Multinomial Logit Approach

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Determinants of Automobile Purchase and Brand Choice in Ghana: Multinomial Logit Approach a

b

c

Kojo Mensah Sedzro , Godfred Amewu , Joseph Darko , Ezekiel d

Nii Noye Nortey & Julius B. Dasah

d

a

Noguchi Memorial Institute of Medical Research, University of Ghana , Accra , Ghana b

Ghana Institute of Management & Public Administration (GIMPA) , Achimota , Accra , Ghana c

ISSER, University of Ghana , Accra , Ghana

d

Department of Statistics , University of Ghana , Accra , Ghana Published online: 14 Nov 2014.

To cite this article: Kojo Mensah Sedzro , Godfred Amewu , Joseph Darko , Ezekiel Nii Noye Nortey & Julius B. Dasah (2014) Determinants of Automobile Purchase and Brand Choice in Ghana: Multinomial Logit Approach, Journal of Transnational Management, 19:4, 303-317, DOI: 10.1080/15475778.2014.948791 To link to this article: http://dx.doi.org/10.1080/15475778.2014.948791

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Journal of Transnational Management, 19:303–317, 2014 Copyright # Taylor & Francis Group, LLC ISSN: 1547-5778 print=1547-5786 online DOI: 10.1080/15475778.2014.948791

Determinants of Automobile Purchase and Brand Choice in Ghana: Multinomial Logit Approach KOJO MENSAH SEDZRO

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Noguchi Memorial Institute of Medical Research, University of Ghana, Accra, Ghana

GODFRED AMEWU Ghana Institute of Management & Public Administration (GIMPA), Achimota, Accra, Ghana

JOSEPH DARKO ISSER, University of Ghana, Accra, Ghana

EZEKIEL NII NOYE NORTEY and JULIUS B. DASAH Department of Statistics, University of Ghana, Accra, Ghana

This study expands on previous studies by examining the purchasers’ criteria choice of automobile in the Ghanaian market and establishing the factors that influence the purchase and estimating the choice of brand. The study examined 1,130 automobile owners. Data was collected on 20 automobile attributes considered important when purchasing an automobile. The preliminary result of the research show that five major factors— interior, safety, value for money, modernity, and economy—influence consumers to purchase a particular automobile. Conclusion can be drawn that a relationship exists between the influencing factors and the brands of automobiles purchased by respondents. KEYWORDS Automobile, brand-choice, Multinomial logistic model

customer

behavior,

Received July 2014; revised August 2014; accepted August 2014. Address correspondence to Godfred Amewu, Ghana Institute of Management & Public Administration (GIMPA), P. O. Box AH 50, Achimota, Accra, Ghana. E-mail: gamewu@ gimpa.edu.gh 303

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INTRODUCTION According to Mowen and Minor (1998), products are made of characteristics or features known as attributes which are used by consumers in making purchase decisions. The number and types of the attributes used by the consumer varies from one product to another. For example, attributes of a fruit juice to a consumer might include price, taste, quality, and packaging. Similarly, the attributes of a mobile phone, considered by a consumer before purchasing, could range from price, brand, interface, and properties (Heikki, Pakota, Pietila¨, & Svento, 2005). Attributes and their importance may vary from one consumer to another and may change over time as consumers gain new experiences and information (Mackenzie, 1986). Consumers have always relied on the product attributes to acquire one product or the other. Many past studies have examined the attributes that drive consumable products such as food, drinks, and telecommunications equipment (Heikki et al., 2005 can we list about three or four references to support the claim). The automobile market in Ghana is one of the most lucrative market environments today due to the availability of car loans from banks and better remuneration at work. Thus, it is of interest to look at the many attributes such as brand image, price, cost of servicing, availability of spare parts, consumption, acceleration, social status, ambiance, ease of driving, safety, age of car, resale value, country of origin, load capacity, durability, comfort, and interior roominess considered in the purchase of these automobiles and provide empirical support for the influencing factors that play a major role in the decision of purchase and choice of brand. In the real world, consumers rely on various information ‘‘cues’’ or characteristics of products in their product evaluations (Nobre, 2011; Peter & Olson, 1987; Richardson, Dick, & Jain, 1994; Schellinck, 1983). Narteh, Odoom, Braimah, and Buame (2010), established multiple factors as key drivers of automobile brand choice in sub-Saharan Africa in the perceptive of brand choice, but ignored to establish the alternative choice that maximizes their preference. As a result, this article expands on similar studies conducted on automobiles and focuses on the alternative decision that the consumer faces in a real purchasing condition of making decisions with reference to the automobile attributes. The main objective of this article is to determine the influencing factors by considering the attributes that characterized the automobile and the brand and also estimate the likelihood of brand choice. Many past studies examined the effects of product attributes focused on exploratory studies; this article also used factor analysis to determine the key variables. The general purpose of the factor analytic technique is to find a way to summarize the information obtained from the attributes identified into a smaller set of new, composite dimensions or variates (factors) with a minimum loss of information. The

