Factors Affecting Malaysian Household Purchase Decisions of Food-Away-from-Home

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Factors Affecting Malaysian Household PurchaseDecisions of Food-Away-from-HomeHelen Lee Siew Heng a & Andrew Tan Khee Guan aa KDU Penang College, School of Social Science , Universiti Sains Malaysia , 11800 Minden,Penang, MalaysiaPublished online: 08 Sep 2008.

To cite this article: Helen Lee Siew Heng & Andrew Tan Khee Guan (2007) Factors Affecting Malaysian Household PurchaseDecisions of Food-Away-from-Home, Journal of International Food & Agribusiness Marketing, 19:2-3, 97-115, DOI: 10.1300/J047v19n02_06

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Factors Affecting MalaysianHousehold Purchase Decisions

of Food-Away-from-Home

Helen Lee Siew HengAndrew Tan Khee Guan

ABSTRACT. The dichotomous-choice Logit model is applied on datafrom the Malaysian Household Expenditure Survey (1998/99) to exam-ine household purchase decisions of food-away-from-home (FAFH).Results indicate that younger, higher educated, wealthier, smaller-sizedfamilies, urban residents, or Malay and Chinese households have a sig-nificantly higher likelihood to purchase FAFH, ceteris paribus. In addi-tion, gender does not impact FAFH purchase decisions in a statisticallysignificant manner. Based on these findings, several observations arenoted to provide policymakers and food industry analysts with a betterunderstanding of the habits and attitudes of Malaysian householdsvis-à-vis FAFH. doi:10.1300/J047v19n02_06 [Article copies available for afee from The Haworth Document Delivery Service: 1-800-HAWORTH. E-mail ad-dress: <[email protected]> Website: <http://www.HaworthPress.com> © 2007 by The Haworth Press, Inc. All rights reserved.]

Helen Lee Siew Heng (E-mail: [email protected]) is Lecturer, KDU PenangCollege and Andrew Tan Khee Guan is Senior Lecturer; both are affiliated with theSchool of Social Science, Universiti Sains Malaysia, 11800 Minden, Penang, Malaysia.

Address correspondence to: Andrew Tan Khee Guan, Senior Lecturer, School ofSocial Science, Universiti Sains Malaysia, 11800 Minden, Penang, Malaysia (E-mail:[email protected]).

The authors are grateful to Edward Norton, Lai Yew Wah, Rudy Nayga, an anony-mous reviewer, and the editor for their helpful contributions.

Research support from the Universiti Sains Malaysia short-term grant (304/PSOSIAL/635066) is acknowledged.

Journal of International Food & Agribusiness Marketing, Vol. 19(2/3) 2007Available online at: http://jifam.haworthpress.com

© 2007 by The Haworth Press, Inc. All rights reserved.doi:10.1300/J047v19n02_06 97

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KEYWORDS. Food-away-from-home, Logit analysis, purchase deci-sions, Malaysia

INTRODUCTION

Malaysian households have often allocated the largest amount ofhousehold expenditures on food products. This includes consumptionof food-at-home (FAH) and food-away-from-home (FAFH).1 Never-theless, while FAH expenditures have declined steadily from a share of33.7% to 22.2% between 1973 and 1999, FAFH expenditures rose from4.6% to 10.9% during the same period (Figure 1) (Department of Statis-tics Malaysia, 2000). This reflects the changing lifestyle of Malaysianhouseholds, whereby, having a meal at home is taking place less oftenwhile eating-out has become more frequent.

Given the increased popularity of the eating out culture, this has pavedthe way for the development of the food industry in Malaysia. Between1999 and 2003, the consumer foodservice market increased by 16% to atotal of 20,235 units. During the same period, consumer food servicetransactions and market worth grew by 22% and 39%, respectively, from1,026 million to RM16,312 million (US$4,315 million)2 in current valueterms (Euromonitor International, 2004). Furthermore, the 60% growth

98 JOURNAL OF INTERNATIONAL FOOD & AGRIBUSINESS MARKETING

FIGURE 1. Household Expenditures on Food-At-Home and Food-Away-From-Home as Percentage of Total Household Expenditure, Malaysia (SelectedYears)

Source: Department of Statistics Malaysia, 2000.

