Keywords

Customer Behavior; Hyperstar Stores; Music; Waiting Time.

Introduction

Consumers are an important market element in any business. Therefore, it is essential to maintain and pay attention to this vital element in the market supply chain. Accordingly, marketing managers always try to use different techniques to drive their customers towards shopping behavior. Many marketers believe that an attractive environment is one of the main strategies to meet customer satisfaction (Baker et al., 1992; Baker & Cameron, 1996; Andersson et al., 2012). Kotler and many other researchers have shown that the environment can influence consumer decisions significantly (Gorn, 1982; Alpert & Alpert, 1990; Ng, 2003). Consumers have special needs and goals, necessitating reference to the environment to meet their demands in a timely and appropriate manner. Sensory stimulation is one of the methods that can make a shopping market environment attractive to customers and lead to their purchase intention (Ng, 2003). Today, music is one of the main tools used to stimulate consumers in the marketing environment, particularly in stores (Baker, 1992; Grewal, 2003). Research has also shown that music can bring peace of mind and reassurance to the consumers (Garlin & Owen, 2006; Turley & Milliman, 2000). Consumers feel more comfortable and spend much more time in stores to receive services when they listen to music (Turley & Milliman, 2000). The waiting time is one of the important dimensions of the effects of music on consumer behavior.

One of the considerably influential factors on customer satisfaction is the speed of response or timely reaction to their waiting time (Taylor & Claxton, 1994). Time is a competitive advantage in today’s increasingly competitive environment, and companies that can promptly meet the needs of customers while simultaneously ensuring quality will be successful. On the other hand, in the case of failure to meet customer demands, time becomes a destructive and negative factor. However, music can counteract the negative effects of waiting time (Andersson et al., 2012). Research has shown that music decreases the adverse effects of waiting and enables customers to neglect time.

Different studies have shown that music affects consumer behaviors. Some service centers use music to shape the tangible environmental factors in the minds of customers based on the type of their audience. They also try to motivate their customers to purchase while shaping an appropriate mental image that leads to purchase behavior. In the meantime, the effect of music on various aspects of consumer behavior has received less attention in Iran. Various service departments and stores have also neglected music from the operational point of view. However, the use of appropriate music can increase customer attention and subsequently enhance sales. Meanwhile, with the development of competition in various markets, those who fail to examine the effects of music on customers' purchase behavior will lose their market share more than competitors. Research in Iran has focused on the role of music in consumer behavior; however, no studies have been conducted so far on the effect of music on the customers' waiting time to receive services. Accordingly, the present study attempts to fill the existing research gap in this field by raising two questions of what effects music can have on consumers’ behaviors toward purchasing a product and how to use music to direct individual desires.

Conceptual Model

Figure 1 indicates the conceptual model of the study.

Methodology

The present study is applied survey research. The statistical population of the research consisted of all customers of hyperstar stores in Tehran province. The study used simple random sampling, and the sample size was 384 due to the unlimited population size. Data were collected using field and library studies, including articles published in scientific journals and relevant dissertations in the university library. The data collection tool included a standard questionnaire designed according to the variables of the research model. The variables of time perception, emotional evaluation of service environment, and emotional responses to waiting were measured using the paper of Hui et al. (1997), while the variable of purchase intention was measured using the questions in the research by Chen (2008). The questionnaire was in the form of a 5-point Likert scale determined based on the theoretical foundations and previous research. Instrument validity and reliability were determined by content validity and Cronbach's alpha test, respectively. The results of reliability testing for the research instrument revealed an alpha coefficient of 0.856, confirming the reliability of the research instrument.

Descriptive and inferential statistics were used to analyze the data, and finally, Amos software and RMSEA, GFI, CR, and other tests were used to examine the fitness of the model and the relationships among research variables. The present study has used a combination of SPSS and AMOS software.

Results

The exploratory analysis was employed to determine the research indicators and the distribution of the questions into the main factors based on the calculated data.

This section included 15 questions distributed among five components. The Bartlett’s test and KMO were first calculated to perform the exploratory factor analysis and ensure a suitable sample size for the analyses.

According to Table 1, the significance level of Bartlett’s test was 0.000, indicating a suitable sample size and data for exploratory analysis (<0.05). The KMO statistic was 0.793, which is at a good level (>0.7) in the following, exploratory analysis is performed.

Table 1 Bartlett’s Test
KMO Index		0.793
	Test statistic	2618.065
Bartlett’s test	Degree of freedom (df)	105
	Significance level (sig)	0.000

The number and matrices of the extracted factors from the research questions were specified in this section. In general, five factors were identified out of these 15 questions, together explaining 73.84% of the total variance of the items. Table 2 presents the results of this analysis.

