Keywords

Consumer, Consumer Behavior, White Goods, Purchase Process, Purchase Decision Process, Role of Women, Kazakhstan.

Introduction

Nowadays, with the development of marketing activities, the increasing competition among businesses has made some research necessary for a better understanding of consumer behavior. Thus, marketing managers began to examine consumer behaviors to get to know the market, to devise a general marketing strategy, to choose a target market, and to determine the marketing mix suitable for their target market. Therefore, the researchers used several models to analyze the target markets and consumer behaviors and tried to identify the factors that affect consumer behavior.

All individuals are members of the society in general and a family in particular. Family affects various choices and decisions regarding the consumption of goods and services. Therefore, it is an important organization for marketing. The role and status of family members in these decisions are also important, especially in the selection of goods and services consumed by all family members. Therefore, marketing managers are closely interested in the effects and roles of family members in the purchase of various goods and services. Family members can have different roles in the decision-making process within the family. For example, men are more dominant in decisions regarding buying a car, but women are more dominant in decisions regarding buying white goods. Researches on this subject showed that the roles of spouses differ according to the characteristics of the family and according to the product (Belch et al., 1985; Davis, 1974; Davis, 1976; Webster, 1995; Dallmann, 2001; Piron, 2002; Barletta, 2003).

In recent years, social and economic changes have shaped the position of women in production and consumption. In many developed and developing countries, women have started to have a more effective and strong identity in every field. The number of women participating in working life is increasing day by day. Therefore, these changes also affect the role of women in purchasing processes in the family. The woman can be in a decisive position depending on factors such as family status, education level, or employment status. In this context, the main purpose of this study is to determine the role of women according to the different demographic structures of the families in Kazakhstan regarding the decision-making stages of the white goods and to reveal the role distribution of the purchasing process. In addition we investigated the role of Kazakh women in the process of purchasing white goods for their families. In this framework, we conducted a survey study. We analyzed the role of women according to the region they lived, where they lived, their employment status, income status, marriage duration, and educational status. We used the Multivariate Analysis of Variance (MANOVA) to test our hypotheses.

Data

It is possible to talk about the concept of family, which are the most important factor affecting the purchasing behavior of consumers, as well as the most determinant and binding factor in terms of affecting individuals. The concept of “family” as a consumption and decision-making unit has long been a focus research area in marketing and consumer behavior (Epp & Price, 2008). One of the smallest and most important institutions of social life is family. Since the family is both a spending and an earning structure, it has a very different place in the social structure (Göksu & Bilge, 2010). Families are generally classified according to the number of members, marriage style, genealogy, residence, and distribution of authority. The classification according to the distribution of authority is more relevant in our study, since it directly affects the roles in the family regarding decision-making. The Kazakh family is generally patriarchal. However, recent social and economic changes have made the power structure in the family more egalitarian. Today, husbands and wives participate almost equally in decision-making processes. The power structure in families determines the influence areas of all family members, therefore spouses. Moreover, in Kazakh culture, the woman (Anne, Ana) is considered as the person who conveys language, belief, religion, custom tradition, tradition, national culture, which is the basis of all the good qualities of the Kazakh people and the world view (Mukatova, 2010). However, the role of women in social relations also determines different forms of familial relations and the degree of development in a society. Therefore, based on power distribution, we can classify Kazakh families under three categories, namely patriarchal kinship families, patriarchal families, and nuclear families (Tursynova et al., 2015).

In recent years, women have started to come to the fore in economic and social fields. Looking across the world, women in developed and developing countries have started to have a more effective and strong identity in economic and social fields. With each passing day, women are participating more in working life and the number of working women is increasing. The number and proportion of women's participation in the workforce in Kazakhstan is around 30% and this is a low ratio compared to the world. As their economic power increase and they start to have a say in production, women's purchasing behavior and consumption behaviors began to change.

The tables below show the population distribution in Kazakhstan, family structure, and supply of white goods.

According to Table 1, the population of the Republic of Kazakhstan is 18,157,300 as of 2019, of which 9,366,000 are women. According to these values, 51.58% of the population is women and 48.42% are men.

Table 1 Distribution of the Population by Gender
Years	Population	In Total Population		Percentage of Total Population
		Female	Male	Female	Male
2016	17,160,800	8,876,200	8,284,600	51.72	48.28
2017	17,415,700	9,002,600	8,413,100	51.69	48.31
2018	17,669,900	9,128,100	8,542,800	51.66	48.34
2019	17,918,200	9,249,700	8,668,500	51.62	48.38
2020	18,157,300	9,366,000	8,791,300	51.58	48.42

According to Table 2, the region of South-Kazakhstan ranks first with 1,471,312 women in terms of women's population, Almaty region comes second with 1,019,155 women and Almaty comes third with 979,406 women. The region of North-Kazakhstan has the lowest women population with 291,677 women. Among the places where the female population is the highest among the urban population, the region of South-Kazakhstan with 687,212 women is the first, Astana city with 534,632 women in the second and Almaty city with 979.406 women in the third place. The lowest female population is in Mangıstau region with 134.317 women. While the province of South-Kazakhstan has the highest rural women population with 784,100 women, Pavlodar region has the lowest rural women population with 110,521 women.