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(in unit terms) of the 100% home delivery or take-away food sector wasprimarily attributed to the increasingly busy work schedules of the mod-ern Malaysian society. With continued global economic recovery, theMalaysian FAFH industry outlook appears promising, with projectedgrowth of 19% in unit terms, 30% in total transactions, and 32% in cur-rent value over the forecast period of 2003-2008 (Euromonitor Interna-tional, 2004).

Notwithstanding its upward growth trend and economic significance,certain aspects of the Malaysian FAFH industry have been neglectedover the years. While micro-level demand studies of FAFH in Westerncultures have been extensively conducted using disaggregated cross-sectional household consumption data (Stewart et al., 2004; Fanninget al., 2002; Capps, Jr. and Park, 1997; Byrne et al., 1996), an extensivereview of the literature revealed that little research attention has beendevoted to examining the profile of consumers who purchase FAFH inthe local region. To date, only Ishida et al. (2003) have used aggregateddata to study the structural changes in food consumption expendituresin West Malaysia. However, Yen and Huang (2002) and Blaylock andBlisard (1992) noted that studies based on micro-level type of data haveproven to provide better insights on how different groups within thepopulation behave compared to studies assuming average effects usingaggregated data.

As such, this study aims to fill this gap by using disaggregatedcross-sectional household expenditures data to quantitatively examinethe sociodemographic factors that affect Malaysian household deci-sions to eat out. Understanding how these factors determine the proba-bility to consume FAFH is important to food service strategy andconsumer insight analysts who are interested in knowing the character-istics that define their industry. In addition, a better understanding ofthese factors enables the government authorities to formulate soundpublic health policies vis-à-vis the Malaysian FAFH industry.

THEORETICAL FRAMEWORK

Becker’s (1965) theory of household production is often used tomodel FAFH analysis. In general, the theory considers how householdschoose the best combination of commodities in order to maximizehousehold utility, while subject to time, resources, and technology

Helen Lee Siew Heng and Andrew Tan Khee Guan 99

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constraints. Following the notations of Manrique and Jensen (1998), ahousehold maximizes its utility function based on:

U = U(Z1, Z2, . . ., Zj, . . ., Zn), (1)

where, U represents the household utility function; and, Zj refers toquantities of home-produced commodity j. This function is subject toconstraints such as the opportunity cost of time for the household, totalexpenditures on market-purchased good being equal to the sum ofnonwage income and wage income of household members, and otherhousehold characteristics (Manrique and Jensen, 1998).

EMPIRICAL MODEL

In household demand studies, the dependent variable is dichotomousin nature when households either report participation/purchase (value =1) or nonparticipation/nonpurchase (value = 0) of the activity or good inquestion. However, the use of the ordinary least squares model in suchcircumstance violates the constant error variance assumption and pro-duces inefficient estimates (Greene, 2003). In this case, Logit analysisis appropriate as its specifications allow for monotonic transformationsto guarantee that predictions (probabilities) lie in the unit interval (Hiseret al., 1999).

The Dichotomous-Choice Logit Model

In general, the Logit model is written as:

Log[P/(1 – P)] = 0 � 1X1 � . . . � nXn � e, (2)

where, P = probability of the consumer to purchase FAFH during thesurvey period; X = explanatory variables hypothesized to influence thisprobability (as listed in Table 1); = coefficients for the explanatoryvariables; and, e = stochastic disturbance term of the regression.

