Optimizing Client Expectations in Delivering Certainty
Abstract This paper presents a framework for analyzing, measuring, ...
Optimizing Client Expectations in Delivering Certainty
2013
Page 1
Introduction
Client expectations are a critical compone...
Optimizing Client Expectations in Delivering Certainty
2013
Page 2
On the contrary, when the
client has low expectations
f...
Optimizing Client Expectations in Delivering Certainty
2013
Page 3
When a provider over performs relative to the perceived...
Optimizing Client Expectations in Delivering Certainty
2013
Page 4
For example, the KPIs for a project driven by regulatio...
Optimizing Client Expectations in Delivering Certainty
2013
Page 5
The Provider Position Matrix (PPM) maps the role undert...
Optimizing Client Expectations in Delivering Certainty
2013
Page 6
service provider. In such case, a routine delivery as a...
Optimizing Client Expectations in Delivering Certainty
2013
Page 7
A complex project may be expected to face challenges in...
Optimizing Client Expectations in Delivering Certainty
2013
Page 8
Opportunity Costs for failure
Operational Risks associa...
Optimizing Client Expectations in Delivering Certainty
2013
Page 9
Inputs are collected from the clients through a questio...
Optimizing Client Expectations in Delivering Certainty
2013
Page 10
A weighted average assessment of the above three dimen...
Optimizing Client Expectations in Delivering Certainty
2013
Page 11
Weight Determinant Client
C1 C2 C3 C4 C5 C6 C7 C8 C9 C...
Optimizing Client Expectations in Delivering Certainty
2013
Page 12
The relative contribution of each determinant to the O...
Optimizing Client Expectations in Delivering Certainty
2013
Page 13
influence the client expectation more than the executi...
Optimizing Client Expectations in Delivering Certainty
2013
Page 14
Conclusion
Exceeding client expectation is a pre-requi...
Optimizing Client Expectations in Delivering Certainty
2013
Page 15
References:
1
J.M.M., P. T. (1991). “Customer (Dis)Sat...
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Avinash kumar

  1. 1. Optimizing Client Expectations in Delivering Certainty Abstract This paper presents a framework for analyzing, measuring, managing and optimizing client expectations that can be applied across diverse project and client types, in delivering certainty and best quality to the project,. Client expectations are a critical component of the diversity experienced across projects and clients. An absence of a framework has resulted in ad-hoc practices to record and manage client expectation, often devoid of well defined methodology or even a “cheat sheet” to guide the service provider. This gap assumes greater significance considering that exceeding client expectations is central to client retention in current times, across industries. This paper provides a framework to identify core determinants of client expectations and defines the metrics to measure the same. The framework builds upon the tenets of consumer behavior to qualify the zone of tolerance for a given client type, as measured by the relationship between client perception and expectations. It then defines a matrix for the service provider to discover its positioning to meet the client’s requirement given its capability (relative to the industry). It finally quantifies the execution quality that not only defines the client satisfaction, but also influences client perception that defines the expectation in future. The framework then quantifies the above three determinants, assigning weights to each, as per nature of client, project, provider or execution. The guidance score on the client expectation is then calibrated for the qualitative and macro environmental factors to accurately reflect the client expectation. Key words Client Expectation, Provider Positioning, Execution Quality, Expectation Framework Author Avinash Kumar heads the Business Solutions team for Banking and Financial Services clients in the North America geography for Tata Consultancy Services Ltd. In his over 20 years of experience, he has worked across several critical engagements for leading Wall Street firms across their global locations. He has been instrumental in establishing several new relationships for TCS thereby providing him deep insight into managing clients' behavior and expectations and setting up the winning teams. Avinash lives in Toronto with his wife and two children. He can be contacted at: Tata Consultancy Services
  2. 2. Optimizing Client Expectations in Delivering Certainty 2013 Page 1 Introduction Client expectations are a critical component of the diversity experienced across projects and clients. Yet, it remains to be one of the most neglected domains where project management frameworks have been designed or applied. This has resulted in ad-hoc practices to record and manage client expectation, often devoid of well defined methodology or even a “cheat sheet” to guide the service provider. This gap assumes greater significance considering that exceeding client expectations is central to client retention in current times, across industries. Client expectation could vary for the same service provider with a long standing relationship, across a variety of opportunities, and could remain static across a variety of service providers. The expectation is driven by the underlying problem statement, diversity in industry practices, choices in technology, impact of implementation risks, opportunity costs, regulatory implications, and the provider’s capability relative to its peers. This paper provides a framework to identify core determinants of client expectations and defines the metrics to measure the same. In doing so, it draws upon the experience of the author from several project executions, published data on managing client expectations, research findings and tools deployed in enhancing the same. Expectation is “Belief” Client expectation is often interchangeably used with client satisfaction. While the latter is a post facto measurement of the outcome itself, client expectation is the belief about service delivery and tolerances around variance in the outcome (Fig 1 – Source: Poiesz and Bloemer 1 ). For this reason, quantification of client expectation lies beyond the conventional Key Performance Indicators (KPIs). Most of the KPIs in project management measure the performance or the outcome leaving out measurement and management of client expectation to the softer skills of the project lead. When a client has high expectations from a provider, it expects high resilience from the provider in managing project diversity and provides little tolerance for the variance.