Table 2 Distribution of the Female Population by Region
Female
	2016	2017	2018	2019	2020
Republic of Kazakhstan	8,876,242	9,002,614	9,128,096	9,249,736	9,366,039
Akmola Region	380,336	380,836	384,649	379,105	380,707
Aktobe Region	418,690	425,311	431,264	436,643	442,524
Almaty Region	1,010,020	976,364	988,617	1,005,419	1,019,155
Atyrau Region	289,229	295,961	302,261	308,629	315,000
Western Kazakhstan Region	322,729	325,616	329,216	331,224	333,644
Zhambyl Region	553,144	559,734	565,448	567,296	567,621
Karagandy Region	722,220	726,490	729,258	727,591	726,014
Kostanay Region	464,889	465,011	465,780	462,659	460,373
Kyzylorda Region	370,223	376,799	382,635	386,167	390,861
Mangystau Region	295,732	305,159	314,868	322,714	331,382
Southern Kazakhstan Region	1,376,722	1,403,230	1,428,599	1,446,631	1,471,312
Pavlodar Region	398,150	399,353	400,599	399,324	397,716
Northern Kazakhstan Region	301,606	299,323	297,896	294,334	291,677
Eastern Kazakhstan Region	731,058	731,130	731,174	727,289	724,015
Nur-Sultan City	421,276	441,270	451,889	503,824	534,632
Almaty City	820,218	891,027	923,943	950,887	979,406
Urban Population
Republic of Kazakhstan	5,011,674	5,212,674	5,314,038	5,423,601	5,513,486
Akmola Region	184,211	184,662	186,467	184,876	184,627
Aktobe Region	263,238	269,019	274,237	279,668	286,605
Almaty Region	239,887	243,721	246,318	247,793	242,174
Atyrau Region	138,669	143,232	147,755	151,857	154,375
Western Kazakhstan Region	165,017	167,621	170,567	174,170	178,472
Zhambyl Region	230,275	233,180	236,543	235,828	233,051
Karagandy Region	577,060	582,610	586,574	587,015	586,945
Kostanay Region	246,863	249,718	253,134	253,806	255,730
Kyzylorda Region	162,123	166,109	170,528	174,139	176,709
Mangystau Region	150,290	133,827	135,320	136,263	134,317
Southern Kazakhstan Region	553,025	641,423	655,857	667,860	687,212
Pavlodar Region	283,412	285,912	288,239	288,022	287,195
Northern Kazakhstan Region	132,220	133,106	136,036	136,158	136,915
Eastern Kazakhstan Region	443,890	446,237	450,631	451,435	455,121
Nur-Sultan City	421,276	441,270	451,889	503,824	534,632
Almaty City	820,218	891,027	923,943	950,887	979,406
Rural Population
Republic of Kazakhstan	3,864,568	3,789,940	3,814,058	3,826,135	3,852,553
Akmola Region	196,125	196,174	198,182	194,229	196,080
Aktobe Region	155,452	156,292	157,027	156,975	155,919
Almaty Region	770,133	732,643	742,299	757,626	776,981
Atyrau Region	150,560	152,729	154,506	156,772	160,625
Western Kazakhstan Region	157,712	157,995	158,649	157,054	155,172
Zhambyl Region	322,869	326,554	328,905	331,468	334,570
Karagandy Region	145,160	143,880	142,684	140,576	139,069
Kostanay Region	218,026	215,293	212,646	208,853	204,643
Kyzylorda Region	208,100	210,690	212,107	212,028	214,152
Mangystau Region	145,442	171,332	179,548	186,451	197,065
Southern Kazakhstan Region	823,697	761,807	772,742	778,771	784,100
Pavlodar Region	114,738	113,441	112,360	111,302	110,521
Northern Kazakhstan Region	169,386	166,217	161,860	158,176	154,762
Eastern Kazakhstan Region	287,168	284,893	280,543	275,854	268,894

Table 3 shows how families provide white goods and the number of white goods per 100 families. According to the table, families have 242 televisions, 186 refrigerators and freezers, 145 washing machines, 234 vacuum cleaners, and 113 computers.

Table 3 How Famılıes Provıde Whıte Goods and the Number of Whıte Goods Per 100 Famılıes
	2016	2017	2018	2019	2020
Televisions	239	247	247	247	242
Refrigerators and Freezers	164	171	177	184	186
Washing Machines	129	134	139	144	145
Vacuum Cleaners	231	234	237	239	234
Computers	102	119	110	113	113

Literature Review

Researching decision making in families is important to identify the most effective family members in the purchasing process. While women may be most effective in purchasing some products, other members can be decisive in others. Historically, family decisions have attracted the attention of many consumer researchers and behavioral scientists.

The review of the literature was carried out according to the systematic review process defined by Castagna et al., (2020); Altarawneh et al., (2020) and Wadesango et al., (2020).

In his research, Spiro (1983) evaluates the influencing strategies used by spouses in resolving disputes in purchasing decisions. He also identifies the characteristics of individuals and situations that affect the spouse's use of strategies. The results show that there are various socio-economic and life cycle variables that distinguishes not only the intensity of the strategy used but also the unique combination or mixture of the impact strategies.

Qualls (1987) examined the impact of gender roles on family purchasing decisions. He found a relatively strong relationship between the gender role, the degree of influence, the harmony of preferences, the resolution of conflicts, and the decision.