Model Regressors

Given the lack of preceding studies on the subject, the selection ofsociodemographic variables likely to affect Malaysian household FAFH


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purchase decisions relies on the previous studies by Kim and Geistfeld(2004), Stewart et al. (2004), Manrique and Jensen (1998), Capps, Jr.and Park (1997), Byrne et al. (1996), and Capps, Jr. et al. (1985). Thefollowing sociodemographic characteristics are therefore hypothesizedto influence FAFH purchase decisions: (1) age of household head,(2) ethnicity/race, (3) education, (4) gender, (5) household size, (6) totalhousehold monthly income, and (7) location (Table 1).

Age of the household head (in number of years) is used in the currentstudy with the assumption that differences in age lead to differences inpreferences and eating habits. While Kim and Geistfeld (2004) andCapps, Jr. et al. (1985) found that age had a negative effect on FAFHexpenditures, such an effect might be expected as younger householdheads are characteristically more active, able, and mobile than theirolder cohorts. On the other hand, older households may be more healthand cleanliness conscious compared with younger households. As such,they may be less likely to eat out and would have higher FAH expendi-tures instead if they perceive FAFH to be unhealthy or unhygienic(Nayga, Jr. and Capps, Jr., 1993). Therefore, FAFH purchase decisionsare hypothesized to be inversely related to age of household head.

McCracken and Brandt (1990) suggest that a dummy variable basedon race be included in household food expenditure studies to allow forthe possibility of cultural and ethnic differences to influence purchasedecisions. However, as with most other Western studies, McCracken


TABLE 1. Description of Explanatory Variables in the Statistical Model

Variable Description Expected Sign

Age Age of household head (in years) �

Race1 1 if Malay household head; 0 if non-Malay n/a

Race2 1 if Chinese household head; 0 if non-Chinese n/a

Education Formal education of the household head (in years) �/�

Gender 1 if male household head; 0 if female �/�

Household size Total number of family members in the household �

Household income Total gross monthly household income (in Ringgit) �

Location 1 if household resides in urban area; 0 if rural �

Age-income Age of household head by income �/�

Race1-income Malay households by income �/�

Race2-income Chinese households by income �/�

Education-income Formal education of household head by income �/�

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and Brandt (1990) categorized race into Caucasians, Blacks, Hispanics,and others in their study. Given the unique Malaysian scenario of threedistinct races amongst its multi-ethnic population (i.e., Malay, Chinese,Indian, and a small proportion consisting those of various other races),the samples in this study are grouped under Malay (Race1), Chinese(Race2), and Indian/others (Race3) (base group) instead. However, no apriori hypothesis regarding the influence of race on FAFH purchasedecisions is made in the current study.

Formal education of the household head (in number of years) is con-sidered due to its possible effect on lifestyles and knowledge of health-related issues. In general, better-educated household managers mayhave broader knowledge about diet and health, and may consequentlyconsume healthier meals (Manrique and Jensen, 1998; Wang et al.,1995). This suggests a possible negative relationship between years ofeducation and purchase decisions, especially if FAFH is viewed as lesshealthy or unhygienic. However, the possibility that number of years offormal education may have a positive effect on FAFH purchase deci-sions cannot be entirely ruled out as well. This is because better-educated households may be more knowledgeable of the availability ofdifferent varieties and facilities of FAFH. As a result, they may have ahigher likelihood to purchase FAFH, especially when purchasing mealsthat have better nutritional quality. In addition, more educated house-holds may face greater time constraints at work compared with less edu-cated households. For example, those who have longer working hoursor greater opportunity cost of time would have less time to cook at homeand, thus, may prefer to eat out more often. As such, the hypothesizedeffect of years of formal education on purchase decisions of FAFH maybe either positive or negative.

Studies by Binkley (2005), Manrique and Jensen (1998), Byrne et al.(1996), and Nayga, Jr. and Capps, Jr. (1993) suggest that different gen-ders of household heads may account for differences in FAFH purchasedecisions. In general, households with women household heads wouldbe more likely to purchase FAH as opposed to FAFH. However, giventhat participation rates of women in the Malaysian workforce have beensteadily increasing of late, from 44.8% in 2000 to 46.7% in 2004 (Min-istry of Finance Malaysia, 2004), this may also lead to a higher likeli-hood to purchase FAFH, particularly amongst women and householdswho have less time to cook at home. As such, the relationship betweengender of household head and FAFH purchase decisions would have tobe further ascertained.