Table 2 Variance Distribution
Factors	Preliminary observations			Total loads extracted			Total squared factor loading
	Total	% Deviation	Cumulative percentage	Total	% Deviation	Cumulative percentage	Total	% Deviation	Cumulative percentage
1	4.511	30.071	30.071	4.511	30.071	30.071	2.837	18.913	18.913
2	2.857	19.048	49.119	2.857	19.048	49.119	2.580	17.201	36.114
3	1.424	9.493	58.612	1.424	9.493	58.612	2.253	15.020	51.133
4	1.259	8.395	67.007	1.259	8.395	67.007	1.795	11.968	63.102
5	1.026	6.840	73.847	1.026	6.840	73.847	1.612	10.745	73.847

Confirmatory Analysis of Exploratory Variables

All dimensions of the research and related questions are presented in the measurement model, indicating pairwise relationships. Correlation relationships are examined in pairs, according to which correlations of ≥ 0.9 mean that the variables should be merged or variables with higher variance remain in the model. Variables with correlations of ≥ 0.9 measure very close concepts (Figure 2).

According to Table 3, all the indicators under study were accepted to evaluate the fit of the confirmatory factor model, supporting the fit of this model.

Table 3 Goodness of Fit Indices For The Measurement Model of Research Variables
Index	Earned value	Acceptable value	Status
GFI	0.962	GFI>90%	Accepted
AGFI	0.912	AGFI>90%	Accepted
CFI	0.921	0.90<CFI<1	Accepted
CMIN/df	2.862	<3	Accepted
RMSEA	0.078	RMSEA<0.08	Accepted

Structural Equations Model

After examining and confirming the measurement model, the research model is presented, and the relationships between the latent variables (the research hypotheses) are studied. The first step is to ensure the fit indices and then examine the assumed relationships between the latent variables (Figure 3).

The fitness indices of the model will be examined in the following.

According to Table 4, all indicators under study were accepted to evaluate the fit of the confirmatory factor model, confirming the fit of this model.

Table 4 The Fitness Indices of The Structural Equations Model
Index	Earned value	Acceptable value	Status
GFI	0.953	GFI>90%	Accepted
AGFI	0.924	AGFI>90%	Accepted
CFI	0.896	0.90<CFI<1	Accepted
CMIN/df	2.652	<3	Accepted
RMSEA	0.028	RMSEA<0.08	Accepted

Table 5 summarizes the results of the regression coefficient, which indicates the extent to which the components affect each other, along with the significance of the coefficients.

Table 5 Regression Coefficients of Research Components (Testing Hypotheses) Based on Total Effects
Hypothesis	Component effect	On component	Regression coefficient	Sig.	Result
1	Emotional evaluation	Music	0.234	0.009	Confirmed
2	Time perception	Music	0.469	***	Confirmed
3	Emotional response	Music	0.129	0.053	Rejected
4	Time perception	Emotional evaluation	0.353	***	Confirmed
5	Emotional response	Emotional evaluation	0.676	***	Confirmed
6	Purchase intention	Emotional response	0.201	0.013	Confirmed
7	Purchase intention	Time perception	0.313	***	Confirmed
8	Purchase intention	Emotional evaluation	0.064	0.439	Rejected
*** represents very small values close to zero

According to the results of the analysis, the following relationships were confirmed between variables:

• Emotional evaluation and music

• Time perception and music

• Time perception and emotional evaluation

• Emotional response and emotional evaluation

• Emotional evaluation and purchase intention

• Time perception and purchase intention

Tables 6 & 7 show the direct and indirect effects of variables on each other.

According to Table 6, the highest coefficient belonged to the relationship between emotional response and emotional evaluation, while the relationship between purchase intention and emotional evaluation had the lowest coefficient.

Table 6 Direct Effects Between Variables
	Music	Emotional evaluation	Emotional response	Time perception	Purchase intention
Emotional evaluation	0.234	0	0	0	0
Emotional response	0.129	0.676	0	0	0
Time perception	0.469	0.353	0	0	0
Purchase intention	0.425	0.064	0.201	0.313	0

According to Table 7, only the indirect relationship between music and purchase intention was confirmed with a coefficient of 0.152.

Table 7 Indirect Effects Between Variables
	Music	Emotional evaluation	Emotional response	Time perception	Purchase intention
Emotional evaluation	0	0	0	0	0
Emotional response	0.158	0	0	0	0
Time perception	0.083	0	0	0	0
Purchase intention	0.152	-0.026	0	0	0

Discussion

This study aimed to investigate the impact of music on customers’ reactions to waiting for services.

