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Vol. 38 (Nº 57) Year 2017. Page 22

Investigating the factors affecting customer purchase activity in retail stores

Investigando los factores que afectan la actividad de compra de clientes en tiendas minoristas

Subhra MONDAL 1; Manmohan MALL 2; Uma Sankar MISHRA 3; Kalyan SAHOO 4

Received: 24/07/2017 • Approved: 21/08/2017


Content

1. Introduction

2. Review of literature

3. Objectives & research methodology

4. Results & its analysis

5. Conclusion

Bibliographic references


ABSTRACT:

This paper tries to study impact of socioeconomic profile of respondents’ influence variables like store image, customer loyalty & satisfaction in terms of price, quality, loyalty, customer care, payment preference and factors which influence the purchasing power of consumers through exploratory factor analysis (done by R language through psych package) where factor structure is assessed through chi-square statistics along with other fit parameters like R2 , RMSEA etc.
Keyword: Store image, Customer satisfaction, Customer Loyalty, Retail Store

RESUMEN:

Este trabajo trata de estudiar el impacto del perfil socioeconómico de las variables de influencia de los encuestados como la imagen de la tienda, fidelización y satisfacción del cliente en términos de precio, calidad, lealtad, atención al cliente, preferencia de pago y factores que influyen en la compra poder de los consumidores a través del análisis factorial exploratorio (hecho por el lenguaje R a través del paquete psicológico) donde la estructura factorial se evalúa a través de estadísticas Chi-Square junto con otros parámetros de ajuste como R2, RMSEA etc.
Keyword: almacene la imagen, satisfacción del cliente, lealtad del cliente, tienda al por menor

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1. Introduction

The customers process information at their own end and approval retail stores to have them materialize. They approach different stores to explore product views and to ship in order to fulfil their needs and desired requirements. They also update themselves about offers and enjoy good brand portfolio. The particular behaviour of selecting a particular retail outlet depends on certain behavioural parameters influenced by reference group, culture, family etc. Here store-image is a major input apart from shopping experience factors which help in patronizing the behaviour. Apart from their traditional factors with the dynamic change in factors like money and luxury it is essential for consumers to evaluate retail stores along with their terms.

Economic provides impetus for customer’s change in demand patterns. The psychology, lifestyle of individual is affected by liberalized economy, information flow, technical change, improving literacy, income growth. All this combining impact on individuals purchasing and consumption behaviour. As a result of which the dramatic change that retail industry in India has gone through. So due to sudden booming industry gains, big retails are in a competition to keep loyal customer into their kitty. So, they want to make the customer loyal. So, customer patronization is the theme of retail industry in India.

India has vast middle class and almost untapped retail industry are the key attractive faces for global retail giants want to enter into newer markets, which in turn will help the India Retail Industry to grow faster.

A large young working population with average age of 24yrs, nuclear families in urban areas, along with increasing working women population are some factors which are attracting newer businessmen to enter the India Retail Industry. The factor of Indian Retail Industry looks maximizing. Purchasing power of Indian urban consumer is growing and branded merchandise in various categories like lifestyle products that are widely accepted by urban India consumer. So focus of customer patronization in depending on branding of retail houses where they are keen to provide values and quality product to the consumers. This customer value providing capacity is the sustainable competitive advantage to the retail stores. As Retail accounts for nearly 10% of country’s GDP and around 8% of total employment, retailing in India is on a booming scale.                                                          

The trends that are driving the growth of retail sector in India are: -

The retailing diaspora in India is fast developing as shopping malls are continuously becoming familiar in large cities. When it comes to development of retail space specially the malls, the Tier II units are no longer behind the race. State Govt. of Odisha is giving facilities to many retail houses to bring out their establishment in Bhubaneswar. India is being seen as a potential goldmine for retail investors from over the world and latest research has rated India as top destination for an attractive emerging retail market. Even though India has well over 5 million retail outlets, the country severely lacks the facility that can resemble a good retail house of international repute.

The organized retail sector is expected to grow stronger than GDP growth in India. Retailing has seen such a transformation over the past decade that its very definition has undergone a sea change. Retail today has changed from selling a product or service to selling a hope, an aspiration and above all an experience that a consumer would like to repeat.

For manufacturers and service providers the emerging opportunities is urban market is to capture and deliver better value to the customers through Retail. In test of time and investment, innovative concepts and models will survive. So, specialist retailers who are specialized by use of modern management technology backed with seemingly unlimited financial resources. Retailing in India is currently Rs. 20,238 billion and organized retail is Rs. 1,020 billion. For retail country things are now brighter and better. More challenges will come to the manufacturers and service providers when market power shifts to organized retail.

Retail sector provided phenomenal inputs to the productivity of goods and services in a larger extent. So the most developed countries, retail sector in the driving force for economy. Here in India, retail industry has come forth as one of the most dynamic and fast paced industry. Day by day new ventures are going the bandwagon of retail. The Indian Retail Industry is gradually itching towards becoming the next boom industry.

