dynamics of consumer's perception, demographic characteristics and consumer's behavior towards selection of a restaurant.pdf

Dynamics of consumers’ perception,demographic characteristics andconsumers’ behavior towards selection ofa restaurant: an exploratory study onDhaka city consumers

Muhammad Sabbir Rahman

Introduction

With the turn of the century, and changes in the socio-economic environment in Bangladesh,

the context of the restaurant business in Dhaka city has changed. Generally, people in

Bangladesh, particularly in Dhaka city are very busy and do not seem to have much time to

go far away to a luxurious hotel to consume delicious food. That is why a number of carry-out

facilities particularly small and medium restaurants have become prevalent in many places

in Dhaka city of Bangladesh in order to meet the existing demand of these lucrative but busy

customers.

To be successful in a restaurant business, foodservice providers are required to deliver not

only good quality products and services, but also a high level of dining satisfaction that will

lead to increased customer’s satisfaction and thus repeat business. For this reason,

restaurant’s marketers need to understand customer’s perception and the characteristics of

their demographic variables.

In addition, investigators have agreed that service quality and customer satisfaction directly

link to customer’s return behavior in the hospitality market (Dube et al. 1994; Lee and Hing,

1995; Johns and Tyas, 1996; Oh and Jeong, 1996; Fu and Parks, 2001). Zeithaml (1996); Oh

(2000); Tam (2004) explained that service quality, customer perceived value, and

satisfaction are highly correlated and predict the customer’s repurchase behavior. Several

studies have been conducted to assess customer’s perception, purchasing behaviors,

customer’s perceived value, customer’s satisfaction; and repurchase intention related to the

hotel and restaurant industry (Dube et al., 1994; Lee and Hing, 1995; Johns and Tyas, 1996;

Fu and Parks, 2001; Tam, 2004).

There is a significant research gap of linking consumers’ perception towards the selection a

restaurant when consumers’ demographic backgrounds play a mediating role. Particularly,

in a country like Bangladesh, empirical research is lacking thus contributing to the need for

new knowledge in this area. Studies focusing on Bangladeshi restaurants’ consumers were

hardly ever reported in previous academic studies. The present study aims to gather primary

data from respondents from multicultural and multi-religious groups who frequently

restaurants. This paper also presents a conceptual framework and exploratory analysis of

the relevant variables.

The findings would make a contribution to the restaurants business managers and the

literature on consumer perception and hospitality research. This study identified a

relationship model to understand the consumers’ perception which is mediated by

DOI 10.1108/17515631211205488 VOL. 13 NO. 2 2012, pp. 75-88, Q Emerald Group Publishing Limited, ISSN 1751-5637 j BUSINESS STRATEGY SERIES j PAGE 75

Muhammad Sabbir

Rahman is a Lecturer in the

Faculty of Management,

Graduate School of

Management, Multimedia

University, Selangor,

Malaysia.

demographic characteristics and thus can be of value to the foodservice industry in their

strategic planning.

Therefore, the overall objective of the present study is to test whether the demographic

characteristics – such as gender, income and age – really mediate customers’ perceptions

in the choice of a particular restaurant or not. Therefore, this study will directly address the

question – ‘‘Do the consumer’s perception towards choosing a restaurant mediating by their

demographic characteristics (age, gender, income)?’’

Background of Dhaka city

Dhaka is the capital of Bangladesh and is one of the major cities of South Asia with a

population of over 12 million, making it the largest city in Bangladesh (Statistical Pocket

Book, 2008). From the report of World Bank (2010) Dhaka is the ninth largest city in the world

and also among the most densely populated cities. Modern Dhaka is the centre of political,

cultural and economic life in Bangladesh (National Web Portal, Government of Bangladesh,

2009). The city, in combination with localities forming the wider metropolitan area, is home to

an estimated 12.8 million as of 2008 (Statistical Pocket Book, 2008). The population is

growing by an estimated 4.2 percent per year, one of the highest rates amongst Asian cities

(Terry, 2006). According to the Far Eastern Economic Review, Dhaka will become a home of

25 million people by the year 2025 (Davis, 2006).