Nakip & Yaraş (1999) examined the role of Turkish women in purchasing decisions according to Engel, Kollat, and Blackwell models. They determined that the role of Turkish women in purchasing decisions of the family varies according to product groups and the distribution of roles within the family according to the employment status of the woman. They found that working women play a more decisive role in purchasing decisions.

Lee & Beatty (2002) studied the effect of family structure on decision-making. They examined whether the gender role and professional status of women have any effect on the adolescents and their parents' influence on the purchasing decisions.

The work of Belch & Willis (2002) is largely based on studies conducted in the 1970s and 1980s to assess the effects of spouses on family decision-making. Since then, very profound changes have occurred in American families. These changes may have affected the nature of household decision-making processes. Hypotheses were developed and tested with an up-to-date sample of 458 men and women to examine whether these early findings are still valid. The results indicate that, with the increasing influence of women in all decision-making areas, significant changes have occurred in the roles in the decision-making processes in families.

The work of Erbil & Pasinlioğlu (2004) determined the role of women in the family decision-making process. They worked on married women who went to the Ordu Maternity and Children's Hospital and who agreed to participate in the study. They determined that spouses gave 42.8% of the decisions in the family.

Özdemir & Tokol (2008) examined the differences between genders in terms of attention and focus, detailed thinking and ability, as well as in terms of the chromosomes, hormones, and brain structure. Apart from the gender differences generally discussed in this study, socio-demographic and cultural differences will also affect the purchasing behavior of women and their reactions to marketing efforts.

Aygün & Kazan (2008) investigated the effects of family members on purchasing decisions and activities in their research. The effects of family members varied significantly depending on the purchase decision processes and the types of the product.

Juyal & Singh (2009) examined the effects of gender roles in the family decision-making process. They interviewed 300 women from the Dehradun region of Uttrakhand by evaluating five different purchasing decisions. They used structured questionnaires in interviews with women. They found differences depending on the family type (large or core), age, education level, and income level.

Kitapçı & Dörtyol (2009) discussed the family buying decision process in Sivas province and drew attention to the changing role of women. They found that in the traditional Turkish family structure, the father is more decisive in the purchase decisions.

Cengiz (2009) studied which spouse is more decisive in purchasing decisions in his field research in Trabzon, Ankara, İzmir, and Diyarbakır. He concluded that husbands are dominant in low-income families, but in the middle and high-income families make decisions jointly or woman is more dominant.

Günay & Bener (2011) examined how married women from Ankara perceive basic social and economic activities in the family within the framework of their gender roles. There was no difference according to the age, education level, employment status, and monthly income level of the families.

In their study on Kazakh woman consumer behavior in Almaty, Potluri et al., (2014) showed that Kazakh women's spending habits and purchasing preferences vary between four different age groups.

Çetin (2016), on the other hand, tried to find out the factors that affect the clothing choices of women university students and to determine whether the brand, physical properties of the clothes, or the socio-economic levels of the families are more effective in their purchasing behavior. They found that the brand is more decisive in students' decisions than price.

“Woman or Man? The effect of Gender Identity Role in Gift Purchasing Behavior” by Kılıçer et al., (2016) investigated the effect of gender and gender roles in gift purchasing behavior. They showed that the gift purchasing behaviors of men and women vary. In that, women attach more importance to the gift and buy more gifts.

Vural & Güllü (2017) examined the role of women in the purchase decisions of families residing in three cities of South Kazakhstan (Shymkent, Turkistan, and Kentav). They revealed the role of women in purchasing in Kazakh families. They concluded that in families with high education levels, and crowded families women take an active role in business life.

Moreover, the role of women in the purchasing process and their decision-making skills are influenced by the type of family (broad or core), education level, age, profession, and income level. Women living in a large family think more about the impact of their purchasing decisions on their close relatives. Their concerns reinforce the social roles of their mother-in-law and father-in-law, who have a higher influence. Women's age also has a significant impact on their purchasing decisions.

Methodology

The Importance and Purpose of the Research

Today’s rapidly changing economic conditions and intense competition environment in Kazakhstan requires businesses to carefully analyze their customers. In this study, we investigated the role of Kazakh women in the process of purchasing white goods for their families. Our main purpose is to determine the role of women according to different demographic variables and to reveal the role distribution at different stages of the purchasing process. We believe that the study will fill an important gap since there is no study on the decision processes of Kazakh customers.

The Universe of the Research and Sampling

As the study universe, we selected Kazakh families living in four different regions of Kazakhstan (North, South, East, and West). We used a face-to-face survey method to save time and to ensure a high return rate. The research was carried out between 01 June 2020 and 30 June 2020, especially on weekdays and at the weekend when shopping malls are busy.

Collection of Research Data

The data is collected from white goods consumers and the number of participants is 720. The questionnaire forms are controlled and the face-to-face method is used. However, we excluded incomplete or incorrect 18 forms. Therefore, we evaluated 702 questionnaires in total. According to this figure, the rate of return is 97.5%.

