Research Article: 2019 Vol: 23 Issue: 2
Severe competition fuelled by liberalisation, privatisation and globalisation has changed the outlook of Indian banking industry during the last two decades. Banks are facing an arena where they all are rushing towards acquiring new customers. Competing banks allure customers by offering better services at lower prices. Customers are no longer loyal and they don’t stick to one bank like earlier; rather they maintain multiple banking relationships to get competitive benefits. As a result, Indian banking industry is encountering a problem known as split banking. Split banking means usage of more than one bank as primary bank by customers. It results in decreased share of customer’s wallet towards a bank and hence, less profitability for banks. In the present study, an effort has been made to identify determinants of satisfaction of exporting SMEs who follow split banking. Exporting SMEs have been specifically focused upon in the research study as they constitute an important segment of banks’ market. Beyond academic interests of the study, findings of the study would also be helpful for commercial banks in increasing their share of wallet among exporting SMEs.
SMEs, Split Banking, Share of Wallet, Satisfaction, Banks
Severe competition fuelled by liberalisation, privatisation and globalisation has changed the outlook of Indian banking industry during the last two decades. It has been transformed to buyers’ market from sellers’ market. Banks are facing an arena where they all are rushing towards acquiring new customers. They offer products and services at competitive prices to acquire new customers. Customers are no longer loyal and they don’t stick to one bank like earlier; rather they maintain multiple banking relationships to get competitive benefits. As a result, Indian banking industry is encountering a problem known as split banking. Split banking means usage of more than one bank as primary bank by customers. It results in decreased share of customer’s wallet towards a bank and hence, less profitability for banks.
Banks are utmost important for SMEs as SMEs look forward to banks first for their financial needs (Peterson & Rajan, 1994; Cole et al., 1996; Berger & Udell, 2002; Carey & Flynn, 2005; Ghosh, 2007; Ruis et al., 2009). But earlier research has reported dissatisfaction of small and medium enterprises with banks (Smith, 1989; Ennew et al., 1993; Chaston, 1993; Chaston, 1994; Orser et al., 1994; Zinger, 2002, Maria et al., 2006; Bbenkele, 2007; Safakali, 2007; Vegholm, 2007; Popli & Rao, 2009; Safakali, 2010; Yesseleva, 2010; Vegholm, 2011). Dissatisfaction with banking services may lead to switching behaviour among customers (Singh & Kaur, 2015; Kaur, 2015). As there are more switching barriers in case of SMEs, so, instead of totally switching to a new bank, they decrease their business with a bank and move towards a new bank resulting in split banking.
SMEs offer profit opportunities for banks that’s why SMEs are considered as an important market segment (Lam & Burton, 2006; Beck et al., 2008). Moreover, their export business increases their importance for banks. Satisfaction with banking services has a direct positive impact on share of wallet of customers towards a bank (Keiningham & Perkins-Munn, 2003; Cooil et al., 2007). Therefore, to decrease the trend of split banking, banks should focus on increasing satisfaction among SMEs. Hence, an effort has been made in the research study to identify determinants of satisfaction of SMEs who follow split banking.
It has been widely researched that relationship lending is most beneficial for small and medium enterprises (Berger & Udell, 1995; Degryse & Cayselle, 2000). In spite of this, SMEs mostly borrow from multiple banks. Earlier research has evidenced split banking as a common norm among SMEs (Ongena & Smith, 2000; Lam & Burton, 2005; 2006; Jobling et al., 2009; Maenpaa. 2012; Wambua & Mugambi, 2013). This may be due to benefits of maintaining multiple banking relationships. Tirri (2007) found that credit tightening is lower for firms having more lending relationships. Another set of studies has also found that firms having multiple creditors have greater debt capacity (Cosci & Meliciani, 2006; Neuberger & Rathke, 2006; Shikimi, 2013). They argued that when banks perform transaction lending, firms borrowing from more than one creditor can increase their debt capacity by promising ex ante up to the full amount of available assets to each one of the investors. Moreover, firms that engage in multiple banking relationships benefit from competition among lending banks in terms of lower probability of credit tightening and more favourable loan conditions (Tirri, 2007; Wambua & Mugambi, 2013). On the other hand, firms who maintain fewer bank relationships tend to pledge personal guarantees to their main banks and are charged a higher interest rate (Ogawa et al., 2009). Bouchellal (2011) also confirmed that firms experienced lower financial costs by maintaining multiple bank relationships. Conversely, Shikimi (2013) argued that cost of credit is positively correlated with the number of banking relationships.