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factor analysis satisfies the objective of identifying the product attributes of automobiles through data summarization. Likewise, the multinomial logit model predicts the choice of brand. It is hoped that the findings of this article will contribute to the existing studies in consumer purchase decisions and brand choice as we established the association of brand type with the influencing factors. In the second section of the article, a review of past related studies is discussed; the third section provides an overview of the methodology, followed by a discussion of results. The final section provides a conclusion and recommendations.

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LITERATURE REVIEW According to Dorsch, Grove, and Darden (2000), consumer choice behavior can be studied through the classic five-step (need; information search; evaluation of alternatives; purchase; post-purchase evaluation) problem-solving paradigm or through the progression of consumer choice from a product class to brand choice. The five-step model is usually suitable for decision making in a real market situation that assumes rational problem-solving behavior and, in most cases, complex decision making. The acquisition of an automobile follows the same traditional view of the buying process, but is only affected by symbolic values related to brands. Consumer choice behavior has some important prevailing conditions that must be considered while studying choice. In the light of the classical problem-solving buying behavior, consumers engage in information search before making the actual choice (Heikki et al., 2005). The consumer decisionmaking process is usually guided by already formed preferences for a particular alternative. This means that consumers are likely to make the choice between alternatives based on limited information search activity (Beatty & Smith, 1987; Moorthy, Ratchford, & Talukdar, 1997) and without detailed evaluation of the other alternatives (Alba & Hutchinson, 2000; Chernev, 2003; Coupey, Irwin, & Payne, 1998; Slovic, 1995). Laroche and Matsui (2003) outline that evaluation of alternatives through information search has gained momentum in recent research. Their study on consumers’ use of five heuristics (conjunctive, disjunctive, lexicographic, linear additive, and geometric compensatory) in the consideration set formation found that conjunctive heuristics is used most often in considering set formation for two product classes in the study (beer brands and fast food outlets). Conjunctive heuristics means that a consumer selects a brand only if it meets acceptable standards—the so-called cutoff point on each key attribute that the consumer regards as important (Assael, 1995, p. 249; Solomon, 2001, p. 280). In this non-compensatory method of evaluation, a

