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May  2022, 18(3): 1809-1833. doi: 10.3934/jimo.2021045

## Identifying and determining crowdsourcing service strategies: An empirical study on a crowdsourcing platform in China

 1 School of Management, Hefei University of Technology, Hefei, 230009, China 2 Department of Industrial and Systems Engineering, University of Florida, Gainesville, 32611, USA

* Corresponding author: Zhanglin Peng

Received  May 2020 Revised  December 2020 Published  May 2022 Early access  March 2021

The crowdsourcing platforms, as mediators and service providers, play a critical role in crowdsourcing initiatives. The service quality of a platform has a direct impact on solver satisfaction, and ultimately affects the platform's continuous operation. Service quality can be measured by service quality attributes (SQAs). Thus, identifying and quantifying SQAs are crucial to enhance solver satisfaction. Besides, choosing pertinent strategies and determining priorities for the SQAs are another core issue. To address these issues, this study proposes a novel decision framework that combines the Fuzzy Analytical Kano (FAK) and the Importance-performance analysis (IPA) models. Firstly, 24 related SQAs are identified from five dimensions of service quality. Secondly, we quantify these SQAs into a polar form representation scheme in accordance with the FAK model. In addition, the pertinent service strategies and priorities of the SQAs are confirmed by using the IPA model and Kano categories. Finally, decision priority rules for corresponding strategies and priorities of SQAs are constructed. An empirical study is presented to demonstrate our proposed decision framework on ZBJ platform, which is one of the most widely used online crowdsourcing platform in China.

Citation: Xu Zhang, Zhanglin Peng, Qiang Zhang, Xiaoan Tang, Panos M. Pardalos. Identifying and determining crowdsourcing service strategies: An empirical study on a crowdsourcing platform in China. Journal of Industrial and Management Optimization, 2022, 18 (3) : 1809-1833. doi: 10.3934/jimo.2021045
##### References:
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Cheng, Research on the optimal combination and scheduling method of crowdsourcing members in a cloud design platform, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 233 (2019), 2196-2209.  doi: 10.1177/0954405419855874. [11] X. Cheng, Q. Gou, J. Yue and Y. Zhang, Equilibrium decisions for an innovation crowdsourcing platform, Transp. Res. Part E Logist. Transp. Rev., 125 (2019), 241-260.  doi: 10.1016/j.tre.2019.03.006. [12] H. Cho, M. Chen and S. Chung, Testing an integrative theoretical model of knowledge-sharing behavior in the context of Wikipedia, J. Am. Soc. Inf. Sci Technol., 61 (2010), 1198-1212.  doi: 10.1002/asi.21316. [13] R. K. S. Chu and T. Choi, An importance-performance analysis of hotel selection factors in the Hong Kong hotel industry: A comparison of business and leisure travellers, Tour Manag., 21 (2000), 363-377.  doi: 10.1016/S0261-5177(99)00070-9. [14] X. N. Deng, K. D. Joshi and R. D. Galliers, An importance-performance analysis of hotel selection factors in the Hong Kong hotel industry: A comparison of business and leisure travellers, MIS Q., 40 (2016), 279-302. [15] B. Gong, X. Chen and C. Hu, Fuzzy entropy clustering approach to evaluate the reliability of emergency logistics system, Energy Procedia., 16 (2012), 278-283.  doi: 10.1016/j.egypro.2012.01.046. [16] J. Howe, The rise of crowdsourcing, Wired Mag., 14 (2006), 1-4. [17] J. Howe, Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business, 1$^st$ edition, Crown Publishing Group, New York, 2008. [18] J. C. Huang, Application of Kano model and IPA on improvement of service quality of mobile healthcare, Int. J. Mob. Commun., 16 (2018), 227-246. [19] L. C. Huang, W. L. Shiau and Y. H. Lin, What factors satisfy e-book store customers? Development of a model to evaluate e-book user behavior and satisfaction, Internet Res., 27 (2017), 563-585.  doi: 10.1108/IntR-05-2016-0142. [20] Y. Huang, P. 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Appl., 36 (2009), 4479-4484.  doi: 10.1016/j.eswa.2008.05.034. [31] B. Li, Y. Cheng, Y. Yuan, G. Wang and L. Chen, Three-Dimensional Stable Matching Problem for Spatial Crowdsourcing Platforms, The 25th ACM SIGKDD International Conference, ACM, 2019. doi: 10.1145/3292500.3330879. [32] S. Li and Q. Xiao, Classification and improvement strategy for design features of mobile tourist guide application: A Kano-IPA approach, Mobile Inf. Syst., 20 (2020), 1-9.  