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Consumer Loyalty Towards their Primary Retail Banking Institutions

Consumer Loyalty Towards their Primary Retail Banking Institutions

 

 

A Study on the Consumer Attitudes and Loyalty Towards their Primary Retail Banking Institutions in Bahrain

CHAPTER 1: INTRODUCTION

Introduction

In the increasingly competitive world of retail banking, organizations must establish a presence that sets them apart from the crowd. Low cost, convenience, broad product lines, and customer service have all been used to segment the banking industry. For small and medium sized banks, customer service has increasingly been the method of choice for making their mark. This strategy has been driven primarily by an inability to compete in other key areas with the larger players in the banking industry, but also by the economics of customer retention.

 

The process of obtaining new customers is a costly and time-consuming venture. Because of this, banks of all sizes have focused a great deal of time and energy on improving customer service practices. The underlying belief is that improved customer service will help to create relationships with customers that are strong enough to prevent them from leaving for potentially more attractive opportunities elsewhere. These improvements in customer loyalty, and therefore retention, are believed to have a significant impact on the long-term profitability of the bank.

 

Since customer retention is a critical goal, the question becomes; what factors affect a customer’s perception of the bank and what actions can the bank take to increase positive perceptions. The answer that has increasingly been the subject of investigation is the link between the attitude of the service provider, the customer interaction behaviors that those attitudes lead to (customer service), and the attitudes that those behaviors generate in the customer (customer satisfaction).

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This relationship between customer service and customer satisfaction has ultimately been connected with customer retention, a concept that has particular importance in the sales and service environment of the banking industry. Front line workers are the primary link between the company and its customers. Every interaction between these employees and their customers has a direct impact on the publics’ opinion of the organization. The employees’ performance in relation to customer service activities can either create a sale for the bank or loose a customer. The loss of a single customer can cost the company a great deal of money in terms of lost sales as well as the cost of replacing that customer; so every interaction is crucial.

 

Affective loyalty is a component of loyalty that focuses on how much a customer likes the bank or how positive their attitude is toward the bank. Conative loyalty focuses on the customer’s intention to keep on using the bank in the future. Fishbein and Ajzen (1975) argued that attitude toward a product predicts attitude toward buying the product. Attitude toward buying the product is assumed to predict intention to buy the product. In marketing literature, attitude toward a product is often used as a direct predictor of intention to buy the product (Brown & Stayman, 1992; Homer, 1990). Since customer satisfaction and brand reputation are the main determinants of loyalty, banks on the Internet should struggle to satisfy their customers and build strong brands.

 

From research in marketing, there is some knowledge about the determinants of loyalty in the physical marketplace.

 

Customer satisfaction is a post-choice evaluation of a specific transaction (Selnes, 1993). Early studies conceptualized customer satisfaction as customer perception of product performance (Anderson, 1973; Cardozo, 1965). Later studies showed that customer satisfaction is a function of customer perception of product performance compared to a set of standards (e.g., expectations, values and norms) related to the confirmation/disconfirmation paradigm (Yi, 1990). In addition to the direct effect of confirmation/disconfirmation, both expectation (i.e., the comparison standard) and perception of brand performance are found to have direct effects on customer satisfaction with a brand (Churchill & Surprenant, 1982; Yi, 1990).

 

Satisfaction is considered to act as an antecedent of loyalty, arising out of direct prior experience (Dick & Basu, 1994). Several studies have found support for the positive relationship between customer satisfaction with a brand and their loyalty toward that brand (Fornell, 1992; Samuelsen et al., 1997; Sandvik & Duhan, 1996). “Loyal customers are not necessarily satisfied customers, but satisfied customers tend to be loyal customers” (Fornell, 1992, p. 7). A satisfied customer has few incentives to change supplier or brand. Therefore, “customer satisfaction makes it costly for a competitor to take away another firm’s customer” (Fornell, 1992, p. 10). Satisfying customers should therefore be of great importance to vendors in keeping their customers loyal. Customers who are satisfied with their bank probably develop a positive attitude toward the bank. As a result, their intention to stay with the bank in the future is likely to be high.

 

 

Aims of the Dissertation 

In the retail banking sector, customer retention is clearly a big concern for gaining a competitive edge. This has triggered intense competition among banks for customer loyalty. With the current economic recession the challenge faced by banks to retain the loyalty of their customers is even higher.

 

Not many studies have been conducted in Bahrain to throw light on the customer behavior and to know what influences their loyalty to their primary retail banks. Here an attempt will be made to find out the factors which attribute the loyalty of the customer towards their primary retail bank in Bahrain

 

The intention of this research is to examine all aspects of customer experience related to checking account, product quality and services, and all touch points of the bank – branches, call centre, sales representatives, online banking.

 

Objectives of the Study

Primary objectives of the study will be to identify:

 

  1. factors which drive overall attitudes towards the banks
  2. what matters to people in banking interactions
  3. rising concerns of customers, and
  4. factors which influence customer loyalty

 

For this purpose, the researcher aims to analyze the relationships that exist between customer service behaviors and customer satisfaction. The variables of interest in this research represent part of a larger body of research described as the “service-profit chain” (Heskett & Sasser, 1997).

 

Significance of the Study

The current study is useful because it adds to the body of literature attempting to quantify the relationship between service workers and the people they serve. This link is critical to the success of all retail sales organizations and the knowledge gained from research of this type has implications that can directly affect the financial performance of these organizations. Minor improvements in a firm’s ability to satisfy their customers can have a major impact on their competitiveness. Research into the link between employees and customers can inform decisions regarding the process of managing and retaining customers through better service quality and customer service. In essence, it can help answer questions such as, should banks spend time and money on customer service training, employee satisfaction efforts, management training, etc.

 

Definition of Terms

The following terms are defined for the purpose of this study:

Affective Loyalty: The component of loyalty that focuses on how much a customer likes the firm or how positive their attitude is toward the firm (Chandrashekaran et al., 2007).

 

Large bank asset classification: Banks with assets over $10 billion (Stiroh & Metli, 2003).

 

Loyalty: A positive attitude toward a brand in addition to intention to re-buy the brand (Chandrashekaran et al., 2007). 

 

Medium bank asset classification. Banks with assets between $500 million and $10 billion (Stiroh & Metli, 2003). 

 

Small bank asset classification: Banks with assets less than $500 million (Stiroh & Metli, 2003).

 

CHAPTER II: REVIEW OF THE LITERATURE

 

Introduction

The transition to a service economy has had important repercussions for organizations and the way they operate (Bowen & Schneider, 1988). In today’s competitive environment, service effectiveness is increasingly seen as a critical organizational objective (Cascio, 1995; Reicheld & Sasser, 1990). To understand the impact of the customer service focus, it is imperative to review the differences between the delivery of service and the traditional delivery of goods. The fundemntal characteristics of delivery of service are complex, dynamic, and dependent upon customer perceibed expectations and imagination (Cascio, 1995; Iacobucci, Ostrom, & Grayson, 1995). Contrary to the product measurement, the analysis of customer service delivery is not a highly structured task. There are three defining features of services that the set them apart from the traditional delivery of goods:

 

  1. First, services are intangible in nature. In contrast to products, or goods, that can be touched and possessed, services tend to be experiential in nature (Berry, 1983).

 

  1. A second distinguishing feature of services is that production and consumption occur simultaneously. It is suggested that the chain of events involved in the creation and consumption of goods differs from the order of events that occurs in the creation and consumption of services (Berry, 1983). Goods are generally produced, then sold, and finally consumed. Services, on the other hand, are generally purchased first, then produced and consumed at the same time.

 

  1. The final defining feature of services is that the consumer is often involved in the production and delivery of the service. With respect to goods, consumers typically have little input into the creation of the product they are purchasing (Schneider & Bowen, 1984).

 

These three characteristics of services (i.e., intangibility, simultaneous production and delivery, and customer participation) mean that the customer service employee, who directly interacts with the customer, is critical to the delivery of quality service. The research suggests that these customer service employees perform two critical functions. First, because of their direct contact with customers, they are essential collectors of information about customer expectations and attitudes as well as a source of suggestions for improving the quality of the service and its delivery (Bowen & Schneider, 1988).

 

 Second, and possibly even more importantly, customer service employees represent the organization to the customer. The service employee is the embodiment of the organization for most customers. Consequently, the behavior of the service employee, and the impact it has on the customer, is a critical factor in defining customer’s perceptions of the company. Given the important roles filled by customer service employees, organizations wishing to deliver quality service must find ways to support and effectively coordinate the behavior of these individuals (Schneider & Bowen, 1992, 1995).

 

A particularly important approach to customer service is one that focuses on moving beyond the delivery of high quality service to the formation of long-term relationships with customers. The benefit of forming such relationships comes from the presumably higher levels of customer commitment and retention. The commitment and retention should lead, in turn, to higher profits for the organization (Schneider, White, & Paul, 1997). Based on this line of reasoning, the manner in which customer service representatives treat customers will have a significant impact on the development of these long-term relationships.

 

Service Quality Literature

Service quality is the customer’s perception of how well their expectations were met during the service encounter (Zeithaml et al., 1990). Researchers typically measure service quality using customer evaluations of five attributes: reliability, empathy, assurance, tangibles, and responsiveness. Service quality has typically been measured by surveying customers both before and after a service experience and differentiating their expectations and perceptions (Zeithaml et al., 1990). Based on the service delivery gap model, perceptions greater than or equal to expectations suggest satisfactory service quality; perceptions less than expectations indicate unsatisfactory service quality (Parasuraman, Zeithaml, & Berry, 1985, 1988; Zeithaml et al., 1993). Most researchers (Bitner, 1990; Bitner et al., 1990; Zeithaml et al., 1990) only measure service quality after the service encounter and rely on this single survey of a customer’s perception to reveal the service delivery gap.

 

Mystery Shopping — Measures of Customer Service Quality

Another way of measuring the quality of the service delivery is by making use of participant-observer evaluations by “mystery shoppers.” Mystery shopping is a method used to measure the quality of customer service provided by employees as it occurs (Grove & Fisk, 1992). Mystery shopping uses trained observers posing as customers to interact with staff and objectively assess service quality. The technique typically focuses on “activities and procedures that do or do not occur rather than gathering opinions about the service experience” (Wilson, 1995, p. 725). Thus, mystery shopping provides an alternative to customer surveys or other feedback techniques that occur after the fact.

 

The validity of the mystery shopper technique hinges on the employee’s believing that the observer is a real customer, not an “undercover” evaluator. To both protect the anonymity of the shopper and to increase the believability of the interaction, mystery shoppers follow a script, or scenario, to solicit responses from the employee. After each interaction, the evaluator independently rates the quality of customer service received based on an established set of criteria.

 

According to one study, service organizations use mystery shopping because customer surveys do not provide sufficient information on weaknesses in the service delivery (Wilson, 1995). Investigating the business randomly and anonymously can have a dramatic effect on employees by creating the belief that their work could be evaluated at any time (Baggs & Kleiner, 1996).

