Identifying and prioritizing factors affecting in-cabin passenger comfort on high-speed rail in China: A fuzzy-based linguistic approach

Abstract Factors affecting customer comfort are crucial for the success of many services such as public transportation, health facilities, and so on. Therefore, the identification and prioritization of such factors is an important demand from stakeholders. This research aims to identify and prioritize the factors that affect in-cabin passenger comfort on high-speed rail (HSR) based on empirical evidence collected from China. For the identification process, the quality-management tool known as quality function deployment (QFD) to capture the voice of the customer is used to discover the most important demands as the driving influencing factors of in-cabin passenger comfort, by utilizing passengers’ feedback regarding their experiences with HSR posted in social media. Such factors will be prioritized by a fuzzy linguistic group decision-making approach based on the adoption of generalized comparative linguistic expressions obtained from a questionnaire investigation on randomly selected HSR passengers, and accomplishing a decision-solving procedure that includes consistency-checking and consensus-reaching processes to achieve an effective, reliable, and agreed prioritization of the factors. The research outputs will suggest the factors of greatest concern among all HSR passenger demands. This study gives HSR operators and designers not only insights and tools for managing HSR passenger demands but also advice for refining the design and service quality of HSR in China.

[1]  Zhibin Wu,et al.  Possibility Distribution-Based Approach for MAGDM With Hesitant Fuzzy Linguistic Information , 2016, IEEE Transactions on Cybernetics.

[2]  Jui-Sheng Chou,et al.  Pricing policy of floating ticket fare for riding high speed rail based on time-space compression , 2018, Transport Policy.

[3]  Mohammed Quaddus,et al.  A multi-phased QFD based optimization approach to sustainable service design , 2016 .

[4]  Da Ruan,et al.  A fuzzy-set approach to treat determinacy and consistency of linguistic terms in multi-criteria decision making , 2007, Int. J. Approx. Reason..

[5]  Burak Efe,et al.  Fuzzy cognitive map based quality function deployment approach for dishwasher machine selection , 2019, Appl. Soft Comput..

[6]  Deok-Hwan Kim,et al.  Robustness indices and robust prioritization in QFD , 2009, Expert Syst. Appl..

[7]  Yanquan Zhou,et al.  Mining customer requirements from online reviews: A product improvement perspective , 2016, Inf. Manag..

[8]  Babak Abedin,et al.  A methodology for project portfolio selection under criteria prioritisation, uncertainty and projects interdependency - combination of fuzzy QFD and DEA , 2018, Expert Syst. Appl..

[9]  Francisco Herrera,et al.  Hesitant Fuzzy Linguistic Term Sets for Decision Making , 2012, IEEE Transactions on Fuzzy Systems.

[10]  Jun Wang,et al.  Some programming models to derive priority weights from additive interval fuzzy preference relation , 2012, Knowl. Based Syst..

[11]  P. L. Biju,et al.  Evaluation of customer requirements and sustainability requirements through the application of fuzzy analytic hierarchy process , 2015 .

[12]  Francisco Herrera,et al.  A 2-tuple fuzzy linguistic representation model for computing with words , 2000, IEEE Trans. Fuzzy Syst..

[13]  Mahmud Mohd. Jusan,et al.  Incorporating user values into housing design through indirect user participation using MEC-QFD model , 2017 .

[14]  Changwan Kim,et al.  A structural equation analysis of the QSL relationship with passenger riding experience on high speed rail: An empirical study of Taiwan and Korea , 2009, Expert Syst. Appl..

[15]  Xilin Zhang,et al.  Research on Element Importance of Shafting Installation Based on QFD and FMEA , 2017 .

[16]  Gülçin Büyüközkan,et al.  A new hesitant fuzzy QFD approach: An application to computer workstation selection , 2016, Appl. Soft Comput..

[17]  Jian Jin,et al.  Translating online customer opinions into engineering characteristics in QFD: A probabilistic language analysis approach , 2015, Eng. Appl. Artif. Intell..

[18]  Andrew W. H. Ip,et al.  Analyze the healthcare service requirement using fuzzy QFD , 2015, Comput. Ind..

[19]  Marcelo Resende,et al.  Mapping service quality in electricity distribution: An exploratory study of Brazil , 2019, Utilities Policy.

[20]  Luis Martínez,et al.  Customizing Semantics for Individuals With Attitudinal HFLTS Possibility Distributions , 2018, IEEE Transactions on Fuzzy Systems.

[21]  Luis Martínez-López,et al.  Analyzing the performance of classical consensus models in large scale group decision making: A comparative study , 2017, Appl. Soft Comput..

[22]  Ralf-Christian Härting,et al.  Potentials of changing customer needs in a digital world - a conceptual model and recommendations for action in tourism , 2018, KES.

[23]  Lincoln C. Wood,et al.  Green hospital design: integrating quality function deployment and end-user demands , 2016 .

[24]  Yung-Hsiang Cheng,et al.  Evaluating web site service quality in public transport: Evidence from Taiwan High Speed Rail , 2011 .

[25]  Jian Zhou,et al.  Using fuzzy non-linear regression to identify the degree of compensation among customer requirements in QFD , 2014, Neurocomputing.

[26]  Weiwei Shen,et al.  Passenger satisfaction evaluation model for Urban rail transit: A structural equation modeling based on partial least squares , 2016 .

[27]  Jui-Sheng Chou,et al.  Influential constructs, mediating effects, and moderating effects on operations performance of high speed rail from passenger perspective , 2013 .

