Bank Selection Process and Market Segmentation: Evidence from Indian Exporting SMEs

Abstract

Liberalization, privatization and globalization caused by economic reforms have induced severe competition in Indian banking sector. To survive in this contemporary, highly competitive business environment, banks should be able to attract new customers by identifying what is valuable to them and how they make bank selection decision. Small and medium-sized enterprise (SME) exporters have been found to be a very important segment of banks’ market because of profit and revenue opportunities presented by them. For appropriate positioning in exporting SMEs’ market, banks are not only required to be aware of their bank selection criteria rather they should also be aware of their different segments. In the present study, an effort has been made to identify the bank selection criteria of exporting SMEs as well as to segment them on the basis of the same. Findings revealed timeliness in services and accommodation of credit needs as critical factors considered by exporting SMEs while selecting a bank. Further, two types of SMEs customers have been identified, namely, transaction-oriented and relationship-oriented SMEs. Transaction-oriented SMEs who are newly established and more price-sensitive should be targeted with transaction-oriented strategy which aims at offering better-quality services at lower costs than competing banks. Conversely, relationship-oriented SMEs who are established focus more on bank features as well as on need accommodation; a relationship-oriented strategy can be followed to acquire such customers.

Keywords

Bank Selection Criteria Market Segmentation SMEs Exports Cluster Analysis

Introduction

The Indian banking sector has experienced a drastic change due to economic reforms that occurred during the last two decades. Banks are in the process of moving into a more competitive financial environment with a wide variety of financial products and services. Increasing competition has incessantly forced banks to find out new ways to take a lead over their competitors in getting new customers and retaining them through innovative and sophisticated products and services. For acquiring new customers, there is a need to understand what is valuable to them and how they make decisions.

Bank selection process initiates the relationship between a customer and a bank and hence is the most important step for banks in acquiring a new customer. Customers often buy products in a hierarchical order moving from relatively simple services to more complex and expensive ones (Devlin, 2002). Further, market positioning, which lies at the heart of marketing, can be done on the basis of bank selection criteria of customers. Positioning means reaching the target market with the products matching to their demands. Aish (2001, p. 15) has described the process of positioning as follows:

Understand how the market is segmented, that is how the heterogeneous market is divided into homogeneous groups of customers according to specific criteria.

Target a segment (segments) of this market, which the organization is going to serve bearing in mind that this will automatically result in selecting the competitors whom the company is going to compete with in that particular market.

Position itself to the chosen target market based on their choice criteria and send a message to the target market showing that it is present and is trying to serve them in a differentiated way compared with the other offers in that particular market.

Hence, for framing a positioning strategy, banks need to segment the market on the basis of some criteria. Only recently, the commercial banking industry has begun to learn and implement marketing techniques that other industries have been applying since decades ago. Banks can follow two types of marketing (positioning) strategies, namely relationship-oriented strategies and transaction-oriented strategies (Mols et al., 1997). Relationship-oriented strategy means the recognition that the bank can increase its earnings by maximizing the profitability of the total customer relationship over time rather than by seeking to extract the maximum profit from any individual product or transaction (Moriarty et al., 1983). However, this strategy can be called as a resource-demanding strategy as it requires patience to build the trust and intimacy needed. Moreover, to improve customer relationship over time, banks will have to accommodate customers’ needs. Conversely, transaction-oriented strategy considers each transaction as independent and perceives the customer as reacting to stimuli such as price and quality of the offer. The strategy does not value long-term relationships in themselves, but rather considers them as a manifestation of a bank’s ability to continuously offer better-quality products or services at lower costs than competing with other banks (Moriarty et al., 1983). These strategies can be adopted on the basis of orientation of customers. If the customers are cost oriented who attach more importance to cost-related factors, banks can follow transaction-oriented strategy. Conversely, if banks have service- and relationship-oriented customers who stress upon long-term relationships and speed of service, a relationship-oriented strategy can be followed (Mols et al., 1997).