  3. 3. Optimizing Client Expectations in Delivering Certainty 2013 Page 2 On the contrary, when the client has low expectations from the provider, there is heightened monitoring, reporting and control - each time there is a variance on the outcome, often coupled with a high tolerance. To measure and manage client expectations, therefore, we need to quantify the degree of control that the client is willing to vest in the provider and the tolerance for variance, amidst uncertainty. As outlined by Parasuraman 2 , the client’s service expectations have two levels, namely, the adequate service level and the desired service level. The adequate service level is the minimum acceptable service level, given the problem statement, and the perceived capability of the provider. The desired service level is the service the customer hopes to receive, including nice to have outcome, and is dependent upon the provider’s past performance or peer reviews about its performance. The difference between the two determines the tolerance zone (Fig 2). Fig 1: Expectations, Performance and Outcome Expectations Performance Outcome Zones of Tolerance KPIs ReliabilityTangibles Responsiveness Assurance and Empathy Missing? Client Need Minimum Outcome Nice to have Outcome Delightful Outcome A Perceived Capability of the Provider to Deliveran Outcome Level B C D Under Performance Over Performance A B C D Expected ServiceLevels forthe Provider Acceptable Service Level Desirable Service Level A B C D Zone of Tolerance Fig 2: Expectation and Zone of Tolerance Thought Leader
  4. 4. Optimizing Client Expectations in Delivering Certainty 2013 Page 3 When a provider over performs relative to the perceived capability, the adequate service level is adjusted to the current need, or the perceived capability, whichever is higher, and the desired service level is pegged at the nice-to-have outcome level. Similarly, when a provider under performs relative to the perceived capability, the adequate service level is reset to the current client requirement, completely disregarding the provider capability, and the desired service level is reset to the past performance or the current need, whichever is higher. This explains for a shift in the client expectation real time, during a project, as the client continuously re-calibrates the expectation with respect to the provider’s ability and the real time performance. The key to measure client expectation, therefore, is to quantify the perceived capability of the provider that drives the adequate service level. This is the level below which the client does not expect the provider to perform. The first step in calibrating client’s expectation, therefore, is to discover the determinants of the adequate service levels and the client perception of the provider’s capability. The perception itself is influenced by The current need of the client and the macro environment influencing the same. The provider’s positioning in the industry and past performance Decoding the Client The client’s perception of the provider can be quantified by developing a Client Outlook Score (COS) that reflects the client’s ability to delegate control to the provider and vest a larger degree of tolerance to variance in outcome. COS reflects the tolerance of the client to withstand variance in delivery and endorse the provider for its contribution, net of the delivery outcome. Greater the COS, higher is the acceptance by the client for the diversity in project execution and lower the expectations from the provider for stringent monitoring, reporting and control. Several factors influence the client’s outlook (Fig 3) such as competitve scenario, regulatory requirement, degree of operational efficiency, opportunity costs and risks, relationship with the provider and choices available with the client, to name a few. The key determinants of COS are as follows: What drives the current requirement What are the risks for the client Who gets impacted with the outcome How is the client engaging with the provider, and What choices does the client have, for meeting its need
  5. 5. Optimizing Client Expectations in Delivering Certainty 2013 Page 4 For example, the KPIs for a project driven by regulation may be entirely different from the one driven by efficiency or profitability. Time to Deliver may be more critical than Cost to Deliver for such projects. If project delays or cost –overruns entail reputation risk, the client will not only closely monitor what has been delivered, but also review as to how was it delivered. Similarly, projects that impact the client’s client and public at large influence client expectations altogether differently than those that impact only internal users. Another sure shot indicator of the client’s trust is the stage and frequency with which provider is engaged with the client. A provider perceived as thought leader is often consulted at the conception stage, while the one seen as a mediocre player gets to perform stereotype executions, even as a follower is often engaged to complement a shortfall in resources, and often characterized with a “Do-as-Directed” posture by the client. Finally, the client’s expectation is driven by the choices it may have on the underlying technology, solution, providers and deployment (scope and time to market). The client is likely to be more demanding in a buyer’s market and more susceptible to the vendor in a greenfield domain. For example, it is quite common for clients to issue Request for Information (RFI) rather than