measurement, 18(3), pp. 205-215.
52. Chen, Y., Shek, D. & Bu, F., 2011. Applications of interpretive and constructionist research methods in adolescent research: philosophy, principles and examples. International journal of adolescent medicine and health, 23(2), pp. 129-139.
53. Chesebro, J. & Borisoff, D., 2007. What makes qualitative research qualitative?. Qualitative research reports in communication, 8(1), pp. 3-14.
54. Chng, S., Abraham, C., White, M. & Skippon, S., 2019. To drive or not to drive? A qualitative comparison of car ownership and transport experiences in London and Singapore. Transportation Research Interdisciplinary Perspectives, Volume 2, p. 100030.
55. Chu, S., 2015. Car restraint policies and mileage in Singapore. Transportation Research Part A: Policy and Practice, Volume 77, pp. 404-412.
56. Chung, Y. & Kim, A., 2020. Effects of mergers and acquisitions on brand loyalty in luxury Brands: The moderating roles of luxury tier difference and social media. Journal of Business Research, Volume 120, pp. 434-442.
57. CNA, 2019. Customers lose about S$331,000 after sudden closure of 2 car dealers, Singapore: s.n.
58. Coff, R., 1999. How buyers cope with uncertainty when acquiring firms in knowledge-intensive industries: Caveat emptor. Organization Science, 10(2), pp. 144-161.
59. Cohen, L., Manion, L. & Morrison, K., 2017. Research methods in education. Routledge.
60. Crotty, M., 1998. The foundations of social research: Meaning and perspective in the research process.. Sage. .
61. Cugini, A., Caru, A. & Zerbini, F., 2007. The cost of customer satisfaction: a framework for strategic cost management in service industries. European Accounting Review, 13(6), pp. 499-530.
62. Curtis, E., Comiskey, C. & Dempsey, O., 2016. Importance and use of correlational research. Nurse researcher, 23(6).
63. Dahiru, T., 2008. P-value, a true test of statistical significance? A cautionary note. Annals of Ibadan postgraduate medicine, 6(1), pp. 21-26.
64. Dawes, J., Meyer-Waarden, L. & Driesener, C., 2015. Has brand loyalty declined? A longitudinal analysis of repeat purchase behavior in the UK and the USA. Journal of Business Research, 68(2), pp. 425-432.
65. De Pelsmacker, P., Janssens, W. & Mielants, C., 2005. Consumer values and fair-trade beliefs, attitudes and buying behaviour. International Review on Public and Non Profit Marketing, 2(2), pp. 50-69.
66. Deaner, R., Balish, S. & Lombardo, M., 2016. Sex differences in sports interest and motivation: An evolutionary perspective. Evolutionary Behavioral Sciences, 10(2), p. 73.
67. DeFond, M., Francis, J. & Wong, T., 2000. Auditor industry specialization and market segmentation: Evidence from Hong Kong. Auditing: A Journal of Practice & Theory, 19(1), pp. 49-66.
68. Delgado‐Ballester, E. & Munuera‐Alemán, J., 2005. Does brand trust matter to brand equity?. Journal of product & brand management, 14(3), pp. 187-196.
69. Desai, S. & Reimers, S., 2019. Comparing the use of open and closed questions for Web-based measures of the continued-influence effect. Behavior research methods, 51(3), pp. 1426-1440.
70. Devaraj, S., Matta, K. & Conlon, E., 2001. Product and service quality: the antecedents of customer loyalty in the automotive industry. Production and Operations Management, 10(4), pp. 424-439.
71. Di Muro, F. & Noseworthy, T., 2013. Money isn’t everything, but it helps if it doesn’t look used: How the physical appearance of money influences spending. Journal of Consumer Research, 39(6), pp. 1330-1342.
72. Dierdorff, E. & Rubin, R., 2007. Carelessness and discriminability in work role requirement judgments: Influences of role ambiguity and cognitive complexity. Personnel Psychology, 60(3), pp. 597-625.
73. Donnely Jr, J. & Ivancevich, J., 1970. Post-purchase reinforcement and back-out behavior. JMR, Journal of Marketing Research, 7(3), p. 399.
74. Dou, W. J. Y. a. W. W., 2019. Competition, profitability, and risk premia. , s.l.: The Jacobs Levy Equity Management Center for Quantitative Financial Research Working Papers Series..
75. Douglas, C. & Michael, F., 1991. On distribution-free multiple comparisons in the one-way analysis of variance. Communications in Statistics-Theory and Methods, 20(1), pp. 127-139.
76. Douglas, S., Craig, C. & Nijssen, E., 2001. Executive insights: Integrating branding strategy across markets: Building international brand architecture. Journal of International Marketing, 9(2), pp. 97-114.
77. Draper, N. & Smith, H., 1998. Applied regression analysis. John Wiley & Sons, Volume 326.
78. Driscoll, D., 2011. Introduction to primary research: Observations, surveys, and interviews. Writing spaces: Readings on writing, Volume 2, pp. 153-174.
79. Eastham, K., Coates, D. & Allodi, F., 1970. The concept of crisis. Canadian Psychiatric Association Journal, 15(5), pp. 463-472.
80. Ebrahim, R. G. A. I. Z. a. F. Y., 2016. A brand preference and repurchase intention model: the role of consumer experience. Journal of Marketing Management, 32(13-14), pp. 1230-1259.