In the commercial banking industry, there is a growing recognition that small and medium-sized enterprises (SMEs) not only represent a viable market segment but also their financial needs are different (Beck et al., 2008; Lam & Burton, 2006). For SMEs, banks are important and almost indispensable business partners as SMEs look first to the banks for their financial needs (Berger & Udell, 2002; Binks & Ennew, 1996; Carey & Flynn, 2005; Cole et al., 1996; Ghosh, 2007; Petersen & Rajan, 1994; Ruis et al., 2009). Banks perform several functions necessary for SMEs’ survival like providing cash management services, arranging letters of credit for assistance in obtaining trade credit, facilitating domestic and foreign exchange transactions and financing through loans. The most important function out of all is providing finance to SMEs. When we consider exporting SMEs, these needs further increase as availability of sufficient finance has been found as a critical success factor for exporting firms (Bell, 1997; Buatsi, 2002; Nwachukwu et al., 2007; Tannous & Sarkar, 1993).

Given the importance of exporting SMEs, banks need to explore the exporting SMEs market. They will have to identify the criteria on which potential SMEs customers determine their bank selection decision. They need to segment the exporting SME market so that a suitable and appropriate marketing strategy can be developed. Hence, an effort has been made to examine the exporting SMEs’ bank selection criteria as well as to segment the exporting SMEs’ market so that appropriate positioning strategies can be developed for the different segments.

Review of Literature

As increasing competition in banking industry has forced banks to formulate strategies for acquiring new customers, researchers and practitioners have shown a great deal of interest in gaining a better understanding of bank selection criteria of customers. Many studies are available that deal with investigating bank selection criteria of customers. There exist a group of studies that deal with bank selection criteria of retail customers. Some studies have focused on SMEs whereas some have stressed upon large enterprises.

A cursory review of literature on bank selection criteria of SMEs has revealed a list of variables that SMEs consider while selecting a bank. Accommodation of credit needs or responsiveness to credit needs has been found to be the most important consideration viewed by SMEs while selecting a banking partner (Buerger & Ulrich, 1986; Jobling et al., 2009; Lam & Burton, 2005; Nielsen et al., 1994, 1998; Trayler et al., 2000). Haines et al. (1991) have found that non-accommodation of credit needs is the main reason for switching by SMEs. He dictates, ‘If a bank wants to induce a customer to look elsewhere for a new banking relationship, all the bank needs to do is to reject the loan application in whole or in part’. As earlier research has evidenced that small enterprises are more constrained than large firms (Riding et al., 2012; Saeed, 2011), they are more dependent upon banks. Hence, they expect the banks to accommodate their credit needs, and this is the reason that this factor has been attached greatest importance at the time of bank selection by SMEs. Further, a wide variety of products and services also named as availability of full services has also been found to be an important factor for consideration by SMEs while selecting a bank (Schlesinger et al., 1987; Trayler et al., 1997, 2000).

As SMEs are already credit constrained (Bell, 1997; Riding et al., 2012; Saeed, 2011; Sharkey et al., 1989; Tannous, 1997; Cooper & Nyborg, 1998), cost of finance has the ultimate impact on the profitability of SMEs. Hence, earlier research has evidenced that cost of finance is viewed as an important factor by SMEs at the time of bank selection (File & Prince, 1991; Maenpaa, 2012; Mols et al., 1997; Nielsen et al., 1994, 1998; Schlesinger et al., 1987; Trayler et al., 2000; Turnbull & Gibbs, 1989). But in spite of its recognized importance at the time of bank selection, it has not been found to be a leading factor (Athanassopoulos & Labroukos, 1999; Locke & Drever, 2008). Further, due to credit constraints, SMEs have to check the collateral demanded by banks. Buatsi (2002) has found collateral to be an important issue focused by SMEs at the time of bank selection. Many loan applications of SMEs are rejected on the basis of non-availability of collateral (Fielden et al., 2000; Singh, 2008; Yesseleva, 2010).