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Nayga, Jr. and Capps, Jr. (1993) indicate that household size, repre-sented by the number of individuals living in the household, determinesdecision making on FAFH purchases. In general, the likelihood to pur-chase FAH is expected to increase, while the converse is true for FAFH,when family size increases. This is primarily due to the economies ofscales in meal preparation at home for more individuals in the family(Stewart et al., 2004; Wang et al., 1995). As such, household size andFAFH purchase decisions is expected to be negatively related.

Total monthly household income (in Ringgit, RM) is included in themodel to account for differences in income levels to affect householdpurchase decisions of FAFH. Due to the unavailability of data to mea-sure opportunity cost of time, this variable also serves as its proxy giventhat wealthier individuals generally have higher levels of opportunitycosts than less wealthy individuals. Stewart et al. (2004) and Ishida et al.(2003) found that wealthier households are more likely to spend onproducts and services, including leisure and other dining amenities suchas full service and ambience. In this context, FAFH can be consideredas a form of leisure, in which it is the time spent outside of householdproduction (e.g., cooking, cleaning, shopping) and labor force activities(Stewart et al., 2004). Low-income households, on the other hand, willhave tighter budget constraints and will have a lower likelihood to pur-chase products such as clothes, entertainment, and other goods, includ-ing FAFH. As such, a positive correlation between this variable andFAFH purchase decisions is expected.

In this study, a dummy variable is assigned a value of 1 for urbanhouseholds and 0 for rural households. This location variable is desir-able as it reflects the availability of eating establishments at differentlocalities and, thus, may affect household FAFH purchase decisions(Ishida et al., 2003; Manrique and Jensen, 1998; Capps, Jr. et al., 1985).In essence, urban households have wider choices of eating establish-ments compared with rural households. Urban households also facegreater time constraints in traveling back and forth their workplace dueto traffic congestion, and thus have less time to prepare or cook at home.In contrast, rural households have a more traditional lifestyle and mayeven produce their own foods for home consumption. Hence, it is hy-pothesized that a positive relationship will result as urban householdsare expected to have a higher likelihood to purchase FAFH than theirrural counterparts.

Lastly, in addition to the main variables listed above, interactionterms between Age-Income, Education-Income, and Race-Income werealso considered given the possibilities that income differences across


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age, level of educational attainment, or ethnic/racial groups may in-fluence FAFH purchase decisions as well.3 However, the expectedrelationships between these variables with the likelihood of FAFH pur-chases will have to be further determined.

DATA

Data used in this study are from the Malaysian Household Expendi-ture Survey 1998/1999 (MHES) from the Department of Statisticsof Malaysia. This secondary data are the most recently available of thenational household food consumption survey. The primary objective ofthe MHES is to collect information on the levels and patterns of con-sumption expenditure by households on a comprehensive range ofgoods and services. Data collection for the entire MHES was carried outfor 12 months, beginning in July 1998 and continued through June1999. Different samples were canvassed every month to ensure thatseasonal variations on account of various festivals during the yearand also the commencement of school terms were taken into consider-ation. In the survey, households were asked to maintain a detailed rec-ord of household expenditures over a one-month period. In addition,sociodemographic characteristics of the respondents were also recorded(Department of Statistics Malaysia, 2000).

The sample was designed using a stratified multi-stage, area proba-bility sampling method, thus ensuring that sociodemographic and geo-graphical considerations are taken into account to reflect the Malaysianpopulation. While a total number of 9,198 households responded tothis survey, several households in the sample have incomplete socio-demographic and other relevant information. As a result, 9,180 obser-vations were subsequently retained after deleting those with missing orsuspect relevant information.