 

In another study, mystery shopping was not only comparable to customer surveys for reliability but offered a more cost effective means for data collection (Fin & Kayande, 1999). “When evaluating the same subjective construct, an individual mystery shopper provides higher quality data than does an individual customer” (Fin & Kayande, 1999, p. 107).

 

Mystery shopping can discern finer differences between locations than can be detected using customer surveys because of the greater attention paid by the respondent (Fin & Kayande, 1999). The fact that mystery shoppers know in advance that they will be evaluating an interaction gives them an advantage over individuals presented with a customer service survey. There may also be further advantage in having individuals who have been trained to be observant providing the assessment especially when they have clearly defined behavioral criteria to assess. Link between Employee Satisfaction and Customer Service

 

Extensive research has been conducted regarding the relationship between job satisfaction and various work-related behaviors including job performance measures such as customer service quality. Satisfied workers have been found to be more conscientious, helpful, and to have greater willingness to report unethical behaviors than dissatisfied workers (Silberstang, 1995). Work satisfaction has been shown to influence attendance at work, pro-organizational behaviors, decisions to leave the organization, and psychological withdrawal behaviors (Cranny, Smith, & Stone, 1992).

 

Researchers have also noted that the methods researchers use to study satisfaction and performance greatly impact the conclusions reached regarding their relationship (Cranny, Smith, & Stone, 1992). They suggest that correlation studies have shown only moderate relationships at best, while intervention research supports a stronger relationship. For example, in a review of 207 studies of the effects of psychologically-based interventions on productivity and performance, investigators reported that 87% of the interventions were successful in raising productivity, as well as job satisfaction (Katzell & Guzzo, 1983).

 

Intervention studies assess the effects of a manipulated independent variable (such as pay, benefits, or supervisory practices), and are believed to take into account various mediating factors (such as extrinsic and intrinsic rewards, and perceived equity) that affect both performance and satisfaction better than correlation studies (Cranny, Smith, & Stone, 1992).

 

Using path analysis, it was found that work satisfaction significantly influences job performance as rated by supervisors (though not as self-reported), which is believed to be due to increased alertness and focused attention (Cranny, Smith, & Stone, 1992). Other research found that job satisfaction is a significant predictor of organizational commitment for female advertising executives (DeConinck & Stilwell, 1996); and organizational commitment of supervisors was positively related to performance (Becker, Billings, Eveleth, & Gilbert, 1996). In studying research and development teams, it was found that satisfaction with pay, advancement, and supervision was related to an increase in patent acquisition, technical quality ratings, and publication of articles, all of which can be considered job performance issues in the research and development field (Keller, Julian, & Kidia, 1996).

 

Service Quality and Customer Satisfaction

 

A study conducted by SAS, Peppers & Rogers Group, (n.d) on measuring customer value in retail banking, done on US retail bank executives, explored challenges and opportunities that retail banks experience in developing and integrating customer value metrics into their product-based business models. This study highlights the fact that competitive advantage will remain elusive and out of reach until retail banks refocus their strategy on customers and customer value management rather than solely on products and product management. While this study’s focus has been from the point of view of the bank this dissertation attempts to understand loyalty from the point of view of the customers and what factors influence this loyalty.  

 

Another report presented by Deloitte Center for Banking Solutions, ( January 5 , 2006) identifies a series of small steps that, over time, will transform a bank to a truly customer-centric organization focused on meeting the needs and wants of its customers.  Almost half of the respondents cited location /access as the primary reason they chose their bank while superior service was chosen as what helps to stem attrition and deepen customer relationships. This dissertation can be considered as an extension of this study as it will be considering the total customer experience with their primary bank and the factors which influence their loyalty to the bank.

 

Armonk, & San Mateo,( April 5, 2009) conductged a research in which anonymous customers proactively contacted the world’s top retail banks through Web and phone inquiries to measure responsiveness to inquiries, performance of call center agents, and follow-up on leads generated from the inquiries. It illustrates that the retail banks can more effectively align employees, business processes, and technology solutions to build customer loyalty, improve customer experiences and drive revenue growth. This study adds to the dissertation topic emphasizing that there are significant opportunities for banks to build customer loyalty and drive revenue.

 

Similarly, Dan Nordale, Lizanne Kaiser,   Vicki Shields, (July 14, 2005) conducted a research to measure customer satisfaction. This report addresses the formidable challenges large consumer and business banks face today to successfully balance the often conflicting objectives of delivering near-term organic growth, fostering long-term customer loyalty, and continually driving greater operational efficiency. This white paper based on in-depth analysis and interviews with retail banks around the world justifies the relevance of the topic of this dissertation.

 

The paper presented by István Szűts, Zsolt Tóth, (May 30-31, 2008) on customer loyalty in retail banking addresses the problems and challenges retail banks face in developing and measuring customer loyalty programs and customer satisfaction.  Though this paper states that the majority of customers feel ambivalent about their relationship with their banks it doesn’t explore the reasons for it.   It does acknowledge that banks have not done enough to sustain loyalty of customers. This paper seems like a good foundation for this dissertation topic as it discusses the concerns of the bank regarding maintaining loyalty of customers and the challenges of running a good loyalty program.

An article written by Ranjay Gulati and James B. Oldroyd (2005) refers to the quest for customer focus by Royal Bank of Canada [RBC] and what it discovered from a survey of 2000 current and potential customers.  The survey results showed a disparity between what the bank thought was important to the customers and what really mattered to the customers.  This research identified four stages of customer focus and the organizational changes needed to move from one stage to the next. This article complements the dissertation topic.   It will be interesting to see what matters to the customers in Bahrain in comparison to the results of the study by RBC.

 

A thesis by Nordman Christina (2004) focuses on both loyal and disloyal customers and the factors that characterize both based on a qualitative study of loyal and disloyal bank customers in the Finnish retail banking market. The thesis has managerial relevance especially for banks since the empirical study was carried out in a retail bank setting. This thesis complements the dissertation topic as it is related to loyalty in  retail banking.  It also throws light on the factors that cause disloyalty which often receives much less attention compared to loyalty.

 

A report by Peter Clark (June 11, 2007 based on the The Loyalty Guide Volume II, gives a more theoretical base to the customer loyalty issue by explaining the business factors that directly influence the loyalty of customers. It also offers practical insights into the ways in which workings of customer loyalty programmes can positively influence customer behavior.The content of this report can provide a conceptual framework against which the data of the primary research for the dissertation can be analyzed.

 

In a similar vien Joseph N. Fry et al. ( n.d) presented an article based on a patronage tracking study conducted with graduates of a Canadian University.  The study tries to provide basic information of the bond between customer and the bank by addressing two main questions – whether customers exhibit loyalty towards their bank and if so, what factors explain the variations in the degree of loyalty observed. This dissertation will be more of an extension to this article and comparisons can be drawn between the results of the primary research for the dissertation and the results of the patronage tracking study in this article.

 

An article “Fun in a Customer Experience Way” by Howard Lax & Rochester (n.d explains that customer loyalty is not the same as customer experience.  It emphasizes that banks’ focus should not be to produce happier feelings but to invest in experiences and processes that build and reinforce customer loyalty. When it comes to building loyalty experiences may be more important than maximizing delightful interactions as a happier customer need not be a more loyal customer. The content of this article can also contribute to the background research for the dissertation.

 

Satisfaction

Customer satisfaction is a post-choice evaluation of a specific transaction (Selnes, 1993). Early studies conceptualized customer satisfaction as customer perception of product performance (Anderson, 1973; Cardozo, 1965). Later studies showed that customer satisfaction is a function of customer perception of product performance compared to a set of standards (e.g., expectations, values and norms) related to the confirmation/disconfirmation paradigm (Yi, 1990). In addition to the direct effect of confirmation/disconfirmation, both expectation (i.e., the comparison standard) and perception of brand performance are found to have direct effects on customer satisfaction with a brand (Churchill & Surprenant, 1982; Yi, 1990).

 

Satisfaction is considered to act as an antecedent of loyalty, arising out of direct prior experience (Dick & Basu, 1994). Several studies have found support for the positive relationship between customer satisfaction with a brand and their loyalty toward that brand (Fornell, 1992; Samuelsen et al., 1997; Sandvik & Duhan, 1996). “Loyal customers are not necessarily satisfied customers, but satisfied customers tend to be loyal customers” (Fornell, 1992, p. 7). A satisfied customer has few incentives to change supplier or brand. Therefore, “customer satisfaction makes it costly for a competitor to take away another firm’s customer” (Fornell, 1992, p. 10). Satisfying customers should therefore be of great importance to vendors in keeping their customers loyal. Customers who are satisfied with their bank probably develop a positive attitude toward the bank. As a result, their intention to stay with the bank in the future is likely to be high.

 

Brand Reputation

A brand is a name, term, symbol, sign or design to identify the goods or services of one seller and to differentiate them from those of competitors (Keller, 1998). Brand reputation has been defined as a perception of quality associated with the brand’s name (Selnes, 1993). A strong brand is a brand with “a set of assets linked to a brand’s name or symbol that adds to the value provided by a product or service to that firm’s customers” (Aaker, 1996, p. 7-8). A strong brand is a signal of quality; it is a risk reducer for customers and it reduces search costs (Keller, 1998). A brand name, or a brand’s reputation, is often used as a heuristic for choice when intrinsic cues or attributes are difficult or impossible to employ (Selnes, 1993). Brand reputation and satisfaction may look like the same construct. In the literature, however, brand reputation is seen more as a long-term and overall evaluation of the product than customer satisfaction (Selnes, 1993).

A positive relationship is expected between brand reputation and customer loyalty to the brand (Sandvik & Duhan, 1996). The reason for this is that a brand’s reputation might function as a social norm and that customers often rely on the beliefs of their reference groups to a great extent. Another reason why brand reputation is important for loyalty is that it may be difficult to evaluate satisfaction with a product directly in some situations. In such situations, the brand reputation is used as a heuristic for evaluating the product. However, an important point to note is that customer satisfaction with a brand is a determinant of brand reputation in some situations. In the insurance industry, for instance, customer satisfaction with a brand is found to have a positive relation to brand reputation (Selnes, 1993). This underscores both the direct and indirect effects of customer satisfaction on customers’ loyalty.

 

Switching Costs

Porter (1980) defined switching costs as the costs of switching from one supplier’s product to another supplier’s product. Fornell (1992) included search costs, transaction costs, learning costs, customers’ habits, emotional costs and cognitive effort in the switching costs construct. As can be seen, switching costs include both economic and psychological values. Switching costs is a common strategy to increase loyalty (Dick & Basu, 1994). Currently, banks are eager to launch loyalty programs where customers obtain substantial benefits by holding most of their banking business within one bank (positive lock-in).

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“Switching barriers make it costly for the customer to switch to another supplier” (Fornell, 1992, p. 10). The implication of this proposition is a positive relationship between switching costs and customer loyalty. High switching costs retain customers from changing banking relationships. Therefore, an increase in switching costs will lead to an increase in loyalty. Since this is a kind of lock-in strategy, it has a positive effect on the intention to keep on as a customer (i.e., conative loyalty), rather than on customers’ affective commitment toward the bank (i.e., affective loyalty). Anecdotal experience suggests that customers will probably perceive high switching costs as something negative, which reduces their flexibility to switch supplier or product.