doi: 10.1155/2020/8816130. [33] W. Li, W. J. Wu, H. M. Wang, X. Q. Cheng, H. J. Chen, Z. H. Zhou and R. Ding, Crowd intelligence in AI 2.0 era, Front Inf. Technol. Electron. Eng., 18 (2017), 15-43.  doi: 10.1631/FITEE.1601859. [34] C. Liang, Y. Hong and B. Gu, Effects of IT-enabled monitoring systems in online labor markets, Social Science Research Network, 2016 International Conference on Information Systems, 36, Dublin, Ireland, 2016. [35] T. X. Liu, J Yang, L. A. Adamic and Y. Chen, Crowdsourcing with all-pay auctions: A field experiment on Taskcn, Manag Sci, 60 (2014a), 2020-2037. [36] J. A. Martilla and J. C. James, Importance-performance analysis, J. Mark, 41 (1977), 77-79. [37] J. C. Nunnally, Psychometric Theory, McGraw-Hill, New York, 1978. [38] M. R. Ogiela and L. Ogiela, Cognitive cryptography techniques for intelligent information management, Int. J. Inf. Manag., 40 (2018), 21-27.  doi: 10.1016/j.ijinfomgt.2018.01.011. [39] S. Oreg and N. Oded, Exploring motivations for contributing to open source initiatives: The roles of contribution context and personal values, Comput. Hum. Behav., 24 (2008), 2055-2073.  doi: 10.1016/j.chb.2007.09.007. [40] F. Y. Pai, T. M. Yeh and C. Y. Tang, Classifying restaurant service quality attributes by using Kano model and IPA approach, Total Qual. Manag. Bus. Excell., 29 (2018), 301-328.  doi: 10.1080/14783363.2016.1184082. [41] A. Parasuraman, V. A. Zeithaml and A. 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##### References:
 [1] S. À. Campo, V. J. Khan, K. Papangelis and P. Markopoulos, Community heuristics for user interface evaluation of crowdsourcing platforms, Future Gener. Comput. Syst., 95 (2019), 775-789.  doi: 10.1016/j.future.2018.02.028. [2] R. Alharthi, E. Aloufi, I. Alrashd, A. Alqazzaz and J. Rrushi, Protecting location privacy for crowd workers in spatial crowdsourcing using a novel dummy-based mechanism, IEEE Access, 8 (2020), 114608-114622. [3] E. W. Anderson and V. Mittal, Strengthening the satisfaction-profit chain, J. Serv. Res., 3 (2000), 107-120.  doi: 10.1177/109467050032001. [4] N. Archak, Money, Glory and Cheap Talk: Analyzing Strategic Behavior of Contestants in Simultaneous Crowdsourcing Contests on TopCoder. com, Proceedings of the 19th International Conference on World Wide Web, Raleigh, North Carolina, 2010. doi: 10.1145/1772690.1772694. [5] S. Balasubramanian, P. Konana and N. M. Menon, Customer satisfaction in virtual environments: A study of online investing, Manag. Sci., 49 (2003), 871-889.  doi: 10.1287/mnsc.49.7.871.16385. [6] B. L. Bayus, Crowdsourcing new product ideas over time: An analysis of the Dell IdeaStorm community, Manag. Sci., 59 (2012), 226-244.  doi: 10.1287/mnsc.1120.1599. [7] C. Berger, R. Blauth and D. Boger, Kano's methods for understanding customer-defined quality, Cent. Qual. Manag., 2 (1993), 3-36. [8] D. C. Brabham, Moving the crowd at iStockphoto: The composition of the crowd and motivations for participation in a crowdsourcing application, First Monday, 13 (2008). doi: 10.5210/fm. v13i6.2159. [9] D. C. Brabham, Moving the crowd at threadless: Motivations for participation in a crowdsourcing application, Inf. Commun. Soc., 13 (2010), 1122-1145.  doi: 10.1080/13691181003624090. [10] J. Chen, R. Mo, J. Chu, S. Yu and F. Cheng, Research on the optimal combination and scheduling method of crowdsourcing members in a cloud design platform, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 233 (2019), 2196-2209.  doi: 10.1177/0954405419855874. [11] X. Cheng, Q. Gou, J. Yue and Y. Zhang, Equilibrium decisions for an innovation crowdsourcing platform, Transp. Res. Part E Logist. Transp. Rev., 125 (2019), 241-260.  doi: 10.1016/j.tre.2019.03.006. [12] H. Cho, M. Chen and S. Chung, Testing an integrative theoretical model of knowledge-sharing behavior in the context of Wikipedia, J. Am. Soc. Inf. Sci Technol., 61 (2010), 1198-1212.  doi: 10.1002/asi.21316. [13] R. K. S. Chu and T. Choi, An importance-performance analysis of hotel selection factors in the Hong Kong hotel industry: A comparison of business and leisure travellers, Tour Manag., 21 (2000), 363-377.  doi: 10.1016/S0261-5177(99)00070-9. [14] X. N. Deng, K. D. Joshi and R. D. Galliers, An importance-performance analysis of hotel selection factors in the Hong Kong hotel industry: A comparison of business and leisure travellers, MIS Q., 40 (2016), 279-302. [15] B. Gong, X. Chen and C. Hu, Fuzzy entropy clustering approach to evaluate the reliability of emergency logistics system, Energy Procedia., 16 (2012), 278-283.  