Previous research is evident of many other variables that seek SMEs’ attention when they are in the process of bank selection. Service speed has been found to be an important criterion for many SMEs in the selection of a new bank (Mols et al., 1997; Nielsen et al., 1994; Schlesinger, 1987; Turnbull & Gibbs, 1989). Bank staff are the representatives of banks; hence, their knowledge, efficiency and relationship management are considered to be important by SMEs while selecting a bank, as reported by many studies (Jobling et al., 2009; Maenpaa, 2012; Mols et al., 1997; Schlesinger, 1987; Turnbull & Gibbs, 1989; Zineldin, 1996). In some studies, bank staff’s ability to provide a long-term relationship has been found to be a critical factor at the time of bank selection (Jones et al., 2002; Mitter, 2012; Nielsen et al., 1998). There exist another set of studies that have reported other variables critically considered by SMEs while finding a new banking relationship. Hemmasi et al. (1996) and File and Prince (1991) reported that SMEs viewed confidentiality of client’s information as a critical factor at the time of bank selection, whereas Edris (1997) reported that the SMEs considered the size of bank assets to be important while finding a new banking relationship.

If banks can segment the market on the basis of their purchasing determinants, they can position themselves in respective segments resulting in less wastage of resources as well as better capturing of the market. Hence, there exist a group of researchers who tried to segment SMEs market on the basis of their bank selection criteria (Anderson et al., 1976; File & Prince, 1991; Jones et al., 2002; Mitter, 2012; Mols et al., 1997). Mols et al. (1997) have concluded that banks can follow two types of strategies to acquire new customers, namely, relationship-oriented strategy and transaction-oriented strategy. Jones et al. (2002) have concluded that small firms are less lured by prices and are more focused on relationships with banks. Mitter (2012) has found that transaction-oriented approach is best suited for large firms whereas small firms can be acquired by relationship-oriented strategy as small firms are less price-sensitive. Anderson et al. (1976) divided bank customers into two clusters on the basis of bank selection criteria, namely, service-oriented and convenience-oriented customers. Another study, File and Prince (1991), has segmented SMEs into three clusters: return seekers who were found to be price-sensitive, relevance seekers and relationship seekers. Further, Mols et al. (1997) suggested transaction-oriented strategy for the customers who focused on price and service quality and a relationship-oriented strategy for the customers who focused on relationship.

A thorough review of literature has provided a list of factors that are considered by SMEs while selecting a new banking relationship. Segmentation has also been done in earlier research on the basis of their bank selection criteria which has concluded mostly two types of strategies for acquiring customers. Earlier studies have focused on bank selection criteria of SMEs which may be different from that of exporting SMEs. While selecting a bank for availing export finance, SMEs may also consider other factors like availability of foreign exchange reserves, global branch network, banks’ authorized dealer code (required for extending packing credit in foreign currency) etc., which are generally ignored by non-exporting SMEs. Moreover, earlier literature has examined bank selection criteria as well as segmentation of SMEs worldwide. However, as per the best of researcher’s knowledge, there is no conclusive evidence of such research in India. It is a well-known fact that more exports are made from SMEs as compared to large enterprises in India. Therefore, it becomes essential to identify bank selection criteria of exporting SMEs in India.

Research Methodology

The present study has been conducted to fill the above-mentioned gap and to achieve the following specified objectives:

To identify the factors influencing the decision of SMEs to select a particular commercial bank for financing exports.

To segment the exporting SMEs market on the basis of their bank selection criteria so that an appropriate targeting and positioning strategy can be adopted.

For achieving these objectives, the following research methodology has been adopted.