Characteristics of Survey Respondents

Descriptive statistics4 of variables in the statistical model are pre-sented in Table 2. While the average age for the general Malaysian pop-ulation is 26.3 years old, the average age of the household head in thecurrent sample is much older at 44.8 years. Ethnically, about 50% ofthe sample household heads are Malay; 29% Chinese; 21% Indian/other races. In contrast, the general population of Malaysia consists of54% Malay; 26% Chinese; and 20% Indian/other races (Department


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of Statistics Malaysia, 2001). Within the entire sample, a householdhead averages about 8 years of formal education (at least high schooleducation).

While the general population of Malaysia consists of 50.4% males,the current sample reports about 83% male household heads. The aver-age household size of the current sample is approximately 4.4 personscompared with 4.6 persons for the national average. The sample house-holds have an average monthly income of about RM2,310 (US$611)compared with the national average of RM2,472 (US$654). About 57%of the current sample resides in urban areas as opposed to 62% for thegeneral Malaysian population. From the sub-sample, whereby respon-dents are categorized under FAFH purchasers or nonpurchasers, 6,605(72%) respondents reported that they purchased FAFH, while 2,575(28%) respondents reported that they did not purchase any FAFH at allduring the survey period (Table 2).5

ESTIMATION RESULTS

Results of the Logit model are presented in Table 3, with the esti-mated coefficients, z-statistics, odds ratio, sample means, and marginaleffects for the respective variables listed accordingly. The explanatoryvariables that are statistically significant in explaining household deci-sions to purchase FAFH include age, race (Race1 and Race2), educa-tion, household size, household income, and location. In other words,out of the eight main explanatory variables, only gender is not a signifi-cant determinant in affecting the likelihood of purchasing FAFH. Inaddition, amongst the various interaction variables considered, onlyEducation-Income was retained as an interaction effect in the finalmodel given its statistical significance.6

The model fits the data well as indicated by the Likelihood Ratio sta-tistic (LR) = 2845.96 and the probability value is almost zero, implyingthat one or more of the total effects in the model is important for predict-ing the probability of FAFH purchases. The model is also found tocorrectly predict 79.96% of the outcomes in the sample, while theMcFadden’s R2 = 0.2612.

Based on the calculated mean values for all the respondents, the “typ-ical” respondent in this sample is assumed to be a 45-year-old, Malaymale, residing in an urban area, with 8 years of formal education, earns atotal monthly income of RM2,310 (US$611) and has 4 household mem-bers (Table 3). By inserting the values for the typical respondent into the


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estimated Logit equation (2), the log of odds of purchasing FAFH isderived as 2.12.7 Thus, the probability of purchasing FAFH is calcu-lated as 0.89,8 which indicates that the typical respondent described ear-lier has an 89% chance of purchasing FAFH. Impacts of the specificsociodemographic variables on FAFH purchasing decisions are furtherdiscussed below.

Age. As expected, the effect of age of household head on the proba-bility of purchasing FAFH is negative and significant. Holding otherthings constant, a year increase in age reduces the odds of purchasingFAFH by 98% (Table 3). In addition, the probability of purchasingFAFH decreases by 0.15% if the typical respondent is a year older, allelse constant. This finding is also supported by the lower average age ofthose who purchased FAFH (43.7 years old) compared with those whodid not purchase FAFH (47.7 years old) (Table 2). These results suggestthat households with older household heads are less likely to purchaseFAFH compared to their younger counterparts.

Race. In terms of ethnicity, both Race1 and Race2 are statisticallysignificant and positively related to the probability of purchasing FAFH.