2. Review of literature

The literature review that was undertaken served to provide a theoretical base in order to develop and justify the research initiative. The purpose of this chapter is to carefully examine existing literature associated with the topic of research. By providing a review of literature, the researcher attempts to not only explain the need for the proposed study but also to appraise the shortcomings and gaps in previous studies. Furthermore, a literature review aids in making the researcher aware of the current progress in the area of study and offers possible insights into the problem statement. As described in the introduction to the thesis, the objective of the current study is to understand the role of different factors in influencing the buying behavior and decision–making of consumers. The following objectives were addressed through the review…

An overview of literature highlighting the importance of store image in consumer behaviour that was studied to address these objectives follows. A review of the dimensions of store image is done to better understand the concept. The relationship between store image and shoppers’ satisfaction level and the store loyalty is discussed. The influence of shopper satisfaction on store loyalty is studied in the final section. 

2.1. Store Image, Customer Satisfaction & Store loyalty

2.1.1. Store Image

Store image is an important marketing tool for retailers because a better image means greater customer flows, fewer walkouts and thus more customer spending each time they visit (Davies and Brooks, 1989). On the other hand, store image is crucial because consumers' decisions on where to shop depend on their perceptions of the available shopping alternatives (Oppewal and Timmermans, 1997). The importance of store image is quite high in the choice of the store because the shopper seeks the store whose image is most congruent with the image he/she has of him/herself with his/her vision of the world and lifestyle (Martineau, 1958). Thus, store image becomes a key factor determining a retailer's strategy. Past research on store image has pointed out that numerous environmental variables of a store (e.g colour, layout, etc) affect consumers’ perception of store image and that specific characteristics tend to be associated with high-image and low-image (Hutcheson and Mutinho, 1998). Baker et a!, (1994) argued that store environment indirectly influences store image through merchandise and service quality inferences.

We found that there are differences of definitions of store image according to scholars. But we can say that store image is an overall attitude of a consumer to the store, its attributes mean various things, and each store has a relative location in the consumer’s mind.

Table 1.1
Definition of Store Image

Scholars

Definition of store image

 

 

Kunkel & Berry

(1968)

 

 

Store image is built up through experience and totally conceptualized or expected strengthening that urge consumers to purchase at the specified store.

 

 

 

Oxenfeldt (1974)

 

 

Store image is a complex of attributes that consumers feel about the store and it is more than a simple sum of objective individual attributes since parts of attributes interact in consumers’ minds.

 

 

 

Zimmer & Golden

(1988)

 

 

Store image means a complex in total dimensions of store attributes that consumer feel and a complex means that store image consists of various attributes.

 

 

 

Berman & Evans

(1995)

 

Store image consists of functional and emotional attributes, these are organized in the perceptual structures of purchasers, and the structures are expectation on overall policies and executions of retailers.

 

In general, store attributes are important to consumers when they make the decision of where to shop. Consumers form impressions about stores and these impressions have a significant impact on store patronage. In general, consumers patronise stores whose image is congruent with their self-perceptions and unconscious needs. Thus, store image and general attitudes toward the store can influence shopping behaviour (Darley and Su-Lim, 1999). Consumers prefer certain attributes to be present in the stores they choose to shop in (Erdem et al, 1999). The preferences for certain store attributes are explained by differences in consumer values. Store attributes are presented by retailers according to their specific functional strategies. Store attributes must be offered as are desired by the targeted consumers. The challenge to retailers isto determine which store attributes are relatively more important to the targeted consumer. Providing appropriated store attributes is not enough to satisfy consumers and guarantee store loyalty. Maintaining the quality of their attributes is the hardest and most critical task to survival in the competitive nature of retailing.

2.1.2. Dimensions of Store Image

The dimensions and properties of store image are depending on the purpose and objects of studies. Consumers make store images based on advertisement, commodities, transmission of words, and shopping experience (Assael 1992). Martineau (1958) indicated that store image consists of layout and architecture, symbols and colour, advertising and sales personnel. The following major dimensions are as follows: -

2.1.3. Components of Store Image and Ways of Measuring Them

A study by Silva and Giraldi (2010) reviewed and summarized the various elements and sub-elements of store image, which are as follows

Table 1.2
Components of Store Image

Components

Subcomponents

Product Price

Low prices, competitive or satisfactory prices, uncompetitive or high prices

Product Quality

Good or bad quality and in-stock brands

Clientele

Characteristics of the customers that shop at the store

Assortment

Range, depth, sells brands that attract customers

Physical Premises

Cleanliness, layout, ease of buying and attractiveness

Product Style

Keeping up with the latest styles

Sales Staff

Attitude of the sale staff, knowledge of the sales staff, number of salespersons, good or bad service, friendliness

Location Convenience

Location near home or work, access, good or bad location

Other Items

Parking, open hours, general convenience, layout

Services

Credit, lay-away plan, delivery and other services

Sales Promotions

Special sales, coupons, special events

Advertising

Quality and style of advertising, media used, credibility of advertising

Store Atmosphere

Layout, lighting, temperature, visual communication, colors, size of sales area, outside and inside decoration, product display, crowding within the store, prestige

Policy on Refunds and Exchanges

Policy on refunds and exchanges

Institutional Aspects

Store reputation

After-sales

Level of satisfaction

Source: Adapted from Kunkel and Berry (1968), Lindquis (1974),
James, et al. (1976), Birtwistle, Clarke and Freathy (1999), Ghosh
(1990), and Hirshman, Greenberg and Robertson (1978).

