Analysis of Purchasing Decisions as a Form of Consumer Brand

Responses

P. Dewi Dirgantari

, Y. M. Hidayat

and B. Widjajanta

Faculty of Economics and Business Education, Universitas Pendidikan Indonesia, Bandung-Indonesia

Keywords: Purchase Decision, Marketing Mix, Meta-Analysis.

Abstract: The purpose of this study is to find out and analyze purchasing decisions that are influenced by the factors

forming the marketing mix. This research is motivated by the gap in research results so that it needs to be

reexamined these factors and/or dimensions. The method used is a meta-analysis by collecting the results of

research published online/online about purchasing decisions that are influenced by the marketing mix

factors. The results obtained from this study indicate that purchasing decisions are influenced by acceptable

marketing mix factors.

1 INTRODUCTION

Marketing is one of the main sources of competitive

advantage in a company (Guercini & Runfola,

2015). As stakeholders of marketing activities,

consumer behavior must be well understood

(Abdeen, Rajah, & Gaur, 2016). Consumer behavior

is the study of how individuals, groups and

organizations choose, buy, use, and spend goods,

services, ideas, or experiences to satisfy the needs

and desires of consumers (Kotler & Keller, 2016:

151). Purchasing behavior gets a lot of attention

from marketers and researchers because of the

significant role it plays in anticipating operational

success and achieving competitive advantage

(Panasuraman et al., 1985).

Purchasing decisions is a process where

consumers know the problem, find information

about a particular product or brand and evaluate how

well each of these alternatives in solving the

problem which then leads to purchasing decisions

and greatly influenced perceived risk (Kotler &

Keller, 2015). Lack of information and knowledge

of a brand and the features of a product can clearly

lead to low purchasing decisions, thereby reducing

the number of purchases (Kotler & Keller, 2015).

Thus, to overcome the low purchasing decisions,

companies must multiply their product information

when consumers carry out information seeking

stages (Shareef et al., 2008). At the information

seeking stage, consumers will seek offline and

online referrals (Chaffey & Smith, 2008).

Companies must provide marketing stimuli that can

be controlled through products, prices,

places/locations and integrated promotions

(marketing mix) to produce the desired response in

the target market (Kotler & Armstrong, 2008). The

marketing mix in turn aims to translate brand

expressions into actual products or services, at

certain prices, which will be sold at certain outlets,

to be promoted through communication activities

and certain channels, and must be supported by

certain services (Sicco Van Gelder, 2005:1)

Research on purchasing decisions has been

carried out to date in various industries such as the

fashion industry (Eckman, Damhorst & Kadolp,

1990), the automotive industry (Purwani &

Dharmmesta, 2002), organic food industry

(Balawera, 2013), industry tourism (Khuong, Thi, &

Thanh, 2016), the food and beverage industry

(Salleh, Ariff, Zakuan, Sulaiman, & Saman, 2016),

the industrial industry (Yulindo, 2011) and the

telecommunications industry (Kakar et al., 2017 ).

The results of the study show that certain

products with low purchasing decisions make the

level of trust in the company low and cause the level

of sales to be very dependent on the purchasing

decisions of goods and services produced (Eckman

et al., 1990). Dewi Pujiani's (2014) showed that the

mix marketing (product, price, place, promotion)

influence buying decisions and the most dominant

dimension is promotion. Whereas Alizar Hasan,

Yumi Meuthia, Berry Yuliandra, and Indah Desfita

(2014) showed that for places/locations there was no

754

Dirgantari, P., Hidayat, Y. and Widjajanta, .

Analysis of Purchasing Decisions as a Form of Consumer Brand Responses.

DOI: 10.5220/0009504507540759

In Proceedings of the 1st Unimed International Conference on Economics Education and Social Science (UNICEES 2018), pages 754-759

ISBN: 978-989-758-432-9

significant relationship to purchasing decisions and

the most dominant dimension was price and Amelia

Tjahjono's, Prof. Dr. Hatane Semuel, MS. and

Ritzky Karina M. R. Brahmana, S.E., M.A. (2013)

shows that the marketing mix consisting of products,

prices, places and promotions affects the decision to

purchase women's clothing online as well as the

social and psychological environment which is the

variable that has the biggest contribution to

purchasing decisions.

Based on the above phenomena, there appears to

be a research gap so that the factors and/or

dimensions need to be reexamined. Thus, it is

necessary to do research related to purchasing

decisions. This research is limited to the factors that

form the marketing mix such as products,

promotions, prices, and places that influence

purchasing decisions. The assumption used in this

study is the amount of research on similar topics,

especially research on purchasing decisions but

different results.

The purpose of this paper is to obtain findings

regarding: the influence of the marketing mix factors

on purchasing decisions with a meta-analysis

approach, so that the synthesis results can be

obtained as a hypothesis for further testing. The

results of this study are expected to be used as input

for policy makers related to purchasing decisions.

2 METHODOLOGY

A meta-analysis method used to obtain further

information about purchasing decisions that

influenced by marketing mix factors from

articles/studies. Articles/studies are obtained through

databases of online journals such as Science Direct,

Springer, Ingenta Connect, Sage, and Research Gate,

etc. with the year published between 2008 and 2018.

