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Identifying Issues in Customer Relationship Managementat Merck-Medco

Gholamreza Torkzadeh and Jerry Cha-Jan ChangDepartment of MISCollege of Business

University of Nevada, Las Vegas

Gregory W. HansenVice President

Customer Service OperationsMerck-Medco

September 2004Revised March 2005

Please direct correspondence to:

Reza TorkzadehDepartment of MISCollege of Business

University of Nevada, Las Vegas4505 Maryland Parkway - Box 456034

Phone: (702) 895-3796E-mail: [email protected]


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1. Introduction

To manage prescription drug benefit for sixty million customers, Merck-Medco has made

a significant investment in its customer relationship management (CRM) over the recent years.

Computer applications and system procedures are developed and used to schedule customer

service representatives and balance call traffic to ensure speed and service quality. A network of

six call centers in five states within the continent of the US handles over 40 million customer

calls per year. Frequently, clients have service penalties associated with the speed of answer and

thus the service is painstakingly managed to avoid penalty.

Ideally, the company would like for the customer service representatives to analyze

customer data online and be able to resolve customer issues at the first contact. Yet, the call

centers and customer service representatives are not always able to resolve all issues online. The

unresolved cases are queued for follow-up by a team of customer service representatives within

each dispensing pharmacy. The majority of customer and client complaints have been traced to

this queuing process. The company has for the past few years struggled to refine this process

with limited success. Reasons for member dissatisfaction are numerous and difficult to prioritize.

In this paper, we report the results of a collaborative effort between academe and practice

to improve customer relationship management at Merck-Medco. With a few exceptions such as

BP and IBM [12, 26], very few studies of this kind are reported in research journals. This study

was designed to accomplish two objectives that together will help management develop

strategies for increasing CRM success. The first objective was to identify primary factors that

result in member dissatisfaction with customer relationship management, more specifically with

the call center. The second objective was to produce a reliable and valid set of measures that can


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be used by the company and others to monitor employee training effectiveness and remedial

plans. The methodology, sample, and procedures were decided with these objectives in mind.

The subjects in this study were people who directly interacted with the system on a daily

basis to make decisions and serve customers. Through close collaboration with the firm, we

collected a large sample that represents over 75% of all user groups. The level of participation

was influenced by the users’ desire to improve the system and the management involvement in

the study. In the following section we will review the literature on CRM and clarify the

construct. The literature review describes call center issues in a broader perspective of customer

relationship management. Section 3 provides the background for Merck-Medco. Section 4

describes research methodology followed by section 5 that describes data analysis and results.

Discussion and conclusions are provided in sections 6 and 7, respectively.

2. Customer Relationship Management

Customer relationship management incorporates information acquisition, information

storage, and decision support functions to provide customized customer service [23]. It enables

customer representatives to analyze data and address customer needs in order to promote greater

customer satisfaction and retention. It helps organizations to interact with their customers

through a variety of means including phone, web, e-mail, and salesperson. Customer

representatives can access data on customer profile, product, logistics and the like to analyze

problems and provide online and rapid response to customer queries.

Companies use CRM to not only create a customer profile, but also to anticipate

customer needs, conduct market research, and prompt customer purchase [26]. It is suggested

that it costs up to twelve times more to gain a new customer than to retain an existing one [33]. A


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problem needs to be resolved on the first contact or the chances are the customer with an option

to go elsewhere will never call back [39].

Because of the potential benefits, organizations commit significant hardware, software,

and human resources and often restructure their processes in order to implement CRM. It took

IBM a 4-year initiative to re-engineer its customer relationship management [26]. However,

despite the extensive commitment, it is suggested that many of these systems fail to fulfill

expectations [33, 35, 46]. The lack of proper integration of data across organizational functions

is suggested as one of the reasons why many companies struggle with their CRM systems [41].

The interplay between technological, organizational, and individual factors also affects outcomes

of these systems [22, 24].