Earlier research has concluded that larger, riskier, less profitable and more opaque firms prefer more lending ties (Farinha & Santos, 2000; Tirri, 2007; Berger et al., 2006). Conversely, Degryse et al., (2004) argued that firms with low profitability or financially distressed firms have fewer lending relationships. Ongena & Smith, (2000) have concluded that firms maintain more banking relationships in countries with inefficient judicial system. Farinha & Santos (2000) argued that likelihood of a firm substituting a single relationship with multiple bank relationships increases with the duration of that relationship (Farinha & Santos, 2000). Berger et al., (2006) analysed the impact of bank ownership on number of lending relationships maintained by firms and concluded that firms with foreign main banks were more likely to have multiple bank relationships as compared to firms having state owned banks as their primary banks. Barboni & Treibich (2013) concluded that number of banking relationships is mainly determined by supply or bank side. It means firms prefer multiple lending when they are credit constrained and unable to stabilize their lending source (Malhotra & Dash, 2011).
Due to problem of information asymmetry and less collateral back up, SMEs are always resource constrained (Wagenvoort, 2003). SMEs prefer multiple banking relationships because it results in their increased debt capacity at competitive prices. But on the other hand, to increase debt capacity, they have to sacrifice the benefits of maintaining a single bank relationship. A firm can procure financial products and services at a lower price from a single financial institution as compared to procured from multiple providers as the bank saves on information and transaction costs (Peterson & Rajan, 1994; Hernandez-Canovas & Martinez-Solano, 2006). If relationship with a single bank is maintained, costs of screening, risk assessment and monitoring involved in all new loan provision are considerably reduced. Therefore, there is tradeoff between benefits and costs of split banking for SMEs.
From the banks’ perspective, increasing share of wallet of customers result in increased profitability (Garland, 2004; Keiningham et al., 2005). Moreover, share of wallet is positively associated with SMEs banking loyalty (Lam et al., 2009). A few studies have tried to explore the reasons behind split banking among SMEs (Lam & Burton, 2005; 2006; Jobling et al., 2009). Lam & Burton (2005) found that the main reasons behind split banking were lack of flexibility, perceived risk and search of specialised banking skills whereas Jobling et al., (2009) stressed upon non accommodation of credit needs as the major cause of split banking pattern followed by SMEs in Australia. Lam & Burton (2006) further argued that SMEs want to have specialized banking skills, less perceived risk and better negotiation position and therefore they go for split banking. Earlier research has concluded a significant relationship between satisfaction and loyalty as well as split banking. Increasing satisfaction among SMEs can result in their increased loyalty and decreased split banking trend. However, no prior studies, to the best of researcher’s knowledge, have analysed determinants of satisfaction of SMEs who follow split banking. The present study seeks to close the gap in empirical literature on split banking among SMEs. So following hypothesis has been framed and tested in the present study:
H1: Service quality factors have significant impact on satisfaction of SMEs who follow split banking pattern.
Research methodology deals with the methods used to achieve the objectives of the study.
To develop and validate the proposed research model, the study adopted mixed research design. Extensive review of literature was done to develop the scale. Further, reviewing of scale was done by industry experts, banking experts and academicians to ensure content validity and appropriate statistical procedures were deployed for complete validation of the model. The model was then empirically tested to identify the determinants of satisfaction of SMEs who follow split banking pattern.