2.2. Customer Satisfaction

Meeting the needs of customers and to create a favorable customer experience is one of the objectives of creating a specific store image. Producing customer satisfaction can result in achieving the long-term objectives of future profits and continued business sustainability. Furthermore, customer satisfaction enhances repetitive buying behavior and the buying of other goods at the same store (Chang and Tu, 2005). Chen-Yu and Hong (2002) found that the manner in which consumers spend their money is oriented towards increasing their satisfaction, a preferred consequence of a marketing plan. Moreover, not only does satisfaction reinforce the determination or resolve to repurchase, but also loyalty to a store (Patterson and Spreng, 1997; Bloemer, Kasper, and Lemmink, 1990; Kincade, Redwine, and Hancock, 1992).

Customer satisfaction is a reaction to anticipation, product functioning after buying, experience with the product, or experience during shopping. The reaction is a response from the assessment of requirements; between pre-buying anticipations, needs or models and the actual experience with shopping- and/or product (Bloemer and De Ruyter, 1998; Howard and Sheth, 1969). As a result, the satisfaction of customers hinges upon whether or not the beliefs contemplated before a shopping event are met (Juyal, 2012).

Customer satisfaction from the retail perspective can be broken down into three elements: satisfaction with shopping systems which encompasses convenience and category of outlet; satisfaction with buying systems which consists of the choice and actual buying of products; and satisfaction of consumes which is an outcome of the use of the product (Westbrook, 1987). Disappointment with any of these elements could result in customer disloyalty, reduced sales, and loss of market share. Three stages make up customer satisfaction: pre-sales (anticipation is about goods, services, advantages, cost, and availability); sales (customer encounters the store setting, goods, service, delivery, quality, etc.); and after-sales (when the customer anticipates maintenance or information, substitution or reimbursement, overhaul or employs the grievance process) (Juyal, 2012).

2.2.1. Store Loyalty

Dick and Basu (1994) classified customer loyalty into brand loyalty, vendor loyalty, service loyalty, and store loyalty. These definitions can further be categorized into a behavioral approach, an attitudinal approach, and a combined approach.

Early studies on loyalty were performed on particular brands which could be assessed from group data and consequently brand loyalty was understood largely to be a behavioral notion. Brand loyalty was also seen as a function of the purchasing history of customers (Kuehn, 1962). Another perspective was provided by Lipstein (1959) who suggested that brand loyalty was a function of the likelihood of the buying of the same product or a function of time for a particular brand. Jacoby and Chestnut (1978) described loyalty as the prejudiced behavioral response of consumers in the selection of one among many options in a period of time and that it could be denoted as a function of the decision-making process.

Store loyalty, from an attitudinal perspective, can be understood as store partiality or psychological loyalty. Thus, it can be described as a positive attitude to a particular store and functionally it can be evaluated as the future likelihood of purchase (Juyal, 2012).

The behavioral and attitudinal approaches were combined by Dick and Basu (1994) who then described store loyalty as positive attitude and repeated buying of consumers so that the idea can be broadly comprehended. They asserted that their idea was suitable since both elements could be assessed. Neither positive attitudes nor repeated purchases singly can be essential and adequate specifications of store loyalty and both must be viewed together from the perspective of consumers.

2.2.2. Dimensions of loyalty

The most common dimension of store loyalty is repurchasing behavior (Juyal, 2012). However, this is a limited perspective. Subsequently, more dimensions have been suggested. These dimensions are:

Favorable word-of-mouth implies that loyal customers become advocates for the service (Payne, 1993). Four variants of the advocacy notion can be recognized:

1. Offering favorable word-of-mouth (e.g., Zeithaml, Berry, Parasuraman, 1996; Andreassen and Lindestad, 1998).

2. Suggesting the service to others (e.g., Stum and Thiry, 1991).

3. Inspiring others to use the service (e.g., Kingstrom, 1983).

4. Supporting the virtues of the service provider (e.g., Kingstrom, 1983).

2.3. Relationship between Store Image and Store Loyalty

Two perspectives exist with regard to the relationship between store image and story loyalty. The first is that the attributes of store image directly affect store loyalty. The second is that store loyalty is influenced by store image itself. Martineau (1958) associated store image and store loyalty by asserting that store image affects store loyalty. On the other hand, Singson (1975) emphasized the attributes of store image and found that price and quality, followed by assortment, were the most significant attributes that influenced store loyalty. Another perspective was provided by Lessig (1973) who found that store loyalty is associated with store image when store image is gauged using store setting, products, costs, and promotions.