The steps of the meta-analysis in this study are as

follows:

1. Formulating research questions. The

problem in this study is related to purchasing

decisions, especially purchasing decisions and

marketing mix formers namely products,

promotions, prices, and places by formulating the

meaning of these two concepts /defining variables

including their relevance.

2. Gathering existing empirical

studies/research. After formulating research

questions, articles/studies was sought using

keywords that are relevant to the topic of purchasing

decisions that are influenced by the marketing mix

factors. A total of 339 articles were obtained from

this step.

3. Selecting studies. Studies that not provide

sufficient information to calculate general metrics

are excluded from the analysis. Researches with

different methods is also excluded, although the

topic is relevant to the research question. Through

this process, 26 studies/studies were obtained that

were in accordance with the criteria and metric

measures relevant to the research formula.

4. Encoding of selected studies/researches.

After a set of studies selected, the next step is

encoding, obtain characteristics studies/researches,

and input it to a spreadsheet program to manage the

processing of statistics from the meta-analysis.

5. Data analysis. At this step, data extracted

from studies/researches can be the basis for various

calculations to get a summary of the results in the

literature.

6. Interpret and present results.

3 RESULTS AND DISCUSSION

After formulating the research questions and

collecting empirical studies/research a total of 339

articles were subsequently obtained:

1. Transform the value of F

count

and t

count

to the

size of the correlation (r). F

count

and t

count

obtained from 26 selected studies are

transformed into correlation values with the

following formula (Hunter & Schmidt, 2004)



√

















2



the results are shown in table 1.

Table 1: Transformation to r value.

No. Author N F



(Ahmad, et al.,

2012)

50 13.530 3.678 0.4689

(Andreti, et al.,

2013)

300 5.962 0.3264

(Saidani &

Ramadhan,

2013)

100 21.406 4.627 0.4234

(Ahmadi, et al.,

2010)

100 12.545 0.7850

5. (Agustim, 2010) 69 19.141 4.375 0.4714

(Purnomo, et al.,

2014)

98 16.977 4.120 0.3876

(Mughal, et al.,

2014)

200 0.2090

(Malombeke, et

al., 2014)

75 5.221 0.5214

9. (Hasan, et al., 160 0.2380

Analysis of Purchasing Decisions as a Form of Consumer Brand Responses

755

No. Author N F



2014)

10.

(Imelda &

Sangen, 2013)

219 0.7800

11. (Miharja, 2013) 96 33.235 5.765 0.5111

12. (Yazia, 2013) 100 16.162 4.020 0.3763

13.

(Tajik & Gorji,

2014)

400 4.280 0.2098

14. (Yosep, 2013) 200 44.099 6.641 0.4268

15.

(Wibowo &

Karimah, 2012)

110 9.087 3.014 0.2786

16.

(Abdullah, et al.,

2013)

150 28.161 5.307 0.3998

17.

(Citrawati &

Sulistiono, 2014)

100 217.684 14.754 0.8304

18.

(Moorthy, et al.,

2014)

71 19.707 4.439 0.4713

19.

(Perdana &

anang, 2018)

100 0.4700

20. (Yu, et al., 2017) 173 0.4100

21. (Kenning, 2008) 276 4.009 2.002 0.1201

22.

(Nawawi &

Ikhaz, 2015)

200 22.693 4.764 0.3207

23.

(Aras, et al.,

2017)

100 8.119 0.6341

24.

(Astuti &

Wijaya, 2015)

100 68.216 8.259 0.6406

25.

(Sipayung &

Sinaga, 2017)

384 275.661 16.603 0.6474

26.

(Hasan, et al.,

2016)

94 23.259 4.823 0.4492

2. Calculate estimated population correlation

average (̅). Estimated average population

correlation is obtained by dividing the average

correlation from the selected studies by the

number of samples (Hunter & Schmidt, 2004)

or written in the formula

r̅ 

∑





∑



with N



is the number of samples in study i and



is the correlation in the study i.

From the Table 1, an estimate of the average

population correlation is obtained

r̅ 

1,699.142

4,025

 0.422

3. Calculates the variance of the population

average. Similar to calculate population

correlation averages, the variance of population

averages is obtained by weighted it with the

sample size (Hunter & Schmidt, 2004)







∑







r̅







∑



Calculation of variance from population

averages is obtained through the following table

Table 1: Calculation of Variances of Average Population.