In a recent study, Goodhue et al. [19] examined challenges and opportunities of CRM in

several organizations. They suggest that the growth of CRM is driven by the changing demands

of the business for quality service, the availability of large amount of data, and the role of

information technology. They suggest that in order to benefit fully from CRM, firms may need to

undergo a major change in organizational culture and business practices. Organizational change

requires significant commitment and has high potential in terms of opportunities and challenges.

The authors recommend different levels of integration, transformation, and application for CRM

depending on the organizational needs and maturity.

A significant portion of customer dissatisfaction is due to employees’ inability or

unwillingness to respond to service failures [38]. In the financial services sector, for example,

more than 70 percent of customers defect because of dissatisfaction with service quality [8].

Organizations with service failure and recovery problems need to communicate commitment to

customers and strengthen bonds [4], CRM can help these organizations.


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In the pharmaceutical industry, CRM has become a great investment and plays a

significant part in managing customer requests. Accurate data, effective processing, and cross-

functional integration are critical success factors in improving customer satisfaction. Product

information (e.g., specification, inventory, price, delivery method) are readily available to

customer representatives to facilitate immediate and accurate response to customers in their first

contact. Callbacks are minimized as they involve cost (in employee time) and the risk of losing

the customer. Processes are streamlined and quality control is imbedded within the system in

order to ensure continuous and consistent monitoring of customer service. Measures of service

quality have been developed and used in research studies of marketing [29], MIS [45], and call

centers [13].

Information technology plays an important role in CRM success [5, 26, 31]. Describing

the role of technology in service quality, Harvey et al. [21] point out the important gains that

come from producing and delivering more value to the customer. They suggest a model that

describes how services that provide ‘value-added partnerships’ can be created through

information technology. Improved service quality is perceived through close interaction and real-

time flow of information.

Integration of telephone communications, database, local area networks and other

information system applications have clearly enhanced the CRM function [1, 36]. Information

technology applications can be used to create customer ‘empowerment’ that will ultimately result

in customer satisfaction [36]. Organizational web sites are increasingly used to deliver services

as well as accumulate customer information. CRM professionals who are trained in information

technology and marketing [32] have been in great demand in recent years [47].


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Members also have the option of using a home delivery pharmacy. Merck-Medco

operates 12 home delivery pharmacies across the country. These pharmacies are divided into

prescription processing centers and prescription dispensing centers. A member can submit a

prescription through five different means: mail, fax, phone voice response system, Internet or by

speaking with a customer service representative. Each client submits all of his or her

prescriptions to the same prescription processing center. Depending upon the type of medication,

the actual prescription could be dispensed through a different prescription dispensing center.

Once a prescription processing center receives a prescription, it is responsible for

entering all information necessary to prepare for dispensing. The process for accomplishing this

varies based on whether the order is for a new prescription or a refill of an existing prescription.

In the case of the refills, members are issued a bar-coded sticker that can be scanned to access all

of the necessary information from the original prescription. A new prescription is more complex.

All documents received through the mail are scanned into images that can be retrieved

later for any purpose. In the case of new prescriptions, once the images are available, a

pharmacist enters the information into the system on the right side of the screen while looking at

the images on the left side of the screen. The order is then subjected to a series of administrative

and professional edits. The administrative edits look for current eligibility, drug coverage

information, account balance and address or personal profile information. The professional edits

include drug utilization review that ensures they are not taking multiple drugs that interact with

each other or an allergy that would be impacted by the drug. If there were any questions about

what the doctor prescribed then a call would be made by a pharmacist.

There are also edits that target specific medications that have lower cost but

therapeutically equivalent alternatives. Pharmacists then call doctors to discuss switching the


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medication to the alternative. This is primarily focused on saving the plan and customer money

while maintaining the same therapeutic outcomes. There are also some disease management edits

that concentrate on prescribing behaviors relative to specific disease states. These would also

prompt a call to a doctor to discuss the appropriateness of the medication prescribed by the

doctor.