doi: 10.1016/j.egypro.2012.01.046. [16] J. Howe, The rise of crowdsourcing, Wired Mag., 14 (2006), 1-4. [17] J. Howe, Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business, 1$^st$ edition, Crown Publishing Group, New York, 2008. [18] J. C. Huang, Application of Kano model and IPA on improvement of service quality of mobile healthcare, Int. J. Mob. Commun., 16 (2018), 227-246. [19] L. C. Huang, W. L. Shiau and Y. H. Lin, What factors satisfy e-book store customers? Development of a model to evaluate e-book user behavior and satisfaction, Internet Res., 27 (2017), 563-585.  doi: 10.1108/IntR-05-2016-0142. [20] Y. Huang, P. V. Singh and K. Srinivasan, Crowdsourcing new product ideas under consumer learning, Manag. Sci., 60 (2014), 2138-2159. [21] Y. Ilbahar and S. Cebi, Classification of design parameters for e-commerce websites: A novel fuzzy Kano approach, Telematics and Inform., 34 (2017), 1814-1825.  doi: 10.1016/j.tele.2017.09.004. [22] L. C. Irani and M. S. Silberman, Turkopticon: Interrupting Worker Invisibility in Amazon Mechanical Turk, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Paris, France, 2013. doi: 10.1145/2470654.2470742. [23] S. L. Jarvenpaa and V. K. Tuunainen, How finnair socialized customers for service co-creation with social media, MIS Q Exec., 14 (2013), 125-136. [24] N. Kano, N. Seraku, F. Takashi and S. Tsuji, Attribute quality and must-be quality, J. Jpn. Soc. Qual. Control, 14 (1984), 39-48. [25] S. H. Khodadad Hosseini and L. Behboudi, Brand trust and image: Effects on customer satisfaction, Int. J. Health Care Qual. Assur., 30 (2017), 580-590. [26] Y. F. Kuo, J. Y. Chen and W. J. Deng, IPA-Kano model: A new tool for categorising and diagnosing service quality attributes, Total Qual. Manag. Bus., 23 (2012), 731-748.  doi: 10.1080/14783363.2011.637811. [27] A. R. Kurup and G. P. Sajeev, Task Personalization for Inexpertise Workers in Incentive Based Crowdsourcing Platforms, 2018 International Conference on Advances in Computing, Communications and Informatics, Bangalore, INDIA, 2018. doi: 10.1109/ICACCI. 2018.8554475. [28] D. M. Lambert and A. Sharma, A customer-based competitive analysis for logistics decisions, Int. J. Phys. Distrib. Logist. Manag., 20 (1990), 17-24.  doi: 10.1108/EUM0000000000350. [29] T. D. Latoza and V. D. H. Andre, Crowdsourcing in software engineering: Models, motivations, and challenges, IEEE Software, 33 (2015), 74-80.  doi: 10.1109/ICSE-SEIP.2019.00043. [30] Y. C. Lee and S. Y. Huang, A new fuzzy concept approach for Kano's model, Expert Syst. Appl., 36 (2009), 4479-4484.  doi: 10.1016/j.eswa.2008.05.034. [31] B. Li, Y. Cheng, Y. Yuan, G. Wang and L. Chen, Three-Dimensional Stable Matching Problem for Spatial Crowdsourcing Platforms, The 25th ACM SIGKDD International Conference, ACM, 2019. doi: 10.1145/3292500.3330879. [32] S. Li and Q. Xiao, Classification and improvement strategy for design features of mobile tourist guide application: A Kano-IPA approach, Mobile Inf. Syst., 20 (2020), 1-9.  doi: 10.1155/2020/8816130. [33] W. Li, W. J. Wu, H. M. Wang, X. Q. Cheng, H. J. Chen, Z. H. Zhou and R. Ding, Crowd intelligence in AI 2.0 era, Front Inf. Technol. Electron. Eng., 18 (2017), 15-43.  doi: 10.1631/FITEE.1601859. [34] C. Liang, Y. Hong and B. Gu, Effects of IT-enabled monitoring systems in online labor markets, Social Science Research Network, 2016 International Conference on Information Systems, 36, Dublin, Ireland, 2016. [35] T. X. Liu, J Yang, L. A. Adamic and Y. Chen, Crowdsourcing with all-pay auctions: A field experiment on Taskcn, Manag Sci, 60 (2014a), 2020-2037. [36] J. A. Martilla and J. C. James, Importance-performance analysis, J. Mark, 41 (1977), 77-79. [37] J. C. Nunnally, Psychometric Theory, McGraw-Hill, New York, 1978. [38] M. R. Ogiela and L. Ogiela, Cognitive cryptography techniques for intelligent information management, Int. J. Inf. Manag., 40 (2018), 21-27.  doi: 10.1016/j.ijinfomgt.2018.01.011. [39] S. Oreg and N. Oded, Exploring motivations for contributing to open source initiatives: The roles of contribution context and personal values, Comput. Hum. Behav., 24 (2008), 2055-2073.  doi: 10.1016/j.chb.2007.09.007. [40] F. Y. Pai, T. M. Yeh and C. Y. Tang, Classifying restaurant service quality attributes by using Kano model and IPA approach, Total Qual. Manag. Bus. Excell., 29 (2018), 301-328.  