No. N









































50 0.469 0.047 0.002 0.109

300 0.326 (0.096) 0.009 2.747

100 0.423 0.001 0.000 0.000

100 0.785 0.363 0.132 13.168

69 0.471 0.049 0.002 0.167

98 0.388 (0.035) 0.001 0.117

200 0.209 (0.213) 0.045 9.086

75 0.521 0.099 0.010 0.739

160 0.238 (0.184) 0.034 5.426

219 0.780 0.358 0.128 28.045

96 0.511 0.089 0.008 0.759

100 0.376 (0.046) 0.002 0.211

400 0.210 (0.212) 0.045 18.043

200 0.427 0.005 0.000 0.004

110 0.279 (0.144) 0.021 2.267

150 0.400 (0.022) 0.000 0.075

100 0.830 0.408 0.167 16.667

71 0.471 0.049 0.002 0.172

100 0.470 0.048 0.002 0.229

173 0.410 (0.012) 0.000 0.026

276 0.120 (0.302) 0.091 25.183

200 0.321 (0.101) 0.010 2.060

100 0.634 0.212 0.045 4.494

100 0.641 0.218 0.048 4.773

384 0.647 0.225 0.051 19.487

94 0.449 0.027 0.001 0.069

Total

4025 154.123

From the table 2 Calculation of variance from

population averages is obtained









.

,

 0.0383

4. Calculates the variance of sampling errors







that obtained from previous step is a

combination of variance in population

correlation and variance in sampling errors, so

that the variance in population correlation must

be corrected by variance in sampling errors. The

variance of sampling errors was formulated as

follows (Hunter & Schmidt, 2004)











1̅













1





UNICEES 2018 - Unimed International Conference on Economics Education and Social Science

756

then the sampling error variance is obtained











10.422









154.8071





 0.0044

then, the impact of sampling errors is obtained















0.0044

0.0383

100%  11.488%

Calculate corrected population correlation

variances. After obtaining the sampling error

variance (





) ), then the population correlation

variance is calculated by using the formula



















 0.03830.0044  0.0339

5. Calculates measurement error correction Y

Measurement errors in a general study occur,

this level of measurement error is measured by

the reliability coefficient of each research

study. The greater reliability coefficient will

produce a small measurement error. Therefore

the population correlation value (r̅obtained in

the second step of analysis needs correction by

involving reliability coefficient in this case on

variable Y. The formula used is











with:



= average measurement error correction

(a) = square root reliability coefficient

Ave(a)= average(a)

to simplify the calculation process, it is

presented in the following table:

Table 3: Reliability Coefficient.

No. r



0.718 0.847

0.824 0.908

0.721 0.849

0.896 0.947

- -

0.639 0.799

0.867 0.931

0.583 0.764

- -

0.867 0.931

- -

0.916 0.957

No. r



- -

0.822 0.907

- -

0.606 0.778

- -

0.891 0.944

0.774 0.880

- -

0.729 0.854

Total

12.296

Average (



)

0.878

6. Calculate corrected population correlations

Next is to calculate the actual or corrected

population correlation values, namely by using

the following formula (Hunter & Schmidt,

2004)



48.0

878.0/422.0



ArAve

Ave



so that the corrected population correlation

obtained is equal to 0.48

7. Calculate corrected variance.

The next step is to calculate the number of

squared coefficients of variation (V) using the

following formula (Hunter Schmidt, 2004)





261.0

878.0/449.0



 aAveaSDV

Furthermore, variance is calculated due to

variations in artifacts

047.0

261.0878.0480.0

222



 VAS



Analysis of Purchasing Decisions as a Form of Consumer Brand Responses

757

Corrected population correlation variances are as

follows













1187.0

014.0

878.0/0.047-0.0339

222





AVAVarVar



assuming the correlation effect size is normally

distributed with the confidence level of 95%, the

interval is

248.0)1187.0(96.1

713.0)1187.0(96.1

96.1











lower

upper

The meta-analysis study found that the corrected

population correlation (ρ) between purchasing

decisions and marketing mix factors was estimated

at 0.48, the variance of the population was 0.014 and

the standard deviation was 0.1187. With a

confidence level of 95%, the acceptance limit is

0.248 < ρ < 0.713, then the corrected population

correlation (ρ) of 0.48 enters the acceptance limit.

Thus, referring to the results of the analysis of

meta-analysis study data regarding the influence of

the marketing mix factors on acceptable purchasing

decisions.

The purchase process occurs when consumers

search for information, compare existing

alternatives, then make purchasing decisions for a

product (Neha & Manoj, 2013), because consumers

before making a purchase decision, will usually

spend time evaluating by looking at suggestions,

reviews or reviews. what consumers have done

before on the product or service that will be bought

(Sciences, 2013). This is where the right time the

company provides marketing stimuli that can be

controlled through products, prices, places and

integrated promotions (marketing mix) to produce

the desired response in the target market (Kotler &

Armstrong, 2008).

4 CONCLUSIONS

Referring to the results of analysis of meta-analysis

study data on the influence of the marketing mix (the

forming factors & marketing mix such as product,

price, place and promotion) on purchasing decisions

shows that the hypothesis states that there is an

influence of the marketing mix (products, prices,

places and promotions) towards purchasing

decisions.

Whereas to minimize the impact of sampling

errors, it is recommended that in future studies be

able to pay attention to the characteristics of the

manufacturing or service industry as well as offline

or online.

ACKNOWLEDGEMENT

The researcher would like to thank the Lembaga

Penelitian dan Pengabdian kepada Masyarakat of

Universitas Pendidikan Indonesia, Bandung who

supports the study.

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