Once all of these edits are identified and resolved, the prescription is then ready to be

dispensed (see figure 1). As mentioned earlier, the type of prescription dictates where it will

actually be dispensed from. There are two automated pharmacies that dispense primarily pills,

tablets and capsules. Therefore, a prescription that is capable of being dispensed through

automation would be electronically transferred to one of these two pharmacies. If the

prescription is a prepackaged item, refrigerated medication, a narcotic, a controlled substance, or

a compounded medication, it is dispensed in any one of seven pharmacies across the country.

The remaining three pharmacies are strictly prescription processing centers and do not dispense

any medications. The following table outlines the locations and functions of each pharmacy in

the United States:

Prescription processing centers only Spokane, WA, Irving, TX and Fairfield, OH

Prescription dispensing centers only Willingboro, NJ

Prescription processing and dispensing Las Vegas, NV, Columbus, OH, Pittsburgh, PAHarrisburg, PA, Wilmington, MA, Parsippany, NJand Tampa, FL

Automated dispensing pharmacies Las Vegas, NV and Willingboro, NJ

-------------------------------------

Insert Figure 1 about here--------------------------------------

Integrated throughout the prescription processing and dispensing process are extensive

customer service capabilities. All information relative to past prescriptions and prescriptions

currently in process is accessible by a customer service representative (CSR) via another


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proprietary system. In addition, client plan design and personal profile information is also

available within this system. With 60 million customers to support, customer service handles

over 40 million calls per year through its network of six call centers. They are located in Tampa

(Florida), Parsippany (New Jersey), Columbus (Ohio), Dublin (Ohio), Irving (Texas) and Las

Vegas (Nevada).

A complex set of systems and applications are used to schedule CSRs and balance call

traffic to ensure the speed with which calls are answered meets the expectations of clients.

Frequently, clients have service penalties associated with the average speed of answer so this is

painstakingly managed to avoid any penalty. These six call centers have the ability to route

telephone calls transparently to a customer. Any question can be answered by any CSR in any

call center via the system. This allows customer service to balance call traffic based on staffing

and demand in real time.

Although the call centers and CSRs have integrated systems, they are not always capable

of resolving a member’s issue via the system. In these cases, there is a follow-up system to

address the member’s question or concern. A common example is when a member calls about

the status of their order that is currently in process. If the member needs the medication earlier

than when the system projects it will be dispensed, then a message is sent electronically to the

dispensing pharmacy. Within each dispensing pharmacy is a team of CSRs who are staffed

exclusively to follow up on member issues that the call center CSRs are unable to resolve. This

team would work these electronic messages called queues. Each queue is defined based on its

pharmacy location and the nature of the request. For instance, a member who had their

medication dispensed from Tampa, FL and needs to have it replaced because it was lost in the

mail would be queued to FLRPLC or Florida Replacement. There are 26 different queues that are


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used by each dispensing pharmacy. If the specific nature of the request does not fit into one of

the first 25 queues then the CSR would use the 26th

queue, which is a general queue. Figure 2

shows a simplified customer service process. The majority of customer and client complaints are

traced to the queuing process of Call Center CSR Enter Unresolved Issues into Queues (third

box) and Dispensing Pharmacy CSR Resolve Queued Issues (fourth box) in Figure 2.

-------------------------------------Insert Figure 2 about here

--------------------------------------

4. Research Methods

Measures of service quality have been developed by researchers in marketing [29, 30],

information systems [45], and call centers [9]. These measures, however, are more general and

do not relate to specific issues such as CSR and queuing process that are important components

of the CRM system at Merck-Medco. Thus, we decided to start with a clean slate for identifying

and prioritizing problems that caused customer dissatisfaction and were primary concerns of the

management at Merck-Medco.

The process started by conducting focus groups in two of the call centers and four of the

pharmacies. Teams of six customer service representatives at each site were asked to brainstorm

a list of scenarios and issues that cause member dissatisfaction relative to the queuing process. A

team of managers, supervisors and CSRs in the Las Vegas site combined the lists and

consolidated them. They primarily sought to eliminate redundancy. Once the consolidated list

was complete, they sent the list out to all of the pharmacies and call centers asking them to add

any items that were not already addressed in the list. Therefore, every site had input. The

descriptions were then edited into the form of neutral problem statements.