Dhaka is also the commercial heart of Bangladesh. The city has a growing middle class

population driving the market for modern consumer and luxury goods (Alastair, 2002). The

main commercial areas of the city include Motijheel, New Market, Gulshan and Farmgate,

while Tejgaon and Hazaribagh are the major industrial areas. Bashundhara-Baridhara is a

developing economic area that include with high-tech industries, corporations and a large

shopping mall (Alastair, 2002). Growth has been especially strong in the finance, banking,

manufacturing, telecommunications and services sectors, while tourism, hotels and

restaurants continue as important elements in the economy of Dhaka (Terry, 2006).

Literature review

The field of consumer research has developed as an extension of the field of marketing

research, focusing almost exclusively on consumer behavior rather than on other aspects of

the marketing process. However, consumer behavior not only involves the specific actions

taken by individuals in buying and using products and services, but also the social and

psychological factors that affect these actions as well (Carman, 1990). According to the

study by Shwu-Ing (2003) a person’s buying choices are influenced by four major

psychological factors: motivation, perception, learning, beliefs/attitude. Some researchers

also categorized influencing factors into internal and external factors (Kaufman, 2002;

Shwu-Ing, 2003). According to Yoo et al. (2000); Pappu and Quester (2006) price, store

image, distribution insanity, advertising spending, and price promotion of the marketing mix

are the determining factors in consumer perception of the marketing mix. Chen (2007)

argued that the marketing mix on consumer behavior is influenced through product, price,

location, and promotion. However, Schiffman and Kanuk (2000) defined perception as the

process by which an individual selects, organizes and interprets stimuli into a meaningful

and coherent picture of the world. They also mentioned that individual consumers have

perceived images of themselves; they also have perceived images of products and brands.

To this end authors such as Anandarajan et al. (2000) have argued that variables such as

gender and age are not correlated with the service users in their workplace.

The mediating effect of age

Age is not simply a chronological construct; rather, individuals become older biologically,

psychologically, and socially (Moschis et al., 1997). Biological age refers to the physical

changes associated with chronological ageing (Dehlin et al., 2000), whereas psychological

age and social age depend on perceptions of a person’s age – how old the person ‘‘feels’’

PAGE 76 jBUSINESS STRATEGY SERIESj VOL. 13 NO. 2 2012

and ‘‘looks’’, what the person ‘‘does’’, and what he or she finds ‘‘interesting’’ (Alalaakkola,

1996; Schiffman and Sherman, 1991). Biological age is continuous with chronology, but

psychological and social age can vary during an individual’s life and can even ‘‘go

backwards’’ – for example, when an individual choice of a particular restaurant can be

affected by their psychological age. On the other hand social age is significantly influenced

by society’s views on how each age cohort (or ‘‘generation’’) lives, behaves, and consumes

(Treguer, 2002; Alalaakkola, 1996). Moreover, social age is affected by so-called ‘‘period

effects’’, whereby events that occur in a certain chronological sequence affect persons in a

similar way, irrespective of an individual’s age. Under the influence of such ‘‘period effects’’,

the behavior of different age groups can be temporarily similar (Alalaakkola, 1996).

According to Ozimek and Zakowska-Biemans (2011) consumer decision-making and

information processing are influenced by individual differences and psychological

processes. They also noted that consumers’ eating behavior is also influenced by

socio-demographic variables such as age, social class, and place of residence.

Wagar and Lindqvist (2010) also explained that age is a relevant factor in service design.

They recommended that service providers therefore need to take age as an important factor

in their service design. Age is a significant marketing phenomenon because it affects the

consumption patterns of individuals and is associated with several important social and

psychographic factors (such as family size, income, and self-image) (Alalaakkola, 1996).

Consumers’ attitudes toward food can be influenced by emotional disposition to the

foodstuff. As concerns taste features it was found that the higher the education of

respondents, the smaller the degree to which they pay attention to this factor (Ozimek and

Zakowska-Biemans, 2011). On the other hand, the impact of culture on food choice is

immense and varied; however there are still many differences in food choices, and in food

likes and dislikes, among members of the same culture (Rozin and Vollmecke, 1986). Thus,

the following research question can be proposed:

RQ1. How does age affect consumer perception of selecting a restaurant?