 

Search Costs

Fornell (1992) included search costs as part of the switching costs construct. In this research, search costs will be treated as a construct of their own since search costs are an important aspect of the economics of electronic markets and a key concept in studying buyers’ behavior. A standard definition of buyers’ search costs in the economic literature is “the cost incurred by the buyer to locate an appropriate seller and purchase a product” (Bakos, 1997, p. 1677). A major impact of electronic markets is that they reduce the costs customers must pay to search for information about products in the market (Bakos, 1991).

 

High search costs are assumed to make it expensive for customers to shop around for the best offer in the market. The implication of this is that it is costly for customers to find alternative brands or suppliers. Therefore, they keep on using the same brand or the same supplier. This implies that high search costs lead to high conative loyalty. However, customers are probably not satisfied with a situation where it is difficult to find alternatives and, thus, having limited flexibility being imposed on them. 

 

Service quality and customer satisfaction are similar concepts and controversy exists regarding the nature of the relationship between them (Oh & Parks, 1997). Some researchers consider service quality to be an outcome of the service encounter while customer satisfaction is a response to service quality. Other scholars suggest that a causal relationship exists but disagree on which comes first. Many researchers agree that customer satisfaction and service quality are distinct constructs that share many attributes (Bitner et al., 1990; Boulding et al., 1993; Carmen, 1990; Parasuraman et al., 1993).

 

Given the overlap in these concepts, it’s not surprising that many investigators (Cadotte et al., 1987; Fornell, 1992; Oliver, 1980; Oliver & Swan, 1989; Spreng et al., 1995) have conducted customer satisfaction research using service quality measures. This confusion within the academic community has led to companies using customer satisfaction and service quality measures interchangeably in assessing service delivery (Devlin, Dong, & Brown, 1993). From an organizational perspective, service quality and customer satisfaction both impact strategic marketing variables such as customer loyalty, trust and commitment, tendency to engage in positive word-of-mouth communications, intention to return, and profitability (Anderson, 1998). In addition, extremely high levels of service quality and customer satisfaction are acknowledged as important components in building loyalty.

 

Even though there is profound debate focusing on the specific nature of customer service effectiveness (Iacobucc. et al., 1995; Gotlieb, Grewal, & Brown, 1994), there is agreement that customer attitudes (e.g., customer satisfaction) are related to important customer behaviors (i.e., loyalty and attrition). This proposition is consistent with a model of attitudes which predicted behavioral intentions (Fishbein & Ajzen, 1977). The past research on attitudes and behavioral intentions, even though relating to different context, further supports the idea that that customer satisfaction leads to behavioral intentions.

Considering the revenue value of repeat customers is more than twice that of new customers, this lack of customer loyalty represents a serious issue in this new economy. The interest in what is referred to as employee identification in this new economy has focused predominantly on issues of retaining employees and maintaining attachment from a variety of increasingly disposable and virtual workers. Part of the problem is that the new economy is characterized by substantially more information sector jobs than qualified people to fill them, resulting in massive turnover for many companies and bidding wars to lure top employees from one organization to another. One recent survey revealed that, among information technology professionals who were not even actively job hunting, “a whopping 62% are willing to consider other opportunities” (Seaberg, 2000, p. 276). Additionally, the New Economy is making use of an increasingly virtual and disposable workforce, which includes telecommuters as well as contingent/contract employees (Tapscott, 2000).

A Study on the Consumer Attitudes and Loyalty Towards their Primary Retail Banking Institutions in Bahrain

Davenport and Pearlson (1998) contended that organizations using more virtual workers forfeit the sense of identification felt by employees in traditional offices. It also may be that whatever attachment does exist is more strongly directed toward one’s team, colleagues, profession, work location, or self. The lack of identification to one’s organization experienced by so many workers in the new economy can be costly to both organizations and employees in terms of turnover, morale, productivity, and so on. It is ironic that “the very technologies that offer employees the freedom to work any time and
anywhere may also lead to a fraying of the ties that bind organization members to each other and to their employer” (Wiesenfeld, Raghuram, & Garud, 1999, p. 777).

 

Although the most relevant theory and research on customer loyalty comes from marketing and advertising scholars (Parasuraman & Grewal, 2000; Schultz & Bailey, 2000), the conclusions for fostering this sort of attachment seem fundamentally communicative (i.e., create and nurture a relationship with the customer). Creating and nurturing customer relationships may be more important in the new economy than ever before, given the nature of this marketplace (Newell, 2000). Although issues such as price continue to play a role in the loyalty equation, competitive pricing alone, even with reward programs (Schultz & Bailey, 2000), does not create long-term customer loyalty.

 

As far as communication strategies go, an overview of the practitioner literature (Newell, 2000) suggested the following for businesses in the new economy:

  1. Provide regular/frequent/constant information (i.e., updates via various channels).
  2. Use one-way, information-based sources (i.e., web sites, e-magazines, kiosks), but do not violate trust by invading privacy.
  3. Offer technologies with high levels of interactivity (i.e., phone, e-mail, and live chat).
  4. Foster community among customers (i.e., newsgroups, online forums, and chatrooms).

Although such guidelines are useful beginning points, they generally say little about either what should be communicated or when these various technologies might be most appropriate. Although we have theories of media use to guide us (Fulk & Steinfield, 1990), it is largely unknown how applicable this work is to online customer service as a means of directly or indirectly fostering customer loyalty. The real focus here should be on the more interactive, two-way communication technologies (e.g., online chat) because these better match most views of customer relations’ management. Utilization of these channels to better listen and respond to customer feedback can be best accomplished with the more interactive communication technologies. Although some channel variety is desirable, having these more interactive tools as available options is critical.

 

As is the case with customer loyalty, economics factor into the identification equation, but that alone has not proven adequate to build employee identification. The high-touch, relationship-focused approach according to O’Connor (2000) is necessary to establish that sense of connection and belonging points directly to the importance of communication. Scott, Corman, and Cheney (1998) were among the most recent to articulate the communication-identification relationship. However, only a handful of studies suggest what this relationship might be amid the sort of online environments characterizing the new economy. For example, Wiesenfeld et al. (1999) found e-mail use was linked to higher organizational identification among more virtual workers. Similarly, Kraut and Attawell (1997) concluded heavier e-mail use was associated with stronger organizational attachment. The more specific communication strategies in the practitioner literature (Kaye & Jordan-Evans, 1999) include the following:

  1. Engage in honest/truthful information-sharing.
  2. Provide recognition and remind employees of their importance.
  3. Network with other intelligent people via various media.
  4. Build supervisor, coworker, and top management relations.
  5. Listen to and understand employees.

 

The findings reviewed previously suggest the value of frequent e-mail use for promoting attachment. Also, emphasis should be placed on the role of two-way interactive technologies (e.g., electronic meeting systems) for fostering a sense of organizational connection and belonging among employees in this new economy. As these technologies develop and are adapted by an increasingly comfortable set of users, it should be quite possible for interactive online channels to help foster strong identification among an organization’s members.

 

The attachment concerns documented here seem especially salient for organizations, their members, and customers in the new economy. Given some of the conceptual similarities between customer loyalty and employee identification as forms of attachment, there may be complementary strategies that can address both areas of concern. As a supplement to economic incentives, communication strategies are part of the solution. Furthermore, the important role of new communication technologies in organizations today suggests that these communication strategies effectively utilize the new media.

Furthermore, the important role of new communication technologies in organizations today suggests that these communication strategies effectively utilize the new media. However, the emphasis should shift from one-way information sharing which seems partly to blame for reducing attachment to focus more on interactive communication between the organization and its customers and employees which holds potential for creating and sustaining both customer loyalty and employee identification in the new economy. The use of technologies for communication may be more challenging than uses strictly for information-sharing, but those organizations that provide such tools and follow through effectively may enhance customer and employee attachment at a time when others continue to lose this battle.  E-Business Today the Internet is used for a wide variety of purposes including email, surfing the World Wide Web, chatting in chat rooms, doing research and retrieving information, games, gossip, and more. The Internet has even begun to evolve beyond personal computers. In fact it is clear that it is becoming commonplace to access the Net through cellular telephones (e.g., Nokia, Ericsson), personal organizers (e.g., Palm Computing, Psion), videogame consoles (e.g., Sega’s Dreamcast or Sony’s Playstation), as well as home appliances (e.g., Electrolux, Whirlpool), vending machines (e.g., Maytag), and automobiles (e.g., GM’s OnStar and Microsoft’s AutoPC) (Venkatraman, 2000). The Internet has become more than a way to keep in touch and share documents. It has emerged as a backbone to business and commerce. For some companies the Internet has meant transitioning and undergoing fundamental organizational change, while for others it means start-up in such an environment from the very beginning (Senge, Scharmer, Jaworski, & Flowers, 2004). The impact of the Internet appears to be widespread with a large number of companies striving toward an e-business (i.e., business conducted over the Internet) and e-commerce (i.e., transactions or sales conducted over the Internet) solutions. With all of the many things people do on the Internet, the largest growing area includes that of business activities online. The Internet’s impact is most obvious in business-to-consumer, or so-called B2C (i.e., companies who sell to consumers online) transactions, however there have been realizations for progress in business-to-business or B2B (i.e., companies that sell to one another online) value chains as well. There are many different predictions on the future growth in these areas. The Gartner group predicts that the global B2B marketplace will be worth more than $7.3 trillion or 7% of the total global economy, by the year 2004 (Memishi, 2000). According to Greenfield and Toole (2000), E-commerce, the sale of products over the Internet, has exploded, with every major retailer linking its future to dotcom sites. It was estimated that there were at least 275,540,000 individuals online as of February 2000. Sales of products online more than doubled in 1999, from $14.9 billion in 1998 to nearly $37 billion. All of the hype about e-business suffered a blow during the so-called dotcom fallout that began around the year 2000. Some dotcoms, or companies who conduct transactions with customers solely online, began to suffer from issues of profitability and their business models were put into question. Researchers and business leaders today have argued that what it takes for success in e-business is fundamentally the same as what it takes for success in traditional business. Many business leaders and researchers do not argue the Internet to be a passing fad or a non issue for companies today, even after some e-businesses have failed. What is at issue is whether any insights have been gained through empirical research that concludes the success factors for e-business as compared to traditional business. Questions remain to be answered regarding whether or not companies must completely re-create themselves for e-business. E-business, short for electronic business refers to business conducted over the Internet (Internet Week, 2000; Pfenning, 2000; Trombly, 2000; Webber, 2002). Ecommerce, short for electronic commerce, refers to transactions of buying and selling done via the Internet (DeFigueiredo, 2000; Greenfield & Toole, 2000; Internet Week, 2000; Lerouge & Picard, 2000; Webber, 2002)

 

Customer Satisfaction

While service quality provides insight into the effectiveness of the service delivery process, customer satisfaction, or the customers’ perception of their experience, is generally the measure of greatest concern to organizations. This concern is driven by the organization’s belief that customer satisfaction leads directly to customer retention. Consequently, researchers continue to explore new models and methods for uncovering meaningful information about customer satisfaction. 