81. Elfil, M. ；. N. A., 2017. Sampling methods in clinical research; an educational review. Emergency, 5(1).
82. Elshiewy, O., Guhl, D. & Boztug, Y., 2017. Multinomial logit models in marketing-from fundamentals to state-of-the-art.. Marketing ZFP, 39(3), pp. 32-49.
83. Emberson, L., 2016. Gaining knowledge mediates changes in perception (without differences in attention): A case for perceptual learning. Behavioral and Brain Sciences, Volume 39, pp. 1-77.
84. Emons, W. & Sheldon, G., 2009. The market for used cars: new evidence of the lemons phenomenon. Applied Economics, 41(22), pp. 2867-2885.
85. Engel, J., 1988. Polytomous logistic regression.. Statistica Neerlandica, 42(4), pp. 233-252.
86. Erba, J. et al., 2018. ampling methods and sample populations in quantitative mass communication research studies: A 15-year census of six journals. Communication Research Reports, 35(1), pp. 42-47.
87. Etikan, I., Musa, S. & Alkassim, R., 2016. Comparison of convenience sampling and purposive sampling. American journal of theoretical and applied statistics, 5(1), pp. 1-4.
88. Fader, P., Lattin, J. & Little, J., 1992. Estimating nonlinear parameters in the multinomial logit model.. Marketing Science, 11(4), pp. 372-385.
89. Farm, A., 2020. Pricing in practice in consumer markets.. Journal of Post Keynesian Economics, 43(1), pp. 61-75.
90. Felix, E., 2015. Marketing challenges of satisfying consumers changing expectations and preferences in a competitive market. International Journal of Marketing Studies, 7(5), p. 41.
91. Felli, J., Noel, R. & Cavazzoni, P., 2009. A multiattribute model for evaluating the benefit-risk profiles of treatment alternatives.. Medical Decision Making, 29(1), pp. 104-115.
92. Festinger, L., 1962. Cognitive dissonance. Scientific American, 207(4), pp. 93-106.
93. Fetscherin, M. & Toncar, M., 2009. Valuating brand equity and product-related attributes in the context of the German automobile market.. Journal of Brand Management, 17(2), pp. 134-145.
94. Field, A., 2013. Discovering statistics using IBM SPSS statistics. s.l.:Sage.
95. Flynn, M., 2017. You Know, It's Just Marketing-Unfair and Deceptive Trade Practices in Car Dealer Buy-Back Offers. In: U. D. L, ed. s.l.:s.n., p. 11.
96. Fonseca, J., 2011. Why does Segmentation Matter? Identifying market segments through a mixed methodology. European Retail Research, 25(1), pp. 1-26.
97. Forganni, A. & Reed, H., 2019. Circumvention of Trade Defence Measures and Business Ethics. Journal of Business Ethics, 155(1), pp. 29-40.
98. Fowler Jr, F. & Fowler, F., 1995. Improving survey questions: Design and evaluation. Sage.
99. Freund, P. & Martin, G., 1996. The commodity that is eating the world: The automobile, the environment, and capitalism. Capitalism Nature Socialism, 7(4), pp. 3-29.
100. Frey, B., 2018. The SAGE encyclopedia of educational research, measurement, and evaluation. Sage Publications.
101. Frost, P. et al., 2015. The influence of confirmation bias on memory and source monitoring. The Journal of General Psychology, 142(4), pp. 238-252.
102. Gaucher, B. & Chebat, J., 2019. How uncivil customers corrode the relationship between frontline employees and retailers. Journal of Retailing and Consumer Services, Volume 46, pp. 1-10.
103. Gensch, D. & Recker, W., 1979. The multinomial, multiattribute logit choice model.. Journal of Marketing Research, 16(1), pp. 124-132.
104. Gensch, D. a. G. S., 1997. Differences in independence of irrelevant alternatives at individual vs aggregate levels, and at single pair vs full choice set.. Omega, 25(2), pp. 201-214.
105. Glantz, S. & Slinker, B., 2001. Primer of Applied Regression & Analysis of Variance. McGraw-Hill, Inc., New York.
106. Glass, G., 1976. Primary, secondary, and meta-analysis of research. Educational researcher, 5(10), pp. 3-8.
107. Gnambs, T. & Kaspar, K., 2015. Disclosure of sensitive behaviors across self-administered survey modes: a meta-analysis. Behavior research methods, 47(4), pp. 1237-1259.
108. Goldman, E., 2006. Coasean Analysis of Marketing. L. REv. ed. s.l.:A. Wis. .
109. Gong, X. et al., 2020. Cross-Side Network Effects, Brand Equity, and Consumer Loyalty: Evidence from Mobile Payment Market.. International Journal of Electronic Commerce, 24(3), pp. 279-304.
110. Greenland, S. et al., 2016. Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. European journal of epidemiology, 31(4), pp. 337-350.
111. Gregory, I., 2003. Ethics in research. A&C Black.
112. Grundmann, R., 2017. The problem of expertise in knowledge societies. Minerva, 55(1), pp. 25-48.
113. Guadagni, P. & Little, J., 1983. A logit model of brand choice calibrated on scanner data.. Marketing science, 2(3), pp. 203-238.