The mediating effect of gender

Socio-demographic characteristics are the key elements of understanding food consumers’

behavior (Marshall, 1995). According to Gentry et al. (2003, p. 1), gender is ‘‘the symbolic

role definition attributed to members of a sex on the basis of historically constructed

interpretations of the nature, disposition, and role of members of that sex.’’ The American

Psychological Association defines gender as ‘‘a psychological phenomenon that refers to

learned sex-related behaviors and attitudes of males and females’’ (American Psychological

Association reprinted in Gerrig and Zimbardo, 2002). Gender is the most universal social

organizing principle (Roopnarine and Mounts, 1987). According to Reiter (1975, p. 159)

gender roles create ‘‘arrangements by which a society transforms biological sexuality into

products of human activity, and in which these transformed needs are satisfied’’. Yeganeh

(2000) argued that gender functions are traditionally divided into distinct feminine and

masculine roles that may vary substantially from one society to another. Generally men are

considered to be more utilitarian compared to women who have a more hedonistic

orientation (Hu and Jasper, 2004). Research has shown that men generally put more

emphasis on convenience when purchasing and have stronger loyalty to a store than women

(Hart et al., 2007). It is also revealed that men under 35 tend to shop more like women than

men, meaning that they like to browse and experience the shopping process (Byrne, 2006).

Teen boys have a strong influence on their family purchase decisions (Byrne, 2006). Men

were found to consider shopping for clothes to be equally appropriate for men and women

(Hill and Harmon, 2007). According to the NPD report, three out of four men shopped for

themselves in 2009 (Reda, 2010). Females and males tend to have different attitudinal and

behavioral orientations, partly from genetic makeup and partly from socialization

experiences (Putrevu, 2001). Thus, gender is often perceived as being an important

predictor of differential outcomes in the literature on social psychology (Correll, 2007).

VOL. 13 NO. 2 2012 jBUSINESS STRATEGY SERIESj PAGE 77

Gender is one of the demographic or socioeconomic variables for customer classification

and product market segmentation (Alexander, 1947; Nysveen et al., 2005). Other

socioeconomic variables include age, marital status, education, income and occupation

(Slama and Tashchian, 1985). Consequently, gender segmentation, differentiation and

positioning have long been applied in marketing, especially regarding clothing, hairstyling,

cosmetics, and magazines (Kotler and Keller, 2006). Nevertheless, marketing research is

rather limited, especially with respect to the interaction of gender in selling-buying situations

and gender’s effects on relationship development (Bhagat and Williams, 2008; Ndubisi,

2006). Empirical research suggests that men and women tend to have different attitudinal

and behavioral orientations in their buying behavior (Homburg and Giering, 2001; Noble

et al., 2006).

Gender differences can affect consumers’ approaches to decision making (Mitchell and

Walsh, 2004) and the decision difficulty (Walsh and Mitchell, 2005), while gender differences

were also found for appearance-related attitudes and behavior (Burton et al., 1994). There

are differences between men and women in their reactions to the same marketing stimuli.

Arnold and Bianchi (2001) proposed gender identity as a variable that impacts on the

success of a relationship marketing strategy when dealing with business-to-consumer

relations. Men and women seem to want different products, and they are likely to have

different ways of thinking about obtaining these (Huang et al., 2003). Consequently, the

following research question is proposed:

RQ2. Does gender have an effect on consumer perception in selecting a restaurant?

The mediating effect of income

Besides gender and age, other demographic characteristics such as income appear to

impact the consumer’s perception of selecting a restaurant. Prior research indicates that

education and income are significant demographics in terms of Web diffusion (Atkin et al.,

1998). Price was shown to be a more important aspect when choosing food for the elderly

(importance of this factor grew with age of respondents). This may be due to a lower level of

income among older consumers and are considered an unattractive segment because of

their relatively lower incomes and health issues, which often stops them from doing

shopping on their own (Stitt et al., 1995, Manandhar, 1995).

Another important factor influencing food choice behavior has to do with the price (Steptoe

et al., 1995). This is explained primarily by better income situation of people with higher

education levels (Gutkowska et al., 2001). However, little research has focused on other

demographic variables such as income in consumers’ perception in the choice of a

restaurant. Consequently, the following research question is proposed:

RQ3. Do income and employment status of consumers have an influence on their

perception in the choice of a restaurant?