2.4. Relationship between satisfaction and store loyalty behavior

Earlier studies (e.g., Hallowell, 1996; Huddleston, Whipple and VanAuken 2003; Sivadas and Baker-Prewitt, 2000) have suggested that customer satisfaction favorably influences post-buying outlooks and intent to buy. As seen earlier, customer satisfaction is a product of the buying experience. A customer’s favorable opinion of store characteristics can result in improved customer satisfaction. This in turn stimulates emotions and consequently results in favorable loyalty intents. Satisfied customers have a higher likelihood of remaining customers, whereas dissatisfied customers are likely to switch to competitors’ sooner or later. Customers who remain generate higher revenues and margins per customer, in the long-term, than do lost or fresh customers (Best, 2012). Sivadas and Baker-Prewitt (2000) suggested that there is a favorable association between affective and conative loyalty. Customers are likely to have favorable opinions towards a specific store if satisfied with their purchases at that store. Consequently, they may recommend the store to friends and also return to it themselves for subsequent purchases.

2.5. Relationship between store image, customer satisfaction, and store loyalty

Bloemer and De Ruyter (1998) suggested a connection between the image of a store, store selection, customer satisfaction, and store loyalty and determined that satisfaction is the outcome of a deliberate assessment of store image. The favorable assessment of store image results in store commitment, first, and then store loyalty. They also found that store image has a direct, favorable influence on store loyalty in addition to an indirect favorable influence on store loyalty through satisfaction. Thus, they found a favorable association between the three aspects and that the influence of store image is facilitated by satisfaction. A significant direct and indirect association between store image and store loyalty facilitate by customer satisfaction was also found by Chang and Tu (2005).

2.6. Nature & Scope of the Study

This study is descriptive in nature and conducted in two phases.  The first phase dealt with developing an appropriate research framework with facts and theories accessed from literature survey on factors influencing store image, satisfaction and loyalty in retail stores in general and department stores in particular. The aim is to develop the framework, which will then be used to serve meeting the research objective and sub objectives. 

The second phase of the study is an empirical study of departmental stores through the shoppers.  The research approach would be Survey Research, through structured questionnaire and Interviews. The standardized and validated questionnaire has been adopted after due pilot testing

This study is limited in its approach. The shopper buying behavior, store factors, satisfaction and loyalty are being examined only in the context of department stores specifically in Odisha including Bhubaneswar, Cuttack, Sambalpur & Rourkela. Selected department stores namely Vishal mega mart, Big Bazar, Pantaloons, Central, are included in the study. 

Yet the study is likely to contribute to the newly developing field of research on managing customer relationships, as issues are examined critically in the context of emerging retailing scenario. This will immensely help the retail sector in integration of right attitudes with service delivery and customer relationships endeavors so as to take more and more market shares and hence profits. The Study will evoke scope for further research in this emerging field for ultimate benefit of society at large.

2.7. Rationale of Study

During the past decades both marketing academics and practitioners have been intrigued by the relationship between satisfaction and loyalty (Dick and Basu, 1994; Fornell et. al., 1996; Hallowell, 1996; Kasper, 1988; LaBarbera and Mazursky, 1983; Newman and Werbel, 1973; Oliver, 1996). Most of these studies, however, have concentrated on products (brands) and to a somewhat lesser extent on services or channel intermediaries. Surprisingly, research on the relationship between store, shopper, and situational factors along with store image, satisfaction and store loyalty has remained limited, both in actual number as well as in scope. Yet, in the present environment of increased competition with rapid market entry of new store concepts and formats the managerial challenge of increasing store loyalty also presents the research challenge of a more in-depth understanding and an empirical estimation of this important type of consumer behavior.

There is some evidence that store loyalty may be (positively) related to store image (Mazursky and Jacoby, 1986; Osman, 1993). However, it has remained unclear what the exact relationship between store, shopper, and situational factors along with store image, satisfaction and store loyalty in a retail setting is. For instance, one question that has been left unanswered concerns the issue whether there is a direct relationship between store image and store loyalty and whether there is an indirect relationship via store satisfaction. This study is an attempt to acknowledge the shoppers’ buying behaviour and perceived store image that contributes to his/ her store satisfaction and loyalty. 

Figure 2.1
Theoretical Framework

2.8. Research Gap

The review of existing literature revealed certain gaps in knowledge. For instance, the relevance of the various theories with regard to retail evolution has not been studied in the context of the Indian retail sector. Similarly, studies do not appear to have been undertaken to assess the applicability and suitability of the concept and theories of consumer behaviour in the Indian context.

Furthermore, studies in the Indian context were found to chiefly focus on retail formats or the challenges in the retail sector. Also, most of the available studies associated with store image, store loyalty, or customer satisfaction have been performed in foreign contexts and the focus of these is from a generic marketing or sales perspective and not the customer perspective.