A sample of three hundred exporting SMEs of Punjab (India) have been selected through multi stage sampling technique. In India, bulks of exports are made from hosiery, apparel, and cycle and sports industry. Punjab is hub of these industries and hence has been selected for sample constitution. First of all, three major districts who are major contributors in export turnover of Punjab are selected namely Ludhiana, Amritsar and Jalandhar. SMEs of these districts were selected on the basis of proportionate quota sampling technique. Further, four industries namely, hosiery, engineering, apparel and sports were selected and lists of exporting SMEs of these industries have been taken from respective Export Promotion Councils (EPCs). Finally, sample has been selected from the lists as per the decided quotas of districts above.
Primary data have been used in the study which has been collected through a structured questionnaire. A pilot survey was conducted with a sample size of thirty SMEs to improve the overall structure of questionnaire.
The study is primarily based upon primary data which was collected with the help of structured questionnaire. A total of thirty two statements measuring satisfaction of exporting SMEs were extracted through extensive review of literature as well as pilot survey. A five point Likert scale ranging from 5 to 1 where 5 stands for ‘Highly Satisfied’ and 1 for ‘Highly Dissatisfied’ was used to measure responses. Overall satisfaction of exporting SMEs has been measured on same scale. Another question has been added in the questionnaire regarding number of primary banks being used by exporting SMEs.
Data Analysis Techniques
Exploratory factor analysis was applied with the help of PASW 18 (IBM’s software) to reduce data from many variables measuring SMEs’ satisfaction to factors. Further Confirmatory factor analysis was run through AMOS 18 (IBM’s software) to ensure reliability and validity of the scale. Structural Equation Modelling (SEM) was deployed with the help of AMOS 18 to propose the model measuring determinants of satisfaction of exporting SMEs. Finally, group moderation effects have been applied under SEM to explore the determinants of SMEs’ satisfaction having different share of wallet towards banks.
A descriptive analysis of the data indicates that that most of the SMEs are sole proprietary concerns (37%) followed by 32 percent partnership firms and 31 percent company form of organizations. When share of wallet is analyzed, it has been found that forty eight percent of the sampled SMEs are using more than three banks for financing their export activities. Forty three percent are using two or three banks whereas only nine percent of the sampled SMEs are using only one bank. Hence, split banking has been found as a common norm among exporting SMEs.
The collected data were analyzed through appropriate statistical tools. First of all exploratory factor analysis was applied to club variables into factors based on correlation. Reliability and validity was checked through confirmatory factor analysis. After validation of scale, group moderation effects under structural equation modeling have been applied in order to explore the determinants of exporting SMEs’ satisfaction having different share of wallet towards banks. The results of the analysis are as follows:
Exploratory Factor Analysis
Exploratory factor analysis was applied to reduce data from many variables measuring SMEs’ satisfaction to factors. All the assumptions of factor analysis like Bartlett test of sphericity and KMO measure of sampling adequacy were checked and found satisfactory. Factor analysis extracted six factors altogether explaining 85.324 per cent of the variance as shown in Table 1. Cronbach’s alpha was found to be 0.942 which showed reliability of the scale. Fitness of EFA was checked through reproduced matrix where only 8 percent non redundant residuals were found with absolute values greater than 0.05 which indicates that EFA model has good fit.
|Table 1 Factors Measuring Exporting Smes’ Satisfaction|
|Factors||Variables||Eigen Value||Variance explained|
|Financial Factors||Interest Rates||5.504||17.199|
|Bank Charges Clearly Defined and Explained|
|Process Quality||Loan Processing Time||5.497||17.178|
|Adequacy of Amount Sanctioned|
|Transparency in Sanctioning Loan|
|Method of Assessing Working Capital Requirements|
|Flexible Repayment Options|
|Ease in Filling Export Credit Sanction Application Form|
|Timely Release of Credit After Sanctioning of Loan|
|Bank personnel||Availability of Trained Staff||4.882||15.256|
|Reliability of Bank Staff|
|Relationship Management of Bank Officials|
|Behaviour of Bank Staff|
|Easy Access to Decision Makers|
|Staff Having Knowledge of Customer Business|
|Service Speed & Efficiency||Procedural Formalities||4.728||14.774|
|Quick Response to Customer Queries|
|Error Free Records and Lesser Mistakes|
|Quick Redressal of Complaints|
|Modernization in Work Processing|
|Branch Characteristics||Flexibility in Branches||4.201||13.129|
|Loan Sanctioning Power of Branch|
|Arrangements with Other Banks In Case Of Restricted Letter of Credit|
|Convenient Operating Hours|
|Customized Services||Accommodation of Credit Needs||2.492||7.788|
|Innovativeness in Introducing New Schemes|
|Wide Range of Products and Services|
Principal component analysis with varimax rotation was applied which loaded all the variables onto six factors namely, financial factors, process quality, bank personnel, service speed & efficiency, branch characteristics and customized services. Table 1 shows variables along with factors and their respective factor loadings.
