AN EMPIRICAL STUDY OF SERVICE QUALITY
PERCEPTION IN BRAZILIAN PUBLIC SECTOR
Student: Lie Koba
A Dissertation
Presented to the Faculty of EU Business School in Partial Fulfilment of the Requirements for the Degree: MBA in International Business
AN EMPIRICAL STUDY OF SERVICE QUALITY
PERCEPTION IN BRAZILIAN PUBLIC SECTOR
[Objective of the research]
Understand perception of service quality of the public services in Brazil.
[Value]
Fill gap in knowledge of user perception in the North region.
[Presentation structure]
LITERATURE REVIEW
1.1 Public Sector
1.2 Definition of Service Quality
1.3 Service Quality Models
2. METHODOLOGY
3. FINDINGS
3.1 SERVPERF dimensions
3.2 Hypotheses testing
4. LIMITATIONS
5. CONCLUSION
[Presentation structure]
LITERATURE REVIEW
1.1 Public Sector
1.2 Definition of Service Quality
1.3 Service Quality Models
2. METHODOLOGY
3. FINDINGS
3.1 SERVPERF dimensions
3.2 Hypotheses testing
4. LIMITATIONS
5. CONCLUSION
1. LITERATURE REVIEW
1.1 Public sector
Part of the economy owned or controlled by government. (Britannica)
In Brazil: 1808~ (Imperial period)
– 1939: first regulation
– 1988: Promulgation of the constitution
– 1995: State reform plan (based on New Public Management)
After: ‘Extent to which a product or service meets and/or exceeds consumer’s expectations’ (Magd and Curry, 2003:265).
1.2 Definition of service quality
Before: ‘Conformance to standards and specifications’ (Crosby, 1979, cited in Sharabi and Davidow, 2010:190)
Service quality Customer satisfaction
(Mohanty, 2012)
Intangibility
Perishability
Inseparability
Heterogeneity
User
Provider
Service
Inseparability of production and consumption in service. Quality is not only result but the process.
Example: If waiter is rude or slow, food might be good but overall quality will be lacking (sharabi and Davidow, p. 190)
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① Nordic Model: technical and functional (Grönroos)
② GAP model (Parasuraman, Zheitaml and Berry)
1.3 Service Quality Models
SERVQUAL
Service quality =
Perception – Expectation
(22 questions + 22 questions)
X
SERVPERF
Service quality = Perception
(22 questions)
✔
Service quality dimensions:
Tangibles
Empathy
Responsiveness
Safety
Reliability
③ Multi-level model / Retails Service Quality model (Dabholkar)
④ Hierarchical model (Brady and Cronin)
Gap 1: Expectations gap
Gap 2: Standards gap
Gap 3: Performance gap
Gap 4: Communicatio gap
Ghotbabadi et al. (2012)
Inseparability of production and consumption in service. Quality is not only result but the process.
Example: If waiter is rude or slow, food might be good but overall quality will be lacking (sharabi and Davidow, p. 190)
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Conceptual framework
Point of interest:
To what extent these components influence the perception of service quality?
Inseparability of production and consumption in service. Quality is not only result but the process.
Example: If waiter is rude or slow, food might be good but overall quality will be lacking (sharabi and Davidow, p. 190)
7
[Presentation structure]
LITERATURE REVIEW
1.1 Public Sector
1.2 Definition of Service Quality
1.3 Service Quality Models
2. METHODOLOGY
3. FINDINGS
3.1 SERVPERF dimensions
3.2 Hypotheses testing
4. LIMITATIONS
5. CONCLUSION
2. Research methodology
Descriptive
Stratified sampling
Online survey
SERVPERF questionnaire
Data organization and analysis by Microsoft Excel
(1) Amazon
(2) South-Central
(3) Northeast
Manaus city
Fig.1 Geo-economic regions
[Survey location]
[Presentation structure]
LITERATURE REVIEW
1.1 Public Sector
1.2 Definition of Service Quality
1.3 Service Quality Models
2. METHODOLOGY
3. FINDINGS
3.1 SERVPERF dimensions
3.2 Hypotheses testing
4. LIMITATIONS
5. CONCLUSION
3. FINDINGS
3.1 Demographics
Number of respondents: 100
Valid answers: 95
[Comments]
Age influences the perception of quality
Few respondents from higher ages: limitations in use of online survey
Gender
Services
Age
Sivesan and Karunanithy (2013) indicate that demographical factors that influence service quality perception are age, income levels and education level. Another study carried out by Christia and Ard (2016) showed a similar result and concluded that age, income and ethnicity are factors that influence the quality perception
11
%
Female Male 0.557894736842105 0.442105263157895
%
Road paving Public transportation Electric power supply / street lighting Public school / university Hospital / Emergency care units Department of traffic Postal services Public safety Water supply Cleaning / garbage collection service Airport 0.252631578947368 0.147368421052632 0.136842105263158 0.115789473684211 0.0947368421052632 0.0631578947368421 0.0631578947368421 0.0631578947368421 0.0315789473684211 0.0210526315789474 0.0105263157894737 # respondents
Road paving Public transportation Electric power supply / street lighting Public school / university Hospital / Emergency care units Department of traffic Postal services Public safety Water supply Cleaning / garbage coll ection service Airport 24.0 14.0 13.0 11.0 9.0 6.0 6.0 6.0 3.0 2.0 1.0
[Comments]
Lowest mean score: Reliability
Highest mean score: Tangibles
Actual service quality in public sector does not meet user’s expectations
Service quality perception in North differs from the South
(1) Completely disagree; (2) Partially disagree; (3) Indifferent; (4) Partially agree; (5) Completely agree
Comparative data
3. FINDINGS
3.2 SERVPERF dimensions
Scores considering STD DEV is max. 3.8.