Request for Proposal (RFP) for domains where client has limited competence or information and is expecting the provider to provide thought leadership and solution for the underlying problem statement. Provider’s Positioning Once the Client Outlook Score is arrived at, it becomes essential for the provider to instill the trust in the client by positioning itself in the right quadrant of the problem statement (Fig 4). This is the time to calibrate the pre-performance client expectation by an appropriate posturing by the provider, and drive the expectation during service delivery. Drivers Risks Impact •Compliance •Competition •Efficiency •Excellence •Reputation •Legal •Financial •Operational •User •End-client •Public at large •Regulator Fig 3: Determinants of Client’s Outlook Involvement •Early •Frequent •Need Based •Tardy Choices •Technology • Solution •Provider •Deployment
  6. 6. Optimizing Client Expectations in Delivering Certainty 2013 Page 5 The Provider Position Matrix (PPM) maps the role undertaken by the provider relative to the current problem statement and the provider’s perceived competence. This model draws upon the theory of zone of tolerance 3 that suggests that the service quality results from customers comparing their expectations prior to receiving service to their perceptions of the service experience itself. A higher PRR demonstrates a provider in control and in an appropriate role to deliver the solution, as also perceived by the client. This increases the client’s acceptance to diversity of outcome, whereas, a weak PRR implies either an under-play or an ambitious positioning of the provider with respect to the current need and therefore a higher expectation from the client on monitoring and control from the provider. For a Business-As-Usual (BAU) requirement, the client would expect higher maturity and faster on- boarding of the team. For a next generation project, the provider would be expected to demonstrate thought leadership and business use cases. For a new compliance that needs to be implemented, the client may seek faster time to market, low risk and re-use of existing technology or assets. In a multi- vendor environment, the ask from the client would be a crisp collaboration across the stakeholders. It therefore becomes imperative for the provider to profile the problem statement with its own capabilities in communicating the strategy it would adopt in delivering the relevant solution. Quite often, a provider positions itself in the leadership quadrant in an effort to win the business, notwithstanding that the KPIs for a leadership role are significantly different from those for a routine Provider Capability (relative to Industry) Client’sNeed BAU Niche Complex NextGen Low Average Strong ThoughtLeader Own and Drive the Solution Lead the Solution with Industry Collaboration Forge Alliance with Industry Leaders Invest for future growth Augment Resources / Fill the gapCourse Correct Co-Invest with the client Fig 4: Provider’ Position Matrix
  7. 7. Optimizing Client Expectations in Delivering Certainty 2013 Page 6 service provider. In such case, a routine delivery as against a state of the art delivery goes against the provider, even if the entire projects KPIs are met. Similarly, an under posturing for a BAU problem statement erodes trust of the client, and the client may not perceive value for money if the provider low balls (See: Case Study) Execution Quality Even if there is a judgment error in the pre-sales or pre-performance phase, there is an opportunity for the provider to reset expectations during actual execution. According to Berry and Parasuraman 6 a performance below the tolerance zone will engender customer frustration and decrease customer loyalty. A performance level above the tolerance zone will pleasantly surprise customers and strengthen their loyalty. The consistency of delivery can significantly influence client expectation and can be measured by the Execution Quality (Fig 5). Impact of Perception and Expectation – A Ryanair Case study 4 : In a survey conducted for Ryanair, the client perception and expectation were measured using the SERVQUAL 5 dimensions (Reliability, Responsiveness, Assurance, Empathy, and Tangibles) and the client profile (namely age and purpose of travel). Client’s perception of service delivery was higher than their expectation on tangible dimensions such as kiosk check-in, ticket quality, dedicated luggage belts etc and this resulted in a higher satisfaction. The gap between the perception and expectation was wider for the youngsters (18-29 yrs) than the senior citizens. The seniors expected a more comfortable experience, thereby lowering the tolerance zone. Also, their perception was lower than their expectation in responsiveness and empathy, leading to lower satisfaction. For tourists and people visiting family or travelling for personal reasons, the expectations were quite lower than the perception, yielding a higher client satisfaction. People traveling on business had highest expectations with lowest perceptions about the airline, resulting in lowest satisfaction score on Reliability. Being a low cost carrier, people expect little on the service but more on reliability, tangible experience and responsiveness. Their expectation on empathy and assurance is low, primarily driven by Ryan Air’s past performance but the client’s believe that Ryanair has the ability to improve the service delivery on these dimensions, which could reset client expectation and behavior in future.