TABLE 3. Summary Statistics for Logit Analysis of Household Decisions toPurchase FAFH

ExplanatoryVariables

Coefficients()

Z-Statistics OddsRatio (e)

SampleMeans

MarginalEffects

Constant �1.7963 �9.9173* 0.1659 � �

Age �0.0152 �6.2106* 0.9849 45 �0.001500

Race1 0.9589 14.2717* 2.6088 1 0.131300a

Race2 1.5336 17.1743* 4.6350 0 0.081900a

Education level 0.1161 9.2667* 1.1231 8 0.011100

Gender 0.1006 1.3511 1.1059 1 0.010000a

Household size �0.0619 �4.8039* 0.9400 4 �0.005900

Household income 0.0015 20.5236* 1.0015 2310 0.000140

Location 0.5829 10.0524* 1.7913 1 0.069800a

Education-income �0.0001 �9.6700* 0.9999 18480 �0.000007b

Likelihood Ratio (9 d.f.) = 2845.96; Probability (LR stat) = 0.000000; Percent Correct Predictions = 79.96%;

McFadden’s R2 = 0.2612.*5% level of significance.aFor discrete change of dummy variable from 0 to 1.bCalculation based on Norton et al. (2004) and Ai and Norton (2003).

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The results reveal that the odds of Malay and Chinese household headsto purchase FAFH are respectively 2.6 and 4.6 times as high as the oddsof Indian/other race households, ceteris paribus (Table 3). In addition,the marginal effects indicate that the probability of Malay and Chinesehouseholds to purchase FAFH increases by 13.1% and 8.2% respec-tively in relation to Indian/other ethnic groups. Thus, these positiveeffects suggest that Malay and Chinese households have a slightlyhigher tendency for eating-out compared to Indian households.

Education Level. The effect of education level on decisions to pur-chase FAFH is statistically significant and positive as the probability ofpurchasing FAFH increases by 1.1% if the typical respondent possessesan additional year of formal education (Table 3). This finding is alsosupported by the breakdown amongst purchasers and nonpurchasers ashousehold heads average 8.7 years of formal education (secondarylevel) for the former group, compared to only 5.7 years (primary level)for those who did not purchase (Table 2). This effect may be rational-ized by the greater time constraints at work for those with higher educa-tion levels. Hence, such households have a higher tendency to eat outcompared to less educated households with presumably lower opportu-nity cost of time.

Gender. Gender of household head has a statistically insignificanteffect on household decisions to purchase FAFH (Table 3). Similar to theresults of Kim and Geistfeld (2004), this indicates that once the effects ofother factors are controlled for, whether the household head is male or fe-male does not affect the likelihood of FAFH purchases. However, this ef-fect is not totally unexpected since whenever households eat out, theynormally dine together. Hence, it does not make any difference to FAFHpurchase decisions if gender of household head is male or female.

Household Size. Household size is statistically significant and nega-tively related to household decisions to purchase FAFH (Table 3). Theodds of eating-out decreases by 0.9 for each additional household mem-ber, while its probability decreases by 0.6% for each additional house-hold member. This effect can be attributed to the greater financialburden of eating-out as family size increases. As a result, larger familiesare more likely to dine at home as opposed to eating out.

Household Income. Total gross monthly household income is statisti-cally significant and positively related to the probability of purchasingFAFH (Table 3). Each RM1,000 increase in household income leads toabout a 14% increase in the probability of purchasing FAFH. Evidenceamongst those who purchased also indicates that total household monthlyincome average about RM2,776 (US$734) for this group compared to


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only RM1,197 (US$316) amongst nonpurchasers (Table 2). This ob-served effect is consistent with the previous study of Stewart et al.(2004), which suggested that wealthier households are more likely topurchase FAFH as they do not mind paying for value-added servicesprovided by eating establishments, such as full-service restaurants andfast-food joints. This finding implies that FAFH can be considered as anormal good since the probability of purchasing FAFH is greater forhouseholds with higher income.

Location. Location differences of households have a statistically sig-nificant effect on household decisions to purchase FAFH (Table 3). Thefinding indicates that, ceteris paribus, the odds of an urban household topurchase FAFH are about 1.8 times greater than the odds of a householdfrom an urban area. Meanwhile, urban households are also about sevenpercentage points more likely to purchase FAFH than rural households.The most likely explanation for this effect is that urban households havegreater availability and choice of eating establishments offered to themcompared to rural households. For example, most full-service andfast-food restaurants are located in the urban areas. Based on these re-sults, the hypothesis that urban households have higher tendencies topurchase FAFH than their rural counterparts is valid.