Reliability analysis has been done through calculating values of Cronbach alpha and composite reliability for all the constructs which are more than 0.80. This indicates good consistency among all the items within each dimension.
Confirmatory Factor Analysis
Reliability and validity of the scale was checked through Confirmatory factor analysis (CFA). So it was necessary to check fitness of CFA model. The CFA model was found to be fit as CFI was 0.926.
Content validity of the questionnaire was confirmed by getting questionnaire checked by professionals and academicians. For checking construct validity, separate measurement model was specified for each construct. CFI values for all the six dimensions in the scale were more than 0.90 indicating strong construct validity. Average Variance Extracted (AVE) of each construct was found to be more than 0.5 as well as composite reliability was greater than AVE, indicating presence of convergent validity. Further, AVE of each construct is greater than Maximum Shared Squared Variance and Average Shared Squared Variance statistics thereby concluding discriminant validity of the instrument. Criterion validity has been established by correlating the scores of dimensions of service quality with the overall satisfaction with respect to service quality, which is considered to be the outcome construct. Results of correlations showed presence of criterion related validity.
Structural Equation Modeling
Structural Equation Modelling (SEM) method has been applied in order to analyse the impact of individual service quality constructs on the satisfaction level of exporting SMEs. The analysis has been done with the help of AMOS 18 so as to propose a model for measuring determinants of satisfaction of exporting SMEs.
The results indicated that p-values of all dimensions of service quality except branch characteristics and process quality are less than 5 percent level of significance. Hence, it can be stated that except branch characteristics and process quality, all the dimensions of service quality provided by banks have significant impact on overall satisfaction of exporting SMEs and they altogether explain 77.8% variation in the endogenous variable ‘overall satisfaction’. The results also indicate that the most influential factor is service speed & efficiency followed by customized services, financial factors and bank personnel. The fitness indices have been calculated to check the fitness of the model. All the values of Goodness of Fit Measures i.e. NFI (Normed Fit Index), CFI (Comparative Fit Index) and TLI (Tucker Lewis Index) are greater than 0.90 which indicates that structural model has a good fit.
As the main objective of the study was to explore the determinants of SMEs’ satisfaction having different share of wallet towards banks, exporting SMEs have been divided into three groups as shown in Table 2 on the basis of their responses to number of banks being used. One way ANOVA has been applied to compare these three groups with respect to their overall satisfaction as well as their determinants of satisfaction with banks. Results indicated significant differences among three groups with respect to overall satisfaction as well as determinants of satisfaction. On the basis of SEM results, following model has been proposed to identify the determinants of satisfaction of SMEs who follow split banking (Figure 1).
|Table 2 Results of Group Moderation Effects|
|Endogenous||Exogenous||Standardized Regression Weights (p-value)|
|Using one bank (n=28)||Using two or three banks (n=128)||Using more than three banks (n=144)|
|Overall Satisfaction||1) Financial Factors||.145 (.283)||.175 (.006)|
|2) Process Quality||.023 (.868)||-.006 (.912)||-.378 (.000)|
|3) Bank Personnel||.129 (.024)||.090 (.201)|
|4) Service Speed & Efficiency|
|5) Branch Characteristics||.205 (.110)||.041 (.506)||.131 (.074)|
|6) Customised Services||.115 (.381)|
Group moderation effects have been applied under Structural Equation Modeling (SEM) to explore the determinants of SMEs’ satisfaction having different share of wallet towards banks, results of which are displayed in Table 2.