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consumer would eliminate a brand that does not fulfill the standards on one or two of the most important attributes, even if is positive on all other attributes. Most published studies of automobile choice concentrate on brand awareness, recommendation, and brand loyalty. We could state some supporting articles, however, for this study we limit ourselves to consumeroriented choice, referring to a decision on which alternative to purchase from a set of alternatives. Consumer choice behavior can be approached by utilizing different choice models (e.g., Bockenholt & Dillon, 2000; Chintagunta, 1999; Swait & Adamowicz, 2001) or neural networks to model selection decisions (e.g., Papatla, Zahedi, & Zekic-Susac, 2002). Papatla et al. (2002) examined empirically brand choice and store choice in regard to margarine, detergent, and tissue. The research found that while neural networks have higher probability of resulting in a better performance, hybrid models guaranteed equal or better results than stand-alone models. Furthermore, many decision strategies used by consumers can change due to person-specific, context-specific, and task-specific factors (Dhar, Nowlis, & Sherman, 2000; Swait & Adamowicz, 2001). Therefore, mathematical modeling has its limitations in regard to the fact that consumers tend to utilize different approaches to make choices. Similarly, consumer choice also can be approached from the perspective of conscious and non-conscious choice (e.g., Fitzsimons et al., 2002). Many choice situations occur outside of conscious awareness and with limited information search (Kivetz & Simonson, 2000) and many choices have both conscious and non-conscious motives. Fitzsimons and colleagues (2002) found that in many cases, non-conscious influences affect choice much more than is traditionally believed by researchers. Studies on automobile choice in Africa and other developing countries is sparse. Several academic articles that dealt with automobile brand choice focused on developed countries and grasped the consumer decision-making process (De Haan, Mueller, & Peters, 2006; Diamantopoulos, Schlegelmilch, & Palihawadana, 2011; Lieven, Muhlmeier, Henkel, & Waller, 2011, Tang, Lou, & Xiao, 2011). Narteh, Odoom, Braimah, & Buame, 2010 examined key drivers of automobile brand choice in sub-Saharan Africa. The study was built on 7 key attributes (automobile features, brand awareness, image and emotional connection, price, accessibility, and external influence) related to automobile owners who had rated the attributes according to order of importance. The research, which focused on purchase of car brand, showed that the selection of car brand is based on a multiplicity of factors, with some directly attributed to the brands while others are external cues associated with the brands. This confirms the outcome of other related studies (Radder & Huang, 2008; Ross & Harradine, 2004; Shabbir, Kauffmann, Ahmad, & Qureshi, 2009; Tang et al., 2011).The results from these studies indicate that multiple factors have underpinned consumer brand choices.

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However, the results on the relative importance of these factors have not been consistent. For instance, some scholars have found awareness as a major determinant of brand choice (Keller, 2001; Srinivasan, Vanhuele & Pauwels, 2010; Huang & Sarigollu, 2012) while others found brand image (Baek et al., 2010; Erdem & Swait, 2004; Freling & Forbes, 2005), or accessibility (Kim, 2008; Lin & Chang, 2003; Van Auken, 2003), price (Chattopadhyay, Shivani, Krishnan, & Pillania, 2009; Ching, Erdem, & Keane, 2009; Erdem, Swait, & Valenzuela, 2006) as the major determinants of brand choice. Subsequently, other external factors such as country of origin (COO or CO) and impact of family and friends influenced consumer choice of brands (Evanschitzky, Wangenheim, Woisetschlager, & Blut, 2008; Wang & Yang, 2008).

THE MULTINOMIAL LOGISTIC MODEL Individuals have well-ordered preferences for any set of choice alternatives (e.g., products, brands, etc.), and they choose an alternative that maximizes their preferences. Multinomial logistic model (MNL) is a logistic regression model having a dependent variable with more than two levels (Agresti, 2000). The MNL offers a way to operationalize the theory of rational choice within a probabilistic framework. (McFadden, 1974). In this study, the dependent variable has five levels indicating Kia, Mercedes, Nissan, Toyota, and other brands. The predictor variables as brand types covariates are interior (interior of the car), safety, value for money, modernity, and economy. The goal of using MNL is to model the odds of choice of brand of automobile as a function of the covariates and to express the results in terms of the odds ratios for a different alternative brand of automobile. The model assumes: (1) the customer has an unobservable preference or utility for each of the brand choice alternatives; (2) the customer chooses the brand that provides highest utility (McFadden, 1974); (3) the random disturbance terms are independently and identically distributed. Consider n the independent observation with p explanatory variables denoted by the vector x0 ¼ (x1, x2,. . .xp), with an outcome variable of k categories. To construct the logit, one of the categories must be considered the base level and all the other logits constructed relative to it. (Chatterjee & Hadi, 2006). In this study, we have five categories of outcome variables with other brands as the base level (i.e., reference category). Let P(Y ¼ 1jx) ¼ pj. denote the probability of an observation falling in the jth category. The model is then expressed as:   pj ðxi Þ ¼ b0j þ b1j x1i þ b2j x2i þ . . . þ bpj xpi þ e; log pk ðxi Þ j ¼ 1; 2; . . . ; ðk  1Þ;

i ¼ 1; 2; . . . ; p

ð1Þ

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Since all the p0 s add to one, this reduces to logðpj ðxj ÞÞ ¼

eðb0j þb1j x1i þb2j x2i þ...þbpj xpi þeÞ k1 P ðb þb x1i þb x2i þ...þb xpi þeÞ 2j pj 1þ e 0j 1j