Data Collection Instrument

As mentioned above, research was conducted in the past to analyze the bank selection criteria of SMEs which may be different from that of exporting SMEs. While selecting a bank, exporting SMEs may consider other factors also which are generally ignored by non-exporting SMEs. Hence, a self-structured questionnaire has been used in the present study which has been made after exploratory interviews with SMEs exporters as well as after thorough review of literature. Originally, it contained 24 statements measuring bank selection criteria of exporting SMEs. After pre-testing, one item was dropped. The owners or managers of exporting SMEs were asked to rate their opinions about these statements on the Likert scale of one to five where one stands for least important and five stands for very important. The questionnaire also contained questions regarding demographic profile of the sampled SMEs.

A pilot survey was conducted with a small sample size of 30 exporting SMEs. This enabled the researcher to develop an insight to bring about the required modifications in the overall configuration or taxonomy of the questionnaire by incorporating suggestions received from responding SMEs. In conjunction with this qualitative assessment, quantitative assessment was also done for further refinement of scale items. For this, the corrected item-to-total correlation was computed. Item-to-total correlation equal to or greater than 0.4 is considered acceptable. Out of the total 24 items, 23 items were chosen for the scale.

Further, reliability and validity analysis has been conducted through structural equation modelling (measurement model), which resulted in a reliable and valid instrument for measuring bank selection criteria of exporting SMEs.

Sampling Procedure

Multistage sampling technique has been used to select a sample of 300 exporting SMEs of Punjab, India. In India, bulks of exports are made from hosiery, apparel, bicycle and sports industry. Punjab is a hub for these industries and hence has been selected for sample constitution. In the first stage of sample selection, three districts, that is, Amritsar, Jalandhar and Ludhiana of Punjab have been selected because of their major contribution to export turnover of Punjab. These districts altogether contribute 92.5 per cent of total export turnover of Punjab. Further, quotas of SMEs from these three districts have been decided on the basis of their contribution to export turnover of Punjab. Finally, the sample has been selected from these districts on the basis of quota sampling. Quota sampling ensures that the composition of the sample is the same as that of the population with respect to the characteristics of interest. Hence, this sampling technique attempts to obtain representative samples at a relatively lower cost (Malhotra & Dash, 2011).

In the second stage, main exporting industries of Punjab have been analyzed on the basis of their export turnover. Four industries have been found to be contributing 71 per cent of the total export turnover of Punjab, namely engineering, hosiery, apparel and sports. A list of exporting SMEs of these industries was taken from the respective Export Promotion Councils (EPCs) for the year 2011–2012. In the third stage, exporting SMEs have been selected from the lists of EPCs as per the decided quotas of districts on the basis of convenience sampling.

Results and Discussion

A descriptive analysis of the data indicates that most of the SMEs are from partnership firms (56.7 per cent) followed by sole proprietary concerns (37 per cent). Only 6.3 per cent replies have come from company form of business organizations. Age-wise analysis shows that there are 39.3 per cent of firms whose age is less than 10 years of age followed by 34 per cent having more than 20 years of age and 26.7 per cent having 10–20 years of age. A major chunk (94 per cent) of the sample is from small enterprises (including micro enterprises). Only 6 per cent of the sampled firms are found to be medium enterprises. It is due to less number of medium enterprises in Punjab as currently there are only 67 medium enterprises in Punjab. Seventy per cent of the respondents are using public sector banks as their primary banks whereas rest of the sample is dependent upon private sector banks.

Data collected have been analyzed through a series of validated tools and procedures. The critical step involved in the development of a measurement scale is the assessment of the reliability and validity of constructs. Reliability and validity analysis has been done to confirm reliability and validity of the scale. The exploratory factor analysis (EFA) of the collected data has been conducted to identify the latent variables. Further, confirmatory factor analysis (CFA) has been performed in order to confirm the findings. The analysis has been done with the help of PASW (version 18) and AMOS (version 18). The results of the analysis are described in the following sub-sections.