 

While no single definition for customer satisfaction exists, the complexity of the processes involved in a customer’s arriving at judgment of satisfaction or dissatisfaction continues to provide opportunity for study. Most researchers broadly define customer satisfaction and dissatisfaction as the consumer’s judgments regarding a business’s success or failure in meeting expectations. When expectations are met, satisfaction results; unmet expectations lead to dissatisfaction (Oliver, 1980). Research dating back to the early 1960’s (Cardozo, 1965) suggests that customer satisfaction is a consequence of the confirmation or positive disconfirmation of expectations, and that customer dissatisfaction is a by-product of negative disconfirmation of expectations (Day, 1984; Oliver, 1980; Olshavsky & Miller, 1972; Olson & Dover, 1976).

 

Customer satisfaction has been discussed using many different models and taxonomies. Customer expectations, the disconfirmation paradigm, service quality, customer delight, customer loyalty, and defection are among the popular topics in the customer satisfaction literature. The disconfirmation paradigm (Oliver, 1977, 1980) is the most widely used and explored measure of customer satisfaction. Oliver developed the disconfirmation paradigm from adaptation level theory (Helson, 1964). According to Oliver, expectations and disconfirmation are the two cognitive processes involved in customer satisfaction. Positive disconfirmation (performance exceeding expectations) and negative disconfirmation (performance below expectations) produce the affective outcomes called customer satisfaction and dissatisfaction. Each service encounter influences the expectation level for the next visit. Numerous researchers have applied the paradigm to their research (Bearden & Teel, 1983; Swan & Trawick, 1981; Tse & Wilton, 1988).

 

One study suggested that the factors that determine customer satisfaction differ between goods offerings and service encounters (Churchill & Surprnant, 1982). Studies have also shown an interaction between goods and service activity in achieving customer satisfaction (Bearden & Teel, 1983; Cadotte, Woodruff, & Jenkins, 1987; Oliver, 1993). One study concluded that food quality and personal service interact in the dining experience to determine customer satisfaction or dissatisfaction (Oliver, 1993).

 

One researcher introduced the notion that customer satisfaction involves cognitive and affective aspects in pre-purchase, purchase, and post-purchase phases of buying goods and/or receiving services (Westbrook, 1980). While many other conceptualizations exist, there is agreement that satisfaction is a perception or judgment a customer makes following a service encounter in which goods and/or services are exchanged (Yi, 1990). This evaluation of satisfaction is highly heterogeneous. It differs from customer to customer, encounter to encounter, and firm to firm, supporting the need for new insights in customer satisfaction between and across industries. One study emphasized that satisfaction is a process spanning the consumption period and that research of the post-purchase phase is critical to new knowledge development (Tse, Nicosia, & Wilton, 1990).

 

Prior experience, the relationship or history that a customer has with a business, moderates the customer’s service quality judgment and level of satisfaction (Oh & Parks, 1997). The satisfaction judgment a customer makes after each transaction may be a transaction-specific judgment (Bitner, 1990) or a cumulative global judgment based on multiple interactions with the firm or product (Cronin & Taylor, 1994; Ostrom & Iacobucci, 1995).

 

One study identified prior experience as an important aspect of the service encounter, and therefore customer service, because it influences subsequent service encounters and the future relationship between the customer and the service provider. Customers continually update their beliefs and expectations regarding a service, and with each visit they incorporate new information with their existing knowledge about the provider. Each service encounter yields a service quality judgment that results in updated expectations for the next visit (Tax, Brown, & Chandrashekaran, 1998). Two belief-updating processes were adapted to analyze customer dissatisfaction with complaint handling, and in each case the mitigating effect of prior experience (or lack thereof) was demonstrated (Aaker, 1991). Expectations are personal norms based on experience with the product (Woodruff, Cadotte, & Jenkins, 1983). Support for the idea was provided that expectations vary with the consumer and are formed from past experiences as well as word-of-mouth and advertisements about a firm (Zeithaml et al., 1990). Comparisons have been viewed as being made against six types of expectations: ideal (Miller, 1977), desired (Spreng & Olshavsky, 1993; Zeithaml et al., 1993); equitable and ideal outcomes (Tse & Wilton, 1988), values (Westbrook & Reilly, 1983), acceptability (Miller, 1977; Zeithaml et al., 1993), and should-be (Boulding, Kalra, Staelin, & Zeithaml, 1993). These six expectation types are represented in six different customer satisfaction models. Recent work has begun to accept all six models as valid, recognizing that customers hold multiple expectations simultaneously (Spreng, MacKenzie, & Olshavsky, 1996).

 

After the traditional customer satisfaction paradigm was extended to consider the affective role (Westbrook & Oliver, 1981), other researchers introduced five types of satisfaction evoked by feelings:

  1. Contentment (acceptance or tolerance),
  2. Pleasure (an evoked positive experience ending with happiness),
  3. Relief (aversive state is removed),
  4. Novelty (interest or excitement due to expected or unexpected events), and
  5. Surprise (delight or outrage due to far exceeded or unmet expectations) (Oliver & Swan, 1989).

 

Regardless of how customers form expectations or arrive at satisfaction, all customers have expectations (Tse & Wilton, 1988). Expectations change as encounters with a firm change. Meeting or exceeding these evolving expectations determines the financial success of a firm (Fornell, Anderson, & Lehman, 1994).

 

Relationship Quality

Trust and commitment, the constructs of relationship quality, are critical to long-term relationships between customers and service providers (Dwyer, Schorr, & Oh, 1987; Gronroos, 1994; Gummesson, 1994, 1998). Trust and commitment potentially grow or shrink with each service encounter.

 

Trust is the confidence the customer has in the service provider’s reliability and integrity. Trust is a major determinant in the success of a relationship between a customer and a service provider (Wilson, 1995). According to another source reliability over multiple service encounters adds to a customer’s trust in the organization (Ganesan, 1994). Trust has been linked to outcome in complaint handling (Kelley & Davis, 1994). Higher levels of customer satisfaction have predicted higher levels of trust (Smith & Bolton, 1998).

 

Commitment is the customer and service provider’s desire to continue their relationship (Morgan & Hunt, 1994). Higher levels of customer satisfaction have been correlated with higher levels of customer commitment across service encounter outcomes and in particular in regard to outcomes due to complaints lodged by the customer (Kelley & Davis, 1994; Smith, 1998; Smith & Bolton, 1998; Weun & Trocchia, 1996).

 

Awareness, exploration, expansion, commitment, and dissolution have been identified as the stages in the relationship between customer and service provider. Throughout these stages, customers use satisfaction or dissatisfaction to form their intentions to repatronize the business, engage in negative or positive word-of-mouth communications about the business, or exit and not return. Customers return or defect from service businesses for multiple reasons, only one of which is satisfaction level (Dwyer et al., 1987). The latest satisfaction craze has been called a satisfaction trap because between 65% and 85% of satisfied customers will defect (Reichheld, 1996).

 

Researchers have used intentions to repatronize a service to test the validity of service quality and customer satisfaction models. Research has demonstrated that low levels of service quality and low customer satisfaction are related to switching behavior (Bitner, 1990; Boulding et al., 1993; Cronin & Taylor, 1992). Research has also demonstrated that only extreme levels of satisfaction result in loyal customers (Bitner, 1990).

 

According to one study, this means a very positive satisfaction level (nine or ten on a ten-point scale) (Hart, 1988). Although loyalty has been defined by repeated buying behavior, it has been suggested this is unwise because of the convenience factor (Jacoby & Chestnut, 1978). It was agreed that a customer’s loyalty must be measured further by analyzing the customer’s beliefs, affects, and intentions (Oliver, 1999). A proposed framework combining beliefs, affects, and intentions in the creation of customer loyalty framework suggests there are three components of customer loyalty. First, the customer must prefer the product to the competitor’s product. Second, the preference for the product must coincide with an emotional preference for the brand. Third, the end result is the consumer has a higher intention to remain with that brand (Dick & Basu, 1994).

Customer loyalty reflects buying behavior and customer satisfaction is an attitude. It is surmised that customer satisfaction is relevant only to the degree to which it increases customer loyalty. The retention of secure customers is extremely important to the longevity of an organization. Companies that do a better job of maintaining their customers generate better financial results than do companies with poor retention records. Copancino (1997) estimated that a 5% reduction in the number of customers decreased profits by 50% or more. Profits generated by the average customer increase in each of the first four or five years of the vendor-customer relationship. Copancino (1997) attributed this to several factors. The cost of acquiring a new customer is significant in most industries in terms of advertising, promotion, closing, and initial setup for a new customer. Additionally, there are many revenue-related benefits of a loyal customer such as improving the margin of a base purchase, the opportunity to generate incremental sales, reduced operating costs, and referrals. The single act of obtaining positive referrals is among the most powerful factors in increasing profits for a company. Jones and Sasser (1995) identified a company’s best friends and worst enemies as the loyalist/apostle and the defector/terrorist, respectively. The loyalist is a customer completely satisfied with a company’s products and service who continues returning to that company. Loyalists are a company’s bedrock. Customers that are dissatisfied, quite dissatisfied, or neutral are defectors. Defectors may have very unreasonable demands, devour excessive resources, and wreak havoc on employee morale (Jones & Sasser, 1995). For these reasons, savvy service organizations such as Nordstrom, Sewell Village Cadillac Company in Dallas, Texas, and Southwest Airlines, regularly fire customers they cannot properly serve. These companies recognize counterproductive efforts and discontinue them. The most dangerous defectors are terrorists. Terrorists are customers who are quick to share their anger and frustration with others following a perceived bad experience. Unfortunately, terrorists are far more apt to spread the word, are more tenacious, and are more effective at telling their negative experiences than are apostles. Like many apostles, terrorists had bad experiences, but no one listened, no one responded, and no one successfully resolved the problem. There is a lesson to learn from these mistakes. Customers with concerns or complaints need attention and empathy, not to be patronized or worse, to be ignored. Industry may want to reconsider its customer service efforts in order to capture the returns available from this vital complaint information. Listening to the right customers is essential to problem resolution.

 

There are many reasons why satisfied customers defect. Jones and Sasser (1995) conducted extensive research on the relationship between customer satisfaction and customer defection or loyalty. They found that companies ignored or did not accord enough importance to the following aspects of this relationship:

  1. Except in a few rare instances, overall customer satisfaction is the key to securing customer loyalty and generating long-term financial performance.
  2. Even in markets with relatively little competition, providing customers with outstanding value may be the only reliable way to achieve sustained customer satisfaction and loyalty.
  3. Very poor service or products are not the only cause and may not even be the main cause of high dissatisfaction. Often, the company has attracted the wrong customers initially or has an inadequate process for turning around the right customers who may have had a bad experience.
  4. Different satisfaction levels reflect different issues and, therefore, require different actions. 5. Even though the results of customer satisfaction surveys are an important indicator of the health of a business, relying solely on them can prove fatal. Based on the aforementioned information and recommendations, quality improvement (also known as total quality management) is a requirement to remain competitive in the new economy environment.