Questionnaire Form and Measurement

We made a wide literature review and tried to determine scales that would best represent our variables. We created the questionnaire by translating the selected scales into Kazakh and Russian. The questionnaire form consists of five parts. The chapters consist of questions about need recognition, determination of alternatives, evaluation of alternatives, purchase decision and purchasing, and post-purchase evaluation stages. All statements are measured with a 5-point Likert type scale between 1 (I strongly agree) and 5 (I strongly disagree). The advantage of the Likert scale is its usability. Indeed, the participants answer the Likert rating comfortably in face-to-face, telephone, and mail survey methods (Nakip & Yaraş, 2017). We made sure that the questionnaire conforms to the general rules and format in terms of number, design, and application. The form consists of 54 questions in total.

Hypotheses

Within the scope of this research, the following hypotheses are developed and tested.

H₁            The role of women in the decision-making stages of purchasing white goods varies according to the region where the family lives.

H₂            The role of women in the decision-making stages of purchasing white goods varies according to the average monthly income of the family.

H₃            The role of women in the decision-making stages of purchasing white goods varies according to age groups.

H₄            The role of women in the decision-making stages of purchasing white goods varies according to payment methods.

H₅            The role of women in the decision-making stages of purchasing white goods varies according to brands.

H₆            The role of women in the decision-making stages of purchasing white goods varies according to the quality of the product.

H₇            The role of women in the decision-making stages of purchasing white goods varies according to the price of the product.

Analysis and Findings

Statistical Analysis

We measured each of the five stages of the purchasing process with a separate statement set. There are a total of 54 questions. We designed the survey to include up to 14 questions for each of the first four stages and evaluated the post-purchase evaluation stage within the scope of these statements. We expected from the factor analysis to reduce these statements to the five sub-factors of our model. Therefore, we did not perform a factor analysis for all statements. Since we performed separate factor analyses for the statements of each stage, one or at most two factors is performed for each stage and the statements of each stage are used directly in statistical analysis.

The statistical analysis includes the demographic characteristics of the participants and a reliability test. We used the Multivariate Analysis of Variance (MANOVA) to test our hypotheses. MANOVA analysis revealed that we could use more than two dependent variables in single factor multiple variances. It is possible to increase the number of dependent variables as well as the number of independent variables. Of course, as the number of factors increases, factor levels increase in different dimensions, and the model becomes increasingly complex. Therefore, you should choose a reasonable number of factors (Nakip & Yaraş, 2017).

Demographic Analysis

The frequency and distribution tables for the demographic characteristics of the participants are given below.

As seen in Table 4; 174 participants live in Northern Kazakhstan, 185 in Southern Kazakhstan, 169 in Eastern Kazakhstan, and 174 in Western Kazakhstan. The distributions are 24.8%, 26.4%, 24.1%, and 24.8%, respectively.

Table 4 Dıstrıbutıon of the Partıcıpants by the Regıons of Kazakhstan
Kazakhstan Region	Frequency	Percent	Cumulative Percent
Northern Kazakhstan	174	24.8	24.8
Southern Kazakhstan	185	26.4	51.1
Eastern Kazakhstan	169	24.1	75.2
Western Kazakhstan	174	24.8	100.0
Total	702	100.0

As seen in Table 5, 58.8% of the participants live in urban and 41.2% live in rural areas.

Table 5 Dıstrıbutıon of the Partıcıpants by the Place Of Resıdence
Place of Residence	Frequency	Percent	Cumulative Percent
Urban	413	58.8	58.8
Rural	289	41.2	100.0
Total	702	100.0

As can be seen in Table 6, 306 participants are male and 396 are female. Their distribution is 43.6% and 56.4%, respectively.

Table 6 Dıstrıbutıon by Gender
Gender	Frequency	Percent	Cumulative Percent
Male	306	43.6	43.6
Female	396	56.4	100.0
Total	702	100.0

As seen in Table 7, 463 participants are married and 239 are single. Their distribution is 66.0% and 34%, respectively. As seen in Table 8, 24.1% of the participants graduated from a high school or under (primary, secondary and high school), 43.4% graduated from a university and 32.5% are post graduates.

Table 7 Dıstrıbutıon by Marıtal Status
Marital Status	Frequency	Percent	Cumulative Percent
Married	463	66.0	66.0
Single	239	34.0	100.0
Total	702	100.0

Table 8 Dıstrıbutıon by Educatıon Status
Education Status	Frequency	Percent	Cumulative Percent
Primary, Secondary and High School	169	24.1	24.1
University	305	43.4	67.5
Post Graduate	228	32.5	100.0
Total	702	100.0

As seen in Table 9, 15.4% of the participants are students, 20.9% are workers, 36.3% are civil servants and 27.4% are coming from other professions (retired, unemployed, and tradesman/trader).

Table 9 Dıstrıbutıon by Professıon
Profession	Frequency	Percent	Cumulative Percent
Student	108	15.4	15.4
Worker	147	20.9	36.3
State Officer	255	36.3	72.6
Other	192	27.4	100.0
Total	702	100.0

As seen in Table 10, 25.1% of the participants have low-income (-100000 Tenge) and 25.4% have middle income-lower (101000-150000 Tenge). However, 26.2% of the participants have middle income-upper (151000-250000 Tenge) and 23.4% have high income (251000+Tenge) (1 Dollar=325 Tenge, at the time of the study).