114. Guido, G., Pino, G. & Peluso, A., 2018. The impact of disgust on consumers’ purchase intentions: an empirical assessment. Journal of Consumer Marketing, 35(1), pp. 105-115.
115. Gupta, S. et al., 2006. Modeling customer lifetime value. Journal of service research, 9(2), pp. 139-155.
116. Guy, R. & Norvell, M., 1977. The neutral point on a Likert scale. The Journal of Psychology, 95(2), pp. 199-204.
117. Hand, C., 2015. How the Internet Changed History. s.l.:ABDO.
118. Hanson, D. a. G. M., 2007. The mix of qualitative and quantitative research in major marketing journals, 1993‐2002. European journal of marketing, 41(1/2), pp. 58-70.
119. Harris, J., 2012. Data is useless without the skills to analyze it. Harvard Business Review, Issue 13.
120. Hasan, U. & Nasreen, R., 2012. Cognitive dissonance and its impact on consumer buying behaviour. IOSR J Bus Manag, Issue 1, pp. 7-12.
121. Hausman, A., 2000. A multi-method investigation of consumer motivations in impulse buying behavior. Journal of consumer marketing, 17(5), pp. 403-426.
122. Hegelund, E. et al., 2020. The influence of educational attainment on intelligence. Intelligence, Volume 78, p. 101419.
123. Hendrickx, J., 2000. Special restrictions in multinomial logistic regression.. Stata Technical Bulletin, Volume 56, pp. 18-26.
124. Henry, E. & Pene, H., 2001. Kaupapa Maori: Locating indigenous ontology, epistemology and methodology in the academy. Organization, 8(2), pp. 234-242.
125. Hettrick, S. et al., 2014. Uk research software survey 2014. 
126. Hiller, J., 2016. Epistemological foundations of objectivist and interpretivist research. Epistemological Foundations , Volume 11, pp. 99-127.
127. Hilton, J. & McCleary, M., 2019. Preconceived Notions about Poverty Held by Preservice Teachers.. Journal of Teacher Education and Educators, 8(2), pp. 95-114.
128. Hinds, P., Chaves, D. & Cypess, S., 1992. Context as a source of meaning and understanding. Qualitative health research, 2(1), pp. 61-74.
129. Hirschey, M., 1982. Economies of scale in advertising. Managerial and Decision Economics, 3(1), pp. 24-29.
130. Hodge, D. & Gillespie, D., 2003. Phrase completions: An alternative to Likert scales. Social Work Research, 27(1), pp. 45-55.
131. Hohenstein, N., Sirgy, M., Herrmann, A. & Heitmann, M., 2007. Self-congruity: Antecedents and consequences.. Aix en Provance: France University Paul Cezanne, pp. 118-130.
132. Holbrook, M. & Hulbert, J., 1975. Multi-attribute attitude models: A comparative analysis.. ACR North American Advances, Volume 2, pp. 375-388.
133. Honeycutt, E. & Ford, J., 1996. Potential problems and solutions when hiring and training a worldwide sales team. Journal of Business & Industrial Marketing.
134. Horowitz, J., 1991. Modeling the choice of choice set in discrete-choice random-utility models. Environment and Planning a, 23(9), pp. 1237-1246.
135. Hoyer, W. et al., 2020. Consumer behaviour. Cengage AU.
136. Huff, S. & Johnson, A., 2014. Clicking through overload: When choice overload can actually increase choice. Journal of Direct, Data and Digital Marketing Practice, 16(1), pp. 24-35.
137. Hughes, G. a. G. J., 1971. Automobile self-congruity models reexamined.. Journal of Marketing Research, 8(1), pp. 125-127.
138. Hunt, S. & Vitell, S., 2006. The general theory of marketing ethics: A revision and three questions. Journal of macromarketing, 26(2), pp. 143-153.
139. Hyde, K., 2000. Recognising deductive processes in qualitative research. Qualitative market research: An international journal, 3(2), pp. 82-90.
140. Iversen, G., Norpoth, H. & Norpoth, H., 1987. Analysis of variance. Sage, Volume 1.
141. Jager, J., Putnick, D. & Bornstein, M., 2017. II. More than just convenient: The scientific merits of homogeneous convenience samples. Monographs of the Society for Research in Child Development,, 82(2), pp. 13-30.
142. Jamaludin, A. & Ahmad, F., 2013. Investigating the relationship between trust and intention to purchase online. Business and Management Horizons, 1(1), pp. 1-9.
143. Jashari, H., 2016. The First and Second Hands Data in Doctoral Research Work. European Journal of Social Science Education and Research, 3(2), pp. 133-138.
144. Jenn, N., 2006. Common ethical issues in research and publication. Malaysian family physician: the official journal of the Academy of Family Physicians of Malaysia, 1(2-3), p. 74.
145. Jin, Y. et al., 2017. s.l.: s.n.
146. Jordan, S. & Messner, M., 2020. The use of forecast accuracy indicators to improve planning quality: Insights from a case study.. European Accounting Review, 29(2), pp. 337-359.
147. Joshi, A., Kale, S., Chandel, S. & Pal, D., 2015. Likert scale: Explored and explained. Current Journal of Applied Science and Technology, pp. 396-403.