Conceptual framework

Above all researchers in this study found that there is very limited literature focusing on

consumers’ perception in the choice of a restaurant’s services when gender, age and

income play a mediating role. Based on the literature review; this study proposed a

conceptual framework of studying dynamics of consumers’ perception their demographic

characteristics towards their behavior of selection a restaurant. This framework emphasizes

the following independent, mediating and dependent variables: consumers’ perception,

consumers’ age, consumers’ income, consumers’ gender and consumer’s behavior towards

choosing a restaurant. The schematic diagram is presented below in Figure 1.

The following null hypothesis is presented by referring to the conceptual framework which

will be tested through statistical procedure:

H1. Consumers’ perception plays a significant role in their behavior towards

choosing a restaurant, when consumers’ ages play mediating role.



choosing a restaurant, when consumers’ income plays mediating role.


choosing a restaurant when consumers’ gender differences play mediating role.

H4. Consumers’ age plays a significant role in their behavior towards choosing a

restaurant.

H5. Consumers’ income plays a significant role in their behavior towards choosing a

restaurant.

H6. Consumers’ gender differences play a significant role in their behavior towards

choosing a restaurant.

Research methodology

The major purpose of this study is to learn the consumers’ perception in the choice of a

restaurant’s services where demographic variables like gender, age and income act as a

mediating variable. In order to guarantee the representativeness of the population,

convenient sampling method was employed, according to the criteria of significant location

in Dhaka city (like Motijheel, New Market, Gulshan and Farmgate, Tejgaon,Hazaribagh and

Bashundhara-Baridhara). The survey questionnaire consisted of six distinct sections, each

section contained relevant questions pertaining to different parts of the study.

Questionnaires were systematically distributed utilizing a convenient sampling method.

The sampling frame for conducting the principal component analysis was comprised of 350

respondents. A seven-point scale was used ranging from ‘‘strongly disagree’’ to ‘‘strongly

agree.’’ Initially a total of 400 samples were distributed among the potential respondents for

this study, of which 370 questionnaires were received. After the screening process was

completed, only 350 responses were considered complete and valid for further data

analysis. This represents a response rate of 87.50 percent, which was considered to be

adequate based on time, cost, certainty and geographical constraints.

Data analysis procedure

The first stage of the data analysis utilized exploratory factor analysis (EFA) to identify the

factor structure for measuring the factor that affect consumers’ perception in the choice of a

restaurant’s services. The second part of the data analysis utilized confirmatory factor

analysis (CFA) to confirm the factor structure. Hence in this study structural equation models

were used to test the hypothesis. It seemed appropriate for the study because of the multiple

dependence relationships in the proposed models (Joreskog and Sorbom, 1996).

Figure 1 Theoretical framework for the proposed study


According to Anderson and Gerbing (1988) measurement of a model can be tested on the

complete data set using a confirmatory factor analysis (CFA). In addition Joreskog and

Sorbom (1996) also mentioned the necessity of using structural equation modeling (SEM).

The application of SEM demonstrates advantages of ‘‘measurement and prediction’’

(Kelloway, 1998, p. 2) over standard multiple regression methods. To examine the general fit

of the proposed model and to test the research questions, factor constructs were employed

in this study which was based on maximum likelihood derived from an earlier exploratory

factor analysis (Fish, 2005). Fit indices included in the current investigation are the

comparative fit index (CFI); the goodness-of-fit index (GFI); the normed fit index (NFI);

Tucker Lewis Index (TLI); the root mean square error approximation (RMSEA) (Bentler and

Bonett, 1980; Joreskog and Sorbom, 1996; Tucker and Lewis, 1973; Fornell and Larcker,

1981).

Data analysis

Reliability coefficient of all the items in the instruments

In order to measure the reliability for a set of two or more constructs, Cronbach alpha was

used for this study. It is a method alpha coefficient values range between 0 and 1 with higher

values indicating higher reliability among the indicators (Hair et al., 1992). In accordance

with the Cronbach alpha test, the total scale of reliability for this study varies from 0.80,

indicating an overall higher reliability factors. From Table I showed that the reliability of this

study is substantial in every perspective.

