AN EMPIRICAL INVESTIGATION OF THE COMPONENTS OF QUALITY SERVICE OPERATIONS: A B2B ANALYSIS

AN EMPIRICAL INVESTIGATION OF THE COMPONENTS OF QUALITY SERVICE OPERATIONS: A B2B ANALYSIS Kristen Bell DeTienne Brigham Young University, 590 TNRB, Provo, UT 84602, HKristen_DeTienne@byu.eduH 801-422-4189 Adam S. Holland University of Wisconsin Law School, 975 Bascom Mall, Madison, WI 53706, Hasholland@wisc.eduH, 608-262-2240 ABSTRACT Previous research has analyzed the relationship between satisfaction and loyalty for Business to Customer (B2C) relationships, but little research has analyzed Business to Business (B2B) relationships. Our study analyzed nine factors that drive satisfaction and loyalty within B2B relationships. We analyzed 2,103 telephone survey responses completed by the customers of a large, multinational staffing agency. In our analysis we utilized stepwise discriminant analyses and a multi-dimensional outcome variable. Results show that when the company solves its customers needs better than the competition, its customers are highly satisfied. Also, increased customer loyalty is reported when the company communicates well and quickly solves problems. Keywords: Customer satisfaction, loyalty, service quality Introduction Research shows that customer satisfaction and loyalty lead to competitive advantage and significantly impact business success. Understanding the variables that impact customer satisfaction and loyalty is essential for a number of documented reasons. First, customer satisfaction drives retention; and retaining customers is less expensive than acquiring new ones. Second, satisfying current customers is the best way to attract new customers as the highly satisfied customers will communicate their satisfaction by word of mouth to potential customers. Third, highly dissatisfied customers will also communicate their dissatisfaction by word of mouth to potential customers and the company will lose the potential business. Fourth and most importantly, most companies obtain their highest margins and profits through repeat (loyal) customers. Each of these factors will be discussed in detail later. The purpose of this article is to examine several dimensions of service quality that impact customer satisfaction and loyalty in business to business (B2B) relationships. We also show how this study provides important advances in this field. To do this, we first review the factors that are expected to impact customer satisfaction and loyalty. Next, we present our research questions, methods, and results. Finally, we review the implications for practice and for research. 706

The Importance of Customer Satisfaction and Loyalty Customer satisfaction is important for four main reasons. First, satisfaction is a major driver of retention and retaining customers is less expensive than attracting new ones (Reinartz & Kumar, 2000). In fact, research documents that acquiring new customers can be five times more costly than servicing existing ones (Reichheld, 2001). Second, satisfying customers is the best way to create new ones (Reichheld & Sasser 1990). On average, each highly satisfied customer will tell three to five other people about their positive experience (Reichheld & Sasser, 1990; Technical Assistance Research Programmes [TARP], 1986). Third, dissatisfied customers will communicate their dissatisfaction and likely scare away potential customers (TARP, 1986). Finally, the fourth and most powerful reason that customer satisfaction is important is because high levels of satisfaction will increase customer loyalty (Jones & Sasser, 1995; Reichheld, 2001). One study documented that by retaining just 5% more customers, companies can increase profit per customer between 25% and 100% (Reichheld, 2001). Another study showed that totally satisfied customers were six times more likely to repurchase products than those customers who were just satisfied (Jones & Sasser, 1995). Dimensions of Service Quality Research suggests several quality elements will predict satisfaction, positive word-of-mouth, and loyalty (dependent variables). A few of the elements have been studied in B2B relationships. However, most of the elements have been researched only in business to customer (B2C) relationships. Thus, by studying these elements in B2B customer relationships, this research provides an understanding of the differences between B2C and B2B customer satisfaction. The discussion of each of the items on the following list was deleted for these conference proceedings. For a complete discussion, send an email to Hdetienne@byu.eduH Anticipating and meeting the underlying needs of the customer How well the business fulfills the customers requests Time required to address customers needs Effective communication with customers Innovative at meeting customers needs Relative service quality Follow-up on service performance Invoice accuracy Ability to minimize employee turnover Advances over Prior Research This study provides four major advances over prior research. Difference between Dimensions of Service Quality and Overall Customer Satisfaction. Prior satisfaction research has overlooked the difference between factors of service quality and overall customer satisfaction (Oliver, 1997). According to Oliver, quality is, A judgment of performance excellence; thus, a judgment against a standard of excellence. On the other hand, satisfaction is, the customers fulfillment response, the degree to which the level of fulfillment is pleasant or unpleasant (Oliver, 1997). Therefore, it is important for research to study separately dimensions of service quality and their impact on overall customer satisfaction. 707