148. Joshi, A., Kale, S., Chandel, S. & Pal, D., 2015. Likert scale: Explored and explained. Current Journal of Applied Science and Technology, pp. 396-403.
149. Jung, N. a. S. Y., 2016. The impact of corporate reputation on brand attitude and purchase intention. Fashion and Textiles, 3(1), p. 20.
150. Kahn, C., Pennacchi, G. & Sopranzetti, B., 2005. Bank consolidation and the dynamics of consumer loan interest rates.. The Journal of Business, 78(1), pp. 99-134.
151. Kapferer, J. & Tabatoni, O., 2011. Are luxury brands really a financial dream. Journal of Strategic Management Education, 7(4), pp. 1-16.
152. Kasim, T., Haracic, M. & Haracic, M., 2018. The improvement of business efficiency through business process management. Economic Review: Journal of Economics and Business, 16(1), pp. 31-43.
153. Kassarjian, H., 1971. Personality and consumer behavior: A review.. Journal of marketing Research, 8(4), pp. 409-418.
154. Kelley, K., Clark, B., Brown, V. & Sitzia, J., 2003. Good practice in the conduct and reporting of survey research. International Journal for Quality in health care, 15(3), pp. 261-266.
155. Kieruj, N. & Moors, G., 2010. Variations in response style behavior by response scale format in attitude research. International journal of public opinion research, 22(3), pp. 320-342.
156. Kilkenny, M. & Robinson, K., 2018. Data quality:“Garbage in–garbage out”, s.l.: s.n.
157. King, G., Chao, X. & Duenyas, I., 2016. Dynamic customer acquisition and retention management. Production and Operations Management, 25(8), pp. 1332-1343.
158. Kiyotaki, N. & Moore, J., 2002. Evil is the root of all money. American Economic Review, 9(2), pp. 62-66.
159. Koh, W. & Lee, D., 1994. The vehicle quota system in Singapore: an assessment. Transportation Research Part A: Policy and Practice, 28(1), pp. 31-47.
160. Komorita, S., 1963. Attitude content, intensity, and the neutral point on a Likert scale. The Journal of social psychology, 61(2), pp. 327-334.
161. Koppelman, F. & Wen, C., 1998. Nested Logit Models Which Are You Using?. Transportation Research Record, 1645(1), pp. 1-7.
162. Kossinets, G. & Watts, D., 2009. Origins of homophily in an evolving social network. American journal of sociology, 115(2), pp. 405-450.
163. Kothari, C., 2004. Research methodology: Methods and techniques.. New Age International. .
164. Kotter, J. & Schlesinger, L., 1979. Choosing strategies for change. Volume Harvard Business Review, pp. 106-114.
165. Kriska, S., Sass, M. & Fulcomer, M., 2013. Assessing Limitations and Uses of Convenience Samples: A Guide for Graduate Students.. 
166. Kurt, D., Inman, J. & Argo, J., 2011. The influence of friends on consumer spending: The role of agency–communion orientation and self-monitoring. Journal of Marketing Research, 48(4), pp. 741-754.
167. Kwok, H. & Lewis, R., 2011. Bayesian hierarchical modeling and the integration of heterogeneous information on the effectiveness of cardiovascular therapies.. Circulation: Cardiovascular Quality and Outcomes, 4(6), pp. 657-666.
168. Landon Jr, E., 1974. Self concept, ideal self concept, and consumer purchase intentions.. Journal of consumer research, 1(2), pp. 44-51.
169. Lavrakas, P., 2008. Encyclopedia of survey research methods. s.l.:Sage Publications.
170. Lee, K. & Lim, G., 2002. Cycle and carriage vs. parallel importers of Mercedes Benz cars. Asian Case Research Journal, 6(1), pp. 45-54.
171. Leech, N., Barrett, K. & Morgan, G., 2014. IBM SPSS for intermediate statistics: Use and interpretation. s.l.:Routledge.
172. Leonard, W. a. W. M., 1970. Automakers and dealers: a study of criminogenic market forces. Law and Society Review, pp. 407-424.
173. Leotti, L., Iyengar, S. & Ochsner, K., 2010. Born to choose: The origins and value of the need for control.. Trends in cognitive sciences, 14(10), pp. 457-463.
174. Leuthesser, L., Kohli, C. & Harich, K., 1995. Brand equity: the halo effect measure.. European journal of marketing, 29(4), pp. 57-66.
175. Liu, Y., Laguna, J., Wright, M. & He, H., 2014. Media mix modeling–A Monte Carlo simulation study.. Journal of Marketing Analytics, 2(3), pp. 173-186.
176. Livingston, E., 2004. The mean and standard deviation: what does it all mean?. Journal of Surgical Research, 119(2), pp. 117-123.
177. Locander, W. & Hermann, P., 1979. The effect of self-confidence and anxiety on information seeking in consumer risk reduction. Journal of Marketing Research, 16(2), pp. 268-274.
178. Loke, A., 2014. The lemon law and the integrated enhancement of consumer rights in Singapore. Sing. J. Legal Stud., p. 285.
179. Loo, W. & Ong, E., 2016. The 2016 Amendments to Singapore's Consumer Protection (Fair Trading) Act-A Missed Opportunity. U. Tas. L. Rev, Issue 36, p. 15.