3. Objectives & research methodology

3.1. Objectives

The above section logically provides inputs for formulating objectives for the study. The following objectives could serve the purpose of this study.

As far as hypothesis for the study is concerned; the study tries to test the study proposed model i.e. factor structure through exploratory factor analysis. In exploratory factor analysis the factor structure is assessed through statistic along with other fit parameters like R2, RMSEA etc. So it is possible for the researcher to assess if the study proposition has any evidence from the data.

3.2. Data set

The data set is 241 X 27 data matrix which means there are 241 rows and 27 columns. The study assumes that there are six factors in the study (F1 to F6) namely socioeconomic profile, customer loyalty and satisfaction, quality of service, price of the product, promptness of service & customer care and store image. These variables (27) were given certain notation so as to make the analysis more manageable in the article. The following is the description to the study constructs and their respective variables. F stands for and Q stands for study variable. These notations were used throughout the report while interpreting the data.

Table 3.1
Description to study constructs

Study construct

Notation

F1: Consumer Profile

Q1 to Q5

F2: Customer loyalty and satisfaction

Q1.1 to Q1.5

F3: Quality of Service

Q6 to Q10

F4: Price of Product

Q11 to Q13

F5: Promptness of Service or Customer Care

Q14 to Q17

F6: Image of the Retail Store

Q18 to Q21

3.2.1. Reliability analysis

Reliability in statistics is the overall consistency of a measure. In this study the study variables were measured with the help of Likert five-point scale, where the items 5 being highly agreed and 1 being highly disagreed. (Here explain any few variables with their items). There are umpteen numbers of measures to measure the reliability of the scale. As per the theory, a measure is said to have a high reliability if it produces similar results under consistent conditions.  It is the characteristic of a set of test scores that relates to the amount of random error from the measurement process that might be embedded in the scores. Scores that are highly reliable are accurate, reproducible, and consistent from one testing occasion to another. One of the measures which is highly used in practice is Cronbach alpha. Perhaps, this might be due to its simplicity and easy to interpret (Tavakol, M., Dennick, R., 2011).  The Cronbach alpha is defined as below.

Parallel analysis

In an attempt to overcome the subjective weakness of Cattell’s (1966) scree test, presented two families of non-graphical solutions (Courtney, M. G. R., 2013 & Cattell, R. B., 1966). The first method, coined the optimal coordinate (OC), attempts to determine the location of the scree by measuring the gradients associated with eigenvalues and their preceding coordinates. The second method, coined the acceleration factor (AF), pertains to a numerical solution for determining the coordinate where the slope of the curve changes most abruptly.

3.2.2. Factor analysis

The factor analysis is divided into two parts the first is common factor analysis or general factor analysis (a successor to reliability analysis) and the latter is exploratory factor analysis to test the study structure. The analysis was one in R language through psych package. The common factor analysis assumes that there exists only one common latent trait in the study. In other words, all the variables assumed to contribute positively to some certain underlying trait in the data. So, the study assumes that all the study variables are trying to explain a common latent trait known as customer satisfaction which is the principal aim of the investigation. The second part tries to follow the exploratory factor analysis as in theory. That is the analysis tries to identify the structure as suggested by the default number of factors identified through either VSS or parallel analysis.

The factor analysis, in fact, is a data reduction technique which seeks to identify certain latent traits in the data without proposing any structure by the researcher as a verifiable hypothesis. Confirmatory factor analysis is reverse case of this observation, where the research hypothesis that whether a particular factor structure is identifiable through data analysis exists or not. The exploratory factor analysis is all about identifying a good fit for proposed structure through an objective function known as discrepancy function, which is defined as below.

4. Results & its analysis

The very first part of analysis is reliability analysis. The study has 26 study variables all those variables are measured with the help of Likert five-point scale. The study assumed as having certain constructs, which need to be verified through factor analysis. So that about the question of reliability of such items of the variables is obvious. Cronbach alpha is one of the most sought after measure used to assess reliability of the scale especially in those research that are advised for factor analysis. The following table shows the summary statistics of reliability analysis in R.

Table 4.1
Summary statistics of Reliability analysis

Raw Alpha

Std.Alpha

G6(Smc)

Average_r

S/N

ASE

Mean

SD

0.96

0.96

1

0.47

22.97

0.01

3.47

0.59

 

The alpha value is 0.96 which means the scale employed for the study is excellent. The Guttman’s Lambda is one and greater than alpha which shows the evidence that there might be lumpiness in the data which is a favorable sign for factor analysis. However, the magnitude of difference is rather insignificant. The following table shows the item wise statistics of reliability analysis.