Of the numerous issues facing management during the past decade, none has had the impact, or caused as much concern, as the quality of products and services. This interest in quality has been due, in part, to foreign competition, the trade deficit, and consumer demand (Omachonu & Ross, 1994). Total Quality Management (TQM) is a means of implementing quality strategies in a start-to-finish process that integrates interrelated functions at all levels of a firm. W. Edwards Deming, the “Father of Quality” popularized quality control in Japan in the early 1950s. Deming defined quality as uniformity and dependability with market-driven costs. He created the “Deming chain reaction” of quality which is based on the premise that as quality improves, costs will decrease due to less rework, fewer mistakes, fewer delays, and fewer snags. Better use of machine-time and material will increase productivity resulting in an increase in market share, which will allow companies to stay in business. In turn, the chain reaction initiates the provision of jobs and more jobs in industry. Deming stressed worker pride and satisfaction rather than the establishment of quantifiable goals. His overall approach focused on improvement of the process in that the system, rather than the worker, is the cause of variation. To aid in the “carrying out” of this chain reaction, Deming created his “universal fourteen points” for management, summarized here: Deming’s Fourteen Points (Deming, 1982, p. 23)

  1. Create consistency of purpose with a plan.
  2. Adopt the new philosophy of quality.
  3. Cease dependence on mass inspection.
  4. End the practice of choosing suppliers based solely on price.
  5. Identify problems and work continuously to improve the system.
  6. Adopt modern methods of training on the job.
  7. Change the focus from production numbers (quantity) to quality.

. Drive out fear.

  1. Break down barriers between departments.
  2. Stop requesting improved productivity without providing methods to achieve it.
  3. Eliminate work standards that prescribe numerical quotas.
  4. Remove barriers to pride of workmanship.
  5. Institute vigorous education and retraining.
  6. Create a structure in top management that will emphasize the preceding thirteen points every day.

 

The rewards of higher quality are positive, substantial, and pervasive. Singhal and Hendricks (1997) found strong statistical evidence that firms that have won quality awards outperform firms in the control sample on operating-income based measures. Over a 10-year period, from six years before to three years after the year of winning the first quality award, the median change in the operating-income for the test sample is 107% higher than that of the control sample. Singhal and Hendricks (1997) also found that there is reasonably strong evidence that firms that have won quality awards do better in terms of sales growth than the control firms. Over the 10-year period, the median change in sales for the sample group was 64% higher than that of the control sample.

 

Customer loyalty has been defined as “a deeply held commitment to rebuy or repatronize a preferred product/service consistently in the future, thereby causing repetitive same-brand or same brand-set purchasing despite situational influences and marketing efforts having the potential to cause switching behavior.” It was suggested that the loyal customer would pursue the product against all odds and at all costs (Oliver, 1999, p.34). Research has shown that loyalty may be determined by one good service encounter with a service organization (Solomon, Surprenant, Czepiel, & Gutman, 1985). In addition, it was suggested that loyalty can occur at any stage of a business relationship (Oliver, 1999). Disappointment and Defection

 

Disappointment is defined as a deeper affective state or a more extreme dissatisfaction felt when service goes differently than expected (Zeelenberg & Pieters, 1999). It is suggested that a disappointment model detailing the emotion of disappointment (one of 32 emotions identified by Frijda, Kuipers, & Schure, 1989) is important to services marketing researchers because disappointment and regret are related to decision making (Inman, Dyer, & Jia, 1997). Research into the relationship between disappointment and the behavioral intentions of complaining, engaging in negative word-of-mouth communications, and defecting showed more disappointed customers complaining and engaging in negative word-of-mouth but not more defecting. This confirmed past studies (Inman et. al., 1997). Defection is a falling away from loyalty or habit in buying behavior (Heskett et al., 1997). It is the final behavioral response that dissolves the relationship between customer and service provider. Defection has been shown to lead to reduced market share, lower profitability, and increased cost (Reichheld & Sasser, 1990; Rust & Zahorik, 1993; Rust, Zahorik, & Keiningham, 1995). Studies have linked switching behavior to service failures in retail stores (Kelley et al., 1993) and to dissatisfaction in the insurance industry (Crosby & Stephens, 1987). Research focused on quality, satisfaction, or service encounters has yielded only partial information about defecting behavior.

 

Extremely satisfied customers usually do not defect (Jones & Sasser, 1995). However, even customers who communicate their satisfaction do defect (Liljander, Roos, & Strandvik, 1998; Roos, 1999). Some natural defection occurs due to factors beyond the control of the provider. Researchers refer to defection that is not natural and that could possibly be avoided or revoked by applying improved business behaviors as nonattritive defection. Both satisfied and dissatisfied customers defect attritively and nonattritively.

 

Three categories for nonattritive defection were presented in one study. First, price may impact defection by being too high, by increasing, by being unfair, or by being deceptive. Second, convenience may influence defection through location, hours of operation, wait time, or availability of appointments. Finally, core service failures have a direct effect on customer defection (Keaveney, 1995). Mistakes, billing errors, and service catastrophes were identified as the subcategories of core failures.

 

In one study, 20% of respondents who switched service providers mentioned inconvenience; 44% of respondents said their defection was related to core service failures, and 34% defected due to personal interactions with the service provider (Keaveney, 1995). In another study defection was categorized as revocable and irrevocable by applying the attributes of relationship length, switching determinants (push, sway, and pull), emotions, voice, and length of process. Irrevocable defections were characterized by medium relationship length, being pushed from the provider by product and service failures, the experience of strong emotions, having complained often with no response, and having taken between two and four months to make the decision to defect (Roos, 1999). It has also been found that long-time customers want no failures (Smith & Bolton, 1998). Several studies have indicated that dissatisfied customers have a higher likelihood of defecting than satisfied customers (Loveman, 1998; Rust & Zahorik, 1993; Solnick & Hemenway, 1992).

 

Service Profit Chain

In recent years, many companies have invested considerable resources into programs for measuring and increasing employee satisfaction (Heskett et al., 1994; Heskett, Sasser, & Schlesinger, 1997). The assumption underlying these activities is that increased employee satisfaction ultimately leads to increased customer satisfaction. This supposed link between employee satisfaction and customer satisfaction is a central element of a conceptual framework referred to as the “service profit chain” (Heskett et al., 1994; Heskett, Sasser, & Schlesinger, 1997) or “value profit chain” (Heskett, Sasser, Schlesinger, 2003). This model suggests a causal chain linking employee satisfaction to financial performance through the mediating constructs of employee loyalty, customer satisfaction and customer loyalty. However, the suggested link between employee satisfaction and customer satisfaction is based on somewhat limited and anecdotal evidence.

 

As stated above, much of the research linking the areas of employee satisfaction, customer satisfaction, and customer loyalty has been summarized in The Service Profit Chain and The Value Profit Chain (Heskett, Sasser, & Schlesinger, 1997). These two books summarize the interrelationship between the corporate policies, employee satisfaction, value creation, customer loyalty, and profitability. At the core of the research is the link between employee satisfaction and customer satisfaction. The authors describe the need for a “seamless integration of all components in the service-profit chain” (Heskett, Sasser, Schlesinger, 2003) They suggest that organizations must guide and support employees since the employees play a central role in ensuring customer satisfaction and the benefits it creates (Heskett, Sasser, & Schlesinger, 2003).

 

Although customer satisfaction (Fornell et al., 1996; Oliver, 1996) and employee satisfaction (Behrman & Perreault, 1982, 1984; Churchill, Ford, & Hartley, 1985) have been widely studied constructs within a number of disciplines, the research relating the two constructs to each other is sparse and contradictory. Despite the plausibility of the link between employee and customer satisfaction, systematic theory-driven research based on sound empirical analysis in this area is scarce. More importantly, existing empirical research on this link is subject to numerous limitations. First, several studies have collected data exclusively from employees (Schlesinger & Zornitsky, 1991), rather than using data from both sides of the dyad. The problem with such an approach is that highly satisfied employees might rate customer satisfaction higher than dissatisfied employees based on their generally positive perception of the company (common method bias). Second, data analysis has typically been based on bivariate approaches (Loveman, 1998; Schlesinger & Zornitsky, 1991; Tornow & Wiley, 1991), rather than exploring the relationship between employee and customer satisfaction within a more comprehensive causal network. Third, existing research has typically been exploratory (Schlesinger & Zornitsky, 1991; Loveman, 1998; Tornow & Wiley, 1991) and neglected the theoretical justification of the link between employee and customer satisfaction.

 

More recently, research testing the service chain paradigm has shown mixed results (Abbott, 2003; Gelade & Young, 2005; Silvestro, 2002; Silvestro & Cross, 2000; Spinelli & Canavos, 2000; Yoon, Seo, & Yoon, 2004). A study of six metropolitan, full-service hotels found a relationship between employee and guest satisfaction. Results indicated that for employees, monetary factors can be dissatisfiers, but employees responded favorably to appreciation, participative decision making, and team work. Further, results showed that hotel guests responded favorably to courteous staff members who were fast and competent (Spinelli & Canavos, 2000). On the other hand, in exploratory research on the UK business-to-business (B2B) sector on the relationship between employee satisfaction and profits, results showed that morale can be very low, yet employees work hard to keep customers loyal and to maximize company profits (Abbott, 2003).

 

In one study of a major UK grocery retailer, results showed an inverse relationship between employee satisfaction and measures of productivity, efficiency, and profitability. In other words, contrary to prediction, the most profitable stores were those in which employees were least satisfied. Further, employee loyalty, as measured by length of service, also appeared to be inversely related to productivity and profitability. The researchers argued that pressure to obtain maximum store efficiency may be causing dysfunctional managerial behavior at the store level (Silvestro, 2000). In a related study, relationships were found among profit, customer loyalty, customer satisfaction, service value, internal service quality, output quality, and productivity. However, no support was found for the hypothesis that these were caused by employee satisfaction and loyalty. Further, the researchers found a strong association between employee dissatisfaction and store profitability (Silvestro & Cross, 2000).

 

In a study of relationships among organizational climate, employee attitudes, customer satisfaction, and sales performance in the retail-banking sector, it was found that customer satisfaction was a mediator between employee attitudes and sales performance in a large sample (55,200 employees, with an overall response rate of 67%) of bank branches in multiple organizations. However, although mediation effects of borderline significance were found when the sample size is large, the effects were too small to be of real importance. The results suggest that other accounts of the service profit chain model may be better at explaining the relationship between employee attitudes and business performance (Gelade & Young, 2005).