180. Louviere, J., Pihlens, D. & Carson, R., 2011. Design of discrete choice experiments: a discussion of issues that matter in future applied research. Journal of Choice Modelling, 4(1), pp. 1-8.
181. Lusardi, A. & Mitchell, O., 2014. The economic importance of financial literacy: Theory and evidence.. Journal of economic literature, 52(1), pp. 5-44.
182. Madnick, S., Wang, R., Lee, Y. & Zhu, H., 2009. Overview and framework for data and information quality research. Journal of Data and Information Quality (JDIQ), 1(1), pp. 1-22.
183. Majid, U., 2018. Research fundamentals: Study design, population, and sample size. Undergraduate research in natural and clinical science and technology journal, Volume 2, pp. 1-7.
184. Malhotra, N. & Peterson, M., 2001. Marketing research in the new millennium: emerging issues and trends. Marketing Intelligence & Planning, 19(4), pp. 216-232.
185. Malle, B., Guglielmo, S. & Monroe, A., 2014. A theory of blame. Psychological Inquiry, 25(2), pp. 147-186.
186. Mallette, M. & Barone, D., 2013. On using Google forms. The Reading Teacher, 66(8), pp. 625-630.
187. Markovich, S., 2008. Snowball: A dynamic oligopoly model with indirect network effects. Journal of Economic Dynamics and Control, 32(3), pp. 909-938.
188. Martin, I., Stewart, D. & Matta, S., 2005. Branding strategies, marketing communication, and perceived brand meaning: The transfer of purposive, goal–oriented brand meaning to brand extensions. Journal of the Academy of Marketing Science, 33(3), pp. 275-294.
189. Martínez-Mesa, J. et al., 2016. Sampling: how to select participants in my research study?. Anais brasileiros de dermatologia, 91(3), pp. 326-330.
190. Matsueda, R., 2013. Rational choice research in criminology: A multi-level framework. Handbook of rational choice social research, pp. 283-321.
191. May, A., 2004. Singapore: the development of a world class transport system. Transport Reviews, 24(1), pp. 79-101.
192. May, A., 2004. Singapore: the development of a world class transport system. Transport Reviews, 24(1), pp. 79-101.
193. McCullough, M., Kurzban, R. & Tabak, B., 2013. Cognitive systems for revenge and forgiveness. s.l.:s.n.
194. McFadden, D., 1973. Conditional logit analysis of qualitative choice behavior. Volume 4, pp. 105-142.
195. McHugh, M., 2013. The chi-square test of independence. Biochemia medica: Biochemia medica, 23(2), pp. 143-149.
196. Mehta, S., Saxena, T. & Purohit, N., 2020. The New Consumer Behaviour Paradigm amid COVID-19: Permanent or Transient?. Journal of Health Management, 22(2), pp. 291-301.
197. Meng, Q., Lu, Z. & Ohtman, M., 2015. Determinants of certificate of entitlement premium for cars under vehicle quota system in Singapore. Journal of the Eastern Asia Society for Transportation Studies, Volume 11, pp. 126-140.
198. Min, S., Zhang, X., Kim, N. & Srivastava, R., 2016. Customer acquisition and retention spending: An analytical model and empirical investigation in wireless telecommunications markets. Journal of marketing research, 53(5), pp. 728-744.
199. Mirkin, B., 2001. Eleven ways to look at the chi-squared coefficient for contingency tables. The American Statistician, 55(2), pp. 111-120.
200. Moen, Ø., Havro, L. & Bjering, E., 2017. Online consumers reviews: Examining the moderating effects of product type and product popularity on the review impact on sales. Cogent Business & Management, 4(1), pp. 1-20.
201. Mohr, J. & Nevin, J., 1990. Communication strategies in marketing channels: A theoretical perspective.. Journal of marketing, 54(4), pp. 36-51.
202. Montag, J., 2017. Identifying odometer fraud in used car market data. Transport Policy, Volume 60, pp. 10-23.
203. Morewedge, C. et al., 2020. Evolution of consumption: A psychological ownership framework. Journal of Marketing, 85(1), pp. 196-218.
204. Murphy, P. & Dacin, M., 2011. Psychological pathways to fraud: Understanding and preventing fraud in organizations. Journal of business ethics, 101(4), pp. 601-618.
205. Myers, C., 2003. Managing brand equity: a look at the impact of attributes.. Journal of product & brand management, 12(1), pp. 39-51.
206. Myers, L. & Sirois, M., 2004. Spearman Correlation Coefficients, Differences between. Encyclopedia of statistical sciences.
207. Newman, I., 2000. A Conceptualization of Mixed Methods: A Need for Inductive/Deductive Approach to Conducting Research.. 
208. Nimon, K. & Oswald, F., 2013. Understanding the results of multiple linear regression: Beyond standardized regression coefficients. Organizational Research Methods, 16(4), pp. 650-674.
209. Nisbett, R. E. & Wilson, T. D., 1977. The halo effect: Evidence for unconscious alteration of judgments.. Journal of Personality and Social Psychology, 35(4), pp. 250-256.
210. Noo-urai, N. & Jaroenwisan, K., 2016. Sustainability Marketing: A Changing of Marketing Concept Lead to Sustainable Business.. International Journal of Business and Social Science, 7(4), pp. 114-119.