Table 4.2
Item-wise statistics of reliability analysis

 

N

RAW.R

STD.R

R.COR

R.DROP

MEAN

SD

Q1

188

0.22

0.23

0.23

0.18

4.49

0.5

Q2

188

0.84

0.84

0.84

0.83

3.45

0.84

Q3

188

0.11

0.08

0.07

0.04

2.56

1.11

Q4

188

0.87

0.87

0.87

0.86

1.71

0.46

Q5

188

0.68

0.68

0.68

0.62

3.3

1.33

Q1.1

188

0.89

0.89

0.89

0.88

3.79

0.64

Q2.1

188

0.92

0.92

0.92

0.91

3.91

1.12

Q3.1

188

0.93

0.92

0.92

0.92

3.99

1.06

Q4.1

188

0.99

0.99

0.99

0.99

3.55

0.84

Q5.1

188

0.99

0.99

0.99

0.99

3.57

0.81

Q6

188

0.85

0.85

0.85

0.83

3.6

0.89

Q7

188

0.93

0.92

0.92

0.92

4.07

1.25

Q8

188

0.8

0.8

0.8

0.77

2.63

1.07

Q9

188

0.85

0.84

0.84

0.83

4.18

1.01

Q10

188

0.93

0.92

0.92

0.92

3.69

0.63

Q11

188

0.93

0.94

0.94

0.92

3.5

0.92

Q12

188

0.04

0.05

0.05

0

1.46

0.57

Q13

188

0.54

0.56

0.56

0.52

1.69

0.46

Q14

188

0.7

0.72

0.72

0.68

4.22

0.72

Q15

188

0.54

0.55

0.55

0.52

1.66

0.47

Q16

188

0.94

0.93

0.93

0.93

4.23

1.15

Q17

188

0.49

0.5

0.5

0.47

4.19

0.51

Q18

188

0.02

0.05

0.05

-0.02

4.36

0.55

Q19

188

0.66

0.65

0.65

0.64

4.23

0.64

Q20

188

0.93

0.92

0.92

0.92

3.72

0.88

Q21

188

0.58

0.58

0.58

0.55

4.53

0.5

 

Fig.4.1: Graphical representation of  and

The above graph is the plot of and  it is clear that there are sudden drops in the value for the first few and the last few variables. The alpha observed to be steady for only few intermediate variables. First and last few variables related huge effect on consistency. Variables related to consumer profile (F1) and service image (F6) has certain influence on internal consistency. For instance, variables related to gender, type of the store; price has very bad influence on the internal consistency. Alpha value falls down drastically when these variables removed. So they seem to be more important preserving scale measurement. Overall, there appears to be inconsistencies across the items of the study.  

4.1. Exploratory Factor analysis

As it was mentioned in the previous section the analysis for this section was done through exploratory factor analysis. The following show the preliminary analysis required for factor analysis namely determination of optimum number of factors and statistical diagnosis like KMO test, Bartlett’s test of spherecity.

Table 4.3
Determination of number of factors

No. of factors 

MAP

DoF

P Value

1

0.088141

48850.88

299

0

2

0.101065

48277.37

274

0

3

0.114559

47577.25

250

0

4

0.122489

46941.19

227

0

5

0.131579

45946.66

205

0

6

0.161507

45479.85

184

0

7

0.194119

44346.81

164

0

8

0.232286

43828.89

145

0

There are number of ways to determine the optimum number of factors. The method used for this research is parallel analysis and very simple structure (VSS). The above table shows the summary statistics for very simple structure. VSS is fit for those structures that are not so complex (Revelle, W. 2015). VSS methodology is such that chi-square value is extracted until the P Value is not significant. But from the table it is clear that the P Values are significant for all components (factors). The analysis did not give any clue as how many factors must be retained for analysis. The situation demands parallel analysis (Raiche, G. 2010).

Fig. 4.2: Scree plot

4.2. Parallel Analysis

Parallel analysis is basically a simulation technique where the method computes the Eigen values of the correlation matrices of the uncorrelated normal variables. The values of Eigen values are retained based on the threshold values specified by the researcher. The following table shows the numerical output for the parallel analysis in R.

Table 4.4
Parallel analysis

 

Eigenvalues

Prop

Cumu

Par.Analysis

Pred.eig

OC

Acc.factor

AF

1

1.734659

0.066742

0.066742

1

1.672576

 

NA

(< AF)

2

1.623116

0.06245

0.129192

1

1.597369

 

0.03741

 

3

1.548982

0.059598

0.18879

1

1.514351

 

-0.00738

 

4

1.467469

0.056462

0.245252

1

1.449408

 

0.017392

 

5

1.403348

0.053995

0.299247

1

1.371739

 

-0.01204

 

6

1.327183

0.051064

0.350311

1

1.314596

 

0.019651

 

7

1.27067

0.04889

0.3992

1

1.263845

 

0.006123

 

8

1.220279

0.046951

0.446151

1

1.209339

 

-0.00351

 

9

1.166381

0.044877

0.491028

1

1.163855

 

0.008563

 

10

1.121045

0.043133

0.534161

1

1.1094

(< OC)

-0.00839

 