Reliability Analysis

In the research study, the internal reliability has been measured with the help of Cronbach alpha statistic as well as composite reliability (CR). For a measure to be acceptable, Cronbach alpha and CR should be above 0.7 (Malhotra & Dash, 2011). Owing to multidimensionality of bank selection criteria construct, Cronbach’s alpha has been computed separately for all the dimensions identified. All alpha coefficients are above 0.80, indicating good consistency among the items within each dimension. The results are shown in Table 2.

Exploratory Factor Analysis

The variables measuring bank selection criteria of exporting SMEs are factor analyzed with the help of PASW 18. Prior to the extraction of factors, the Bartlett test of sphericity (approximate chi square = 5727.693, df = 253, significance = 0.000) and the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy (value = 0.838) have confirmed that there are significant correlations among the variables to warrant the application of EFA. Only factors with Eigen values greater than one have been selected and loadings greater than 0.5 have been included in the analysis (Hair et al., 2010). Five factors have been extracted explaining 76.177 per cent of the variance as illustrated in Table 1.

Table 1.

Factors Influencing Exporting SMEs’ Bank Selection Criteria

Sr. No.	Factor-wise Dimensions	Factor Loadings	Eigen Value	Percentage of Variance Explained	Cumulative Percentage of Variance
F₁	Financial Issues		4.811	20.916	20.916
a.	Collateral Requirements	0.910
b.	Margin Requirements	0.887
c.	Absence of Hidden Charges	0.841
d.	Requirement Regarding Guarantees	0.837
e.	Competitive Interest Rates	0.831
g.	Fee Structure	0.792
F₂	Personnel Attributes		4.156	18.067	38.983
a.	Employees Having up-to-Date Knowledge of Bank Products	0.871
b.	Confidentiality of Client’s Information	0.868
c.	Helpful and Courteous Employees	0.852
d.	Easy Access to Loan Officer	0.845
e.	Transparency in Operations and Fair Dealings	0.694
f.	Knowledge of Customer’s Business	0.642
F₃	Bank Features		3.958	17.210	56.193
a.	Availability of Foreign Exchange Reserves	0.852
b.	Bank Ownership	0.823
c.	Reputation and Credibility of Bank	0.811
d.	Bank’s Ability to Provide a Long-Term Relationship	0.788
e.	Global Branch Network	0.735
f.	Interbranch Facilities	0.732
F₄	Timeliness in Services		2.570	11.173	67.366
a.	Procedural Formalities	0.923
b.	Prompt Provision of Services	0.907
c.	Time Taken for Sanctioning Loan	0.901
F₅	Accommodation of Credit Needs		2.026	8.811	76.177
a.	Wide Range of Products and Services	0.881
b.	Willingness to Accommodate Credit Needs	0.879

Source: Author’s calculations.

The reliability of the collected responses in the research survey has been tested using composite Cronbach’s co-efficient alpha. The value of Cronbach’s alpha is found to be 82.9 which indicates significant level of reliability in the responses. Anti-image correlations matrix has been generated which represents KMO measure of sampling adequacy for individual variables which is found to be sufficiently high for all variables.

The variables have been then rotated using the varimax rotation. The results indicate that all the variables have been loaded onto the five factors as expected, and there is no cross-loading of any variable. Table 1 represents the possible explanation of factors along with their significant variables. Nomenclature of the factors derived has been done on the basis of highest-factor loadings of the variables loaded on the particular factor and their common tone.

Weighted average scores (WAS) of various factors extracted are calculated to measure the relative importance of different factors in bank selection criteria of exporting SMEs. The results indicate that the most important factors considered by SMEs while selecting a bank is timeliness in services (WAS = 4.68) and accommodation of credit needs (WAS = 4.55) followed by personnel attributes (WAS = 3.95), financial issues (WAS = 3.71) and bank features (WAS = 3.62).