211. Northcraft, G. & Ashford, S., 1990. The preservation of self in everyday life: The effects of performance expectations and feedback context on feedback inquiry.. Organizational Behavior and Human Decision Processes, 47(1), pp. 42-64.
212. Nuseir, M., 2018. Impact of misleading/false advertisement to consumer behaviour. International Journal of Economics and Business Research, 16(4), pp. 453-465.
213. O'Connor, M., 1989. Models of human behaviour and confidence in judgement: A review.. International Journal of Forecasting, 5(2), pp. 159-169.
214. Obilor, E. & Amadi, E., 2018. Test for Significance of Pearson’s Correlation Coefficient. International Journal of Innovative Mathematics, Statistics & Energy Policies, 6(1), pp. 11-23.
215. Okulicz-Kozaryn, A., N. T. & Tursi, N., 2015. Luxury car owners are not happier than frugal car owners. International Review of Economics, 62(2), pp. 121-141.
216. Olivola, C. & Wang, S., 2016. Patience auctions: The impact of time vs. money bidding on elicited discount rates. Experimental Economics, 19(4), pp. 864-885.
217. Onwuegbuzie, A. & Leech, N., 2005. On becoming a pragmatic researcher: The importance of combining quantitative and qualitative research methodologies. International journal of social research methodology, 8(5), pp. 375-387.
218. Ormston, R., Spencer, L., Barnard, M. & Snape, D., 2014. The foundations of qualitative research. Qualitative research practice: A guide for social science students and researchers, Volume 2, pp. 52-55.
219. Owen, R., 2019. Net Promoter Score and its successful application. In Marketing Wisdom, pp. 17-29.
220. Palinkas, L. et al., 2015. Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Administration and policy in mental health and mental health services research, 42(5), pp. 533-544.
221. Pannucci, C. & Wilkins, E., 2010. Identifying and avoiding bias in research. Plastic and reconstructive surgery, 126(2), p. 619.
222. Paredes-García, W. & Castaño-Tostado, E., 2019. Optimal designs for estimating a choice hierarchy by a general nested multinomial logit model. Communications in Statistics-Theory and Methods, 48(23), pp. 5877-5888.
223. Paterson, J. & Wong, V., 2016. Consumer Protection, Statute and the Ongoing Influence of the General Law in Singapore. SAcLJ, Volume 28, p. 1079.
224. Pignatiello, G., Martin, R. & Hickman Jr, R., 2020. Decision fatigue: A conceptual analysis. Journal of health psychology, 25(1), pp. 123-135.
225. Pimentel, J., 2019. Some Biases in Likert Scaling Usage and its Correction. International Journal of Science: Basic and Applied Research, 41(5), pp. 181-191.
226. Pöppel, E., 1990. The Advantage of Preconceived Ideas.. Interdisciplinary Science Reviews, 15(4), pp. 327-333.
227. Porter, S., Whitcomb, M. & Weitzer, W., 2004. Multiple surveys of students and survey fatigue. New directions for institutional research, Volume 121, pp. 63-73.
228. Qi, Y., Li, Q. & Du, F., 2018. Are Rich People Perceived as More Trustworthy? Perceived Socioeconomic Status Modulates Judgments of Trustworthiness and Trust Behavior Based on Facial Appearance. Frontiers in psychology, Volume 9, p. 152.
229. Ramrattan, L., 2011. Dealership competition in the US automobile industry. The American Economist, 45(1), pp. 33-45.
230. Ramsey, R., Marshall, G., Johnston, M. & Deeter-Schmelz, D., 2007. thical ideologies and older consumer perceptions of unethical sales tactics. Journal of Business Ethics, 70(2), p. 191.207.
231. Rassin, E., 2003. A psychological theory of indecisiveness. Netherlands journal of psychology, 67(1), pp. 1-11.
232. Ratner, B., 2009. The correlation coefficient: Its values range between+ 1/− 1, or do they?. Journal of targeting, measurement and analysis for marketing, 17(2), pp. 139-142.
233. Raudenbush, S. & Liu, X., 2000. Statistical power and optimal design for multisite randomized trials. Psychological methods, 5(2), p. 199.
234. Reis, H. et al., 2011. Familiarity does indeed promote attraction in live interaction. Journal of personality and social psychology, 101(3), p. 557.
235. Richards, K., 2003. Qualitative inquiry in TESOL. Springer.
236. Riley, R. & Van Doren, C., 1992. Movies as tourism promotion: A ‘pull’factor in a ‘push’location. Tourism management, 13(3), pp. 267-274.
237. Romaniuk, J. & Huang, A., 2020. Understanding consumer perceptions of luxury brands. International Journal of Market Research, 62(5), pp. 546-560.
238. Rosenberg, M., 1981. Conceiving the self.. RE Krieger., 6(2), pp. 212-214.
239. Rosenzweig, P., 2014. The halo effect:... and the eight other business delusions that deceive managers.. s.l.:Simon and Schuster. .
240. Rothman, S., 2007. Understanding data quality through reliability: A comparison of data reliability assessment in three international relations datasets. International Studies Review, 9(3), pp. 437-456.
241. Ryan, M. & Bonfield, E., 1975. The Fishbein extended model and consumer behavior.. Journal of consumer research, 2(2), pp. 118-136.