ð2Þ

j¼1

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for j ¼ 1, 2,. . ., (k  1). The model parameters are estimated by the method of maximum likelihood. To construct the likelihood function, let  1 if customer i chooses brand k Yki ¼ 0 if customer i does not chooses brand k Then the individual ith probability of choosing product 1 or choice alternative 1 (Pik) can be i

e A1 express as : Pik ¼ P Ai for k ¼ :1; 2; . . . K e k

ð3Þ

k

Consider the likelihood that Pðyki ¼ 1Þ is a random sample of n customers whose choices we have observed. The sample likelihood is the product of the likelihoods that individuals in the sample chose the alternative brand that they actually did, can be represented as: n

i

Lðb1 ; b2 ; . . . ; bj Þ ¼ P P PðYki ¼ 1ÞYk ;

ð4Þ

i¼1 keC

Where C is the set of alternatives (the choice set) and b0 s are the unknown parameters of the individuals’ utility function to be estimated, substituting for Pðyki ¼ 1Þ from Equation (3), we obtain: 0 n B e LðÞ ¼ P P @P i¼1 keC

Rbj Xjki

e

Rbj Xjki

1Yki C A

ð5Þ

k

To simplify the estimation, we take the log L: LnðLÞ ¼

n X X i¼1 keC

Yki

X

bj Xjki

 Ln

j

X

P e

bj Xjki

!

j

ð6Þ

keC

The likelihood equations are found by taking the first partial derivatives to 0: n X  @LnðLÞ X ¼ Yki  Pki Xjki ¼ 0; @bj i¼1 keC

for j ¼ 1; 2; . . . J

ð7Þ

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This gives a set of J equations in J unknowns, which can be solved by setting the equations to zero.

RESEARCH METHODOLOGY This section outlines the methodology employed. Exploratory research was conducted using random target population for generalizability of the results.

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Research Instrument A structured questionnaire was prepared and self-administered to the respondents. The questionnaire was divided into three sections. Section one consisted of information on the automobile brand. In section two, respondents were asked to rate the relative importance of 20 automobile attributes considered when purchasing an automobile in relation to the brand. They were measured on a 5-point Likert scale ranging from 1 (not important at all) to 5 (very important). The last section collected information on the demographic characteristics of the respondents.

The Study Population, Sampling Methods, and Data Collection The study population was made up of automobile owners within the catchment areas of Accra, Kumasi, Takoradi, Tamale, and Ho. Due to unavailability of the sampling frame and the impracticability to include every individual, the sample area was stratified into four zones (southern, northern, eastern and western). A central location technique was adopted as a way of recruiting respondents. The central locations include public places such as the automobile and driver’s license office, malls, filling stations, car washing bays, and tertiary campuses, where it is easy to access a respondent with car. This sampling technique was used because it is fast, inexpensive, the subjects are readily available, and it is also suitable for an exploratory study. From 1,200 questionnaires administered, 1,130 were usable. The remaining ones were discarded due to excessive missing observation, thereby yielding a response rate of 94%. Such a response rate was considered sufficient for statistical reliability and generalizability (Tabachnick, Fidell & Osterlind, 2001). Data was captured using CSPRO and analyzed using Statistical Package for Service Solution, version 21.

RESULTS Respondents’ Profile The sample was skewed toward the male population (see Table 1) with 68% of the respondents being male, and 32% were female. The respondents

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K. M. Sedzro et al. TABLE 1 Demographic Characteristics of Respondents

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Variable Gender Male Female Age group 19–25 26–35 36–45 46–55 56þ Highest educational level JHS=SHS Professional Tertiary Post-graduate PhD Others Work status Professional=Technical Administrative=Managerial Clerical Sales Services Agric=Animal=Fishing=hunting, etc. Production & related workers Lecturer=Teachers Personal monthly income Less than GHC100 GHC 100–500 GHC 501–1000 GHC 1001–2000 GHC 2001þ Refused