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Hypothesis 1: Customer-orientation approach influence the perception of satisfaction in service quality.
Hypothesis 2: Continuous improvement practices influence the perception of satisfaction in service quality.
Hypothesis 3: Innovation influences the perception of satisfaction in service quality.
Null hypothesis: There is no other factor that influence satisfaction in service quality.
Dependent variable: User satisfaction
3. FINDINGS
3.3 Hypotheses testing
H1: Customer orientation approach influence the perception of satisfaction in service quality.
Null hypothesis is rejected.
Low scores due to lack of:
competition in public sector
employees’ autonomy to provide efficient solutions
3.3 Hypotheses testing
Questionnaire
Regression analysis
Conclusion
H2: Continuous improvement practices influence the perception of satisfaction in service quality.
Null hypothesis is not rejected.
Further study recommended
Other factors might be influencing average low score
Opportunity for user’s feedback unknown
Questionnaire
Regression analysis
Conclusion
3.3 Hypotheses testing
Different results from other study (Koval et al, 2018) in service industry.
Koval
, O.,
Nabareseh
, S.,
Chromjakova
, F. and
Marciniak, R. (2018), “Can continuous improvement lead to satisfied customers? Evidence from the services industry”,
The TQM Journal, Vol. 30 No. 6, pp. 679-700.
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H3: Innovation influences the perception of satisfaction in service quality.
Null hypothesis is rejected.
Low average score due to:
Cost control / resource scarcity
Collaboration atmosphere (organizational culture)
Questionnaire
Regression analysis
Conclusion
3.3 Hypotheses testing
Hypothesis 1: Customer-orientation approach influence the perception of satisfaction in service quality.
Hypothesis 2: Continuous improvement practices influence the perception of satisfaction in service quality.
Hypothesis 3: Innovation influences the perception of satisfaction in service quality.
3.3 Hypotheses testing
Null hypothesis is rejected.
Null hypothesis: There is no other factor that influence satisfaction in service quality.
Dependent variable: User satisfaction
Null hypothesis is rejected.
Null hypothesis is not rejected.
[Comment]
Proposed model is statistically significant Jointly, factors influence user satisfaction
Dependent variable: user satisfaction
3.3 Hypotheses testing
To check:
Only innovation has positive coefficient
P-values higher than 0,05
Analysis of overall data and Cronbach’s alpha
Measurement of internal consistency
Likert-type questionnaire
To confirm whether questions measure the same latent variable
[Comments]
Mean scores lower than medium neutral point
Standard deviation of continuous improvement slightly higher than other items need further study
Tangibles and Responsiveness: individual coefficients lower than 0,70 judged to not affect research
Total coefficient: 0,93
Total coefficient 0,93 in both conditions (only with SERVPERF and with the items included)
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[Presentation structure]
LITERATURE REVIEW
1.1 Public Sector
1.2 Definition of Service Quality
1.3 Service Quality Models
2. METHODOLOGY
3. FINDINGS
3.1 SERVPERF dimensions
3.2 Hypotheses testing
4. LIMITATIONS
5. CONCLUSION
5. CONCLUSION
Small sample population for each service evaluated
Data from specific fields used for comparative analysis rather than general sector
SERVPERF data from Northeast region not available
4. LIMITATIONS
Public service quality perception is not homogenous across country
Service quality perception score is low in Manaus city
Users are not satisfied
‘Customer-orientation’ and ‘innovation’ are statistically significant factors that influence customer satisfaction.
‘Continuous improvement’ is not statistically significant in public service satisfaction recommended further study
Most critical dimension: Reliability
The lowest score
Continuous improvement
– Not intended to state that continuous improvement is not relevant to the quality management.
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Sources:
Ghotbabadi, A. R., Baharun, R. & Feiz, S. (2012) A Review of Service Quality Models. In: 2nd International Conference on Management. Available from: (Accessed: 07/04/19).
Magd, H. & Curry, A. (2003) Benchmarking: Achieving best value in public-sector organisations, Benchmarking. 10(3) pp. 261-286.
Mohanty, R. P. (2012) Understanding service quality. Production Planning and Control: The Management of Operations. pp.1-16. DOI: 10.1080/09537287.2011.643929.
Sharabi, M. & Davidow, M. (2010) Service quality implementation: problems and solutions. International Journal of Quality and Service Sciences. 2(2) pp. 189-205. DOI 10.1108/17566691011057357.
Wegrich, K. (no date) Public Sector. In: Encyclopaedia Brittanica. Available at: (Accessed: 10/06/19).
Thank you!
Questions?
Under construction
To check dimensionality: exploratory fator analysis
In short, you’ll need more than a simple test of reliability to fully assess how “good” a scale is at measuring a concept. You will want to assess the scale’s face validity by using your theoretical and substantive knowledge and asking whether or not there are good reasons to think that a particular measure is or is not an accurate gauge of the intended underlying concept. And, in addition, you can address construct validity by examining whether or not there exist empirical relationships between your measure of the underlying concept of interest and other concepts to which it should be theoretically related.
Quality may traditionally be understood in terms of such notions as validity (the extent to which an instrument measures what it claims to measure, rather than something else) and reliability (the extent to which an instrument can be expected to give the same measured outcome when measurements are repeated) (Taber,
2013a).
(
article
/10.1007/s11165-016-9602-2) Taber, 2017
Taber examined how Cronbach is used and interpreted in published articles in Science Education Journals. He concluded that there is no clear consensus on the labels to describe alpha values. Terms are arbitrary (satisfactory,, high, fairly high, etc).