Table 10 Dıstrıbutıon by Income Level
Income Group (Thousand Tenge)	Frequency	Percent	Cumulative Percent
Low Income (-100)	176	25.1	25.1
Middle Income-Lower (101-150)	178	25.4	50.4
Middle Income-Upper (151-250)	184	26.2	76.6
High Income (251+)	164	23.4	100.0
Total	702	100.0

Gender-Region Relationship Analysis

In the first hypothesis, we wanted to test whether the role of women varies according to the regions where the family resides. There is no difference in the two-factor (Gender + Kazakhstan Region) MANOVA analysis. Because, as seen in Table 11, the value of the Hotelling's T-square test was found to be meaningless at the level of 0.6698 and 0.394, respectively for region and gender. Therefore, we rejected the H1. This proves that women and men behave in the same way in purchasing according to the regions they reside in.

Table 11 Multıvarıate Analysıs of Varıance Results Showıng the Role of Women Accordıng to the Regıons where the Kazakh Famıly Resıdes as per the Decısıon-Makıng Stages of Whıte Goods (MANOVA)
Effect		Value	F	Hypothesis	Error	Significance
Intercept	Pillai's Trace	0.977	5934.991b	5.000	693.000	0.000
	Wilks' Lambda	0.023	5934.991b	5.000	693.000	0.000
	Hotelling's Trace	42.821	5934.991b	5.000	693.000	0.000
	Roy's Largest Root	42.821	5934.991b	5.000	693.000	0.000
Region	Pillai's Trace	0.017	0.782	15.000	2085.000	0.699
	Wilks' Lambda	0.983	0.782	15.000	1913.469	0.699
	Hotelling's Trace	0.017	0.783	15.000	2075.000	0.698
	Roy's Largest Root	0.014	1.896c	5.000	695.000	0.093
Gender	Pillai's Trace	0.007	1.032b	5.000	693.000	0.398
	Wilks' Lambda	0.993	1.032b	5.000	693.000	0.398
	Hotelling's Trace	0.007	1.032b	5.000	693.000	0.398
	Roy's Largest Root	0.007	1.032b	5.000	693.000	0.398

Gender-Monthly Average Income Relationship Analysis

In the second hypothesis, we tried to test whether the role of women varies according to the monthly average income of the family. There is no difference in the two-factor (Gender + Income Rate) MANOVA analysis. Because, as seen in Table 12, the value of Hotelling's T-square test was found to be meaningless at the level of 0.136 and 0.444, respectively for monthly income and gender. Therefore, we rejected the H2. This proves that women and men behave in the same way in purchasing according to their monthly income.

Table 12 Multıvarıate Analysıs of Varıance Results Showıng the Role of Women Accordıng to Income Levels of Kazakh Famıly as of the Decısıon-Makıng Stages of Whıte Goods (MANOVA)
Effect		Value	F	Hypothesis	Error	Significance
Intercept	Pillai's Trace	0.977	5884.674b	5.000	693.000	0.000
	Wilks' Lambda	0.023	5884.674b	5.000	693.000	0.000
	Hotelling's Trace	42.458	5884.674b	5.000	693.000	0.000
	Roy's Largest Root	42.458	5884.674b	5.000	693.000	0.000
Income Rate	Pillai's Trace	0.030	1.400	15.000	2085.000	0.138
	Wilks' Lambda	0.970	1.403	15.000	1913.469	0.137
	Hotelling's Trace	0.030	1.405	15.000	2075.000	0.136
	Roy's Largest Root	0.023	3.253c	5.000	695.000	0.007
Gender	Pillai's Trace	0.007	0.948b	5.000	693.000	0.449
	Wilks' Lambda	0.993	0.948b	5.000	693.000	0.449
	Hotelling's Trace	0.007	0.948b	5.000	693.000	0.449
	Roy's Largest Root	0.007	0.948b	5.000	693.000	0.449

Gender-Age Group Relationship Analysis

In the third hypothesis, we tried to test whether the role of women is different according to the age groups. There is no difference in the two-factor (Gender + Age Groups) MANOVA analysis. Because, as seen in Table 13, the value of Hotelling's T-square test is found to be insignificant at the level of 0,266 and 0,447, respectively for age and gender. Therefore, we rejected the H3. This proves that women and men behave in the same way in purchasing according to age groups.

Table 13 Multıvarıate Analysıs of Varıance Results Showıng the role of Women Accordıng to Age Groups of Kazakh Famıly as of the Decısıon-Makıng Stages of Whıte Goods (MANOVA)
Effect		Value	F	Hypothesis	Error	Significance
Intercept	Pillai's Trace	0.977	5762.095b	5.000	693.000	0.000
	Wilks' Lambda	0.023	5762.095b	5.000	693.000	0.000
	Hotelling's Trace	41.574	5762.095b	5.000	693.000	0.000
	Roy's Largest Root	41.574	5762.095b	5.000	693.000	0.000
Age Groups	Pillai's Trace	0.026	1.199	15.000	2085.000	0.265
	Wilks' Lambda	0.975	1.198	15.000	1913.469	0.265
	Hotelling's Trace	0.026	1.197	15.000	2075.000	0.266
	Roy's Largest Root	0.016	2.166c	5.000	695.000	0.056
Gender	Pillai's Trace	0.007	0.952b	5.000	693.000	0.447
	Wilks' Lambda	0.993	0.952b	5.000	693.000	0.447
	Hotelling's Trace	0.007	0.952b	5.000	693.000	0.447
	Roy's Largest Root	0.007	0.952b	5.000	693.000	0.447

Gender-Brand Relationship Analysis

In the fifth hypothesis, we tried to test whether the roles vary according to the brand. The two-factor (Gender + Brand) MANOVA analysis showed a difference (Table 14).