doi: 10.1080/14783363.2016.1184082. [41] A. Parasuraman, V. A. Zeithaml and A. Malhotra, E-S-QUAL: A multiple-item scale for assessing electronic service quality, J. Serv. Res., 7 (2005), 213-233.  doi: 10.1177/1094670504271156. [42] N. B. Peddibhotla and M. R. Subramani, Contributing to public document repositories: A critical mass theory perspective, Organ. Stud., 28 (2007), 327-346.  doi: 10.1177/0170840607076002. [43] M. K. Poetz and M. Schreier, The value of crowdsourcing: Can users really compete with professionals in generating new product ideas?, J. Prod. Innov. Manag., 29 (2012), 245-256.  doi: 10.1111/j.1540-5885.2011.00893.x. [44] P. Prasarnphanich and C. Wagner, The role of wiki technology and altruism in collaborative knowledge creation, Data Process Better Bus. Educ., 49 (2009), 33-41. [45] L. Qian, Construction and Evaluation of Customer Satisfaction Index System in E-Shopping, 2015 Fifth International Conference on Instrumentation & Measurement, Computer, Communication and Control, 2015. doi: 10.1109/IMCCC. 2015.71. [46] M. Ramezani, M. Bashiri and R. Tavakkoli-Moghaddam, A new multi-objective stochastic model for a forward/reverse logistic network design with responsiveness and quality level, Appl. Math. Model, 37 (2013), 328-344.  doi: 10.1016/j.apm.2012.02.032. [47] S. Sarker, M. A. Razzaque, M. Mehedi, A. Almogren and M. Zhou, Optimal selection of crowdsourcing workers balancing their utilities and platform profit, IEEE Internet Things J., 6 (2019), 8602-8614.  doi: 10.1109/JIOT.2019.2921234. [48] L. Shun and X. Quan, Classification and improvement strategy for design features of mobile tourist guide application: A Kano-IPA approach, Mob. Inf. Syst., 20 (2020), 1-9. [49] N. Subramanian, A. Gunasekaran, J. Yu, J. Cheng and K. Ning, Customer satisfaction and competitiveness in the Chinese E-retailing: Structural equation modeling (SEM) approach to identify the role of quality factors, Expert Syst. Appl., 41 (2014), 69-80.  doi: 10.1016/j.eswa.2013.07.012. [50] Y. Sun, Y. Fang and K. H. Lim, Understanding sustained participation in transactional virtual communities, Decis. Support Syst., 53 (2012), 12-22.  doi: 10.1016/j.dss.2011.10.006. [51] B. Veloso, F. Leal, B. Malheiro and F. Moreira, Distributed trust & reputation models using blockchain technologies for tourism crowdsourcing platforms, Procedia. Comput. Sci., 160 (2019), 457-460.  doi: 10.1016/j.procs.2019.11.065. [52] Z. Wen and L. Lin, Optimal fee structures of crowdsourcing platforms: Optimal fee structures of crowdsourcing platforms, Decis. Sci., 47 (2016), 820-850.  doi: 10.1111/deci.12201. [53] B. Xu and D. Li, An empirical study of the motivations for content contribution and community participation in Wikipedia, Inf. Manage., 52 (2015), 275-286.  doi: 10.1016/j.im.2014.12.003. [54] Q. Xu, R. J. Jiao, X. Yang and M. Helander, An analytical Kano model for customer need analysis, Des. Stud., 30 (2009), 87-110.  doi: 10.1016/j.destud.2008.07.001. [55] V. Xuan Tran, H. Tsuji and R. 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Services provided by ZBJ.com
Kano's two-dimensional quality model
Importance-performance Analysis
The proposed decision framework
IPA map for identifying the corresponding strategy of each SQA
IPA map of ZBJ.com
Description of SQAs
 Regulation Content The regulation of service providers on information dissemination or sale ...ZBJ platform has the responsibility to maintain the normal operations of the crowdsourcing platform and properly store the information submitted by the solvers...delete the information that defies the relevant rules of ZBJ platform......the information provided by the solvers must be checked by ZBJ platform before posting... The regulation on use of communication tools ...ZBJ platform has the right to examine and supervise information in official communication tools in accordance with laws and regulations. The regulation on handling disputes with ZBJ platform To maintain order, ZBJ platform will fairly handle disputes among solvers... The evaluation regulation The trading evaluation entrance will stay open so that seekers and solversare able to evaluate each other... within 30 daysafter the completion of a transaction.ZBJ platform has the right to shield and delete abnormal trading evaluations... The service regulation of ZBJ Mall Solvers are able to use Pig Coins at the ZBJ Mall. The reward distribution regulation ZBJ platform has the right to charge 2%-20% of rewards as technical service fees from service providers according to different membership levels... collect 20% of reward as technical service fees from non-member