Count

Percent

768 362

68.0 32.0

110 540 305 105 70

9.7 47.8 27.0 9.3 6.2

95 105 550 340 35 5

8.4 9.3 48.7 30.1 3.1 0.4

280 355 90 135 125 25 20 90

24.8 31.4 8.0 11.9 11.1 2.2 1.8 8.0

25 130 315 415 125 120

2.2 11.5 27.9 36.7 11.1 10.6

varied in age, ranging from 18 to over 50. The highest proportion of the respondents fell into the 26–35 age group. They accounted for 48% of the total respondents. This was followed by the 36–45 age group (27%).The educational level of the respondents was generally high. Only 8.4% of the sample had received primary education, while 9.3% received professional education, and 81.9% reported having obtained tertiary education. In terms of occupation, respondents in the professional=managerial position (doctors, accountants, bank managers, etc.) and sales=services represented 56.2% and 23% of the total respectively. Those working as a lecturer=teacher and those in the clerical field were 8% respectively of the entire population. When monthly household income was examined, 13.7% of the respondents were in the ‘‘less than GHC 500’’ income group. This group of respondents was considered a low-income group. The middle-income group

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consisted of those who earn between GHC 501–2000, representing 64.6% of the sample population. Those who earned GHC 2001 and above are labeled as high-income group earners. These groupings were considered logical because of the current cost of living in Ghana.

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Extraction of Factors Influencing Purchase Decision Data reduction method was applied to extract latent factors influencing the purchase of automobiles, based on the 20 attributes of automobiles using principal component factor analysis. The Kaiser-Meyer-Olkin measure of sampling adequacy, a measure of whether the distribution of values is adequate for conducting factor analysis, was 0.800, which was acceptable. The Bartlett’s test of sphericity was statistically significant (1096.129, df ¼ 190, P ¼ 0.000), conforming the multivariate normality of the data. Cronbach’s alpha was computed on each of the Likert scales items contained in the survey instrument. The alpha coefficients for the scales=attributes show that the majority are highly realizable and acceptable, with alpha scores that exceed 0.50, the threshold recommended by Nunnally (1967) for exploratory research. The value of the alphas indicates the scales possessed a high level of internal consistency. The overall alpha for the scale was found to be 0.845. Principal component analysis with orthogonal varimax was conducted to assess the underlying structure for the 20 items of the survey instrument. Five factors were extracted. After rotation, the first factor accounted for 13.1% of the total variance; the second factor accounted for 11.2%; the third accounted for 10.9% and the fourth and fifth accounted for 10.8% and 9.5% respectively. Table 2 displays the items and factor loadings for the rotated factors, with loadings less than 0.55 omitted to improve clarity. Factor 1, which depicts interior, loads most strongly on the first two items, with loadings in the first column. Factor 2, which is associated with safety, is composed of three items with loadings in column 2 of Table 2. Factor 3, which represents value for money, comprised the item ‘‘resale value and country of origin of make of car’’ in the third column. The fourth factor, which relates to economy, comprised four items with ‘‘the price of the car’’ as the highest loading (0.789) followed by ‘‘availability of spare parts’’ with loadings of 0.687. Factor 5, which comprised 2 items in column 5 is tagged as modern; ‘‘modern with sleek look that will impress my friends’’ is the strongest loading.

Determining the Influencing Factors for Purchasing a Particular Brand of Automobile To determine whether the predictors interior (interior de´cor of the car), safety, value for money, economy, and modern would influence the choice

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TABLE 2 Factor Loadings for the Rotated Factors Factor loadings

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1

2

3

4

5

Interior quietness 0.829 Interior roominess 0.820 Ride comfort 0.676 Safety 0.736 Durability 0.589 Ease of driving 0.572 Brand image Resale value 0.763 Country of origin of make of car 0.714 Loading capacity Age of the car My car should fit my social status cost of servicing the car 0.776 The price of the car 0.687 Availability of spare parts 0.621 Ability to consume less fuel. 0.591 Modern with sleek looks, that 0.801 will impress my friends Interior cabin environment (Ambiance) 0.671 Off road performance A powerful engine that provides a fast acceleration Eigen values 2.617 2.442 2.256 2.159 2.061 % of variance 13.084 12.208 11.278 10.794 10.305

Communality 0.771 0.737 0.69 0.606 0.701 0.508 0.414 0.659 0.566 0.372 0.632 0.526 0.696 0.515 0.515 0.447 0.667 0.651 0.445 0.413

Note: Loadings