11

1.067318

0.041066

0.575227

1

1.070865

 

0.014955

 

12

1.028546

0.039574

0.614801

1

1.022579

 

-0.00909

 

13

0.980687

0.037732

0.652533

1

0.979419

 

0.004796

 

14

0.937624

0.036076

0.688609

1

0.938382

 

0.001963

 

15

0.896523

0.034494

0.723103

1

0.893166

 

-0.00381

 

16

0.851613

0.032766

0.755869

1

0.854005

 

0.00551

 

17

0.812213

0.03125

0.78712

1

0.820983

 

0.005403

 

18

0.778216

0.029942

0.817062

1

0.77535

 

-0.01128

 

19

0.732941

0.0282

0.845262

1

0.741967

 

0.010603

 

20

0.698269

0.026866

0.872129

1

0.706547

 

-0.00213

 

21

0.661469

0.02545

0.897579

1

0.665784

 

-0.00483

 

22

0.619843

0.023849

0.921428

1

0.626561

 

0.000724

 

23

0.578941

0.022275

0.943703

1

0.584444

 

-0.00305

 

24

0.53499

0.020584

0.964287

1

0.548153

 

0.001078

 

25

0.492117

0.018934

0.983222

1

NA

 

-0.01316

 

26

0.436082

0.016778

1

1

NA

 

NA

 

-----

Fig.4.3
Parallel analysis

The acceleration can be detected for the first two factors, although ten factors need to be retained to preserve the gradient. In other words, the factors after OC might not be able to explain the variance in the data for they all had same gradient. Technically speaking AF shows the abrupt change in the curve, so that only those factors might be able to explain most of the variance in the data. So based on parallel analysis, it was decided to go for two factor structure. However, there is also certain analysis against common factor structure only for comparison. The analysis was performed in R and following is the summary statistics of exploratory factor analysis.

Table 4.5
Summary statistics for factor analysis

Chi^2

P Value

Fit

RMS

CRMS

Objective Fn.

R^2

Proportion of Variance

439.9652

1.95092E-07

0.93014

0.121416

0.126585

276.2538

0.999667

0.55

The above table shows the summary statistics for common factor analysis (see more details in research methods). The value is approximately 439 with a P Value of 1.95092E-07 (approx. zero). So the structure is not a null model. There is evidence in support of alternative hypothesis that there exists certain hidden structure in the data and the structure can be explained through common factor. The following is the output for common factor analysis.

Table 4.6
Common factor analysis

Variable

F

U^2

Comm.

Q1

-0.20223

0.959104

0.040896

Q2

0.841812

0.291353

0.708647

Q3

0.067717

0.995414

0.004586

Q4

0.861006

0.258668

0.741332

Q5

-0.67037

0.550598

0.449402

Q1.1

0.896103

0.196999

0.803001

Q2.1

0.922481

0.149028

0.850972

Q3.1

0.929803

0.135466

0.864534

Q4.1

0.986417

0.026981

0.973019

Q5.1

0.987854

0.024144

0.975856

Q6

0.861997

0.256961

0.743039

Q7

0.943156

0.110458

0.889542

Q8

0.811882

0.340847

0.659153

Q9

0.844937

0.286082

0.713918

Q10

0.932432

0.130571

0.869429

Q11

0.939177

0.117947

0.882053

Q12

-0.00465

0.999978

2.16E-05

Q13

-0.53665

0.712009

0.287991

Q14

0.685213

0.530483

0.469517

Q15

-0.53411

0.71473

0.28527

Q16

0.928771

0.137384

0.862616

Q17

0.527365

0.721886

0.278114

Q18

0.033446

0.998881

0.001119

Q19

0.651467

0.575591

0.424409

Q20

0.925526

0.143401

0.856599

Q21

0.608083

0.630235

0.369765

In this case the structure might be the one which is determined through VSS and parallel analysis. It was clear from the parallel analysis that there exist two factors in the data. Gender (-0.2022), occupation (-0.670374209) are significant with quality (-0.00464965, -0.534106935) and price (-0.53664782). Rest of the variables has positive loadings to the common factor. Only education found to have positive loading to the factor along with rest of the variables related to loyalty & satisfaction, customer care and image. So the data analysis supports the following structure in the data.

However certain variables in quality and price observed to be significant relationships with their respective factors. For instance,

Now it is clear that quality affects image, creates favorability towards store and might not prefer to have too much quality and they also think that quality of product and customer care might not be an important retail stores. This appears to be rather more important observation. The idea that too much quality is not expected by customers and they opine that quality affects image and their favor. At the same time, they also opine that the product quality and quality of service at customer care is not so important. So it is evidently clear that service is at the pinnacle at the retail stores not the product. Customers may also not expect customer care just as membership clubs, discounts, complaints etc. The structure for price is as follows:

It is clear from the above structure that price affects image, switching and variety. For instance, customers have same level of opinion on both switching and image, which means price affects image, too much price makes customer switch shows that customers rather sensitive to pricing of products. The product variety is also an important variable that affects pricing. The following diagram shows the factor structure for common factor analysis.