solvers... The handling infringement rule ZBJ platform has the obligation to receive complaints and investigate alleged infringements...ZBJ platform has the right to warnings, rectification within a time limit, deletion of the infringing content, deduction of credit value, and retreat in unilateral infringement... The management regulation of service providers According to the business circumstance, ZBJ platform will formulate corresponding incentive policies to help solvers sustain development...30-day turnover conversion rate as a performance indicator. If 30-day turnover conversion rate is higher than the average value, they will be rewarded withPig Coins. The service agreement ZBJ platform has the obligation to ensure the normal operation of the crowdsourcing platform on the basis of the existing technical level, avoid service interruptions, resume service as soon as possible, and ensure that solvers' online communication activities run smoothly.ZBJ platform will not sell or lend personal or corporate information to anyone unless the solver has prior permission to do so. Source: https://cms.zbj.com/rules-28
 Regulation Content The regulation of service providers on information dissemination or sale ...ZBJ platform has the responsibility to maintain the normal operations of the crowdsourcing platform and properly store the information submitted by the solvers...delete the information that defies the relevant rules of ZBJ platform......the information provided by the solvers must be checked by ZBJ platform before posting... The regulation on use of communication tools ...ZBJ platform has the right to examine and supervise information in official communication tools in accordance with laws and regulations. The regulation on handling disputes with ZBJ platform To maintain order, ZBJ platform will fairly handle disputes among solvers... The evaluation regulation The trading evaluation entrance will stay open so that seekers and solversare able to evaluate each other... within 30 daysafter the completion of a transaction.ZBJ platform has the right to shield and delete abnormal trading evaluations... The service regulation of ZBJ Mall Solvers are able to use Pig Coins at the ZBJ Mall. The reward distribution regulation ZBJ platform has the right to charge 2%-20% of rewards as technical service fees from service providers according to different membership levels... collect 20% of reward as technical service fees from non-member solvers... The handling infringement rule ZBJ platform has the obligation to receive complaints and investigate alleged infringements...ZBJ platform has the right to warnings, rectification within a time limit, deletion of the infringing content, deduction of credit value, and retreat in unilateral infringement... The management regulation of service providers According to the business circumstance, ZBJ platform will formulate corresponding incentive policies to help solvers sustain development...30-day turnover conversion rate as a performance indicator. If 30-day turnover conversion rate is higher than the average value, they will be rewarded withPig Coins. The service agreement ZBJ platform has the obligation to ensure the normal operation of the crowdsourcing platform on the basis of the existing technical level, avoid service interruptions, resume service as soon as possible, and ensure that solvers' online communication activities run smoothly.ZBJ platform will not sell or lend personal or corporate information to anyone unless the solver has prior permission to do so. Source: https://cms.zbj.com/rules-28
Explanations of five Kano categories
 Category Explanation Must-be quality attribute (M) Customers become extremely unsatisfied by the non-fulfillment of this attribute, however, when the attribute is provided or fulfilled, the degree of customer dissatisfaction will be alleviated, but not promoted to satisfaction. One-dimensional quality attribute (O) Customer satisfaction has a linear and positive relationship with those attributes. When this quality attribute is absent, customer satisfaction will proportionally decrease. Attractive quality attribute (A) Customer satisfaction soars sharply as these attributes increase but the absence of an attribute would be unlikely to lead to customer dissatisfaction. Indifferent quality attribute (I) Customer satisfaction would not be influenced by this set of attributes regardless of fulfillment. Reverse quality attribute (R) Increasing these attributes would lead to more customer dissatisfaction.