Confirmatory Factor Analysis

CFA provides enhanced control for assessing unidimensionality (i.e., the extent to which items in a factor measure a single construct) than EFA and is more in line with the overall process of construct validation. In this study, CFA model is run through AMOS 18 and the key model statistics are shown in Table 2. The CFA model has been found fit as comparative fit index (CFI) value, an incremental model fitness index, has been found as 0.895 (Malhotra & Dash, 2011).

Table 2.

Reliability and Validity Indices for Five Dimensions

Sr. No.	Constructs	Cronbach α	CR	AVE	MSV	ASV	CFI
1	Financial Issues	0.936	0.918	0.653	0.179	0.069	0.978
2	Personnel Attributes	0.896	0.901	0.611	0.179	0.045	0.981
3	Bank Features	0.881	0.898	0.594	0.085	0.038	0.957
4	Timeliness in Services	0.903	0.904	0.757	0.019	0.009	1
5	Accommodation of Credit Needs	0.833	0.852	0.738	0.064	0.021	1

Source: Author’s calculations.

Validity Analysis

Validity is defined as the extent to which an instrument measures what it aims to measure. There are different types of validity including content validity, face validity, construct validity, convergent and discriminant validity, etc. These different types of validities are discussed below:

Content validity: For the present study, content validity of the instrument has been ensured as dimensions of bank selection criteria and items have been identified from the literature and thoroughly reviewed by professionals and academicians.

Construct validity: Establishing construct validity involves the empirical assessment of unidimensionality, reliability and validity (convergent and discriminant validity). In the present study, in order to check for unidimensionality, a measurement model has been specified for each construct and CFA is run for all the constructs taken together. Individual items in the model are examined to verify how closely they represent the same construct. CFI of 0.90 or above for the model implies that there is a strong evidence of unidimensionality (Byrne, 2009). The CFI values obtained for all the five dimensions in the scale are above 0.90 as shown in Table 2. This indicates a strong evidence of unidimensionality for the scale.

Convergent validity: Convergent validity can be established through average variance extracted (AVE) which is defined as the variance in the indicators or observed variables that is explained by the latent construct. For convergent validity, CR should be greater than AVE and AVE should be greater than 0.5 (Malhotra & Dash, 2011). The values for AVE are summarized for all the five dimensions in Table 2. AVE of each construct is more than 0.5 as well as CR is greater than AVE, thereby demonstrating strong convergent validity.

Discriminant validity: Discriminant validity is ensured if a measure does not correlate very highly with other measures from which it is supposed to differ. For discriminant validity, AVE of each construct should be greater than MSV (maximum shared squared variance) and ASV (average shared squared variance) statistics (Malhotra & Dash, 2011). As shown in Table 4, AVE of each construct is greater than MSV and ASV statistics, thereby demonstrating the discriminant validity of the instrument.

Cluster Analysis

Cluster analysis has been applied on the respondent SMEs in order to segment them on the basis of their behaviour towards the extracted factors. A priori approach has been used in cluster analysis to decide the number of clusters. In a priori approach, the number of clusters is decided based upon the theory or earlier literature (Malhotra & Dash, 2011). Hence, K-means clustering procedure has been used as the number of clusters is specified in advance. Wagstaff et al. (2001) have suggested that where background information is available regarding the number of clusters, K-means clustering procedure can be used. In K-means clustering, the specified number of nodes and points closest to them are used to form initial clusters, and through an iterative rearrangement final K clusters are determined (Nargundkar, 2007).

Earlier literature suggested two types of clusters for customers of banks (Mols et al., 1997). The K-means clustering has been applied in order to analyze the cluster properties on the basis of the above extracted factors from EFA. The results indicate that the number of respondents in cluster I is 173 and in cluster II is 127. Hence, these two clusters have significant number of respondents which should be analyzed. The analysis of variance (ANOVA) test is applied in order to identify the significant difference between the various cluster centres. The results of ANOVA test is shown in Table 3.

Table 3.