Difference between Satisfied and Highly Satisfied Customers. One of the most valuable discoveries of prior research has been to correct the commonly held belief that average satisfaction is enough to guarantee customer loyalty. In reality, average satisfaction does not make customers loyal. Research shows that except in a few rare instances, complete customer satisfaction is the key to loyalty and generating long-term financial performance (Jones & Sasser, 1995). Most customers maintain their business relationships and are loyal customers because they have been highly satisfied by the company s performance (Oliver, 1997). In other words, capital spent on maximizing loyalty should not be focused on merely making dissatisfied customers satisfied, but rather making satisfied customers highly satisfied. Recent studies suggest that it is more valuable for us to understand the factors that distinguish between satisfied and highly satisfied. Thus, a study focused on satisfied versus highly satisfied will advance our understanding of the complicated customer satisfaction relationships and will help companies to know which variables to focus on. Multi-Dimensional Outcome Measure. Much of the prior customer satisfaction research has used a uni-dimensional measure as the outcome variable (Anderson & Fornell, 2000). Recent studies show that customer loyalty is a multi-dimensional element that must take into account three distinct components (Appelbaum, 2001). The Gallup organization suggests that research should examine three elements using three questions: Overall, how satisfied are you with [brand]? (Satisfaction) How likely are you to continue to choose/repurchase [brand]? (Repurchase Intention/Loyalty) And, How likely are you to recommend [brand] to a friend/associate? (Positive Word-of-Mouth). Gallup shows that by combining these questions into an index, researchers can more accurately predict loyalty than by using satisfaction or word-of-mouth alone. Satisfaction and Loyalty in Business-to-Business Relationships. A great deal of research and literature exists concerning customer satisfaction within B2C relationships. However, little research has looked at B2B relationships dealing with satisfaction and loyalty. To summarize, this study adds to customer satisfaction and loyalty research in the following ways. First, we avoid confusing dimensions of service quality with overall satisfaction. Second, we focus our research on the most highly satisfied customers. Third, we use multi-dimensional outcome measure to focus on loyalty. And fourth, our study is focused on B2B relationships instead of B2C relationships as B2B relationships constitute an emergent research area. Research Questions Our goal was to answer four questions about the variables that predict satisfaction, loyalty, and positive word-of-mouth. Our first question builds on the previous research that demonstrates the need to study the factors that distinguish between satisfied and highly satisfied customers: RQ1: Which factors are most important when predicting whether B2B customers are satisfied versus highly satisfied? By researching this question we hope to give companies a rough indication of how they can make their satisfied customers highly satisfied and thus increase customer loyalty. 708

RQ2: Which factors are most important when predicting whether B2B customers will intend (versus definitely intend) to continue doing business with the company? The answer to this question may be more important than the first because customers repurchase intention often has a stronger impact on business success. RQ3: Which factors are most important when predicting whether B2B customers are likely versus highly likely to recommend the company to potential customers? Recommending a company to a friend or associate (positive word-of-mouth) is usually a good indicator of whether or not a customer will be loyal. RQ4: Which factors are most important when predicting whether B2B customers will be loyal or not loyal? As previous research suggests, using a combination of the responses to the previous three questions should provide a better indication of loyalty than by using any of them individually. Methods Phone calls were placed to 21,000 business customers of a large, multinational staffing agency. Telephone surveys were completed by 2,103 respondents, for a response rate of 10.01%. Customers were asked to rate on a scale of 1 to 5 (where 1 was poor and 5 was excellent ), how satisfied are you with [the agency] in the following areas? Independent Variables 1. Our ability to respond quickly with the employees you need 2. How well the employees we send you fit your requests 3. Our ability to minimize employee turnover 4. The value of our follow-up calls regarding employee performance 5. Our effectiveness in solving problems for you 6. The accuracy of our invoices 7. How well we communicate information that is relevant to your staffing needs 8. How innovative we are in meeting your staffing needs 9. Based on your experiences with supplemental staffing, how does our service compare to that of others? (Relative Service Quality) Dependent Variables. Using the same 1 to 5 scale, the survey then asked three questions similar to those used to calculate Gallup s CEII customer loyalty index. The questions were: (1) Overall, please rate your satisfaction with our service delivery [satisfaction], (2) How likely are you to continue doing business with the agency? [loyalty], and (3) How likely would you be to recommend the agency to others? [word-of-mouth]. Results Our analysis was conducted in two stages. We first conducted a stepwise analysis to differentiate between factors that would aid in predicting the outcome variables.tp PTAfter a list of factors was created for each outcome variable, we performed a discriminant analysis on the results to determine which of the factors were most important when predicting the outcome variables. The results of the stepwise analysis will be discussed first, followed by discriminant analysis. 709