 Category Explanation Must-be quality attribute (M) Customers become extremely unsatisfied by the non-fulfillment of this attribute, however, when the attribute is provided or fulfilled, the degree of customer dissatisfaction will be alleviated, but not promoted to satisfaction. One-dimensional quality attribute (O) Customer satisfaction has a linear and positive relationship with those attributes. When this quality attribute is absent, customer satisfaction will proportionally decrease. Attractive quality attribute (A) Customer satisfaction soars sharply as these attributes increase but the absence of an attribute would be unlikely to lead to customer dissatisfaction. Indifferent quality attribute (I) Customer satisfaction would not be influenced by this set of attributes regardless of fulfillment. Reverse quality attribute (R) Increasing these attributes would lead to more customer dissatisfaction.
Description of SQAs
 Dimen–sion No. SQA Explanation Ref Effici–ency 1 The speed of overall crowdsourcing The speed of completing the overall crowdsourcing process. [41] 2 Ease of use The platform is simple to use [41] 3 Platform layout The layout and architecture of the platform are clear and well organized. [41] 4 Information collection and feedback The capability of information collection and feedback of crowdsoucing platform. [15] Availa–bility 5 Usage scenarios There are several ways, such as websites, apps, and WeChat Subscriptions, to use crowdsourcing platforms. [19] 6 Classification retrieval systems This system offers a tool for solvers to search for what they need. [45] 7 IT-based monitoring systems This system supervises false or invalid documents and illegal information, etc. [55] 8 Solver reputation systems This system offers a comprehensive score according to solvers' service attitudes, service speed scores, etc. [22] 9 Recommendation systems This system recommends appropriate tasks for solvers and service providers for seekers. [1] 10 Incentive mechanism The means by which the platform encourages solvers to participate in crowdsourcing initiatives, such as Pig Coins. [62] Fulfill–ment 11 Platform's Image The impressions that solvers have of the platform. [19] 12 Service fulfillment The platform makes accurate promises about their service. [41] 13 Service credibility The platform provides genuine service. [5] 14 Real-time service The platform provides real-time services. [19] Responsi–veness 15 Finding problems If a problem, such as a website crash or hacker attack, occurs, the platform is able to find it immediately. [41] 16 The time of recovering platform The time taken by the platform to recover normal operations. [49] 17 The efficiency of handling complaints The time taken to handle complaints and disputes. [59] 18 The attitude of handling complaints The attitude of the platform toward handing complaints and disputes. [59] 19 Finding new requirements The platform is able to find the requirements of customers in a timely manner. [49] 20 Fulfilling new requirements The platform is able to satisfy the requirements of customers in a timely manner. [49] Privacy 21 The protection of solver information The platform protects the information, such as individual and transaction information, of the solvers. [41] 22 The security of payment means The payment means of the platform is secure. [45] 23 Solutions protection The platform protects solutions and does not let them be used by or leaked to others. [35] 24 Intellectual property protection The platform provides clear intellectual property agreements. [46]
