A symbol-based fuzzy decision-making approach to evaluate the user satisfaction on services in academic digital libraries

Document Type: Original Manuscript

Author

College of Mathematics and Computer Science, Guangdong Ocean University

Abstract

Academic libraries play a significant role in providing core services that include research, teaching and learning. User
satisfaction is an important indicator for evaluating the performance of library service. This paper develops a method
for measuring the user satisfaction in a group decision-making environment. First, the performance of service is
evaluated by using questionnaire survey. The scores are recorded by using some simple symbols. Second, the symbol
information along with nonresponse items in questionnaires are fused into an intuitionistic fuzzy information. Third, an
experimental analysis is provided to illustrate the validity and effectiveness of introduced method in this paper. Finally,
the theoretical and practical implications of current model are discussed, the important limitations are recognized, and
some main advantages and future research directions of current method are shown in conclusions.


Keywords


[1] M. Aggarwal, Discriminative aggregation operators for multi criteria decision making, Applied Soft Computing, 52
(2017), 1058-1069.
[2] R. Ali, S. Lee, T. C. Chung,
Accurate multi-criteria decision making methodology for recommending machine learning
algorithm
, Expert Systems with Applications, 71 (2017), 257-278.
[3] K. T. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets and Systems, 20(1) (1986), 87-96.
[4] T. Baleˇzentis, A. Baleˇzentis,
Group decision making procedure based on trapezoidal intuitionistic fuzzy numbers:
MULTIMOORA methodology
, Economic computation and economic cybernetics studies and research, 50(1) (2016),
103-122.
[5] J. F. Bot´ıa, A. M. C´ardenas, C. M. Sierra,
Fuzzy cellular automata and intuitionistic fuzzy sets applied to an optical
frequency comb spectral shape
, Engineering Applications of Artificial Intelligence, 62 (2017), 181-194.
[6] F. J. Cabrerizo, J. A. Morente-Molinera, I. J. P´erez, J. L´opez-Gij´on, E. Herrera-Viedma,
A decision support system
to develop a quality management in academic digital libraries
, Information Sciences, 323 (2015), 48-58.
[7] J. Chai, J. N. K. Liu, E. W. T. Ngai,
Application of decision-making techniques in supplier selection: A systematic
review of literature
, Expert Systems with Applications, 40(10) (2013), 3872-3885.
[8] T. Y. Chen,
An interval-valued intuitionistic fuzzy permutation method with likelihood-based preference functions
and its application to multiple criteria decision analysis
, Applied Soft Computing, 42 (2016), 390-409.
[9] S. Das, D. Guha,
Power harmonic aggregation operator with trapezoidal intuitionistic fuzzy numbers for solving
magdm problems
, Iranian Journal of Fuzzy Systems, 12(6) (2015), 41-74.
[10] R. Decker, A. Hermelbracht,
Planning and evaluation of new academic library services by means of web-based
conjoint analysis
, The Journal of academic librarianship, 32(6) (2006), 558-572.
[11] D. Efthymiou, C. Antoniou,
Understanding the effects of economic crisis on public transport users’ satisfaction
and demand
, Transport Policy, 53 (2017), 89-97.
[12] J. C. Fagan,
The dimensions of library service quality: A confirmatory factor analysis of the LibQUAL+instrument,
Library & Information Science Research,
36(1) (2014), 36-48.
[13] N. Fuhr, G. Tsakonas, T. Aalberg, M. Agosti, P. Hansen, S. Kapidakis, C. P. Klas, L. Kov´acs, M. Landoni, A.
Micsik, et al.
Evaluation of digital libraries, International Journal on Digital Libraries, 8(1) (2007), 21-38.
[14] Z. Gong, X. Xu, Y. Yang, Y. Zhou, H. Zhang,
The spherical distance for intuitionistic fuzzy sets and its application
in decision analysis
, Technological and Economic Development of Economy, 22(3) (2016), 393-415.
[15] Z. Hao, Z. Xu, H. Zhao, R. Zhang,
Novel intuitionistic fuzzy decision making models in the framework of decision
field theory