 

Such a model was suggested by other recent studies (Koys, 2001; Yoon, Seo, & Yoon, 2004). In a cross-lagged regression study that hypothesized that employee satisfaction, organizational citizenship behavior, and employee turnover would affect profitability and customer satisfaction, Koys (2001) collected data from the units of a regional restaurant chain using employee surveys, manager surveys, customer surveys, and organizational records. Results showed that employee attitudes and behaviors at the first measurement point were related to organizational effectiveness at a later measurement point. Another study (Yoon, et al., 2004) looked at the effects of contact employee supports on employee responses and customer service evaluation. This study combined perceptions from customers and the employees with whom they had contact. Sources of support included organization support, supervisory support, and customer’s participation. These factors were hypothesized to affect the attitudes and behaviors of employees, and thereby to affect customer’s perceptions of service quality provided by employees. Results showed that three sources of support for employees had significant impacts on job satisfaction and employee service quality, and that perceived organizational support and customer participation affected service effort. Results also showed that employee service effort and job satisfaction strongly affected customers’ perceptions of employee service quality.

 

Finally, a four-point theoretical formulation to explain the mirror relationship between employee and customer satisfaction has been proposed (Ellis, Gudergan, & Johnson, 2001) that includes level of focus on customer satisfaction and level of risk-aversion in staff members. The model proposes that the greater the customer satisfaction focus in the contract between the organization and service-providing staff members, the stronger will be the satisfaction mirror. Second, the model proposes that in the presence of a customer-satisfaction-focused [i.e., outcome– based] contract between staff and organization and more risk-averse frontline service staff members, the weaker will be the satisfaction mirror. Third, the model proposes that the greater the customer service focus of the contract between the organization and risk-averse staff, the greater will be the satisfaction mirror. Finally, with a behavior-based, customer-service-focused contract between organization and staff, as the number of outcomes the organization desires from the staff member increases, the satisfaction mirror will weaken.

 

Hypotheses

Given that the results of past research and the development of the “service-profit chain” model (Heskett & Sasser, 1997) a pattern emerges that suggests a relationship between the variables being considered in this study. The evidence suggests a relationship that runs from employee perceptions (job satisfaction) through employee behavior (customer service quality) to customer perceptions (customer satisfaction).

 

The question for the organization participating in this study was whether the relationships that have been shown to exist elsewhere between employee satisfaction, customer service quality, and customer satisfaction could be measured internally. The first question was whether an employees’ level of satisfaction had a measurable association with the quality of the customer service they provide.

Hypothesis 1. There will be a positive and significant correlation between measures of employee satisfaction and measures of customer service quality as evaluated by a mystery shopper survey.

 

Since employee satisfaction has been shown to correlate with customer satisfaction directly (regardless of customer service quality), the second question was whether employee satisfaction had a measurable association with customer satisfaction.

 

Hypothesis 2. There will be a positive and significant correlation between measures of employee satisfaction and measures of customer satisfaction.

 

Finally, while there is limited and contradictory empirical evidence in this regard, the relationship that exists between customer service quality and customer satisfaction was considered.

 

Hypothesis 3. There will be a positive and significant correlation between measures of customer service quality as measured by the mystery shop survey and measures of customer satisfaction.

 

 

 

CHAPTER 2 METHODOLOGY

 

The purpose of the present chapter is to describe the methods and procedures used to test the following null hypotheses:

 

H10: There is no relationship between measures of employee satisfaction and measures of customer service quality as evaluated by a mystery shopper.

 

H20: There is no relationship between measures of employee satisfaction and measures of customer satisfaction.

 

H30: There is no relationship between measures of customer service quality and measures of customer satisfaction. The chapter is organized as follows. The first section describes the setting of the study.

 

This is followed by sections describing the sample, instruments used, data collection procedures, and data analysis procedures.

 

Setting

Data were collected at a midsize retail bank operating in Texas and California. This particular bank had identified customer service as their key market differentiator and had implemented multiple programs in support of this strategic goal over the last several years. Training had been conducted at all levels of the organization to emphasize service quality and the marketing department The purpose of the present chapter is to describe has positioned two different metrics to collect performance data.

For this study, both questionnaire survey and telephonic interview methods were employed. This study focused  on 24 retail banks and 6 Islamic retail banks registered with the Central Bank of Bahrain [CBB]. Customers included in the study were randomly selected from the above banks. A pilot study on 6 customers was prior to actual survey in order to ensure all the  participants understood the survey forms and there was no ambiguity in the questions asked. Banking is one of the main sectors in Bahrain. One of the key reasons to choose this topic for the dissertation is the increase in competition and the challenge to retain the loyalty of banking customers within the constraints thrown in by the current economic recession.  There is also a professional angle to choosing this topic as the findings of this dissertation will be of great interest to the banking clients of researcher’s organisation who are struggling to understand customers and prevent customer attrition

 

Sample

Telephone interviews using structured questionnaire was used as the primary data collection method. Since the researcher’s company handles sales outsourcing for banks in Bahrain the customers for telephone interviews were randomly selected from the company’s in-house customer database, other personal contacts and acquaintances.The sample size being considered for the dissertation was 150 considering the available time frame. Attention was paid to make the sample representative of the population in Bahrain like:

 

In addition, attempt was made to take the feedback from senior managers of the banks regarding the steps they have been taking to retain the loyalty of customers. Data collected was quantitatively analyzed using SPSS software.  Summary of the findings are presented in tables, charts and graphs in the next chapter.

 

For the customer service quality (mystery shop) survey, a total of 150 “mystery shops” were conducted across the 30 banking centers involved in the research. The target of each assessment, the banking centers, received an average of four mystery shops during the period under investigation.

 

Instruments

Three separate tools were utilized in this study. The interviews were recorded and transcribed while the questionnaire data was analyzed through SPSS software.

 

Customer service quality was measured via the questionnaire form (Appendix A) implemented with the help of six data collectors who were short listed and briefed about the study to assist in data collection process. This study objectively assessed the quality of service delivery at each banking center utilizing professionally trained “shoppers.” Each banking center was visited and phoned four times and judged on the quality of the service they provided. The questionnaire were worded such that individual items could be scored easily from the list of options given.

 

Customer Satisfaction Survey

In addition to the mystery questionnaire surveys, semi structured interviews were also conducted with the management of the banks. This was a script-based phone survey conducted with random participants from the management of the banks of each banking center. Also developed with input from an external vendor, this proprietary survey assessed the level of satisfaction the customer had in their recent (within 3 months) interactions with their primary banking center. The tool contained 30 items and covered areas including: satisfaction with interpersonal interactions, speed, accuracy, and problem resolution. In addition, it covered intent to refer others and maintain a relationship with the bank. The survey utilized multiple question formats including “yes/no” and two different Likert-type scales for various questions.

 

Procedures

 

Data Collection Procedures

Employee satisfaction data were collected online using the “Zoomerang” online survey system. This is an online data collection tool which allows for anonymous distribution of surveys via email and links to a web-based survey system. Employees received an email containing informed consent information and instructing them on where to go and how to complete the survey. Participants could opt-out of the survey by simply ignoring the email. Request was made for the participants to include their banking center number and/or location so that data analysis could be accomplished at the business unit. Optional demographics were also collected but were not included in this study owing to low response rates and the aggregation of data to the banking center level. Survey data were downloaded from the “Zoomerang” system and imported into SPSS (Statistical Package for the Social Sciences) for further analysis.

 

Data Aggregation

In order to effectively utilize the sources of data available to the researcher, a common level of analysis needed to be determined. This presented problems for the employee and customer satisfaction data as they represent personal attributes that can only be understood in terms of an individual’s perceptions of the characteristic that comprise satisfaction (Schneider, 1990). The aggregation of individual perceptions is reasonable if the grouping makes theoretical sense given the researcher’s objective (Schneider, 1990).

 

In the current study, each data set was collected from a separate population with the only commonality being the banking center for which the employee worked, the customer shopped, or the mystery shopper visited. Aggregating attitudes is based on a belief in the existence of local influences on satisfaction such as manager behavior, coworkers, environment and clientele (Ostroff, 1993b) that create work units with similar levels of satisfaction. It has been observed (Ostroff, 1992, 1993a) that job satisfaction, and other attitudinal variables, may be influenced as much by situational variables as by individual differences.

 

Finally, statistical techniques for analyzing multivariate data assume that requirements for specific sample sizes are met. Given the small size of individual business units as well as informed consent requirements, sample size was variable as well as uncontrollable. Accordingly, it made both conceptual and practical sense to aggregate employee satisfaction perceptions gathered in the current study at the business unit level.

“The best organizational level for analysis is the one in which within-unit differences are as low as possible” (Allen & Wilburn, 2002, p. 108). To ensure that the aggregation of employee satisfaction data was appropriate, preliminary analysis was conducted on the raw job satisfaction data to determine if the within-unit differences were lower than the total sample.

 

Data Analysis Procedures

Data from the two independent surveys were analyzed using a combination of statistical methods. Given aggregation issues discussed above, and the lack of empirical soundness of the customer satisfaction survey, several analyses were conducted prior to hypothesis testing.

 

The employee satisfaction survey was analyzed to determine whether aggregation to the banking center level was appropriate. Analysis of variance (ANOVA) was used to determine if the total amount of variation within each of the business units was less than the variability within the total sample. Within groups sum of squares were compared to ensure that they were smaller than the total sum of squares for each employee satisfaction variable. This was done first because if business units are not homogeneous, then aggregation should not be attempted. The customer service quality data was analyzed to determine whether aggregation of the four independent surveys into a single banking center average was appropriate. Cronbach’s Alpha was calculated and Analysis of variance (ANOVA) was used to determine if the total amount of variation within the surveys of a given banking center was less than the variability for the total sample. Within groups sum of squares were compared to ensure that they were smaller than the total sum of squares for each survey. This was done first because a lack of homogeneity within each set of surveys suggests inter-rater reliability issues that would undermine the validity of the aggregated customer service quality data.

 

The customer satisfaction survey was analyzed to determine if subscales existed. Inspection of the 18 items included on the survey suggested that it may have been tapping two different constructs. Factor analysis was conducted utilizing a maximum likelihood approach to determine if subscales were viable for inclusion in further analysis. If factor analysis did not establish the existence of logical subscales, a single customer satisfaction scale would be utilized for further analysis.

 

Following these initial analyses, the resulting databases were aggregated into a single matrix with the banking center as the common unit of analysis. Descriptive and frequency statistics were run and correlation matrixes were utilized to summarize the relationships between customer satisfaction, customer service quality, and employee satisfaction, along with employee satisfaction subscales.

 

Finally, multiple regression analyses were utilized to explore the relationships that exist between the three primary variables (employee satisfaction, customer service quality, and customer satisfaction) and the nine employee satisfaction subscales involved. Each scale was regressed alone and in combination with the other variables to obtain an overall picture of the associations between these measures.