Fig. 4.4
Structure diagram for common factor analysis

4.3. Bi-factor analysis

As it was mentioned in the research methodology, the parallel analysis shows evidence that there might exist two factors in the data. So it was proposed a two factor structure for analysis. the following is the summary statistics for bi-factor structure.

Table 4.7: Summary statistics for bi-factor structure

Chi^2

P Value

Fit

RMS

CRMS

Obj. Fun.

R^2

Prop. Of Var.

319.8607

0.02955

0.949211

0.1060971

0.11555

274.0437

0.9996649

0.997705

0.577

0.091

 

The P Value (0.02) is not as significant as in the case of common factor analysis. So, though there is certain level of evidence in support of alternative hypothesis i.e. in support of proposed structure (bi-factor structure) but it is not overwhelming. The fit is nearly one (0.94), R2 value is close to one (0.99) proportionate variance looks fair (0.577). So thought there is sufficient evidence in support of bi-factor structure but not overwhelming. The following table shows the factor scores.

Table 4.8: Factor loadings for bi-factor structure

 Variable

F1

F2

U^2

Comm.

Q1

-0.2088

-0.18911

0.911742

0.088258

Q2

0.846617

0.057501

0.262523

0.737477

Q3

0.066317

-0.05318

0.99342

0.00658

Q4

0.862292

-0.05864

0.255623

0.744377

Q5

-0.66016

0.408656

0.446127

0.553873

Q1.1

0.896818

-0.08124

0.195626

0.804374

Q2.1

0.92695

0.037201

0.123419

0.876581

Q3.1

0.931088

-0.06668

0.132307

0.867693

Q4.1

0.984497

-0.17702

0.024717

0.975283

Q5.1

0.985205

-0.20077

0.019053

0.980947

Q6

0.866831

0.05609

0.227702

0.772298

Q7

0.946425

-0.00402

0.09577

0.90423

Q8

0.810652

-0.13436

0.340078

0.659922

Q9

0.846051

-0.06232

0.283648

0.716352

Q10

0.935196

-0.01914

0.119647

0.880353

Q11

0.940095

-0.07966

0.115979

0.884021

Q12

0.023341

0.906501

0.164871

0.835129

Q13

-0.51613

0.726582

0.274167

0.725833

Q14

0.675658

-0.38906

0.439594

0.560406

Q15

-0.52642

0.310996

0.655778

0.344222

Q16

0.923446

-0.28053

0.111895

0.888105

Q17

0.539306

0.325065

0.563521

0.436479

Q18

0.042255

0.281205

0.915871

0.084129

Q19

0.651031

-0.08997

0.575573

0.424427

Q20

0.919943

-0.28848

0.11514

0.88486

Q21

0.619349

0.293808

0.488002

0.511998

 

Though, bi-factor model has nothing special to explain from the factor one (which is common factor in previous section) but certain variables turned interesting with respect to their contribution. For instance, in the bi-factor model occupation has opposite to gender. Occupation is trying to explain second factor through variety, quality of the product, empathy of service personnel, reputation second factor together with education. Whereas this was not the case in common factor model. Education was not trying to explain promptness in service and payment for the same product. It shows that the second factor has rather more information to reveal which was not possible through the first factor.  The following is the structure diagram for bi-factor analysis.

Fig 4.5
Bi-facture structure

5. Conclusion

The study has sufficient evidence in support of common factor analysis which shows that there exists good amount of convergence among the study variables. All variables are trying to explain certain latent trait in the data. As from the analysis there was evidence that occupation along with gender explains the respondent’s preferences of quality and payment. The observation is also true through the bi-factor (two factor) exploratory analysis. The only difference between the models is that in bi-factor analysis it is education which characterizing the factor but not the occupation but gender remained same. So in both the model gender observed as significant along with price, customer care and payment for services. Occupation and education has different response to these variables. This may be due to intervening effect of certain other variables like loyalty, billing process, sales person helps, home delivery and etc. So, it is evident that occupation wise differences are sensitive to these intervening variables. Loyalty is significant together with price, quality, and favorability. The study also has evidence in support of the assumption that customers can wait if they find the store favorable.

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1. Research Scholar in Faculty of Management Studies, IBCS. SoA (Siksha ‘O’ Anusandhan University), Bhubaneswar, India. Corresponding author. Email: subhra.mondal05@gmail.com

2. Assistant Professor in Centre for Management Studies. North Eastern Regional Institute of Science and Technology (NERIST) . Email: mallmanmohan79@gmail.com

3. Associate Professor in Faculty of Management Studies, IBCS. SoA (Siksha ‘O’ Anusandhan University), Bhubaneswar, India. Email: uma_mishra_mba@yahoo.co.in

4. Professor in King’s Business School, Ghana. Email: . Email: kalyan46@rediffmail.com


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