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The next step was to use a team of three CSRs from the call center and three CSRs from

the pharmacy in NV, since they were co-located, to categorize the list. They reviewed the entire

list and then brainstormed various categories. These categories were then narrowed to a list that

the team felt would capture all of the individual statements. The statements were then assigned to

each category through consensus discussion. Once every statement had been assigned a category,

one more consolidation step took place to eliminate categories with only a few statements. The

final list included 54 statements in five categories.

The list was then formatted as a survey where a five-point scale from strongly agrees to

strongly disagree was applied. A brief opening paragraph provided context for the survey

participants. A pilot survey of 25 CSRs was completed to assess the time to take the survey and

ensure there were no confusing statements. After completing the survey, the team of 25 was

asked for input on the clarity of the survey itself. The consensus was that the survey form was

fine and required no further modifications.

The survey was then converted to both an online version and hard copy. The online

version was administered to a sample of CSRs in the call centers via the system they had access

to. Because the CSRs in the pharmacies used a different system, they were unable to complete

the survey online. They therefore filled out the hard copy version. The current staff of 1500

CSRs in the call centers and the staff of 400 CSRs in the pharmacies completed a total of 1460

surveys – a 77% response rate.

5. Data Analysis

There are two objectives in this data analysis. The first is to identify salient factors that

affected CRM process at Merck-Medco. This would help the company develop an action plan for

improving the process. An exploratory factor analysis is an appropriate tool to identify these


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salient factors. The second objective is to develop a reliable and valid instrument that can be

used to assess the effectiveness of the action plan. A confirmatory factor analysis is

recommended to reinforce confidence in the instrument [7].

In order to accomplish both objectives, data were randomly split into two equal parts. The

first half is used with exploratory factor analysis to determine the salient factors and produce a

set of items that measure CRM process failure at Merck-Medco. The second half is used with

confirmatory procedures to modify and finalize the factors and their measures produced based on

the first part of data.

Exploratory analysis

First, as suggested by Churchill [11], the researchers purified the items (to eliminate

‘garbage items’). Two criteria were used to eliminate the items: corrected-item total correlation

(each item’s correlation with the sum of the other items in its category) and reliability. The

domain sampling model provides a rationale for corrected-item total correlation procedure. The

key assumption in the domain sampling model is that all items, if they belong to the domain of

the concept, have an equal amount of common core. If all the items in a measure are drawn from

the domain of a single construct, responses to those items should be highly inter-correlated. After

this, an exploratory factor analysis of the remaining items was conducted to identify items that

were not factorially pure. Items that loaded on more than one factor at 0.50 or above were

eliminated. This cut-off point is higher than what has been used by other researchers [15].

Exploratory factor analysis using principle components as extraction method with

Varimax rotation was used to determine the number of factors. If the factor analysis would result

in ambiguous structure or produce many items with multiple loadings, we reevaluated the

corrected-item total correlation results and carefully examined close-call items. To the extent


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possible, we also considered item content to make sure all items within a factor measured similar

content.

This was possible because the researchers were familiar with the nature of the problem

through discussions with the staff at Merck-Medco. This was also necessary since we had

developed the initial list of items based on practice rather than theory. Thus, an iterative process

of using corrected item-total correlation and exploratory factor analysis was used to determine

the number of factors. Reliability was calculated at each stage to make sure it remains higher

than 0.80.

The process started with 13 initial factors and resulted in 7 factors with 21 items shown in

Table 1 and described in Table 2. Eigen values for the seven factors are greater than 1.0 and

range from 5.374 to 1.002. All factor loadings are above 0.64, much higher than the commonly

used threshold of 0.5. The seven factors accounted for 65% of variances. The seven factor

solution was easily interpreted and labeled as standard operating procedure compliance (5

items), accountability and ownership (4 items), callback information content (3 items), customer

contact process (3 items), billing issues (2 items), dispensing and replacement process (2 items)

and queuing procedure (2 items).