 

 

 

CHAPTER 3 RESULTS

 

Preliminary Data Analysis

Prior to conducting analyses relevant to the hypotheses put forth in this study, the researcher needed to examine two of the measurement tools in greater detail. In particular, the employee satisfaction survey was considered in light of the aggregation issue. The question of whether it made empirical sense to aggregate the employee satisfaction data to the banking center level needed to be answered. In addition, exploration of the customer satisfaction survey was needed to determine if subscales exist that could be included in the analysis or whether a single score should be utilized. The question of how best to construct the single customer satisfaction score would need to be pursued if subscales were not possible.

 

Employee Satisfaction

While aggregation of the employee satisfaction survey data to the banking center level was necessary from a practical standpoint, given the focus of the other measures involved in the study, the employee level data was first analyzed to determine if problems existed in the raw data that would prevent aggregation. Descriptive statistics were run to provide insight into the appropriateness of items on the employee satisfaction survey.

 

Analysis of the raw employee satisfaction data showed no significant problems. Skewness (-2.14) was noted on item 7, but subscales showed no indication of being significantly skewed (skewness statitistic >+/-2) (Tabachnick & Fidell, 2001).

In order to ensure that within-group variance for each subscale was lower than the total variance, an ANOVA was run. Table 1 shows that this is the case.

 

Table 1: Employee Satisfaction Survey ANOVA

Subscale

 

SS

DF

MS

F

Sig

Pay & Remuneration

Between Groups

4064.73

128

31.75

1.35

.01

 

Within Groups

12491.76

532

23.48

 

 

 

Total

16556.50

660

 

 

 

 

Promotion Opportunity

Between Groups

3439.35

128

26.87

1.43

.00

 

Within Groups

9971.21

532

18.74

 

 

 

Total

13410.56

660

 

 

 

 

Management Behavior

Between Groups

4552.36

128

35.56

1.88

.00

 

Within Groups

10051.14

532

18.89

 

 

 

Total

14603.50

660

 

 

 

 

Fringe Benefits

Between Groups

2752.66

128

21.50

1.28

.03

 

Within groups

8886.78

532

16.70

 

 

 

Total

11639.44

660

 

 

 

 

Management Behavior

Between Groups

4552.36

128

35.56

1.88

.00

 

Within Groups

10051.14

128

35.56

1.88

.00

 

Total

14603.50

660

 

 

 

 

Total Employee Satisfaction

Between Groups

119990.80

128

937.42

1.51

           .00

 

Within Groups

329388.24

532

619.15

 

 

 

Total

449379.05

660

 

 

 

 

 

The within groups sums of squares for each subscale was lower than the total sum of squares. This created a heterogeneity index of less than 1 for each scale analyzed (Tabachnick & Fidell, 2001). Based on this information, the employee satisfaction survey data were aggregated by banking center in order to make further analysis and comparisons to the other data sets possible. All results hereafter refer to the aggregated data.

 

Customer Service Quality Customer service quality was measured using data collected through a questionnaire forms. Each banking center was assessed. Descriptive statistics of this data showed some issues with skewness (Mystery Shop 2 and 4), but the aggregated score was not significantly skewed (+/- 2.0) (Tabachnick & Fidell, 2001) (Table 2).

Table 2: Customer Service Quality Descriptive Statistics

 

N statistic

Min statistic

Max statistic

M statistic

Std. Statistic

Skewness statistic

SE

Kurtosis Statistic

SE

Bank 1

129

29

100

86.62

15.56

-1.78

.21

3.01

.42

Bank 2

129

18

100

87.29

16.78

-2.29

.21

5.23

.42

Bank 3

129

43

100

89.51

12.54

-1.52

.21

1.97

.42

Bank 4

129

32

100

91.74

13.32

-2.48

.21

6.25

.42

Total banks score

129

226

400

355.17

32.01

-1.42

.21

2.88

.42

 

In order to ensure the appropriateness of aggregating this data into a single average score per banking center, Cronbach’s alpha was calculated. This analysis showed the there is a lack of consistency (inter-rater reliability) between the four independent assessments (Cronbach alpha = .22). An ANOVA was run to determine if the variability between the scores for a given banking center was lower than the total variance. Table 3 shows that this is the case.

Table 3: Customer Service Quality ANOVA

 

 

Sum of

Squares

df

Mean

Square

F

Sig

Between Group

 

32804.10

128

                128 256.28

 

 

Within Group

Between Items

2089.48

3

696.49

3.46

.016

 

 

              Residual(a)

77136.18

384

200.87

 

 

 

Total

79225.67

387

204.71

 

 

Total

 

112029.78

515

217.53

 

 

 

 

The within groups (between items) sums of squares was lower than the total sum of squares creating a heterogeneity index of less than 1 for each scale analyzed (Tabachnick & Fidell, 2001). This suggests that while inter-rater reliability may be poor, there is greater homogeneity in the aggregated banking center data than in the total data set. Based on this information, and the practical need to aggregate the data, the customer service quality survey data were aggregated to produce a single average score per banking center. It should be noted that, while this aggregation was necessary to make further analysis and comparisons to the other data sets possible, it does suggest limitations to any associations made in subsequent analyses. Customer Satisfaction

 

Preliminary examination of the customer satisfaction survey included in this study suggested that it may be composed of multiple subscales which would be of interest for further analysis. Initially, descriptive statistics from the raw data were considered to help identify possible issues within the data set. The outcome of this analysis is shown in Table 5 below.

 

This analysis showed that many of the individual items on the survey were highly skewed (skewness statistic >+/- 2.0) (Tabachnick & Fidell, 2001). In particular, the dichotomous items (1, 7-10, & 14) on the scale had skewness statistics ranging from 1.7 to -9.6. Likert scale items (2-6, 13, & 15-18) faired better but still suffered from considerable skewness, ranging from -.75 to -2.71.

Table 4: Customer Satisfaction Descriptive Statistics

 

N statistic

Min statistic

Max statistic

M statistic

Std. Statistic

Skewness statistic

SE

Kurtosis Statistic

SE

main reason for choosing primary bank

3608

1

2

1.83

.37

-1.73

.04

1.01

.08

primary reason to continue with the bank

3820

1

5

4.66

.75

-2.66

.04

7.53

.07

of rising concerns

3810

1

5

4.39

1.03

-1.78

.04

2.45

.07

Exceptional

service

3832

1

5

4.63

.78

-2.54

.04

6.89

.07

 

 

 

 

 

 

 

 

 

 

 

Based on the descriptive analysis, and given that further planned analyses (i.e., multiple regression) could not accommodate the inclusion of both Likert and dichotomous data, it was determined that the dichotomous items would be removed from the survey. This left nine survey questions for further analysis. Question five, “overall satisfaction,” was also excluded from the factor analysis as it represents a single measure of the overall construct the researcher is attempting to measure. Results of the scales created from the factor analysis will be compared with question five to determine their validity.

 

To determine if subscales exist, factor analysis was conducted on the eight variables remaining on the customer satisfaction survey (Table 5). A maximum likelihood extraction method with oblique rotation was used to extract factors with eigen values of at least one. Two factors were subsequently extracted, accounting for 66% of the total variance.

 

Table 5: Customer Satisfaction Survey Factor Analysis – Total Variance Explained

Factor

 

Initial Eigenvalues

Extraction Sums of Squared

Loadings

Rotation

Sums of

Squared

Loadings

 

Total

% of

Variance

Cumulative

%

Total

% of

Variance

Cumulative

%

Total

1

3.996

49.952  

49.952

3.584

44.795

44.795

3.416

2

1.308

16.350

66.302

.926

11.573

56.368

2.171

 

The low proportion of variance accounted for in the data suggested potential problems in the viability of individual subscales given that a large amount of variance was left unexplained. Utilizing cutoff scores of =.5 for inclusion in a scale and =.3 for exclusion from other scales, the following factors were obtained (Table 6).

 

Customer Satisfaction Factor Analysis – Rotated Factor Matrix Factor Questions: 1 2 1. Employee showed interest .828 2. Hours of operation 3. Employee was knowledgeable .771 4. Employee gave options .777 5. Employee gave good service .833 6. Plan to return for business checking .585 .568 7. Plan to return for business loan .773 8. Plan to return for business investment .808

 

Visual inspection of the questions contained in factor two suggested a focus on customer loyalty (i.e. intent to repatronize) rather than customer satisfaction. This, along with the fact that it contained only two items after the crossloaded question was removed, led to the decision to use only the first factor in further analysis. To ensure that there was significantly correlated with question fifteen (overall satisfaction), a person product moment correlation was calculated. Results showed a significant correlation, r = .76 (N = 124) with a two-tailed significance level of .01.

 

Considering the results of previous analyses, it was determined that the best customer satisfaction scale would utilize the four Likert-type items discussed above. This would provide the most robust measure of customer satisfaction combining the variability of four separate questions while maintaining a strong correlation to the overall satisfaction question embedded in the survey. The resulting Total Customer Satisfaction score was used for all comparative analyses.

 

Primary Analysis

With preliminary analyses completed, and the data aggregated to the banking center level, analyses were conducted to determine the relationships between the variables in regard to the stated hypotheses. Descriptive statistics for the aggregated data set were reviewed to determine whether issues existed within the data. No problems associated with skewness or kurtosis were noted, with all skewness scores falling between 0 and .84 and kurtosis scores falling between 0 and .74 (Tabachnick & Fidell, 2001).

 

Given that a single outlier is capable of considerably changing the value of a correlation, outliers were removed using standardized values. Z-scores greater than three in the overall employee satisfaction, customer service quality, or customer satisfaction scales were removed (5 total subjects) and the analyses were run based on the new dataset (Barnett & Lewis, 1984).

 

Pearson product moment correlations were calculated for the aggregated data set including all employee satisfaction subscales, customer service quality, and customer satisfaction. Results of a Pearson product moment correlation coefficient with one-tailed significance indicated moderate but significant positive relationships between most subscales on the employee satisfaction survey. Significant correlations (=.05 level) ranged from r = .20 (operating condition and fringe benefits) to r = .72 (contingent rewards and promotion opportunities) (N = 124). Several subscales did not correlate with each other, but all nine subscales showed significant positive correlation to the overall employee satisfaction scale. Correlations ranged from r = .44 (fringe benefits) to r = .85 (contingent rewards) and all were significant at the .01 level (N = 124).

 

Comparison of the aggregated customer service quality (mystery shop) and customer satisfaction data was less noteworthy. The customer satisfaction scale showed no statistically significant associations with any of the employee satisfaction subscales. For the customer satisfaction scale, correlation coefficients ranged from r = 0 to r = .11, suggesting that the customer satisfaction scale has no relationship to the employee satisfaction or customer service quality scales.

 

The customer service quality scale was negatively correlated with the “operating conditions” subscale of the employee satisfaction survey; r = -.18, significant at the .05 level (N=124). This suggests a slight negative relationship between customer service quality and the employee satisfaction questions regarding operating conditions, but the size of the relationship is small and may the direction seems counter intuitive. Despite the existence of only a single correlation between the customer service quality scale and the “operating conditions” subscale of the employee satisfaction survey, multiple regression analysis was used to assess the predictive power of the multiple employee satisfaction subscales involved in the study. The employee satisfaction survey subscales were looked at as a group, and in combination with the customer service quality data.