Education-Income. While the main effects of education and incomerespectively imply that household heads with higher education levels orhigher income levels are more likely to purchase FAFH, the coefficienton the interaction term between both variables is negative and statisti-cally significant (Table 3). Further, as income of higher educated house-holds increases by RM1,000, these households are about 0.7% lesslikely to eat out. One speculative reasoning for this effect is that highereducated households who are more mindful of health considerationsand are able to afford it may actually be sacrificing convenience by con-suming FAH rather than eating-out, particularly if domestic help isavailable. However, it is of interest to note that the negative relationshipfor this interaction variable does not mean that there is always a dimin-ishing relationship between the two variables but, instead, varies overthe sample of observations. For example, amongst observations with apredicted value of purchasing FAFH less than 0.2, the interaction effectis positive, not negative (see left-hand side of Figure 2).

Predicting the Probability of Purchasing FAFH

Given a set of values in the explanatory variables, the probabilityof purchasing FAFH can be predicted (Table 4). Selected examples


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discussed in more detail below show predictions of the probability ofpurchasing FAFH for different changes in one of the respondent’s char-acteristics with other variables held constant. The typical respondentwith sociodemographic characteristics as previously described (with an89% chance of purchasing FAFH) is used as the base for comparisonpurposes (Example 1).

If the typical respondent is older, say 75-year-old instead of 45-year-old, with other factors held constant, the probability of purchasingFAFH falls from 0.89 to 0.84 (Example 2). However, this probabilityfalls appreciably to 0.70 if total monthly household income is onlyRM1,000 (US$264) instead, all else constant. On the other hand, thisprobability increases to 0.92 if the respondent is younger (aged 25 in-stead of 45), ceteris paribus (Example 3). If the typical respondent doesnot have any formal education, holding other factors constant, it is pre-dicted that the probability of purchasing FAFH would fall from 0.89 to0.77 (Example 6). Conversely, the predicted probability increases from0.89 to 0.96 if the typical respondent possesses 17 years of education(Example 7). Total monthly household income has a strong positiveimpact in prompting FAFH purchase decisions as well. Holding otherfactors constant, if the respondent earns a total monthly householdincome of RM6,000 (US$1587) instead of RM2,310 (US$611), the


FIGURE 2. Interaction Effects of Education-Income Against Predicted Proba-bilities

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TABLE 4. Selected Predictions of the Probability of Purchasing FAFH

Example Characteristics PredictedProbability

1. Age 45, urban location, Malay, male, 8 years of formaleducation, total monthly household income of RM2,310,and 4 family members

0.89


0.84


0.92

4. Age 45, urban location, Chinese, male, 8 years of formaleducation, total monthly household income of RM2,310,and 4 family members

0.94

5. Age 45, urban location, Indian/other races, male, 8 yearsof formal education, total monthly household income ofRM2,310, and 4 family members

0.76


0.77


0.96

8. Age 45, urban location, Malay, female, 8 years of formaleducation, total monthly household income of RM2,310,and 4 family members

0.88


0.90


0.85


0.99


0.70

13. Age 45, rural location, Malay, male, 8 years of formaleducation, total monthly household income of RM2,310,and 4 family members

0.82

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predicted probability of purchasing FAFH increases significantly from0.89 to 0.99 (Example 11). However, this probability falls appreciablyto 0.70 if total monthly household income is only RM1,000 (US$264)instead, all else constant (Example 12). Similarly, if the typical re-spondent resides in a rural location instead of an urban area, the proba-bility of purchasing FAFH falls from 0.89 to 0.82, all else constant(Example 13).