, Information Fusion, 33 (2017), 57-70.
[16] H. Hashemi, J. Bazargan, S. M. Mousavi, B. Vahdani,
An extended compromise ratio model with an application
to reservoir flood control operation under an interval-valued intuitionistic fuzzy environment
, Applied Mathematical
Modelling,
38(14) (2014), 3495-3511.
[17] H. Hashemi, J. Bazargan, S. M. Mousavi,
A compromise ratio method with an application to water resources
management: An intuitionistic fuzzy set
, Water resources management, 27(7) (2013), 2029-2051.
[18] M. Hassaballah, A. Ghareeb,
A framework for objective image quality measures based on intuitionistic fuzzy sets,
Applied Soft Computing,
57 (2017), 48-59.
[19] F. M. Heath, C. Cook, M. Kyrillidou, B. Thompson,
ARL Index and other validity correlates of LibQUAL+ scores,
portal: Libraries and the Academy,
2(1) (2002), 27-42.
[20] R. Heradio, F. J. Cabrerizo, D. Fern´andez-Amor´os, M. Herrera, E. Herrera-Viedma,
A fuzzy linguistic model to
evaluate the quality of library 2.0 functionalities
, International Journal of Information Management, 33(4) (2013),
642-654.
[21] G. Hesamian, M. G. Akbari,
Semi-parametric partially logistic regression model with exact inputs and intuitionistic
fuzzy outputs
, Applied Soft Computing, 58 (2017), 517-526.
[22] W. Hong, J. Y. L. Thong, W. M. Wong, K. Y. Tam,
Determinants of user acceptance of digital libraries: An
empirical examination of individual differences and system characteristics
, Journal of Management Information
Systems,
18(3) (2002), 97-124.
[23] J. Jeng, What is usability in the context of the digital library and how can it be measured?, Information technology
and libraries,
24(2) (2005), 3-12.
[24] C. Kahraman, M. Keshavarz Ghorabaee, E. K. Zavadskas, S. Cevik Onar, M. Yazdani, B. Oztaysi,
Intuitionistic
fuzzy EDAS method: an application to solid waste disposal site selection
, Journal of Environmental Engineering and
Landscape Management,
25(1) (2017), 1-12.
[25] S. Kao, H. Chang, C. Lin,
Decision support for the academic library acquisition budget allocation via circulation
database mining
, Information Processing & Management, 39(1) (2003), 133-147.
[26] K. Krishnaiyer, F. F. Chen,
Web-based Visual Decision Support System (WVDSS) for letter shop, Robotics and
Computer-Integrated Manufacturing,
43 (2017), 148-154.
[27] M. Kucukvar, S. Gumus, G. Egilmez, O. Tatari,
Ranking the sustainability performance of pavements: An intuitionistic fuzzy decision making method, Automation in Construction, 40 (2014), 33-43.
[28] R. Ladhari,
A review of twenty years of SERVQUAL research, International Journal of Quality and Service Sciences,
1(2) (2009), 172-198.
[29] F. Ma, Z. Mo, Y. Luo,
Empirical research on a model to measure end-user satisfaction with the quality of database
search results
, The Journal of Academic Librarianship, 40(2) (2014), 194-201.
[30] S. M. Mousavi, F. Jolai, R. Tavakkoli-Moghaddam,
A fuzzy stochastic multi-attribute group decision-making approach for selection problems, Group Decision and Negotiation, 22(2) (2013), 207-233.
[31] S. M. Mousavi, B. Vahdani, R. Tavakkoli-Moghaddam, N. Tajik,
Soft computing based on a fuzzy grey group compromise solution approach with an application to the selection problem of material handling equipment, International
Journal of Computer Integrated Manufacturing,
27(6) (2014), 547-569.
[32] S. M. Mousavi, S. Raissi, B. Vahdani, S. M. Hossein,
A fuzzy decision-making methodology for risk response
planning in large-scale projects
, Journal of Optimization in Industrial Engineering, 7 (2011), 57-70.
[33] S. M. Mousavi, B. Vahdani, S. S. Behzadi,
Designing a model of intuitionistic fuzzy VIKOR in multi-attribute
group decision-making problems
, Iranian Journal of Fuzzy Systems, 13(1) (2016), 45-65.
[34] A. S. O. Ogunjuyigbe, T. R. Ayodele, O. A. Akinola,
User satisfaction-induced demand side load management in
residential buildings with user budget constraint
, Applied Energy, 187(2017), 352-366.
[35] C. Rao, J. Zheng, C. Wang, X. Xiao,
A hybrid multi-attribute group decision making method based on grey linguistic