Table 14 Multıvarıate Analysıs of Varıance Results Showıng the Role of Women ın the Kazakh Famıly ın Terms of the Brand (MANOVA)
Effect		Value	F	Hypothesis	Error	Significance
Intercept	Pillai's Trace	0.970	4497.806b	5.000	694.000	0.000
	Wilks' Lambda	0.030	4497.806b	5.000	694.000	0.000
	Hotelling's Trace	32.405	4497.806b	5.000	694.000	0.000
	Roy's Largest Root	32.405	4497.806b	5.000	694.000	0.000
Brand	Pillai's Trace	0.106	7.760	10.000	1390.000	0.000
	Wilks' Lambda	0.896	7.821b	10.000	1388.000	0.000
	Hotelling's Trace	0.114	7.882	10.000	1386.000	0.000
	Roy's Largest Root	0.090	12.528c	5.000	695.000	0.000
Gender	Pillai's Trace	0.007	0.938b	5.000	694.000	0.456
	Wilks' Lambda	0.993	0.938b	5.000	694.000	0.456
	Hotelling's Trace	0.007	0.938b	5.000	694.000	0.456
	Roy's Largest Root	0.007	0.938b	5.000	694.000	0.456

The model turned out to be meaningful as a whole. However, while there is no difference in terms of gender, there is a difference at the 0.000 significance level in terms of the brand (F Value: 7.882).

As seen in Table 15, in the first stage (Need Recognition), there is a difference between the first and second-degree brand preferences, and between the first and third-degree brand preferences. On the other hand, there is no difference between second and third-degree brand preferences.

Table 15 Results of LSD (Smallest Sıgnıfıcant Dıfference) Analysıs Showıng the Dıfferences between the Brand Choıces that Make Sense
Purchasing Stages	Brand Importance Rating	Standardized	Significance Level
Need Recognition	1 2	0.4997	0.000
	1 3	0.4414	0.000
	2 3	0.0583	0.307
Determination of Alternatives	1 2	0.3461	0.000
	1 3	0.2484	0.000
	2 3	0.0976	0.031
Evaluation of Alternatives	1 2	0.3217	0.000
	1 3	0.1943	0.001
	2 3	0.1274	0.004
Purchase Decision and Purchasing	1 2	0.1891	0.039
	1 3	0.0877	0.282
	2 3	0.1015	0.115
Post-Purchase Evaluation	1 2	0.2650	0.000
	1 3	0.2902	0.000
	2 3	0.0252	0.599

In the second stage (Determination of Alternatives) there is a difference between first and second-degree brand preferences, between the first and third-degree preferences, and between the third and second-degree brand preferences.

In the third stage (Evaluation of Alternatives) there is a difference between the first and second-degree brand preferences, between the first and third-degree brand preferences, between the third and second-degree brand preferences.

In the fourth stage (Purchasing Decision and Purchasing), there is a difference between first and second-degree brand preferences. On the other hand, there is no difference between the first and third-degree brand preferences and between second and third-degree brand preferences.

In the fourth stage (Purchasing Decision and Purchasing), there is a difference between first and second-degree brand preferences. On the other hand, there is no difference between the first and third-degree brand preferences and between second and third-degree brand preferences.

As seen in Table 16, in the first stage, the average of the first brand preference (3.29) is higher than the second (2.79) and third brand preference averages (2.85). In other words, in this stage, the first-degree brand preference comes to the fore. According to the MANOVA analysis, there is no gender difference in Kazakh society and the weights of men and women are equal. On the other hand, it can be said that the first-degree brand choice is more important than the second and third-degree brand preferences in this stage.

Table 16 Comparıson of Averages by Brand Preferences
Purchasing Stages	Brand Importance Rating	Arithmetic Mean
Need Recognition	1	3.29
	2	2.79
	3	2.85
Determination of Alternatives	1	2.92
	2	2.57
	3	2.67
Evaluation of Alternatives	1	2.72
	2	2.40
	3	2.52
Purchase Decision and Purchasing	1	2.97
	2	2.79
	3	2.89
Post-Purchase Evaluation	1	3.04
	2	2.77
	3	2.75

In the second stage, the average of the first brand preference (2.92) is higher than the second (2.57) and third brand preference averages (2.67). Therefore, at this stage, first-degree brand preference comes to the fore. According to the MANOVA analysis, there is no gender difference in Kazakh society and the weights of men and women are equal. On the other hand, the first-degree brand preference is more important than the second and third-degree brand preferences in this stage.

In the third stage, the average of the first brand preference (2.72) is higher than the second (2.40) and third brand preference averages (2.52). Therefore, at this stage, first-degree brand preference comes to the fore. According to the MANOVA analysis, there is no gender difference in Kazakh society and the weights of men and women are equal. On the other hand, at this stage, first-degree brand preference is more important than the second and third-degree brand preferences.