 Dimen–sion No. SQA Explanation Ref Effici–ency 1 The speed of overall crowdsourcing The speed of completing the overall crowdsourcing process. [41] 2 Ease of use The platform is simple to use [41] 3 Platform layout The layout and architecture of the platform are clear and well organized. [41] 4 Information collection and feedback The capability of information collection and feedback of crowdsoucing platform. [15] Availa–bility 5 Usage scenarios There are several ways, such as websites, apps, and WeChat Subscriptions, to use crowdsourcing platforms. [19] 6 Classification retrieval systems This system offers a tool for solvers to search for what they need. [45] 7 IT-based monitoring systems This system supervises false or invalid documents and illegal information, etc. [55] 8 Solver reputation systems This system offers a comprehensive score according to solvers' service attitudes, service speed scores, etc. [22] 9 Recommendation systems This system recommends appropriate tasks for solvers and service providers for seekers. [1] 10 Incentive mechanism The means by which the platform encourages solvers to participate in crowdsourcing initiatives, such as Pig Coins. [62] Fulfill–ment 11 Platform's Image The impressions that solvers have of the platform. [19] 12 Service fulfillment The platform makes accurate promises about their service. [41] 13 Service credibility The platform provides genuine service. [5] 14 Real-time service The platform provides real-time services. [19] Responsi–veness 15 Finding problems If a problem, such as a website crash or hacker attack, occurs, the platform is able to find it immediately. [41] 16 The time of recovering platform The time taken by the platform to recover normal operations. [49] 17 The efficiency of handling complaints The time taken to handle complaints and disputes. [59] 18 The attitude of handling complaints The attitude of the platform toward handing complaints and disputes. [59] 19 Finding new requirements The platform is able to find the requirements of customers in a timely manner. [49] 20 Fulfilling new requirements The platform is able to satisfy the requirements of customers in a timely manner. [49] Privacy 21 The protection of solver information The platform protects the information, such as individual and transaction information, of the solvers. [41] 22 The security of payment means The payment means of the platform is secure. [45] 23 Solutions protection The platform protects solutions and does not let them be used by or leaked to others. [35] 24 Intellectual property protection The platform provides clear intellectual property agreements. [46]
Fuzzy Kano questionnaire
 (Dys)-functional Question Like Must-be Neutral Live-with Dislike Functional Question $x_1$ $x_2$ $x_3$ $x_4$ $x_5$ Dysfunctional Question $y_1$ $y_2$ $y_3$ $y_4$ $y_5$
 (Dys)-functional Question Like Must-be Neutral Live-with Dislike Functional Question $x_1$ $x_2$ $x_3$ $x_4$ $x_5$ Dysfunctional Question $y_1$ $y_2$ $y_3$ $y_4$ $y_5$
Scores for functional and dysfunctional forms of SQAs
 Answers to the Kano question Functional form of the question Dysfunctional form of the question I like it that way 1 -0.5 It must be that way 0.5 -0.25 I am neutral 0 0 I can live with it that way -0.25 0.5 I dislike it that way -0.5 1
 Answers to the Kano question Functional form of the question Dysfunctional form of the question I like it that way 1 -0.5 It must be that way 0.5 -0.25 I am neutral 0 0 I can live with it that way -0.25 0.5 I dislike it that way -0.5 1
Score for perceived importance
 Not important Somewhat important Important Very important important Extremely important 0.......0.1.......0.2......0.3.......0.4........0.5........0.6.......0.7.......0.8.......0.9.......1
 Not important Somewhat important Important Very important important Extremely important 0.......0.1.......0.2......0.3.......0.4........0.5........0.6.......0.7.......0.8.......0.9.......1
Kano evaluation table