Factor analysis

The survey results obtained from 350 respondents have been explained in this section

through the principal component analysis (PCA). It was carried out to explore the underlying

factors associated with 28 items. The constructs validity was tested through Bartlett’s Test of

Sphericity and The Kaiser-Mayer-Olkin Measure of sampling adequacy. Result for the

Bartlett’s Test of Sphericity and the KMO revealed from this study that both were highly

significant. As such, this study concluded that this variable was suitable for the factor

analysis (Table II).

For this study, the general criteria were accepted items with loading of 0.60 or greater. The

result showed that total variance of the two factors was 53.070 percent. Table III presents

values indicating affiliation of the items to a factor. The higher loading (factor) indicates the

stronger affiliation of an item to a specific factor. The findings of this study indicate that each

of the four dimensions (consumers’ perception, consumers’ age, gender and income) is

homogeneously loaded to the different factors.

Reliability test of each item under each factor after factor analysis

Reliability is the degree to which the observed variable measures the ‘‘true’’ value and is

‘‘error free’’. According to Hair et al. (2006) reliability measures shows greater consistency

Table II KMO and Bartlett’s Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.791Bartlett’s Test of Sphericity Approx. chi-square 864.786

Df 92Sig. 0.000

Table I Reliability analysis for all variables

Cronbach’s alpha Cronbach’s alpha based on standardized items No. of items

0.802 0.803 32


than less reliable measures (Table IV). Reliability coefficients (Cronbach’s alpha) were

computed for the items that formed each factor. The reliability coefficients for the four

factors: consumers’ perception, age, gender and income were 0.812, 0.838, 0.782 and

0.779 respectively. As Table IV shows, all alpha coefficients for the data exceed the minimum

standard for reliability of 0.70 recommended by Nunnally (1978). Thus, the results indicate

that these multiple measures are highly reliable for measuring each construct.

Confirmatory factor analysis

The second phase of data analysis consisted of confirmatory factor analysis. To this end the

structural equation method (SEM) was applied, using maximum likelihood estimation

method to test the hypothesis of the study (Bentler, 1995). Although this study had employed

EFA for verifying grouping and loading pattern of measuring scale items, it had further

attempted to screen EFA examination by conducting CFA among all the exogenous

variables (consumers’ perception, age, gender, income variables) with measuring items

retained by EFA.

Consumers’ perception

From the result of EFA as shown in Table III, we retained four measuring items for consumers’

perception towards choosing a restaurant. This study retained all these items after

conducting CFA, given that all those indicators were loaded with loading factor more than

0.60. For consumer’s perception, the modification indices for the covariance between the

measurement errors of cp1 (‘‘The restaurant gives customers good value for money’’) and

cp2 (‘‘Staff is sensitive to your needs and wants’’) was 16.834; cp1 (‘‘The restaurant gives

customers good value for money’’) and cp4 (‘‘Re-visit the restaurant’’) was 11.278; cp2

(‘‘Staff is sensitive to your needs and wants’’) and cp4 (‘‘Re-visit the restaurant’’) was 18.549.

Table III Factor loading matrices following rotation of four-factor solutions

Items Consumers’ perception (CP) Age Gender Income

The restaurant gives customers good value for money (cp1) 0.662Staff is sensitive to your needs and wants (cp2) 0.694Choices of food meet customers’ needs (cp3) 0.621Re-visit the restaurant (cp4) 0.760Restaurants always serve food according to age 0.651Different age group has different taste 0.631Young people like to eat more on restaurant 0.685Restaurant owners make menu according to the age 0.767Male customers always like to eat in restaurant 0.671Female consumers always influence male to choose restaurant 0.712Young consumers always like eating out 0.639Older consumers do not like to eat outside 0.621Lower income group customers eat street restaurant 0.671Restaurant services base on income class 0.681Middle income group are the main consumers 0.632Restaurants’ environment base on income class 0.681

Notes: Extraction method: principal component analysis; rotation method: Varimax with Kaiser Normalization; Rotation converged in fiveiterations

Table IV The reliability coefficients for derived factors

Factor Number of cases Number of items Cronbach’s alpha

Consumers’ perception (CP) 350 4 0.812Age 350 4 0.838Gender 350 4 0.782Income 350 4 0.779


The correlation of these errors was logically possible; therefore the model was revised to

incorporate this path (Figure 2). After adding this parameter, the measurement model fit

indices of price showed an adequate fit: x2/d.f.¼1.78 (x2¼23.226, d.f ¼ 13); GFI ¼ 0.985,

AGFI ¼ 0.979, CFI ¼ 0.989, NFI ¼ 0.963, TLI ¼ 0.973 and RMSEA ¼ 0.050.