CONCLUSION

This study aims to examine the sociodemographic factors affectingMalaysian household decisions to eat out. The results indicate that par-ticipation in the Malaysian FAFH market is significantly determinedby age of the meal planners, race, education, household size, incomelevels, and location of residence, holding all else constant. This sug-gests that younger, wealthier, more educated, smaller family size, urbanresidents, or both Malay and Chinese ethnic groups are more likely toeat out than their older, less affluent, less educated, larger family size,rural, or Indian/other ethnic households. However, higher income andbetter-educated households may actually be more willing to forgo the ex-pediency of eating-out and prefer to dine in instead.

Several notable implications can be culled from these results. First,similar to the trend in other countries, it is expected that the shift inMalaysian food purchase decisions toward FAFH will likely continuewith increased income and urbanization arising from economic growth.As such, this provides great business development opportunities for theMalaysian consumer foodservice industry. Second, when consideringthe viability of future business locations, the foodservice industry mayconsider targeting younger markets, in urban locales, and those withhigh disposable incomes. These sociodemographic characteristics arefound to define those who are more willing and able to spend on FAFHto satisfy their lifestyle and nutritional needs. Third, since FAFH is typi-cally high in fat, sodium, sugar, calories, and monosodium-glutamate(MSG) contents, public health policies and awareness campaigns aimedat ensuring optimal nutrient intake should be used to educate Malaysianswith the above sociodemographic profiles to be more selective of theirdietary intake. This is in light of the possibility that education plays aprominent role in FAFH consumption decisions.


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Finally, while this study acts as a catalyst to further research onMalaysian household demand, particularly on FAFH, two possiblecaveats are in order due to the secondary nature of the data. First, thedata do not discriminate amongst FAFH acquired from fast-food restau-rants, roadside hawkers, dine-in restaurants, or other types of food out-lets. Therefore, insights on cross-substitutions between these differentsources of FAFH, and also with FAH, are unavailable. Second, whileadditional information such as price, quantity consumed, marital status,number of working adults, number of children and their ages, avail-ability of domestic help, number of working hours, health status, andreasons for consumption or nonconsumption may provide a morecomprehensive indication of sociodemographic effects, these data werenot available.

NOTES

1. FAFH is defined as various types of food consumed in restaurants, coffee shops,coffee houses, stalls, and so on. It also includes food bought in restaurants and othereating places but consumed at home.

2. US$1.00 = RM3.78; RM1.00 = US$0.26 (approximately, as of January 18,2006).

3. As suggested by a reviewer.4. In the interest of brevity, only a succinct discussion of the characteristics of the

survey respondents is provided. A more comprehensive discussion can be obtainedfrom the authors upon request.

5. In this study, respondents reporting FAFH expenditures of RM30.00 (US$8) andbelow were also classified as nonpurchasers. This amount was derived based on a nom-inal amount of spending of RM1.00 (US$0.26) per day for subsistence spending awayfrom home.

6. It is important to note that Hoetker (2007), Norton et al. (2004), Ai and Norton(2003), and Huang and Shields (2000), among others, have all recently noted that contraryto popular practices, interpretation of the coefficient of the interaction term betweentwo variables for nonlinear models (such as the Logit) is not as straightforward com-pared with linear models. As such, we proceed with the computation and interpretationof the interaction term in the current study with mindful considerations.

7. The calculation for the log of odds:

Log[P / (1 P ]−∧

) = �1.1263 – 0.0152(45) � 0.9589(1) � 1.5336(0)

� 0.1161(8) � 0.1006(1) – 0.0619(4)� 0.0015(2310) � 0.5829(1) – 0.0001(18480)

= 2.12.

8. The calculation for the probability of purchasing FAFH: P(Y = 1) = exp(2.12)/1 �exp(2.12) = 0.89.


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SUBMITTED: March 2006FIRST REVISION: October 2006

SECOND REVISION: November 2006ACCEPTED: January 2007

doi:10.1300/J047v19n02_06


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Documents

Factors Affecting Malaysian Household Purchase Decisions of Food-Away-from-Home