 

Results

The first hypothesis was that employee satisfaction would be positively associated with customer service quality. In order to analyze this relationship, the employee satisfaction subscales were entered as independent variables to determine their predictive power in relation to the customer service quality scale. Results of the multiple regression analysis were significant with only 9% of the total variance explained (r2 = .09, F = 1.01). Collinearity diagnostics suggest that multicollinearity was an issue in this analysis. The condition index summarizes the findings with an index over fifteen indicating a possible multicollinearity problem and a condition index over thirty suggesting a serious multicollinearity problem (Tabachnick & Fidell, 2001). Condition index scores for the employee satisfaction subscales and customer service quality regression ranged from 18.3 to 66.4.

 

The second hypothesis was that employee satisfaction would be positively associated with customer satisfaction. In order to analyze this relationship, the employee satisfaction subscales were entered as independent variables to determine their predictive power in relation to the customer satisfaction scale. Results of the multiple regression analysis were non-significant with only 7% of the total variance explained (r2 = .07, F = .89). Collinearity diagnostics suggest that multicollineartiy was an issue in this analysis as well, with condition index scores ranged from 18.3 to 66.4.

 

The third hypothesis was that there would be a positive relationship between customer service quality and customer satisfaction. This relationship could not be assessed utilizing multiple regression analysis, since there were only two scales involved. Based on the previously referenced correlation matrix, customer service quality and customer satisfaction were not significant correlated.

 

 

 

CHAPTER 4 DISCUSSION

 

The current study found that, for the midsized retail banks in Bahrain, employee satisfaction was not significantly correlated with customer service quality or customer satisfaction. Minimal support for a link between customer service quality and the “operating conditions” subscale of the employee satisfaction survey was found (r = -.18, p = .05), but the direction is counter intuitive and may be attributable to issues with the data rather a true relationship (Cronbach’s alpha = .22).

 

Implications for Theory

These findings, while contrary to the popular notion of the relationship between these variables, are consistent with a small but growing body of research that has shown mixed results regarding the service-profit chain concept (Abbott, 2003; Gelade & Young, 2005; Silvestro, 2002; Silvestro & Cross, 2000; Spinelli & Canavos, 2000; Yoon, Seo, & Yoon, 2004). In particular, a UK based study suggested that employee satisfaction can be very low, but employees will continue to work hard to keep customers satisfied and to maximize company profit (Abbott, 2003). While not considered in the current study, variables such as work ethic or pride-in-work could play a significant role in the relationship between employee satisfaction and customer service quality. Although an employee may not be satisfied with his/her job, they may continue to provide a high level of customer service quality because of their personal beliefs about work.

 

In addition to the effect of work ethic and pride, it is also possible that the service context and relationships that form between the service provider and the customer play a role in the employee satisfaction-service quality relationship. The retail banking environment is one in which customer relationships with one’s banker can be stronger than one’s relationship with the bank. The researcher has heard many anecdotal accounts of customers closing accounts to follow a banker to another bank rather than maintain their accounts with the bank. This suggests that dissatisfied employees may continue to strive for high levels of service quality in hopes of forming personal loyalty with their customers. When combined with the issues of work ethic and pride, this combination could explain the lack of correlation between these two variables.

 

The assumed link between employee satisfaction and customer satisfaction has also been undermined by recent research. Silvestro and Cross (2000) found no relationship between employee satisfaction and customer satisfaction. While their study involved only a small number of grocery stores, it did question one of the fundamental components of the service-profit chain. In the highly competitive banking industry, where significant pressure is placed on managers to improve their customer satisfaction scores, it is possible that the stress associated with this drive undermines employee satisfaction. Attempts to increase customer satisfaction by constantly focusing on it may create a working environment that is less satisfying. While the inverse relationship suggested by this breakdown was not found in the current study, it is an issue that may confuse the analysis.

 

In addition to the issue of employee stress, the connection between employee satisfaction and customer satisfaction is complicated by the nature of the employee- customer interactions. Although the retail banking industry is often thought of as a commodity, there are still aspects of price, convenience, and product availability which are outside of the employees’ control but which impact customer satisfaction. In addition, the bank involved in this study utilizes multiple customer loyalty programs (i.e., lower rates for multiple accounts and airline points for maintained checking balance) that may impact a customer satisfaction independent of employee satisfaction. These variables, not assessed by the current study, could play a role in mediating the relationship between employee satisfaction and customer satisfaction.

 

The link between customer service quality and customer satisfaction seems even more direct than the relationship posed by the other hypotheses, and therefore harder to understand when data fail to support it. It seems obvious that good service would lead to high levels of customer satisfaction, but it appears that in fact the relationship is more complex and not a direct one. The limited research that exists does suggest that service quality stems as much from behind-the-scenes processes (e.g., operations, processing, and supply chain management) as it does from actual interactions between employees and customers (Iacobucci et al., 1995). In contrast, findings indicate that customer satisfaction stems from customers’ experiences in service situations (e.g., interactions with service providers) (Iacobucci et al., 1995). The interplay of the behind-the-scenes interactions with the direct delivery of services to the customer makes this a difficult area to quantify. In addition, issues of price, convenience, and product availability, discussed above, are likely contributors to this relationship.

 

Limitations

While the lack of correlation between the three variables may be an accurate representation of the data, there are some problems which impacted the analysis and limit the usefulness of the study. First, two of the three measured variables in the study were designed idiosyncratically, rather than conceptually or empirically. That is, the participating organization made available internal metrics for use in this analysis (customer service quality and customer satisfaction). These metrics were designed by marketing firms with significant involvement from senior leaders of the organization. This committee approach to survey design, combined with annual changes in the items based on the shifting desires and needs of the organization, have led to survey tools that are poorly constructed.

 

The customer satisfaction survey suffered from a lack of conceptual or empirical development. The combination of scale types (dichotomous and Likert-type) precluded the use of some items in the analysis and factor analysis produced only one usable scale from the remaining items. A much better approach would have been to develop measures that were more consistent with a specific model of customer satisfaction rather than utilizing a method of survey construction and development that lacked theoretical and methodological rigor. In addition, the annual changes to the survey prevent the collection of longitudinal data and prevent the development of reliability and validity estimates for the survey.

 

The measurement of customer satisfaction has a set of unique problems that makes its measurement more problematic than that of employee satisfaction. Peterson and Wilson (1992) showed that the nature of most self-report customer satisfaction measures cause them to have skewed distributions. They noted that this skew will lead to an underestimation of the “true” relationship between customer satisfaction measures and measures of other variables. In the current study, the skewed distribution of the customer satisfaction data was managed through the creation of a single scale which utilized the most appropriate items from the initial survey (skewness = -1.4), but skew may still have impacted the results.

 

Finally, many variables likely to affect customer satisfaction were not considered in this study. These variables include individual differences of the customers (e.g., gender, race, negative affectivity, impulsivity); the degree to which the customer coproduces the service; the specific financial product; the degree of customer focus across retail banking functions such as human resources, marketing, and operations; and the expectations and needs of the customers (Schneider & Bowen, 1995). Inclusion of these variables may have changed the outcome of the analysis.

 

The constant modification of the items prevents long-term comparison or estimations of reliability and validity. In addition, the inter-rater reliability of the measures seems suspect and the consistency of the four assessments per banking center is questionable. The small amount of data also creates issues for the analysis.

 

The primary limitation of the employee satisfaction survey was that it did not include items identified in previous research as pertinent in the formation of satisfaction or dissatisfaction. In particular, goal emphasis, role stress, work ethic, autonomy, and job challenge were not considered (Burke et al., 1992; James & James, 1989). The size of the sample available for this study made analysis difficult and results less certain. While sample size was not controllable for this study, future efforts should use a time sample design or multiple organizations such that deeper analysis can be conducted. Finally, the current study included data from a single organization, at a single point in time. These limitations lead to questions about generalizability. For example, when data are aggregated at the business unit level, factors in the external environment of the local offices, such as economic conditions or local demographics, could affect either customers, employees, or both in ways that might change the proposed relationships between the groups’ attitudes. These issues could not be addressed in this study and need to be considered in future research.

 

In the current study an issue was raised regarding multicollinearity in the regression analyses. Since the goal of this study was simply to predict one variable from a combination of other variables, multicollinearity was not a critical concern. The predictions are still accurate, and the overall r2 quantifies how well the model predicts the values. In this case no predictive ability was found. Had the goal been to understand how the multiple variables impacted each other, then multicollinearity would have been a more serious problem (Lewis-Beck, 1995).

 

Multicollinearity occurs because two (or more) of the independent variables involved in the regression are related – they measure essentially the same thing (Lewis-Beck, 1995). In the current study, employee satisfaction subscales were used as independent variables to predict customer satisfaction and customer service quality. The subscales on this survey were shown to be highly correlated, thus creating the multicollinearity issues. Given that the single, “total employee satisfaction” score (see table 11) was not correlated with customer satisfaction or customer service quality, further development of the scales did not seem warranted. Another method for reducing the impact of collinearity is to increase sample size. This approach was not possible in the current investigation but should be considered in future studies.

 

Conclusions

While the current study did not support the service-profit chain model, it did add support to the idea that this model as currently formulated is too simplistic to fully encompass the relationships among the multiple variables involved (Silvestro, 2002; Silvestro & Cross, 2000; Spinelli & Canavos, 2000; Yoon, Seo, & Yoon, 2004). Future research should be aimed at the development of a model that incorporates all the variables associated with employee satisfaction, customer service quality, and the resulting customer satisfaction. While many research efforts have attempted to link employee satisfaction, customer satisfaction, and profitability, there has been less effort to attend to the multitude of intermediate variables in the service profit chain. The current research underscores the complexity of this relationship and suggests that attempts should be focused on clearly understanding the linkages that exist at this micro level before turning attention to broader issues such as profitability and growth. In addition, efforts should be focused on potential differences which exist between various retail industries. There is likely a difference between the customer service requirements from a personal banker and those from a grocery store clerk.

 

More standardized approaches to measuring the variables under consideration should also be developed. In particular, the mystery shopping approach to customer service quality needs further exploration and refinement. Mystery shopping appears to be a vendor-driven data collection approach that makes limited use of the science of measurement and survey construction. The face validity of question items and the willingness of vendors to change measures frequently, coupled with the lack of focus on analysis of scale reliability and validity, leads to metrics that are inadequate for decision making purposes.

 

Finally, organizations should utilize a more rigorous approach to measurement rather than getting caught up in the quick and easy methods offered by many assessment vendors. This particular study focused on only Bahrain retail banking sector, but the recommendations are generally relevant, as it has been the author’s experience that many organizations operate in a similar manner to the one under consideration. Measures of attitude and behavior such as service quality and customer satisfaction are difficult to create and interpret. Organizations need to pay greater attention to the methodology of the data collection as well as the nature of the surveys they are using. By better application of the scientific method and the use of better constructed tools, organizations will achieve greater returns on the investments they make in these processes.