Age, gender and income

From EFA as shown in Table III, have retained four measuring items for age, gender and

income variable. This study has retained all these items after conducting CFA, as all those

indicators were loaded with loading factor more than 0.70. The measurement model fit

indices of age, gender and income showed an adequate fit: x2/d.f.¼1.32 (x2¼15.852,

d.f ¼ 12); GFI ¼ 0.983, AGFI ¼ 0.973, CFI ¼ 0.979, NFI ¼ 0.967, TLI ¼ 0.943 and

RMSEA ¼ 0.040; x2/d.f.¼1.222 (x2¼13.452, d.f ¼ 11); GFI ¼ 0.973, AGFI ¼ 0.963,

CFI ¼ 0.969, NFI ¼ 0.957, TLI ¼ 0.946 and RMSEA ¼ 0.050; x2/d.f.¼1.20 (x2¼16.852,

d.f ¼ 14); GFI ¼ 0.962, AGFI ¼ 0.951, CFI ¼ 0.951, NFI ¼ 0.937, TLI ¼ 0.935 and

RMSEA ¼ 0.040.

Statistical significance of parameter estimates

In this stage of data analysis this study utilized critical ratio (C.R) value, which represents the

parameter of an estimate divided by its standard error. Based on a probability level 0.05 the

test statistic needs to be .^1.96 before the hypothesis (that estimates equals 0.0) can be

rejected. On the other hand, it is also important to note that non-significant parameters can

be indicative of a sample size that is too small (Byrne, 2001).

Hypotheses testing

The structural equation model was examined to test the relationship among the constructs.

After adjustment of the model by observing the modification indices value goodness-of-fit

indicates for this model were chi-square/df ¼ (159.769 /61) ¼ 2.61, GFI ¼ 0.929,

AGFI ¼ 0.916, CFI ¼ 0.894, NFI ¼ 0.828, RMSEA ¼ 0.07. Figure 2 depicts the full model.

After observing the statistical test it is been clear that all the paths are not significant at

p , 0:05. (H1) consumers’ perception plays a significant role on their behavior towards

choosing a restaurant, when consumers’ age plays a mediating role. Therefore null

hypotheses H1 is accepted at 0.5 level of significance p . 0:000. Regarding the H2:

Consumers’ perception plays a significant role on their behavior towards choosing a

restaurant, when consumers’ income plays mediating role. Therefore, this null hypothesis is

accepted at p , 0:000. H3: consumer’s perception plays a significant role on their behavior

towards choosing a restaurant when consumer’s gender differences play mediating role this

null hypothesis is also accepted at p , 0:000. H4: consumers’ age plays a significant role on

Figure 2 Dynamics of consumers’ perception towards their behavior of choosing a

restaurant (for total sample): default model


their behavior towards choosing a restaurant. Therefore null hypotheses H4 is accepted at

0.5 level of significance p . 0:000. On the other hand, H5: consumers’ income plays a

significant role on their behavior towards choosing a restaurant at 0.5 level of significance

p . 0:000 this statement is nearly rejected. However, H6: consumers’ gender differences

play a significant role on their behavior towards choosing a restaurant is rejected at 0.5 level

of significance p . 0:000.

Among all the significant variables, consumers’ age has the highest estimate 1.315 towards

consumers’ behavior of choosing a restaurant (Table V). The second highest estimate is

1.018 for consumers’ perception towards consumers’ behavior of choosing restaurant when

incomes play a mediating role. The third highest estimate is 0.726 for consumers’ perception

towards consumers’ behavior of choosing restaurant when age differences play a mediating

role. Lastly the fourth highest estimate is 0.641 for consumers’ perception towards

consumers’ behavior of choosing restaurant when gender differences play a mediating role.

On the other hand consumers’ income and their gender differences individually do not have

any significant influences over their behavior of choosing a restaurant.