242. Schaeffer, K. & Sclar, E., 2002. The Automobile Era: A Cultural Analysis'. Delivering Sustainable Transport. Emerald Group Publishing Limited, pp. 116-126.
243. Schober, P., Boer, C. & Schwarte, L., 2018. Correlation coefficients: appropriate use and interpretation. Anesthesia & Analgesia, 126(5), pp. 1763-1768.
244. Schwarzkopf, S., 2011. The consumer as ‘‘voter,’’‘‘judge,’’and ‘‘jury’’: Historical origins and political consequences of a marketing myth. Journal of Macromarketing, 31(1), pp. 8-18.
245. Shah, D., Kumar, V. & Zhao, Y., 2015. Diagnosing brand performance: Accounting for the dynamic impact of product availability with aggregate data.. Journal of Marketing Research, 52(2), pp. 146-165.
246. Sharma, G., 2017. Pros and cons of different sampling techniques. International journal of applied research, 3(7), pp. 749-752.
247. Sharp, L. & Frankel, J., 1983. Respondent burden: A test of some common assumptions. Public Opinion Quarterly, 47(1), pp. 36-53.
248. Shephard, R. a. F. R., 1974. The law of diminishing returns. Zeitschrift für Nationalökonomie, 34(1-2), pp. 69-90.
249. Shi, L. et al., 2019. Variable selection and validation in multivariate modelling.. Bioinformatics, 35(6), pp. 972-980.
250. Shim, I. & Von Peter, G., 2007. Distress selling and asset market feedback. Financial Markets, Institutions & Instruments, 16(5), pp. 243-291.
251. Shocker, A. & Srinivasan, V., 1979. Multiattribute approaches for product concept evaluation and generation: A critical review.. Journal of Marketing Research, 16(2), pp. 159-180.
252. Siedlecka, E., Capper, M. & Denson, T., 2015. Negative emotional events that people ruminate about feel closer in time. PloS one, 10(2), p. e0117105.
253. Singer, E. & Couper, M., 2017. Some methodological uses of responses to open questions and other verbatim comments in quantitative surveys. Methods, data, analyses: a journal for quantitative methods and survey methodology (mda), 11(2), pp. 115-134.
254. Singh, J., 1990. Identifying consumer dissatisfaction response styles: An agenda for future research. European Journal of Marketing, 24(6), pp. 55-72.
255. Sirdeshmukh, D., Singh, J. & Sabol, B., 2002. Consumer trust, value, and loyalty in relational exchanges. Journal of marketing, 66(1), pp. 15-37.
256. Sirgy, M., 1986. Self-congruity: Toward a theory of personality and cybernetics.. s.l.:Praeger Publishers/Greenwood Publishing Group. .
257. Sivarajah, U., Kamal, M., Irani, Z. & Weerakkody, V., 2017. Critical analysis of Big Data challenges and analytical methods.. Journal of Business Research, Volume 70, pp. 263-286.
258. Small, K., 1987. A discrete choice model for ordered alternatives. Econometrica: Journal of the Econometric Society, 55(2), pp. 409-424.
259. Soroka, S., Fournier, P. & Nir, L., 2019. Cross-national evidence of a negativity bias in psychophysiological reactions to news.. Proceedings of the National Academy of Sciences, 116(38), pp. 18888-18892.
260. Spence, A., 1980. Notes on advertising, economies of scale, and entry barriers. The quarterly journal of Economics, 95(3), pp. 493-507.
261. Srinivasan, V., 1979. Network models for estimating brand-specific effects in multi-attribute marketing models.. Management Science, 25(1), pp. 11-21.
262. Stankevich, A., 2017. Explaining the consumer decision-making process: Critical literature review. Journal of International Business Research and Marketing, 2(6), pp. 7-14.
263. Stewart, D. & Kamins, M., 1993. Secondary research: Information sources and methods. Sage, Volume 4.
264. Stice, D., Stice, E. & Stice, J., 2017. Cash Flow Problems Can Kill Profitable Companies. SSRN, 8(6), pp. 1-22.
265. Storchmann, K., 2004. On the depreciation of automobiles: An international comparison. Transportation, 31(4), pp. 371-408.
266. Sutton, R., 2010. The boss as human shield. Harvard business review, 88(9), pp. 106-9.
267. Taber, K., 2018. The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48(6), pp. 1273-1296.
268. Taherdoost, H., 2016. Sampling methods in research methodology; how to choose a sampling technique for research. How to Choose a Sampling Technique for Research, Issue April 10, 2016.
269. TAN, Y., 2009. Personal property security interests in Singapore. In The Reform of UK Personal Property Security Law, pp. 429-485.
270. Tansey, O., 2007. Process tracing and elite interviewing: a case for non-probability sampling. PS: Political Science and Politics, 40(4), pp. 765-772.
271. Tavakol, M. & Dennick, R., 2011. Making sense of Cronbach's alpha. International journal of medical education, Volume 2, p. 53.
272. Taylor, C., 2017. Native advertising: The black sheep of the marketing family. International Journal of Advertising, 36(2), pp. 207-209 .