  8. 8. Optimizing Client Expectations in Delivering Certainty 2013 Page 7 A complex project may be expected to face challenges in the ramp up phase, but slowly transition into steady state, until delivery. However, varying project management skills and provider competence could yoyo the project from a red to an amber to a green, and back to an amber state for Provider A, or start from a green state but degenerate into a red state, by the time it gets completed, for Provider B. A close monitoring of dependencies, available resources, associated constraints and risk mitigation techniques, along the life cycle of the project can lend consistency to client expectation from the team, and resultant support to the project. A project with a high EQ would be consistent with the variance expected across its life cycle. Whereas, a project with a low EQ could, for example, start very well, raise the bar for itself, and create avoidable criticism for pitfalls encountered later in the cycle. Similarly, another project that consistently oscillates between a red-amber-green status will have a low EQ and demonstrate a lack of control. Environmental Factors In addition to the tangible determinants, there are lots of intangible and environmental factors that need to be considered in managing the client’s expectations. Such factors include, but are not limited to Competitive landscape of the solution Advertising and Promotion by the provider Regulatory Requirements Fig 5: Execution Quality Ramp up SteadyState Delivery Expected Execution Provider A Provider B Project Phases EaseofExecution
  9. 9. Optimizing Client Expectations in Delivering Certainty 2013 Page 8 Opportunity Costs for failure Operational Risks associated with the solution Industry benchmarks Communication with the stakeholders – frequency and channels It is difficult to prescribe the degree of impact of each of these, but it is a good practice to engage in a conversation with the client to identify the same and assess their relevance and impact for the underlying problem statement. The Framework The framework for optimizing client expectations brings together the above determinants, by assigning weights to each, and managing the same. It will use a combination of Quantitative as well as Qualitative Analysis, while developing the Client Expectation ratio or the CE Ratio (Fig 6). The quantitative analysis provides us a guidance score for measuring client expectation after assigning weights to each of the determinants. This could be a good starting point, but needs to be validated for each client and project type. The qualitative analysis overlays the macro environment around the current need such as technology available in the industry, performance benchmarks, degree of competition, regulations around the subject etc to arrive at a measure of client expectation which is more relevant for the current context.
  10. 10. Optimizing Client Expectations in Delivering Certainty 2013 Page 9 Inputs are collected from the clients through a questionnaire or interview to understand the drivers, risk and the impact to business for the underlying problem statement. The provider then scans the environment for competition, industry benchmarks and maturity of the client relationship to capture the determinants of the COS. Factors that influence COS directly, versus those that influence it inversely, are weighted accordingly. Based on the inputs, a quantitative score between 1 to 10 is assigned to each attribute that influences the COS. Similarly, capabilities of the provider relative to the client’s need are quantified on a scale of 1 to 10, to reflect the current requirement, provider’s competence and posturing. Finally, the execution quality of past engagements with the client (either from past relationship, or from peer review) is awarded a score between 1 to 10 to represent the impact of variance across the project types and phases. Depending upon the problem statement, client type and the business model different weights may be assigned to each determinant, and further to various attributes that roll in to the determinant, so as to present a fair view of the client expectation. For example, COS may hold a 60% weight, PRR a 30% weight and Execution Quality a 10% weight in the overall CE Ratio calculation. Similarly, attributes within these major dimensions such as Risks, Impact, Choices, Provider Role, may be weighted differently. Some degree of normalization may also be needed across determinants. A guidancescore thatmeasures the performanceof an affiliate on key dimensionslike  ClientOutlookScore (COS)  ProviderPosition Matrix (PPM)  Execution Quality (EQ) Listof EnvironmentalAttributes such as  Competition  Advertisingand Promotion  Regulatory Requirements  Opportunity Costs forfailure  OperationalRisks  Industry benchmarks  Communication Qualitative AnalysisQuantitative Analysis Fig 6: Developing the CE Ratio