In the fourth stage, the average of the first brand preference (2.97) is higher than the second brand preference average (2.79). Therefore, at this stage, first-degree brand preference comes to the fore. According to the MANOVA analysis, there is no gender difference in Kazakh society and the weights of men and women are equal. On the other hand, at this stage, the first-degree brand preference is much more important than the second and third-degree brand preferences.

In the fifth stage, the average of the first brand preference (3.04) is higher than the second (2.77) and third brand preference averages (2.75). Therefore, at this stage, first-degree brand preference comes to the fore. According to the MANOVA analysis, there is no gender difference in Kazakh society and the weights of men and women are equal. On the other hand, at this stage, first-degree brand preference is more important than the second and third-degree brand preferences.

As a result, we can say that we identified the brand as the most important factor in all stages of purchasing.

Analysis Based on Gender-Quality Relationship

In the sixth hypothesis, we tried to test whether the role of women varies according to the quality. The two-factor (Gender+Quality) MANOVA analysis showed a difference.

The model as a whole turned out to be meaningful. However, while there is no difference in terms of gender, there is a difference at 0,000 significance level in terms of quality preference (F Value: 13.707) (Table 17).

Table 17 Multıvarıate Analysıs of Varıance Results Showıng the Role of Women ın the Kazakh Famıly ın Terms of Qualıty (MANOVA)
Effect		Value	F	Hypothesis	Error	Significance
Intercept	Pillai's Trace	0.919	1585.164b	5.000	694.000	0.000
	Wilks' Lambda	0.081	1585.164b	5.000	694.000	0.000
	Hotelling's Trace	11.420	1585.164b	5.000	694.000	0.000
	Roy's Largest Root	11.420	1585.164b	5.000	694.000	0.000
Quality	Pillai's Trace	0.179	13.661	10.000	1390.000	0.000
	Wilks' Lambda	0.829	13.684b	10.000	1388.000	0.000
	Hotelling's Trace	0.198	13.707	10.000	1386.000	0.000
	Roy's Largest Root	0.125	17.360c	5.000	695.000	0.000
Gender	Pillai's Trace	0.007	0.942b	5.000	694.000	0.453
	Wilks' Lambda	0.993	0.942b	5.000	694.000	0.453
	Hotelling's Trace	0.007	0.942b	5.000	694.000	0.453
	Roy's Largest Root	0.007	0.942b	5.000	694.000	0.453

Gender-Price Relationship Analysis

In the seventh hypothesis, we tried to test whether the role of the woman varies according to the price. The two-factor (Gender+Price) MANOVA analysis showed a difference.

The model turned out to be meaningful as a whole. However, while there is no difference in terms of gender, there is a difference at 0.000 significance level in terms of price alternatives (F Value: 12.481) (Table 18).

Table 18 Multıvarıate Analysıs of Varıance Results Showıng the Role of Women ın the Kazakh Famıly ın Terms of Prıce (MANOVA)
Effect		Value	F	Hypothesis	Error	Significance
Intercept	Pillai's Trace	0.974	5104.253b	5.000	694.000	0.000
	Wilks' Lambda	0.026	5104.253b	5.000	694.000	0.000
	Hotelling's Trace	36.774	5104.253b	5.000	694.000	0.000
	Roy's Largest Root	36.774	5104.253b	5.000	694.000	0.000
Price	Pillai's Trace	0.164	12.433	10.000	1390.000	0.000
	Wilks' Lambda	0.842	12.457b	10.000	1388.000	0.000
	Hotelling's Trace	0.180	12.481	10.000	1386.000	0.000
	Roy's Largest Root	0.116	16.077c	5.000	695.000	0.000
Gender	Pillai's Trace	0.007	1.019b	5.000	694.000	0.405
	Wilks' Lambda	0.993	1.019b	5.000	694.000	0.405
	Hotelling's Trace	0.007	1.019b	5.000	694.000	0.405
	Roy's Largest Root	0.007	1.019b	5.000	694.000	0.405

As seen in Table 19, in the first stage, there is a difference between the first and second-degree price preferences, and between the first and third-degree price preferences. However, there is no difference between the second and third-degree price preferences.

Table 19 Resutls of LSD Analysıs Showıng the dıfferences between the Sıgnıfıcant Prıce Optıons
Purchasing Stages	Price Importance Rating	Standardized	Significance Level
Need Recognition	1 2	0.2143	0.002
	1 3	0.2774	0.000
	2 3	0.0632	0.247
Determination of Alternatives	1 2	0.0092	0.864
	1 3	0.1001	0.075
	2 3	0.0910	0.035
Evaluation of Alternatives	1 2	0.0300	0.570
	1 3	0.0230	0.678
	2 3	0.0070	0.869
Purchase Decision and Purchasing	1 2	0.3908	0.000
	1 3	0.3940	0.000
	2 3	0.0032	0.956
Post-Purchase Evaluation	1 2	0.2752	0.000
	1 3	0.0571	0.328
	2 3	0.2182	0.000

In the second stage, there is a difference between the second and third-degree price preferences. On the other hand, there is no difference between the first and second-degree price preferences and between the first and third-degree price preferences.

In the third stage, there is no difference between the first and second-degree price preferences, between the first and third-degree price preferences, and between the second and third-degree price preferences.

In the fourth stage, there is a difference between the first and the second-degree price preferences, and between the first and the third-degree price preferences. However, there is no difference between the second and third-degree price preferences.