Stepwise Analysis. We used a stepwise analysis to determine which of the nine dimensions of service quality would be useful when predicting each of the outcome variables. We used SAS to determine which of the nine variables best predicted the outcome variable individually. After the best variable was found, the program entered a second variable to determine which of the remaining eight variables, combined with the first, would best predict the outcome variable and so on until the program determined that by adding additional variables to the combination, the analysis ability to predict the outcome variable (partial R-square) was not increased. The results of this stepwise analysis are reported in Table 1. The first number indicates the order in which the variable joined the stepwise combination. The second number in parentheses is the incremental partial R-square. To protect from over fitting the model and thereby verify that each variable aided in predicting loyalty, random noise variables were included in the analysis. Satisfied Continue Refer Ability to respond quickly with employees you need 2 (.225) 4 (.015) 1 (.241) [Company]'s service compared to others 3 (.103) 3 (.026) 3 (.045) Effective problem solving 6 (.042) 1 (.161) 2 (.093) Employees fitting your requests 4 (.074) 2 (.062) 5 (.022) Communicating info relevant to your staffing needs 5 (.064) 6 (.006) 4 (.030) Clarity and accuracy of our invoices 7 (.016) 5 (.009) 7 (.007) Ability to minimize employee turnover 9 (.012) 7 (.003) 6 (.008) Value of our follow-up calls 8 (.014) 8 (.002) Innovative in meeting your staffing needs 1 (.493) Table 1: Order that variables entered stepwise analysis Discriminant Analysis. After reviewing the results of the stepwise analysis, we performed four separate discriminant analyses on the relevant dimensions of service quality in each area (satisfaction, continue, refer, loyalty [composite]). For instance, for likeliness to continue, we performed a discriminant analysis using the first seven factors in Table 1 to determine which quality factors distinguish between customers that intend to continue doing business with the agency and those that highly intend to do so. This focus on separating customers who are satisfied from those who are highly satisfied is in accordance with prior research (Jones & Sasser, 1995; Oliver, 1997). The discriminant function was designed to predict whether a customer would score a 3 or a 5 in each of the four areas by analyzing the customers answers to the relevant dimensions of service quality defined through the stepwise analysis. We also removed all customers reporting a 1, 2, or 4 from each analysis for the same reason. Our discriminant prediction is calculated by using the following discriminant function (Rencher, 1995): K i + n j= 1 ( A * M ) (1) ij where K is the discriminant analysis constant, A is the customers rating for a given quality dimension, ij 710