  11. 11. Optimizing Client Expectations in Delivering Certainty 2013 Page 10 A weighted average assessment of the above three dimensions yields a guidance score on the Client Expectation Ratio which represents the client’s perception, the provider’s positioning and the execution variance for the underlying problem statement. The weights can be assigned based on the provider’s past experience with the client and its capability in servicing the current need. It is important to note that some of the underlying factors will directly influence the client expectation, while others may inversely influence the same. An appropriate scoring of the underlying factors will generate an enabling or a limiting score on the client expectation, e.g. high risk in the project will lead to lower client expectation, whereas, use of cutting edge technology and standard automated tools will increase the expectation from the provider. A sample calculation for these variables is tabulated in Table 1. Table 1: Consolidated Data for Determinants of Client Expectation
  12. 12. Optimizing Client Expectations in Delivering Certainty 2013 Page 11 Weight Determinant Client C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 CE-Ratio 4.37 6.46 5.12 4.23 4.78 4.93 4.28 5.35 5.61 5.92 Client Outlook Score (COS) 60% 2.65 3.07 2.25 2.68 2.63 2.88 3.23 3.45 3.68 3.94 Drivers (40%) 20% Compliance 4 2 5 3 4 8 2 9 5 6 30% Competition 3 6 1 2 1 2 4 4 8 9 40% Efficiency 6 2 6 4 3 2 5 4 6 8 10% Excellence 4 2 5 5 4 4 5 4 5 6 Risks (20%) 40% Reputation 5 1 5 5 4 4 4 4 5 6 15% Legal 7 2 6 5 5 5 4 4 5 5 20% Financial 5 2 5 5 5 2 5 5 5 5 25% Operational 4 4 5 5 5 5 5 5 5 5 Impact (15%) 10% User 5 5 1 5 5 5 5 5 5 5 30% End-client 6 8 6 5 5 5 5 5 5 5 35% Public at large 6 6 6 6 5 5 5 5 5 5 25% Regulator 6 5 6 6 6 5 5 5 5 5 Involvement (5%) 50% Early 5 4 5 5 5 5 5 5 5 5 20% Frequent 4 1 4 7 7 6 6 6 5 5 20% Need Based 2 2 1 3 9 2 7 6 6 5 10% Tardy 3 7 6 6 10 1 8 7 6 6 Choices (20%) 20% Technology 9 8 7 2 8 1 9 8 7 6 30% Solution 7 4 9 3 2 6 9 9 8 7 10% Provider 7 7 8 9 9 5 10 9 8 8 40% Deployment 5 3 1 8 8 9 9 9 9 8 Provider Role Ratio (PRR) 30% 1.14 1.50 1.40 1.52 1.72 1.04 1.56 1.61 1.86 1.90 Client Need (25%) 10% Next Gen 7 4 6 6 7 1 8 9 9 9 20% Niche 5 4 6 6 6 7 7 8 8 9 30% Complex 4 3 5 5 6 2 7 7 8 8 40% BAU 5 5 5 5 5 3 6 7 7 8 Provider Capability (15%) 20% Low 4 2 1 5 5 5 6 6 7 7 30% Average 4 4 1 4 6 5 5 6 6 7 30% Strong 5 7 1 5 7 5 5 6 6 6 20% Thought Leader 7 9 6 5 2 5 5 5 6 6 Provider Role (60%) 30% Own 5 2 5 5 3 3 5 6 5 6 15% Lead 3 2 4 5 4 5 4 5 5 5 10% Augment 5 7 5 5 8 5 2 5 7 7 10% Collaborate 5 4 1 5 9 2 7 2 8 5 20% Invest 5 6 1 5 10 3 1 2 5 2 15% Course Correct 8 10 1 6 4 5 8 5 4 9 Execution Quality (EQ) 10% 0.58 0.55 0.59 0.57 0.58 0.37 0.56 0.55 0.38 0.62 Expected (50%) 10% Ramp Up 6 4 6 6 6 6 3 3 2 6 70% Steady State 5 5 6 6 6 6 6 5 1 8 20% Delivery 6 6 6 6 6 6 6 6 5 3 Actual (50%) 10% Ramp Up 4 2 5 5 6 6 6 6 6 5 70% Steady State 6 8 1 5 6 6 6 6 6 6 20% Delivery 5 4 1 5 5 6 6 6 6 6
  13. 13. Optimizing Client Expectations in Delivering Certainty 2013 Page 12 The relative contribution of each determinant to the Overall Client Expectation may be arrived at through a weighted consolidation of the quantified inputs (Fig 7). The clients with highest CE Ratio will typically carry a high expectation for the provider. The degree to which this expectation is influenced by their perception, provider’s posturing and ability to execute can also be measured with this quantitative framework. Using the framework, it is also possible to discover the key determinants influencing client expectation, and their relative influence on the same (Fig 8). For example, being perceived as a Thought Leader, capable of providing Next Gen Solutions and using state of the art technology for the Solution may 0 2 4 6 8 10 Compliance Competition Efficiency Excellence Reputation Financial Operational End-client Publicat large Technology Solution NextGen ThoughtLeader Lead Augment Collaborate Invest RampUp Steady State Delivery C1 C2 C3 High Medium Low Fig 8: Sample Determinants of Client Expectation Clients / stakeholders CERatio Fig 7: Sample CE Ratios 0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 C3 C6 C1 C4 C5 C2 C7 C8 C9 C10 Execution Quality (EQ) Provider Role Ratio (PRR) ClientOutlook Score (COS)
  14. 14. Optimizing Client Expectations in Delivering Certainty 2013 Page 13 influence the client expectation more than the execution quality or efficiency. Measuring Expectation Based on study conducted by Irja Hyvari 7 , there is a strong correlation between critical success factors for projects of varying type (Fig 9). These correlations can be base lined to arrive at KPIs for managing client expectations, as the clients would turn to service providers in delivering these success factors, across project types. Using the above framework, following metrics could be used to measure and manage client expectations:  Adequate Service Levels – The minimum acceptable service level is a sure indicator of client expectation, factoring the service provider’s capability and past performance  Zone of Tolerance - The difference between the adequate service level and the desired service level highlights the client’s expectation on the service provider’s performance in the current bid.  Client perception – The belief that a client holds on the provider’s ability to meet its current requirements, as manifested in client communications (RFI vs. RFP), early involvement vs. late and degree of control vested in the provider Fig 9: Correlation between Project Types and Success Factors End–User commitment Adequate funds / Resources Communication Clear Organization Job Description Client Sub-Contractor Company/Organization size Project Size Project Density (no of cross stakeholder activities / interfaces) Organization Type - Matrix or functional Project Management Experience Positive Correlation Weak Correlation Negative Correlation KPIs for Managing Client Expectations Project Diversity
  15. 15. Optimizing Client Expectations in Delivering Certainty 2013 Page 14 Conclusion Exceeding client expectation is a pre-requisite to client retention and growth. It can only be done by an accurate profiling of the client and its current need with respect to the macro environment. An appropriate positioning and posturing is needed by the service provider to ensure that the client expectations are calibrated for the provider’s ability in delighting the client. Once a trust has been established, impeccable execution is needed to retain the same and strengthen the perception for the client. It is time project management frameworks encapsulated the measurement and management of client expectations by defining processes, checkpoints and metrics that deliver the same.
  16. 16. Optimizing Client Expectations in Delivering Certainty 2013 Page 15 References: 1 J.M.M., P. T. (1991). “Customer (Dis)Satisfaction with the Performance of Products. Proceedings from the Euroepan Marketing Academy Conference (pp. 446-462). Dublin: Marketing Thought Around the World 2 A. Parasuraman, L. B. (1991). Understanding Customer Expectations of Service. Sloan Management Review, 39. 3 Robert Johnston. (2002). The Zone of Tolerance: Exploring the relationship between service transactions and satisfaction with the overall service. Warwick Business School, University of Warwick, UK. 4 Nattaphol Thanataveerat, Z. J. (2007, June 07). School of Business. Retrieved from Malardalens University: http://www.eki.mdh.se/uppsatser/foretagsekonomi/VT2007-FEK-D-1520.pdf 5 Parasuraman, B. Z. (1990). Delivering Quality Service; Balancing Customer Perceptions and Expectations. Free Press. 6 A, B. L. (1991). Marketing Services: Competing Through Quality,. New York: Free Press. 7 HYVÄRI, I. (2006). Success of Projects in Different Organizational Conditions. Project Management Journal, 31-41.

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