As shown in Table 2, the determinants of satisfaction of exporting SMEs using only one bank are bank personnel followed by service speed & efficiency. For exporting SMEs using two or three banks, these are customized services followed by service speed & efficiency. Conversely, determinants of satisfaction of exporting SMEs using more than three banks are financial factors followed by service speed & efficiency. It shows that these are financial factors which are main determinants of satisfaction of exporting SMEs using more than three banks. These are the factors which encourage exporting SMEs for “split banking”. This has also been discovered that service speed and efficiency is an important determinant for all groups of SMEs.
Indian banking sector has been witnessing a situation of severe competition during the last two decades. Banks are rushing towards acquiring new customers. Competing banks lure customers by offering lower prices and as a result, customers maintain multiple banking relationships. In other words, customers have primary relationships with more than one bank. Hence, a new term is coined as split banking where customers give their share of wallet to more than one bank.
SMEs offer profit opportunities for banks that’s why SMEs are considered as an important market segment (Lam & Burton, 2006; Beck et al., 2008). Moreover, their export business increases their importance for banks. But earlier research has shown dissatisfaction among SMEs with respect to banking services. Switching barriers prevent SMEs from switching to a new bank totally. Hence, instead of switching to a new bank, SMEs opt for multiple banking relationships. This is the reason that split banking has been found as a common norm among SMEs.
In the present study, an effort has been made to analyse the determinants of SMEs’ satisfaction having different share of wallet towards banks. In other words, reasons behind split banking pattern among exporting SMEs have been explored. A structured questionnaire was used in the research study measuring exporting SMEs’ satisfaction with service quality of banks. Variables measuring exporting SMEs’ satisfaction were factor analysed through exploratory factor analysis and it extracted six factors namely, financial factors, process quality, bank personnel, service speed & efficiency, branch characteristics and customized services. Reliability and validity analysis has been conducted through confirmatory factor analysis for validation of scale.
Consistent with the earlier research, split banking has been found as a common norm among exporting SMEs as forty eight percent of SMEs were using more than three banks. So, three groups were made of exporting SMEs having different share of wallet towards banks. Group moderation effects have been applied under structural equation modeling, taking share of wallet as moderator, to identify determinants of exporting SMEs’ satisfaction having different share of wallet towards banks. The results revealed that the determinants of satisfaction of exporting SMEs using only one bank are bank personnel followed by service speed & efficiency. For exporting SMEs using two or three banks, these are customized services followed by service speed & efficiency. Conversely, determinants of satisfaction of exporting SMEs using more than three banks are financial factors followed by service speed & efficiency. First of all, study concludes that service speed is utmost important for all groups of exporting SMEs. Exporting SMEs have to deal with time bound orders. Further, they are dependent on banks for their financial requirements. So to fulfil their orders in time, they want a speedy service from banks.
For exporting SMEs who are having more than three banks as their primary banks, financial factors are the most important determinant of satisfaction. It shows that these are the factors which encourage exporting SMEs for “split banking”. If we look at determinants of satisfaction of all exporting SMEs, these have been found as service speed & efficiency followed by customized services and financial factors. But if banks want to increase share of wallet of exporting SMEs, they should focus on price competitiveness. Finding corroborate with the findings of Kaur (2015) who concluded that exporting SMEs are lured by prices of banking products. They are not always relationship oriented and any price cannot be charged from them.
The present study does suffer from some limitations. Firstly, data have been collected from Indian exporting SMEs that too from limited region as well as industries. Situational differences may play a role in the outcome of the study. Secondly, only small and medium exporting enterprises have been covered in the present study. The future studies may also cover split banking among corporate sector exporters. Beyond academic interests of the study, findings of the study would also be helpful for commercial banks in increasing their share of wallet from exporting SMEs.
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