Results of ANOVA Test for Comparing Clusters

Sr. No.	Factor Names	Cluster Mean Square	Error Mean Square	F Ratio	Significance
1	Financial Issues	183.905	0.386	476.163	0.000*
2	Personnel Attributes	8.084	0.976	8.281	0.004*
3	Bank Features	20.101	0.936	21.478	0.000*
4	Timeliness in Services	0.063	1.003	0.062	0.803
5	Accommodation of Credit Needs	25.640	0.917	27.951	0.000*

Source: Author’s calculations.

Note: * Significant at p < 0.001.

As shown in the results of ANOVA displayed in Table 3, it is found that Factors I, II, III and V significantly explain the cluster difference. The p-values of these clusters are less than 5 per cent. Hence, with 95 per cent level of confidence, the null hypothesis that ‘there is no difference between cluster centres’ can be rejected. Therefore, these four clusters are helpful in explaining the cluster differences. From the final cluster centres, the features of these two clusters on the basis of their behaviour towards five extracted factors can be identified. The results of final cluster centres on the five extracted factors are shown in Table 4.

Table 4.

Final Cluster Centres

Sr. No.	Factor Names	Cluster
Sr. No.	Factor Names	I	II
1	Financial Issues	0.67083	–0.91381
2	Personnel Attributes	0.14065	–0.19159
3	Bank Features	–0.22179	0.30212
4	Timeliness in Services	0.01237	–0.01685
5	Accommodation of Credit Needs	–0.25048	0.34121

Source: Author’s calculations.

As shown in the results displayed in Table 4, it is found that the factor I (financial issues) is the most important factor in discriminating the two clusters. SMEs of cluster I are those for whom financial issues and personnel attributes are important while selecting a bank but they are least concerned with the other factors. Hence, the SMEs respondents of cluster I can be defined as transaction-oriented customers. Conversely, the respondents in cluster II are highly sensitive to bank features and accommodation of credit needs. They expect high level of efficiency from banks. They take decision of bank selection keeping in mind long-term orientation as they are checking bank features as well as bank’s capability to accommodate their credit needs. Hence, the respondents of cluster II can be labelled as relationship-oriented customers.

After analyzing the demographic properties of the two clusters (displayed in Table 5), it is found that cluster I is mainly composed of those SMEs who are new comers to the industry and their tenure of relationship with banks is not very long. Moreover, their export turnover is also less. Hence SMEs in cluster I are less established firms whose financial needs are more. They compare interest rates and fee structure while selecting a bank for financing their export transactions. As their relationship tenure with banks is not very long, they consider personnel attributes while forming a new banking relationship. Hence, these SMEs are named as transaction-oriented customers. These SME customers react to the market stimuli such as the price of banking products. Banks should follow a transaction-oriented strategy for this type of newly established exporting SMEs. The results negate the findings of Jones et al. (2002) and Mitter (2012) who concluded that small firms are less lured by prices and can be acquired by relationship-oriented strategy.

Table 5.

Demographical Properties of Clusters

Cluster		Relationship with Bank			Age of Enterprise			Export Turnover of SMEs
Cluster	Less than 5 Years	5–15 Years	More than 15 Years	Less than 5 Years	5–15 Years	More than 15 Years	Less than ₹ 10 Crores	10–30 ₹ Crores	More than ₹ 30 Crores
Cluster Number	Cluster I	64	79	30	87	64	22	69	50	54
	Cluster II	23	32	72	31	16	80	27	43	57
Total		87	111	102	118	80	102	96	93	111

Source: Author’s calculations.

