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Dissertation powerpoint presentation

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)

5

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)

6

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.

12

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 ^(https://truelance.net/goto/https://www.emerald.com/insight/publication/issn/1754-2731), Vol. 30 No. 6, pp. 679-700.
^(https://truelance.net/goto/https://highonessays.com/)

15

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)

19

[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.

21

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 ^(https://truelance.net/goto/https://highonessays.com/article/10.1007/s11165-016-9602-2#CR31)).

(
^(https://truelance.net/goto/https://highonessays.com/article/10.1007/s11165-016-9602-2)
article ^(https://truelance.net/goto/https://highonessays.com/article/10.1007/s11165-016-9602-2)
/10.1007/s11165-016-9602-2 ^(https://truelance.net/goto/https://highonessays.com/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).

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