Conclusion

The results of this study suggest that a significant proportion of consumers in Dhaka city of

Bangladesh are very sensitive towards choosing a restaurant services. This research argues

that consumers’ perception always mediating by consumers’ age, gender differences and

their income. On the other hand in some situation age differences can be an important factor

which may also be influenced an individual to choose a restaurant’s services.

Previous studies re-enforce this research findings and corroborate with the literature to

suggest that consumers’ perception can be influenced an individual to choose a

restaurant when age, gender and their income play a significant mediating role (Dube

et al., 1994; Lee and Hing, 1995; Johns and Tyas, 1996; Fu and Parks, 2001; Tam, 2004;

Schiffman and Kanuk, 2000; Wagar and Lindqvist, 2010; Alalaakkola, 1996; Alexander,

1947; Nysveen et al., 2005; Kotler and Keller, 2006; Mitchell and Walsh, 2004; Gutkowska

et al., 2001).

Managerial implications

It is been observed that the majority of research into restaurants customers come under the

perspective of demographic characteristics (age, gender and income).. The present study

explored the influences of the customer’s perception towards selecting a restaurant’s

services when age, gender and income play a mediating role which could contribute

valuable output for the hospitality research.

The results obtained from this exploratory study allow researchers, academicians and

managers to accept that demographic variables can be influenced by the behavior of

customers. To serve the multi diverse consumers in Dhaka city based on this study,

restaurant managers can find directions from the research for the improvement of their

service quality and strategic plan of marketing. Operators of the restaurant who are trying to

understand their customers must measure not only their perception, but also the customer’s

Table V Standard estimation of the main model

Standardized regression weight Estimate S.E. C.R. p value

H1 Age ˆ Consumers’ perception (CP) 0.726 0.064 11.290 0.000**H2 Income ˆ Consumers’ perception (CP) 1.018 0.079 12.884 0.000**H3 Gender ˆ Consumers’ perception (CP) 0.641 0.066 9.675 0.000**H4 Consumers’ behavior (CB) ˆ AGE 1.315 0.067 19.701 0.000**H5 Consumers’ behavior (CB) ˆ INCOME 0.130 0.047 2.794 0.004H6 Consumers’ behavior (CB) ˆ GENDER 0.080 0.038 2.117 0.034


age, gender, and income because those variables also play a substantial role in predicting

customer’s behavior as well.

This study also proposed that restaurants in Dhaka city need to upgrade their internal and

external facilities, for example: foodservice providers should train their employees in a y

professional manner so that they can understand the needs and wants of the customers of

different age, gender and income group. Restaurant owners should offer a variety of food

and ensure that the food is attractive and hygienically prepared. In addition, most of the

customers expect managers to ensure that food handlers follow safe food preparation

procedures at the foodservice work site.

Limitations and direction of further study

The study suffers from a number of limitations. Even though the results are interesting and

support previous studies, there are some limitations due to the scope of this study in using

only customers from Dhaka city. Hence, the results cannot be easily generalized.

This study used convenience sampling procedure which is a limitation and future studies

can utilize stratified random sampling procedure with more samples. On the other hand the

EFA is difficult to generalize to a larger audience and there was a lack of experimental control

for the questionnaire investigation, so the quality of questionnaire cannot be ensured. On the

other hand longitudinal analysis can be conducted to understand more nuances about the

variables. It must also be noted that this study was undertaken in a developing country

where most of the citizen’s income are substantially less than what obtains in developed

economies.

Future studies could examine a restaurant’s service quality, customer satisfaction, restaurant

image for a particular restaurant sector using a larger sample. In addition, further research is

needed to measure the behavior and perception of the senior and working class citizens’

from all the major cities in Bangladesh.

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About the author

Muhammad Sabbir Rahman has a PhD in Business Administration and is a Lecturer in theFaculty of Management at the Multimedia University (Malaysia). His research interestsinclude consumers’ behavior, perception, attitude, brand image, price perception, climatechange, and tourism and tourist market. He has three years of teaching and researchexperience, has written several research papers, both qualitative and quantitative, ininternational refereed journals, in the area of global marketing, e-commerce, internetshopping and internet advertising, Islamic marketing, and consumer behavior. MuhammadSabbir Rahman can be contacted at: [email protected]


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