P group, P quality M is the multiple for the corresponding quality dimension (from Tables 2, 3 and 4), n is the number of quality dimensions attributed to overall satisfaction etc., th i is the ip th and j is the jp dimension. The function is applied as follows: If K n 3 + A3 j * M 3 j ) j= 1 n ( > K5 + ( A5 j * M 5 j ), we predict the customer will answer 3 on the satisfaction/continue/recommend/loyalty question. If K n 3 + A3 j * M 3 j ) j= 1 n ( < K5 + ( A5 j * M 5 j ), we predict the customer will answer 5 on the j= 1 satisfaction/continue/recommend/loyalty question. Once we knew the discriminant functions, we were able to search for the answers to our research questions. The first research question was, which factors are most important when predicting whether the customer will be satisfied (3) or highly satisfied (5)? This importance is calculated as the difference between the multiples from each group for each factor and is listed in Tables 2 through 5 in order of each factor s importance in discriminating between the two groups. Factors with larger multiple differences are more important drivers as can be seen by the discriminant function. Results of the first analysis showed that Ability to respond quickly with employees you need, Employees fitting your requests, and [Company]'s service compared to others are the three most important drivers of separating customers who are satisfied and highly satisfied. In other words, when the company fits its customers needs quickly and much better than other companies can do, its customers are highly satisfied. Results of this analysis are presented in Table 2 below. Calculation category Group 3 Group 5 Difference Multiple Multiple Ability to respond quickly with employees you need 5.18109 7.22662 2.04553 Employees fitting your requests 4.25837 5.82458 1.56621 [Company]'s service compared to others 3.09573 4.37218 1.27645 Communicating info relevant to your staffing needs 3.53353 4.65016 1.11663 Effective problem solving 1.48354 2.4573 0.97376 Innovative in meeting your staffing needs 0.09972 1.05851 0.95879 Clarity and accuracy of our invoices 4.89782 5.58565 0.68783 Value of our follow-up calls 1.31374 1.97013 0.65639 Ability to minimize employee turnover 1.67094 2.27543 0.60449 Disc. analysis constant for overall satisfaction -42.42598-80.67316 Table 2: Linear discriminant function for overall satisfaction (arranged in order from highest multiple difference to lowest) The second research question was, which factors are most important when predicting whether the customer will intend to continue doing business with the company (3) or will highly intend to j= 1 711

continue doing business with the company (5)? Effective problem solving, Employees fitting your requests and Ability to respond quickly with employees you need are the three main drivers in separating those customers that are likely and highly likely to intend to continue doing business with the company. That is, when the company fits its customers needs and solves their problems quickly, customers will intend to continue doing business with the company. The other factors that predict the customer s likeliness to continue are shown in Table 3 below. Calculation category Group 3 Group 5 Difference Multiple Multiple Effective problem solving 0.77716 1.50195 0.72479 Employees fitting your requests 2.54887 3.22004 0.67117 Ability to respond quickly with needed employees 2.60358 3.12456 0.52098 [Company]'s service compared to others 1.96361 2.45975 0.49614 Communicating info relevant to your staffing needs 1.84576 2.20208 0.35632 Clarity and accuracy of our invoices 4.21186 4.52939 0.31753 Ability to minimize employee turnover 0.94735 1.19262 0.24527 Disc. analysis constant for likeliness to continue -26.56029-39.36925 Table 3: Linear discriminant function for likeliness to continue The third research question was, which factors are most important when predicting whether the customer will be likely to recommend (3) or highly likely to recommend (5) the company to potential customers? Ability to respond quickly with employees you need, Effective problem solving and [Company]'s service compared to others are the three most important drivers in separating those customers that are likely and highly likely to recommend the company to potential customers. In other words, when the company solves the problems of its customers quickly and does it better than its competition, its customers are highly likely to recommend it to potential customers. The complete results of this analysis are presented in Table 4 below. Calculation category Group 3 Group 5 Difference Multiple Multiple Ability to respond quickly with needed employees 3.29403 4.39791 1.10388 Effective problem solving 0.92378 1.65214 0.72836 [Company]'s service compared to others 2.12131 2.83243 0.71112 Communicating info relevant to your staffing needs 2.5349 3.21571 0.68081 Employees fitting your requests 2.30483 2.93636 0.63153 Ability to minimize employee turnover 1.01163 1.42489 0.41326 Clarity and accuracy of our invoices 4.05572 4.44639 0.39067 Value of our follow-up calls 0.4135 0.62272 0.20922 Disc. analysis constant for likeliness to recommend -28.44539-47.02409 Table 4: Linear discriminant function for likeliness to recommend The fourth research question was, which factors are most important when predicting whether the customer will be not loyal (3) or loyal (5). Though this study does not take into account which customers were actually retained, it does predict if the customer would score a 3 or 5 to an average of the three previous questions. This average should be a more accurate predictor of actual loyalty than answers to the three previous questions individually; hence a customer is 712