2-tuple
, Iranian Journal of Fuzzy Systems, 13(2) (2016), 37-59.
[36] J. Rezaei, A. Hemmes, L. Tavasszy,
Multi-criteria decision-making for complex bundling configurations in surface
transportation of air freight
, Journal of Air Transport Management, 61 (2017), 95-105.
[37] L. Rong, P. Liu, Y. Chu,
Multiple attribute group decision making methods based on intuitionistic fuzzy generalized
hamacher aggregation operator
, Economic Computation & Economic Cybernetics Studies & Research, 50(2) (2016),
211-230.
[38] G. Tsakonas, C. Papatheodorou,
Exploring usefulness and usability in the evaluation of open access digital libraries,
Information processing & management,
44(3) (2008), 1234-1250.
[39] S. Woodward, K. Berry, S. Bucci,
A systematic review of factors associated with service user satisfaction with
psychiatric inpatient services
, Journal of psychiatric research, 92 (2017), 81-93.
[40] Z. Xu, X. Cai,
Recent advances in intuitionistic fuzzy information aggregation, Fuzzy Optimization and Decision
Making,
9(4) (2010), 359-381.
[41] Z. Xu, R. R. Yager,
Intuitionistic and interval-valued intutionistic fuzzy preference relations and their measures of
similarity for the evaluation of agreement within a group
, Fuzzy Optimization and Decision Making, 8(2) (2009),
123-139.
[42] C. Yue,
Normalized projection approach to group decision-making with hybrid decision information, International
Journal of Machine Learning and Cybernetics,
9(8) (2018), 1365-1375.
[43] C. Yue, A geometric approach for ranking interval-valued intuitionistic fuzzy numbers with an application to group
decision-making
, Computers & Industrial Engineering, 102 (2016), 233-245.
[44] C. Yue,
Entropy-based weights on decision makers in group decision-making setting with hybrid preference representations, Applied Soft Computing, 60 (2017), 737-749.
[45] C. Yue,
Two normalized projection modfels and application to group decision-making, Journal of Intelligent and
Fuzzy Systems,
32(6) (2017), 4389-4402.
[46] C. Yue, Z. Yue,
A soft approach to evaluate the customer satisfaction in e-retailing, In Cao BY. (eds) Fuzzy
Information and Engineering and Decision. IWDS 2016. Advances in Intelligent Systems and Computing
, volume
646, pages 282{296. Springer, Cham, 2018.
[47] Z. Yue, Y. Jia,
A direct projection-based group decision-making methodology with crisp values and interval data,
Soft Computing,
21(9) (2017), 2395-2405.
[48] Z. Yue, Y. Jia,
A projection-based approach to intuitionistic fuzzy group decision making, Scientia Iranica, 24(3)
(2017), 1505-1518.
[49] Z. Yue,
An avoiding information loss approach to group decision making, Applied Mathematical Modelling, 37(1-2)
(2013), 112-126.
[50] Z. Yue,
Group decision making with multi-attribute interval data, Information Fusion, 14(4) (2013), 551-561.
[51] Z. Yue,
An intuitionistic fuzzy projection-based approach for partner selection, Applied Mathematical Modelling,
37(23) (2013), 9538-9551.
[52] Z. Yue,
A group decision making approach based on aggregating interval data into interval-valued intuitionistic
fuzzy information
, Applied Mathematical Modelling, 38(2) (2014), 683-698.
[53] Z. Yue,
TOPSIS-based group decision-making methodology in intuitionistic fuzzy setting, Information Sciences, 277
(2014), 141-153.
[54] Z. Yue, Y. Jia,
An application of soft computing technique in group decision making under interval-valued intuitionistic fuzzy environment, Applied Soft Computing, 13(5) (2013), 2490-2503.
[55] Z. Yue, Y. Jia,
A method to aggregate crisp values into interval-valued intuitionistic fuzzy information for group
decision making
, Applied Soft Computing, 13(5) (2013), 2304-2317.
[56] Z. Yue, Y. Jia,
A group decision making model with hybrid intuitionistic fuzzy information, Computers & Industrial
Engineering,
87 (2015), 202-212.
[57] A. Y¨uksel, M. Rimmington,
Customer-satisfaction measurement, The Cornell Hotel and Restaurant Administration
Quarterly,
39(6) (1998), 60-70.
[58] L. A. Zadeh,
Fuzzy sets, Information and control, 8(3) (1965), 338-353.