Indices of fit were used to evaluate the conceptual model in the form of model fit. The GFI index evaluated the relative values of variances and covariances jointly through the model at a range of 0-1. The GFI value should be ≥ 0.90. The results of the present study showed a GFI value of 0.96. AGFI or adjusted GFI is another index of fit for the degree of freedom. This index represents the mean of squares instead of the sum of squares in the numerator and denominator (GFI-1) and ranges from 0 to 1. The value of AGFI was 0.91 in this study, indicating the good fitness of the model. The RMSEA index is the root mean squared error of approximation and has a value of ≤ 0.05 for good models. Models with an RMSEA of 0.1 have low fitness. As shown by the results, RMSEA was 0.078, which is <0.08 and shows the fitness of the conceptual model. Also, the CFI index >0.90 is acceptable and indicates the model fitness. This index measures the improvement by a comparison of an independent model in which there is no relationship between variables with the proposed model. The CFI index is similar in meaning to the NFI, except that it is fined the sample size. CFI was 0.92 in this research. Finally, the Chi-square/df ratio was 2.86, indicating a value of <3 and subsequently confirming the fitness of the model. The final part of this research examined the relationships between the studied variables, leading to the following results:

There was a significant relationship between emotional evaluation and music (β=0.23, P=0.009). The relationship was direct, considering the positive regression coefficient of 0.23. Besides, the value of the regression coefficient shows the moderate intensity of this relationship while also revealing its significance. According to the SERVQUAL model, the service environment is one of the main dimensions in the evaluation of the services provided to customers. Hui et al. (1997) referred to customers’ evaluation of the service environment as an essential component in their perception of the provided services, stating that music could create an environment leading to more positive evaluations of customers from the service environment. Salespeople in Iranian stores seek to draw the customers’ attention to the services provided and create a good image of the service environment through eye- catching decorations. All these efforts aim to improve the customers’ mental image of the physics of service delivery.

There was a significant relationship between time perception and music (β=0.46, P=0.000). The relationship was direct, considering the positive regression coefficient of 0.46. Besides, the value of the regression coefficient shows the moderate intensity of this relationship. In many service areas, waiting time is changing into a central factor for the customers' satisfaction and their judgment about service quality (Taylor & Claxton, 1994). Many service providers seek to reduce the negative effects of waiting by modifications in the service delivery system. Hui et al. (1997) found a significant relationship between music and waiting time perception. Sometimes, long queues make customers feel exhausted and try to get their required services from other stores. Therefore, stores can use music to provide a peaceful environment for the customers and encourage them to wait.

There was a significant relationship between time perception and emotional evaluation (β=0.35, P=0.000). The relationship was direct, considering the positive regression coefficient of 0.35. Besides, the value of the regression coefficient shows the moderate intensity of this relationship.

There was a significant relationship between emotional response and emotional evaluation (β=0.67, P=0.000). The relationship was direct, considering the positive regression coefficient of 0.67. Besides, the value of the regression coefficient shows the strong intensity of this relationship. In their study, Hui et al. (1997) found a significant relationship between customers' emotional responses and emotional evaluations. Waiting time to receive services can affect customers’ evaluations of the services provided, and a long waiting time to receive services can affect the image of the store in the customers' minds adversely. Even if store services are at an excellent level, a long waiting time is still expected to result in poor customer evaluations.

There was a significant relationship between purchase intention and emotional response (β=0.20, P=0.001). The relationship was direct, considering the positive regression coefficient. Besides, the value of the regression coefficient shows the moderate intensity of this relationship. Hui et al. (1997) also found a significant relationship between these two variables.

There was a significant relationship between purchase intention and time perception (β=0.31, P=0.000). The relationship was direct, considering the positive regression coefficient of 0.31. Besides, the value of the regression coefficient shows the moderate intensity of this relationship. Hui et al. (1997) also found a significant relationship between these two variables. Customers purchase from a store when they evaluate all the conditions, one of which is the waiting time to receive services. When this time increases, customers will undoubtedly try to meet their needs from another store. Hence, the bargaining power of customers’ increases since most stores offer relatively similar services in one commercial area.

Conclusion

The study showed a significant relationship between emotional evaluation and music. Therefore, future research can examine the effects of music type on emotional evaluations and sensory stimulation of individuals to determine the type of music and its effective use.

The study showed a significant relationship between music and time perception. Hence, it is possible to make the waiting time more pleasant for the customers, particularly when they have to wait for a long time to receive services or products.

The results showed a significant relationship between the customers’ emotional perception and their perception of waiting time. Accordingly, given that different groups of people purchase from stores, it is necessary to investigate these factors and manage the waiting time in the best way possible.

The results showed a significant relationship between emotional response and purchase intention. Therefore, customers should be provided with suitable answers to properly manage their willingness to show purchase intentions and become motivated enough to purchase the products from the stores.

The results also showed a significant relationship between the customers' waiting time and purchase intention. Thus, customers’ waiting time needs appropriate management to enhance their desire to use the store services because it can affect their purchase intention.

Finally, it is suggested to examine the repurchase behavior of customers from this store in future studies to investigate their repurchase behavior using the proposed model.

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Received: 12-Apr-2022, Manuscript No. JMIDS-22-11743; Editor assigned: 15-Apr-2022, PreQC No. JMIDS-22-11743(PQ); Reviewed: 28-Apr-2022, QC No. JMIDS-22-11743; Revised: 23-May-2022, Manuscript No. JMIDS-22-11743(R); Published: 30-May-2022