Managers and supervisors who closely collaborated with the researchers throughout the

study could easily relate to these factors. These factors would be useful for Merck-Medco to

make closer examination of their system and develop a useful remedial action plan.

-------------------------------------Insert Table 1 about here

---------------------------------------------------------------------------

Insert Table 2 about here--------------------------------------

Confirmatory analysis


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The results of exploratory factor analysis were encouraging and provided a theoretical

basis to conduct confirmatory factor analysis. Figure 3 presents the confirmatory measurement

model to be tested. The confirmatory factor analysis follows the measurement property

assessment paradigm used in other studies [16, 37, 44] and was conducted using SIMPLIS in

LISERL 8.3. The results are presented in Table 3. Since some items have low factor loading and

the fit indices were less than satisfactory, further refinements were needed.

-------------------------------------Insert Figure 3. about here

---------------------------------------------------------------------------

Insert Table 3. about here--------------------------------------

Following Segars’ [37] procedure to refine and purify the measures, each factor with

more than 3 items is tested separately to improve model fit first. This is done by examining

modification indexes of each single factor measurement model and adding error correlations that

were suggested. One error correlation is added to factor 1 since reading of the item suggests they

could be correlated and no modification was necessary for factor two. These are the only two

factors with more than three items. The next step is to test measurement models with pair of

factors. In this process, items with cross loading were identified and eliminated. A total of 21

paired tests were conducted with only one item eliminated.

The last step is to combine all seven factors into a single measurement model and test the

model fit. This model included the reduced number of items and the error correlation identified

earlier. At this stage, items with factor loading less than .45 [34] or cross loadings were

eliminated one by one. Factors 5 and 7 ended up with only one item and therefore were removed

from the model. The process resulted in a five-factor 13-item model shown in Figure 4. Table 4


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presents the standardized parameter estimates, fit indices, and final item descriptions for this

model. Compared to the initial model, the final model has much better model fit, as shown by the

fit indices. This procedure establishes convergent validity and unidimensionality.

-------------------------------------Insert Figure 4 about here

---------------------------------------------------------------------------

Insert Table 4 about here--------------------------------------

The next step in confirmatory analysis is to examine discriminant validity and construct

reliability. Discriminant validity can be established by comparing the model fit of an

unconstrained model that estimates the correlation between a pair of factors and a constrained

model that fixes the correlation between the factors to unity. Discriminant validity is

demonstrated when the unconstrained model has a significantly better fit than the constrained

model. The difference in model fit is evaluated by the chi-square difference between the models.

A significance of the chi-square difference is a chi-square variate with one degree of freedom.

Tests of all possible pairs for the five factors were conducted and the results are presented in

Table 5. All chi-square differences are significant at .001 level, this supports discriminant

validity. Construct reliability can be assessed using either a formula for composite reliability or

average variance extracted [20, 37]. Those numbers are also presented in Table 5. All factors

except dispensing and replacement process have acceptable composite reliability and average

variance extracted.

-------------------------------------Insert Table 5 about here

--------------------------------------

6. Discussion


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Organizations continuously rethink their processes in order to improve them by extending

the boundaries of information technology application. Practitioners and academics alike have

long accepted the delivery of quality service to customers as a success factor. Information

technology has played an increasingly important role in the successful delivery of this service to

the customer. This study illustrates the complexity of customer relationship management

function and employee concerns regarding the processes involved. These issues are different

from what existing service quality measures and studies address. Service quality studies address

issues of customer (i.e., front end issues) while the current study addresses issues internal to the

system and processes (i.e., back end issues).

There are great expectations for what CRM can accomplish in terms of customer profile,

product information, rapid response, predicting customer needs, retaining customers, conducting

market research, promoting sale, and reducing cost. However, despite considerable

organizational and executive commitments these expectations have not always been

materialized. The perception of a widening gap between the potential of customer relationship

management (that is, what it can ideally achieve) and its actual accomplishments has increased

the need for better understanding of the nature of the problem and for better measures of factors

that influence outcomes. This research was designed to address the perceived gap that exists

between the potential of customer representative management and what it actually accomplished

in a large U.S. pharmaceutical company. Although this research is company specific, we believe

that our findings have relevance to other CRM environments (similar to [17, 27]).