Conversely, analyzing cluster II properties (displayed in Table 5), it is found that cluster II is composed of the firms that are established in the industry. They have longer relationship with banks and have established their names in the export business. Hence, cluster II comprises established firms. Financial issues are least important for this type of exporting SMEs. This may be due to their relationship with banks as longer relationships increase customer loyalty towards banks and reduce their price sensitivity. The results extend the findings of Locke and Drever (2008) who stated that banks, once having acquired a client, probably need not do much to keep them. Firms of cluster II are familiar with the bank staffs; therefore, they give lesser importance to personnel attributes of banks. Further, their export turnover is also more and so the banks entertain them more because of the revenue opportunities they present. These customers expect accommodation of their credit needs from bank. They are more concerned with bank features. They check for the availability of foreign exchange reserves, bank’s AD (authorized dealer) code, its reputation and credibility, etc. Hence, these are those customers who have long-term orientation in their minds regarding relationship with banks. Therefore, these have been named as relationship-oriented customers and banks can follow a relationship-oriented strategy for them. Results corroborate the findings of File and Prince (1991) and Mols et al. (1997) who have also suggested relationship and transaction-oriented segments of SMEs.

Findings and Conclusion

SMEs have been found to be a very important segment of banks’ market because of the profit and revenue opportunities presented by them (Beck et al., 2008; Lam & Burton, 2006). Moreover, their involvement in export further enhances their importance for banks. To attract exporting SMEs market, banks are not only required to be aware of their bank-selection criteria, rather they should also be aware of the different segments of exporting SMEs’ market so as to position themselves in the each identified segment. Hence, an effort has been made in the present study to identify the bank selection criteria of exporting SMEs and to segment them on the basis of the same.

EFA revealed five dimensions measuring bank selection criteria of exporting SMEs, namely, financial issues, personnel attributes, bank features, timeliness in services and accommodation of credit needs. These are significantly different from bank selection criteria of non-exporting SMEs which have been concluded in earlier research. Relative importance of various factors was studied through average scores which concluded timeliness in services and accommodation of credit needs as critical factors considered by exporting SMEs at the time of bank selection. In case of export transactions, there is always a time restriction to fulfil the export order, and if exporting SMEs fail to supply the order in time limit, it may result in loss of order causing financial loss to them. Further, export business requires more funds and does not follow advance payment systems generally. Moreover, there may be delays in payments by importers. Hence, banks should focus on timeliness in services and accommodation of credit needs for acquiring exporting SMEs customers.

Further, results of cluster analysis concluded two SMEs clusters, namely, transaction-oriented customers and relationship-oriented customers. The study indicates that transaction-oriented customers are composed of those SMEs who are newcomers to the industry having less export turnover. These SMEs customers react to the market stimuli such as price of banking products. Banks should follow a transaction-oriented strategy for acquiring newly established exporting SMEs. Eventually, when these enterprises have established themselves and have expanded their export operations, a relationship-oriented strategy can be followed. Conversely, relationship-oriented customers are composed of the firms who are established in the industry; they have established their names in the export business. These are those customers who have long-term orientation in their minds regarding relationship with banks. Therefore, these have been named as relationship-oriented customers and banks can follow a relationship-oriented strategy for them.

The present study makes an addition to the existing literature by concluding that exporting SMEs too are lured by prices of banking products. They are not always relationship-oriented and any price cannot be charged from them. The study will be helpful to banks in framing their targeting strategy. If they want to target newly established exporting SMEs, they should target them with transaction-oriented strategy which aims at offering better-quality services at lower costs than competing with banks. If they want to acquire established firms having more export turnover, they should follow relationship-oriented strategy focusing on accommodating needs of SMEs as well as on their own features.

The research resulted in the development of a reliable and valid scale for assessing bank selection criteria of exporting SMEs. The resulting instrument is devised after a thorough review of the literature and exploratory investigations followed by a series of acceptable validation procedures. While significant findings are obtained from this study, certain limitations are inherent, which may provide extensions for future exploration. Some of the key areas for future research include the following:

The instrument is developed and validated by collecting data from SMEs located in the state of Punjab, India. Further research work can be carried out by covering other states of the country so as to make cross-state comparisons.

The present study has focused only on SMEs. Future studies may also take into account the bank selection criteria of large exporting enterprises.

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