In the fifth stage, there is a difference between the first and second-degree price preferences, and between the second and third-degree price preferences. On the other hand, there is no difference between the first and third-degree price preferences.

As seen in Table 20, the average of the third price preference (2.97) in the first phase is higher than the first price preference average (2.69). Also, the second price preference average (2.91) is greater than the first price preference average. In other words, in the first stage (need recognition), the third-degree price preference comes to fore. According to the MANOVA analysis, there is no gender difference in Kazakh society and the weights of men and women are equal. Accordingly, at this stage, the third-degree price preference is more important than the first and second-degree price preferences.

Table 20 Comparıson of Averages by Prıce Preferences
Purchasing Stages	Price Importance Rating	Arithmetic Mean
Need Recognition	1	2.69
	2	2.91
	3	2.97
Determination of Alternatives	1	2.64
	2	2.65
	3	2.74
Evaluation of Alternatives	1	2.50
	2	2.53
	3	2.52
Purchase Decision and Purchasing	1	3.20
	2	2.80
	3	2.80
Post-Purchase Evaluation	1	2.94
	2	2.67
	3	2.89

In the second stage (determination of alternatives), the average of the third price preference (2.74) is higher than the second price preference average (2.65). Therefore, in this stage, the third-degree price preference comes to the fore. According to the MANOVA analysis, there is no gender difference in Kazakh society and the weights of men and women are equal. Accordingly, in this stage, the third-degree price preference is more important than the second-degree price preferences.

In the fourth stage (purchase decision and purchase), the average of the first price preference (3.20) is higher than the second (2.80) and third price preference averages (2.80). Therefore, in this stage, the first-degree price preference comes to the fore. According to the MANOVA analysis, there is no gender difference in Kazakh society and the weights of men and women are equal. Accordingly, in this stage, first-degree price preference is more important than the second and third-degree price preferences.

In the fifth stage (valuation after purchase), the average of the first price preference (2.94) is higher than the second (2.67) and third price preference averages (2.89). Therefore, in this stage, the first-degree price preference comes to the fore. According to the MANOVA analysis, there is no gender difference in Kazakh society and the weights of men and women are equal. Accordingly, in this stage, first-degree price preference is more important than the second and third-degree price preferences.

Analysis of price has led to confusing results. While the price is very important in the first stage, it loses its importance in the second and third stages. However, in the fourth stage, the price rises to the first degree and becomes important again. This shows that Kazakh consumers do not quite understand the concept of the price compared to the quality and brand, because in the past, the price is determined by the central government.

Conclusion

In this study, we aimed to determine the role of women in Kazakh families with different demographic structures as per the decision-making stages of white goods. We aimed to reveal the role distribution in different stages of the white goods purchase process. We believe that the study will fill an important gap since there is no study on Kazakh customers' decision processes.

Our study has shown that men and women give their decision to buy a white good together.

According to the regions where the family lives, we have shown that men and women show the same behavior and their roles are equal. According to the average monthly income of the family, we showed that men and women show the same behavior in purchasing and their roles are not different. According to the family's age groups, we showed that men and women show the same behavior and their roles are equal. This is the result of the non-discriminatory policy pursued between the sexes during the Soviet era and the value given to women by the Kazakhs.

According to the forms of payment, it turned out that gender is meaningless, but the forms of payment are meaningful. Therefore, we removed the gender variable from our model and examined the differences in the forms of payment. The average of the cash payment is higher than the installments. As the banking sector develops in Kazakhstan, the credit system becomes more widespread, and loan sales increase. As competition between banks increases, credit card usage will increase and become easier.

According to the brand, quality, and prices, we saw that the woman is equal in the purchasing stages. According to this analysis, the brand has first-degree importance in all stages.

Here, we encountered a slightly different result compared to the analysis related to the brand. In the first stage, quality is not important. In the fourth stage (decision to purchase), the quality was found to be of tertiary importance. However, it turned out that quality is of secondary importance at other stages. As a result, quality is in second place in the evaluation of alternatives and post-purchase evaluation stages.

Analysis of price has given confusing results. While the price is very important in the first stage, it loses its importance in the second and third stages. However, in the fourth stage, the price rises to the first degree and becomes important again. This shows that Kazakh consumers did not quite understand the concept of the price compared to the quality and brand. Because in the past, the price is determined by the central government.

This research is the first study on this subject in Kazakhstan. We concluded that Kazakh women while purchasing white goods, decided together with their husbands or family. Besides, the result showed that men and women are equal in the purchasing decision process. As can be seen in other studies on the subject, the woman has an important role in the decision-making process of household needs in Kazakhstan.

This study will fill an important gap in the field and inspire future studies. Conducting similar studies for the other regions of Kazakhstan and with other members of the family will provide a better understanding of consumer behavior and family purchasing decision process. However, as many researchers working in the field of social sciences faced, this study was carried out with various constraints. For example, due to time and cost constraints, the research universe was not considered to cover the whole of Kazakhstan, but only those residing in the North, South, East and West Kazakhstan Provinces. Convenience sampling method, which is one of the non-random sampling methods, was used as the sampling method. Therefore, the results of this research are only valid for the people who were surveyed within the scope and cannot be generalized. With these constraints, it can be recommended that researchers considering working in this field in the future work in different product groups, different geographical regions and longer time intervals.

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