Because of the nature of the problem situation, the approach taken in this study includes a

combination of qualitative and quantitative methods. Case study methodology was used to

determine the scope and boundaries of the problem. Quantitative methods, especially


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multivariate analysis, were used to more specifically identify influential factors within the

determined scope. We feel that a combination of these two methodologies is quite appropriate

for situations where there is no widely accepted theory base for study questions or where existing

theories might not be appropriate for the practical problem at hand. In studying information

technology failure, for example, Lyytinen and Hirschheim [25] argue that any analysis is an

interpretive activity to understand the problem and to find solution. It is prudent to formulate

study questions based on experience and careful case study approach rather than borrowing a

well-established theory from other disciplines that poorly fits the problem.

The company, Merck-Medco, greatly depends on CRM to interact with their customers.

Doctors and patients in the company’s member plans send their prescriptions by mail, fax,

phone, or through website. At automated pharmacies like the ones in Las Vegas, Nevada or

Willingboro, New Jersey, 99% of the prescriptions are filled within 24 hours of receipt. Some of

these pharmacies may fill more than 800,000 prescriptions per week [18]. Before a prescription

is filled, potential drug interactions are automatically checked and issues are flagged and

forwarded to a registered pharmacist for investigation and resolution. There are numerous other

issues that customer service representatives use the system to respond to. For example, a member

patient may request drugs to be sent to a different address while on holiday or may request a

speedy delivery before travel. These messages are received at call centers and queued for action

at pharmacies or packaging centers responsible for that particular package.

For some time, the company has been experiencing problems with their CRM that result

in complaints from customer representatives. This led to a close collaboration between the

company and the researchers and influenced the design, implementation, and data collection for

this study. The study involved broad participation that included senior executives, managers,


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supervisors, and customer representatives. Customer representatives throughout the company

showed interest in addressing CRM issues and streamlining customer query and response

system. Participants from call centers throughout the organization were involved in different

stages of they study including design and data gathering. More than 75% of system users

responded to survey questions.

The results of this study point to numerous processes that are either not followed or

clearly understood by customer service representatives. Through exploratory factor analysis, this

study specifically identifies issues that relate to standard operating procedure, accountability and

ownership, call back information content, customer contact process, billing issues, and

dispensing and replacement process. Collectively these issues have resulted in CRM failure in

the company. Two factors (billing issues and queuing procedure) were dropped during the

confirmatory factor analysis phase in order to improve measurement rigor. Below we describe

each factor that has been identified as a barrier to CRM success in the company and will briefly

address possible remedies. Although the shorter five-factor model is easier to use and supported

by confirmatory analysis, the discussion will include the larger list of actors identified in the

exploratory phase of the study to help the management of CRM at Merck-Medco.

Standard operating procedure – Data analysis suggests that compliance with standard operating

procedure explains current complaints more than any other factor. It includes wrongly sequenced

queues, closing of incomplete files, forwarding incomplete electronic forms, and the like. This

issue relates to employee behavior and may be addressed through training as well as improved

description of procedures. However, training alone or improved description of procedure may

not be adequate if non-compliance is widely spread and has become routine or if the system’s

information product does not readily support compliance with standard operating procedure. The


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current CRM system can be revamped, improved, or redesigned so that it will enforce much of

standard operating procedure and make compliance a part of interaction with the system. Joshi

and Rai [22] studied the influence of information product on work and suggest greater attention

to the need for designing quality systems that not only meet primary information delivery

objectives, but also take into account the task and organizational design issues for the user.

Accountability and ownership – The issue of accountability is another readily identified factor

that has created discontent among CRM users and customer service representatives. Under the

current system, it is difficult to determine who is accountable for events such as filing customer

contact forms without action, violating queue sequence for customer contact form, keeping

commitments made to customers, and not verifying address prior to refill and shipping. This has

created role ambiguity and role conflict with adverse effect on information product intended

outcome [22]. The interaction between task needs and technology application has created a

problem that cannot be addressed through defining responsibilities alone. Levels of responsibility

and accountability need to be established and communicated and violations are readily identified.