classified as not loyal if we predict that they would report between 3 and 3.99, and loyal if they would score between 5 and 5.99. Table 5 below presents the results of this analysis. Calculation category Group 3 Group 5 Difference Multiple Multiple Ability to respond quickly with needed employees 3.33911 4.60774 1.26863 Effective problem solving 0.7931 1.75161 0.95851 Communicating info relevant to your staffing needs 2.15837 3.10162 0.94325 Employees fitting your requests 2.69284 3.58962 0.89678 [Company]'s service compared to others 2.09696 2.92573 0.82877 Clarity and accuracy of our invoices 4.32118 4.78531 0.46413 Ability to minimize employee turnover 1.03279 1.4546 0.42181 Value of our follow-up calls 0.71889 0.97177 0.25288 Discriminant analysis constant for loyalty -27.92034-50.36405 Table 5: Linear discriminant function for loyalty (composite of satisfaction, likeliness to continue, and likeliness to recommend) As shown in Table 5 above, Ability to respond quickly with employees you need, Effective problem solving and Communicating info relevant to your staffing needs are the three most important drivers in separating those customers that are not loyal and loyal. In other words, when the company communicates well with its customers and solves their problems quickly, the company s customers should be loyal. Ranking multiple differences also allows us to quantitatively compare how much more or less important the factors are to each other in terms of separating satisfied and highly satisfied. groups from each other. In other words, a customers score to Ability to respond quickly with employees you need (multiple difference of 2.04553) is over twice as important as their answer to Effective problem solving (multiple difference of 0.97376) in terms of predicting whether they will report they are satisfied (3) or highly satisfied (5). This ranking allows us to better estimate how much attention is needed to improve each dimension of satisfaction or loyalty. A final question to be answered by the analysis was, how accurate are the predictions made by the discriminant analysis? After running each customer s responses through the discriminant function and obtaining a prediction for each, we compared those predictions against the customers true responses. The results of this comparison are listed in Table 6. In summary, the discriminant analysis was 94.7% accurate at predicting satisfaction versus high satisfaction, 80.25% accurate at predicting likeliness to continue versus high likeliness to continue, 86.56% accurate at predicting likeliness to recommend versus high likeliness to recommend, and 90.05% accurate at predicting loyalty. 713

For Overall Satisfaction Satisfied Highly Satisfied Overall Number of customers correctly classified 269 749 1018 Number of customers misclassified 14 43 57 Percent of customers correctly classified 95.05% 94.57% 94.70% For Likeliness to Continue Likely Highly Likely Overall Number of customers correctly classified 166 1057 1223 Number of customers misclassified 53 248 301 Percent of customers correctly classified 75.80% 81.00% 80.25% For Likeliness to Recommend Likely Highly Likely Overall Number of customers correctly classified 185 1077 1262 Number of customers misclassified 29 167 196 Percent of customers correctly classified 86.45% 86.58% 86.56% For Loyalty Not Loyal Loyal Overall Number of customers correctly classified 128 1221 1349 Number of customers misclassified 15 134 149 Percent of customers correctly classified 89.51% 90.11% 90.05% Table 6: Classification results from discriminant analysis using cross-validation Discussion Implications for practice. Drawing on the suggestion of Jones and Sasser (1995), we focused our analyses on determining which factors discriminate between customers that are satisfied but not loyal and those that are highly satisfied and loyal. The analysis determined that the company s promptness at responding to its customers needs, fitting its customers requests, and relative service quality were the three most important drivers of predicting whether customers would be highly satisfied or merely satisfied. The study also found that the company s promptness at responding to the customers needs, effectively solving the customers problems and maintaining good communication with customers were the three most important drivers in predicting whether customers would be loyal or not. We also determined that the analysis was 94.7% accurate at predicting satisfaction and 90.05% accurate at predicting loyalty. Implications for future research. We realize that the study is limited because we are predicting expected loyalty. Though we use a combination of three outcome variables similar to Gallup s CEII loyalty index to determine expected loyalty, the study would be improved by analyzing the nine factors against actual retention. In addition to enhancing the validation of the study, by performing this type of analysis, researchers could study the effectiveness of using the three outcome variables as a predictor of actual loyalty and analyze whether satisfaction or one of the other variables is an accurate predictor of actual loyalty in B2B relationships. Future research could also examine the nature of the variables. That is, are some of the factors that predict satisfaction and loyalty different by nature? For example, it may be that factors like accurate invoices and minimizing turnover do not cause satisfaction when present, but cause dissatisfaction when absent. We also realize that the study is limited by an inability to compare the results against those of companies in other industries. Similar studies within other industries could aid in establishing which of the nine factors are most important to satisfaction and loyalty in B2B relationships in general. 714

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