273. Tellis, G., 2006. Modeling marketing mix. In: Handbook of marketing research. s.l.:s.n., pp. 506-522.
274. The Straits Times, 2016. Man jailed over $3.2m used-car scam, Singapore: The Straits Times.
275. The Stratis Times, 2018. Two parallel import car dealers shut suddenly, Singapore: s.n.
276. Thomas, J. W., 2006. "Marketing mix modeling." , s.l.: Retrieved from.
277. TNP, 2014. 40 car buyers look for missing dealer in showroom showdown: 'This is worse than Jover Chew', Singapore: s.n.
278. TNP, 2018. Used-car dealer jailed 11 months for cheating, Singapore: The New Paper.
279. Toker-Yildiz, K., Trivedi, M., Choi, J. & Chang, S., 2017. Social interactions and monetary incentives in driving consumer repeat behavior. Journal of Marketing Research, 53(4), pp. 364-380.
280. Turner, D., 2020. Sampling methods in research design. Headache: The Journal of Head and Face Pain, 60(1), pp. 8-12.
281. Ursachi, G., Horodnic, I. & Zait, A., 2015. How reliable are measurement scales? External factors with indirect influence on reliability estimators. Procedia Economics and Finance, Volume 20, pp. 679-686.
282. Vafeiadis, T., Diamantaras, K., Sarigiannidis, G. & Chatzisavvas, K., 2015. A comparison of machine learning techniques for customer churn prediction. Simulation Modelling Practice and Theory, Volume 55, pp. 1-9.
283. Valentini, G. & Zavanella, L., 2003. The consignment stock of inventories: industrial case and performance analysis. International Journal of Production Economics, Volume 81, pp. 215-224.
284. van Gils, B. & Proper, H., 2018. Enterprise modelling in the age of digital transformation. In: C. Springer, ed. IFIP Working Conference on The Practice of Enterprise Modeling. s.l.:s.n., pp. 257-273.
285. Vercruyssen, A., Roose, H., Carton, A. & Putte, B., 2014. The effect of busyness on survey participation: being too busy or feeling too busy to cooperate?. International Journal of Social Research Methodology, 17(4), pp. 357-371.
286. Vieira, V., 2017. The Moderating Role of Involvement on the Overchoice Effect in Retail Decision Making. Journal of Relationship Marketing, 16(2), pp. 99-118.
287. Vohs, K., 2015. Money priming can change people’s thoughts, feelings, motivations, and behaviors: An update on 10 years of experiments. Journal of Experimental Psychology, 144(4), p. 86.
288. von Helversen, B., Abramczuk, K., Kopeć, W. & Nielek, R., 2018. Influence of consumer reviews on online purchasing decisions in older and younger adults. Decision Support Systems, Volume 113, pp. 1-10.
289. Walsh, S., 2004. Call Reluctance: The Dark Side of Professional Selling?. Southern Business Review, 29(2), p. 23.
290. Wang, C. et al., 2018. The effects of money on fake rating behavior in e-commerce: electrophysiological time course evidence from consumers. Frontiers in neuroscience, Volume 12, p. 56.
291. Wang, Y. et al., 2017. A Hierarchical Bayesian Approach to Improve Media Mix Models Using Category Data. , s.l.: s.n.
292. Wang, Y. J. L. a. S. Z., 2004. Channel performance under consignment contract with revenue sharing. Management science, 50(1), pp. 34-47.
293. Watts, S. & Stenner, P., 2012. Doing Q methodological research: Theory, method & interpretation. Sage.
294. Weilbaker, D. & Merritt, N., 1992. Attracting graduates to sales positions: The role of recruiter knowledge. Journal of Personal Selling & Sales Management, 12(4), pp. 49-58.
295. Wellington, J., 2015. Educational research: Contemporary issues and practical approaches. Bloomsbury Publishing.
296. Wells, J., 2001. Why employees commit fraud.. Journal of Accountancy, 191(2), p. 89.
297. Wetzel, C., Wilson, T. & Kort, J., 1981. The halo effect revisited: Forewarned is not forearmed.. Journal of Experimental Social Psychology, 17(4), pp. 427-439.
298. Wilkie, W. & Pessemier, E., 1973. Issues in marketing's use of multi-attribute attitude models.. Journal of Marketing research, 10(4), pp. 428-441.
299. Winer, R., 1986. A reference price model of brand choice for frequently purchased products.. Journal of consumer research, 13(2), pp. 250-256.
300. Wirtz, J. & Chew, P., 2002. The effects of incentives, deal proneness, satisfaction and tie strength on word‐of‐mouth behaviour. International journal of service industry management, 13(2), pp. 141-162.
301. Woiceshyn, J. & Daellenbach, U., 2018. Evaluating inductive vs deductive research in management studies. Qualitative Research in Organizations and Management: An International Journal, 13(2), pp. 183-195.
302. Wright, K., 2005. Researching Internet-based populations: Advantages and disadvantages of online survey research, online questionnaire authoring software packages, and web survey services. Journal of computer-mediated communication, 10(3).
303. Yang, L. et al., 2016. Multi-attribute search framework for optimizing extended belief rule-based systems.. Information Sciences, Volume 370, pp. 159-183.
304. Zhao, Z. & Zhao, J., 2015. Car pride: Psychological structure and behavioral implications. In 94th Annual Meeting of the Transportation Research Board, Washington, DC.
305. Zhao, Z. & Zhao, J., 2020. Car pride and its behavioral implications: an exploration in Shanghai. Transportation, 47(2), pp. 793-810.