Information technology is expected to provide appropriate control to management for work

process and quality performance [42]. Information content must be extended to include control

for accountability.

Call back information content – The third factor influencing CRM outcome at the company

relates to the quality of information generated through the use of customer contact form. This

information is unclear or even wrong and is used to generate customer queries and determine

queues. This in turn influences queue procedure that is identified as another factor affecting

customer representatives. Information technology is expected to empower the individual

employee to provide accurate and timely response to customers. Information content plays a


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critical role in employee satisfaction with information system [2, 10, 14]. Lack of employee

confidence in information content has an adverse influence on system use [43]. The system

needs to be revamped to more effectively and more easily control for information quality at data

entry, data integration, and data manipulation level.

Customer contact process – Information technology and CRM are expected to provide the

mechanism through which long-term, individualized relationships with customers can be created

and maintained. Massey, et al. [26] suggest that CRM centers on gaining a steady or increasing

business from current customers, not necessarily a constant stream of new customers. To

accomplish this, the company must ask what makes a specific customer unique and then tailor

services in response to that uniqueness [28]. The current system fails to create an environment

that helps sustain a steady business from customers. The customer contact form was designed to

collect information on a single issue rather than multiple ones. As currently practiced, this form

generates multiple issues and that in turn complicates routing of issues to appropriate pharmacies

or packaging centers. The system should facilitate generation of separate forms for multiple

issues and avoid providing the option of multiple issues on a single form.

Billing questions – This factor illustrates ineffective integration of accounting function (e.g.,

accounts receivable) with CRM in the company. Customer service representatives are not

familiar with how to respond to customer queries for billing; the system does not help them

explain expenses to customers. Customer service representatives have difficulty interpreting

accounting codes on the billing screen. The system needs to more fully integrate accounting

function and help customer representatives respond accurately to billing questions. Lack of

proper integration is suggested as one of the causes of failure in CRM [41].


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Dispensing and replacement process – Since a number of pharmacies and packaging centers are

involved in dispensing medicines to customers, it is important to link back recurring customer

requests or changes to specific center or pharmacy designated to fill that prescription. Customer

service representatives often have difficulty queuing a replacement request. The system does not

automatically identify where a replacement request should be queued. The dispensing and

replacement process is unclear to many customer representatives and the existence of ‘front end’

and ‘back end’ pharmacies makes it more difficult to know where to queue issues. The process

needs to be streamlined and the system should be redesigned to assist customer representatives in

managing dispensing as well as replacement requests. These issues may not have existed at the

time the system was developed. Rapid growth and packaging and dispensing automation have

further complicated the process.

Queuing procedure – There seems to be a significant confusion over the queue system and how

and where customer contact form should be queued. In the current system, queuing occurs

because a customer service representative in a call center cannot systemically resolve the

customers concerns online. There is a need for an environment that integrates all systems at the

company and that will enable customer service representatives in call centers to resolve the vast

majority of member needs without the need to queue anything. The system needs to be

redesigned to eliminate the need for customer service representatives to memorize procedures for

each pharmacy and generate standard form that is easily understood by all representatives and

pharmacy people alike.

These issues can be viewed under two broad categories of ‘people’ and ‘system’. This

grouping facilitates generalization of the issues and determination of remedial actions. Table 6

summarizes our analysis of these issues relative to ‘people’ or ‘technology’. This breakdown is


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similar to Bostrom’s [6] argument that each work system is made up of two interacting

subsystems: the technical and the social. The technical subsystem relates to processes, tasks and

technologies while social subsystem involves people attributes such as skills and attitudes as well

as organizational attributes such as reward systems and authority structure. This model provides

a useful framework that helps identify the interaction between two sets of influential variables.

The interaction between system and people issues is evident in majority of the factors identified

as causing dissatisfaction with CRM in this company. Further, these factors interact among

themselves and sometime exasperate the situation.