 Answers of Dysfunctional Questions Like Must-be Neutral Live-with Dislike Answers of Functional Questions Like Q A A A O Must-be R I I I M Neutral R I I I M Live-with R I I I M Dislike R R R R Q
 Answers of Dysfunctional Questions Like Must-be Neutral Live-with Dislike Answers of Functional Questions Like Q A A A O Must-be R I I I M Neutral R I I I M Live-with R I I I M Dislike R R R R Q
Decision priority rule
 Quadrant Category Decision priority rule Keep Improve Quadrant One Q1-M 1 — Q1-O 2 — Q1-A 3 — Quadrant Two Q2-M — 1 Q2-O — 2 Q2-A — 3 Quadrant Three Q3-M — 4 Q3-O — 5 Q3-A — 6 Quadrant Four Q4-M 4 — Q4-O 5 — Q4-A 6 —
 Quadrant Category Decision priority rule Keep Improve Quadrant One Q1-M 1 — Q1-O 2 — Q1-A 3 — Quadrant Two Q2-M — 1 Q2-O — 2 Q2-A — 3 Quadrant Three Q3-M — 4 Q3-O — 5 Q3-A — 6 Quadrant Four Q4-M 4 — Q4-O 5 — Q4-A 6 —
Importance index and performance index for each SQA on ZBJ platform
 SQA $\overline{x_i}$ $\overline{y_i}$ $|\vec{r_i}|$ $\beta_i$ SQA $\overline{x_i}$ $\overline{y_i}$ $|\vec{r_i}|$ $\beta_i$ S1 0.284 0.283 0.402 0.786 S13 0.362 0.462 0.587 0.664 S2 0.333 0.383 0.508 0.714 S14 0.279 0.353 0.45 0.669 S3 0.35 0.346 0.492 0.79 S15 0.235 0.377 0.445 0.557 S4 0.176 0.307 0.354 0.521 S16 0.251 0.351 0.431 0.621 S5 0.31 0.289 0.424 0.819 S17 0.262 0.345 0.434 0.649 S6 0.339 0.261 0.428 0.915 S18 0.2885 0.377 0.475 0.652 S7 0.276 0.316 0.42 0.718 S19 0.347 0.334 0.482 0.804 S8 0.259 0.229 0.346 0.845 S20 0.266 0.273 0.381 0.773 S9 0.318 0.353 0.476 0.733 S21 0.372 0.462 0.594 0.677 S10 0.285 0.32 0.429 0.728 S22 0.436 0.497 0.661 0.72 S11 0.308 0.3 0.43 0.797 S23 0.364 0.421 0.557 0.711 S12 0.271 0.41 0.492 0.583 S24 0.288 0.363 0.464 0.671
 SQA $\overline{x_i}$ $\overline{y_i}$ $|\vec{r_i}|$ $\beta_i$ SQA $\overline{x_i}$ $\overline{y_i}$ $|\vec{r_i}|$ $\beta_i$ S1 0.284 0.283 0.402 0.786 S13 0.362 0.462 0.587 0.664 S2 0.333 0.383 0.508 0.714 S14 0.279 0.353 0.45 0.669 S3 0.35 0.346 0.492 0.79 S15 0.235 0.377 0.445 0.557 S4 0.176 0.307 0.354 0.521 S16 0.251 0.351 0.431 0.621 S5 0.31 0.289 0.424 0.819 S17 0.262 0.345 0.434 0.649 S6 0.339 0.261 0.428 0.915 S18 0.2885 0.377 0.475 0.652 S7 0.276 0.316 0.42 0.718 S19 0.347 0.334 0.482 0.804 S8 0.259 0.229 0.346 0.845 S20 0.266 0.273 0.381 0.773 S9 0.318 0.353 0.476 0.733 S21 0.372 0.462 0.594 0.677 S10 0.285 0.32 0.429 0.728 S22 0.436 0.497 0.661 0.72 S11 0.308 0.3 0.43 0.797 S23 0.364 0.421 0.557 0.711 S12 0.271 0.41 0.492 0.583 S24 0.288 0.363 0.464 0.671
Kano classification results of SQAs
 SQA S1 S2 S3 S4 S5 S6 S7 S8 Kano result I M I M I I M I SQA S9 S10 S11 S12 S13 S14 S15 S16 Kano result I I M M O I M I SQA S17 S18 S19 S20 S21 S22 S23 S24 Kano result A A A A M M M M Notes: α = 0.1.
 SQA S1 S2 S3 S4 S5 S6 S7 S8 Kano result I M I M I I M I SQA S9 S10 S11 S12 S13 S14 S15 S16 Kano result I I M M O I M I SQA S17 S18 S19 S20 S21 S22 S23 S24 Kano result A A A A M M M M Notes: α = 0.1.
Comprehensive strategies and priorities of SQAs
 SQA Category Priority SQA Category Priority Keep Improve Keep Improve S1 Q4-I —— —— S13 Q2-O —— 2 S2 Q1-M 1 —— S14 Q3-I —— —— S3 Q1-I —— —— S15 Q3-M —— 4 S4 Q3-M —— 4 S16 Q3-I —— —— S5 Q4-I —— —— S17 Q3-A —— 6 S6 Q4-I —— —— S18 Q2-A —— 3 S7 Q4-M 4 —— S19 Q1-A 3 —— S8 Q4-I —— —— S20 Q4-A 6 —— S9 Q1-I —— —— S21 Q2-M —— 1 S10 Q4-I —— —— S22 Q1-M 1 —— S11 Q4-M 4 —— S23 Q2-M —— 1 S12 Q2-M —— 1 S24 Q3-M —— 4 Notes: Q1 is Quadrant One for short.
 SQA Category Priority SQA Category Priority Keep Improve Keep Improve S1 Q4-I —— —— S13 Q2-O —— 2 S2 Q1-M 1 —— S14 Q3-I —— —— S3 Q1-I —— —— S15 Q3-M —— 4 S4 Q3-M —— 4 S16 Q3-I —— —— S5 Q4-I —— —— S17 Q3-A —— 6 S6 Q4-I —— —— S18 Q2-A —— 3 S7 Q4-M 4 —— S19 Q1-A 3 —— S8 Q4-I —— —— S20 Q4-A 6 —— S9 Q1-I —— —— S21 Q2-M —— 1 S10 Q4-I —— —— S22 Q1-M 1 —— S11 Q4-M 4 —— S23 Q2-M —— 1 S12 Q2-M —— 1 S24 Q3-M —— 4 Notes: Q1 is Quadrant One for short.
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