-------------------------------------Insert Table 6 about here--------------------------------------

Finally, it is important to realize that while some of these issues are linked to employee

and user behavior, many of them are inherent attributes of the system and existing processes.

Over the course of the last few years, extensive training efforts have been aimed at addressing

these issues at Merck-Medco with minimal effectiveness, suggesting that systems related issues

are as problematic. Given the number and extent of issues and concerns and the potential impact

that they have on the business, a complete redesign of the systems that customer service

representatives use to access customer and prescription data may be necessary. In any redesign

of the system, one of the key objectives must be to eliminate or minimize the number of issues

that need to be queued. To accomplish this, the multiple systems that are not currently integrated

need to be able to directly communicate with each other. An integrated system will enable a

customer service representative in a call center to resolve member needs online without the need

to queue. Results of this study further suggest a need to streamline processes used to service

customers.


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Table 1. Factor structure

Items Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7

F1-1 .702F1-2 .772

F1-3 .770F1-4 .812F1-5 .649

F2-1 .754F2-2 .814F2-3 .818F2-4 .707

F3-1 .647F3-2 .796

F3-3 .722

F4-1 .695F4-2 .779F4-3 .681

F5-1 .796F5-2 .815

F6-1 .791F6-2 .807

F7-1 .757F7-2 .681

Variance explained 15.22% 12.53% 8.51% 8.18% 7.14% 6.89% 6.47%(Total) 64.95%Eigenvalues 5.374 1.839 1.753 1.442 1.194 1.035 1.002

Factor correlationsFactor 2 .423**Factor 3 .429** .292**

Factor 4 .343** .258** .244*Factor 5 .110** .176** .159** .254**Factor 6 .088* .132** .211** .096** .206**Factor 7 .386** .158** .249** .218** .172** .142**


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Table 3. Completely standardized parameter estimates and t-values

Latent Variable Item Factor Loading (λ) Standard Error t-Value R-Square

F1-1 .56 ---& ---& .32

F1-2 .71 .089 13.85 .50

F1 F1-3 .70 .088 13.75 .49

F1-4 .79 .091 14.70 .62

F1-5 .74 .084 14.25 .55

F2-1 .76 ---& ---& .58

F2 F2-2 .83 .048 20.82 .69

F2-3 .76 .049 19.52 .58

F2-4 .61 .047 15.71 .38

F3-1 .44 ---& ---& .20

F3 F3-2 .75 .14 10.06 .56

F3-3 .75 .16 10.07 .57

F4-1 .67 ---&

---&

.45

F4 F4-2 .66 .089 10.93 .44F4-3 .40 .068 8.24 .16

F5 F5-1 .63 ---&

---&

.40

F5-2 .58 .16 6.46 .34

F6 F6-1 .53 ---& ---& .29

F6-2 .53 .18 5.53 .29

F7 F7-2 .61 ---& ---& .37

F7-2 .41 .073 7.22 .17& Indicates a parameter fixed at 1.0 in the original solution.

Fit Indices: χ2=406.33, df=168, p=0.00000, χ2 /df=2.42, RMSEA=0.044, p(RMSEA


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Table 4. Refined parameter estimates, t-values, fit indices, and item descriptions

Latent Variable Item Factor Loading (λ) Standard Error t-Value R-Square

F1-1 .53 ---& ---& .28

F1 F1-2 .67 .092 13.53 .44

F1-3 .71 .11 12.39 .51

F1-4 .83 .12 12.84 .69

F2-1 .73 ---& ---& .54

F2 F2-2 .85 .063 16.67 .72

F2-4 .61 .052 14.64 .37

F3 F3-2 .74 .14 10.06 .55

F3-3 .77 .11 11.12 .59

F4 F4-1 .69 ---& ---& .47

F4-2 .64 .11 8.72 .41

F6 F6-1 .45 ---&

---&

.21

F6-2 .63 .37 3.66 .39

Fit Indices: χ2

=81.00, df=54, p=0.01014, χ2

/df=1.5, RMSEA=0.026, p(RMSEA


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Applications of computational fluid dynamics (CFD) in the food industry

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