[28]  Ting Yu Huang,et al.  High speed rail passenger segmentation and ticketing channel preference , 2014 .

[29]  Mina Woo,et al.  Assessing customer citizenship behaviors in the airline industry: Investigation of service quality and value , 2019, Journal of Air Transport Management.

[30]  Luis Martínez-López,et al.  Two-stage aggregation paradigm for HFLTS possibility distributions: A hierarchical clustering perspective , 2018, Expert Syst. Appl..

[31]  Chen-Su Fu,et al.  Exploring customer perceptions toward different service volumes: An integration of means–end chain and balance theories , 2019, Food Quality and Preference.

[32]  Y. Moubachir,et al.  A New Approach for the Transition between QFD Phases , 2015 .

[33]  Hsing-Pei Kao,et al.  A new integrated fuzzy QFD approach for market segments evaluation and selection , 2015 .

[34]  Qiang Yang,et al.  Identifying passengers’ needs in cabin interiors of high-speed rails in China using quality function deployment for improving passenger satisfaction , 2019, Transportation Research Part A: Policy and Practice.

[35]  Ingrid Bouwer Utne,et al.  Improving the environmental performance of the fishing fleet by use of Quality Function Deployment (QFD) , 2009 .

[36]  Mateja Kos Koklic,et al.  An investigation of customer satisfaction with low-cost and full-service airline companies , 2017 .

[37]  Patrick Jochem,et al.  Literature vs. Twitter: Empirical insights on customer needs in e-mobility , 2019, Journal of Cleaner Production.

[38]  Laura Eboli,et al.  An ordered regression model to predict transit passengers’ behavioural intentions , 2016, Case Studies on Transport Policy.

[39]  Iván Palomares Carrascosa.  Large Group Decision Making , 2018, SpringerBriefs in Computer Science.

[40]  Zhibin Wu,et al.  Managing consistency and consensus in group decision making with hesitant fuzzy linguistic preference relations , 2016 .

[41]  Morteza Yazdani,et al.  A fuzzy multi attribute decision framework with integration of QFD and grey relational analysis , 2019, Expert Syst. Appl..

[42]  Gi-Tae Yeo,et al.  An Analysis of Port Service Quality and Customer Satisfaction: The Case of Korean Container Ports , 2015 .

[43]  Hao-Tien Liu,et al.  The extension of fuzzy QFD: From product planning to part deployment , 2009, Expert Syst. Appl..

[44]  Ben Clegg,et al.  Analysis of a train-operating company's customer service system during disruptions: Conceptual requirements for gamifying frontline staff development , 2018, J. Rail Transp. Plan. Manag..

[45]  Jian Liu,et al.  Empirical Research of Intercity High-speed Rail Passengers' Travel Behavior Based on Fuzzy Clustering Model , 2012 .

[46]  Piyali Ghosh,et al.  Determining passenger satisfaction out of platform-based amenities: A study of Kanpur Central Railway Station , 2017 .

[47]  Changwan Kim,et al.  Deploying effective service strategy in the operations stage of high-speed rail , 2011 .

[48]  Hongbin Liu,et al.  On Improving the Additive Consistency of the Fuzzy Preference Relations Based on Comparative Linguistic Expressions , 2014, Int. J. Intell. Syst..

[49]  Francisco Herrera,et al.  A model of consensus in group decision making under linguistic assessments , 1996, Fuzzy Sets Syst..

[50]  Karen Holtzblatt,et al.  Contextual Design: Design for Life , 2016 .

[51]  Francisco Herrera,et al.  A group decision making model dealing with comparative linguistic expressions based on hesitant fuzzy linguistic term sets , 2013, Inf. Sci..

[52]  Rafael Caballero,et al.  Assessing the quality of service to customers provided by water utilities: A synthetic index approach , 2017 .

[53]  Gang Liu,et al.  Field study on thermal comfort of passenger at high-speed railway station in transition season , 2016 .

[54]  Melodi Guilbault,et al.  Students as customers in higher education: The (controversial) debate needs to end , 2018 .

[55]  J. Anable,et al.  Customer perceptions of quality of service in public transport: Evidence for bus transit in Scotland , 2016 .

[56]  Huayou Chen,et al.  Decision-making model with fuzzy preference relations based on consistency local adjustment strategy and DEA , 2019, Neural Computing and Applications.

[57]  Xin Zhou,et al.  A hybrid group decision making framework for achieving agreed solutions based on stable opinions , 2019, Inf. Sci..

[58]  Udayan Chanda,et al.  A Bayesian Network Model on the association between CSR, perceived service quality and customer loyalty in Indian Banking Industry , 2017 .

[59]  Yoji Akao,et al.  Quality Function Deployment : Integrating Customer Requirements into Product Design , 1990 .

[60]  Bayu Martanto Adji,et al.  Indonesian experience on travel time use on-board of commuter rail services , 2017 .

[61]  Yi Yang,et al.  Proportional hesitant fuzzy linguistic term set for multiple criteria group decision making , 2016, Inf. Sci..

[62]  Hongbin Liu,et al.  A fuzzy envelope for hesitant fuzzy linguistic term set and its application to multicriteria decision making , 2014, Inf. Sci..

[63]  Francisco Rodrigues Lima Junior,et al.  A multicriteria approach based on fuzzy QFD for choosing criteria for supplier selection , 2016, Comput. Ind. Eng..

[64]  Zhibin Wu,et al.  A consensus model for large-scale group decision making with hesitant fuzzy information and changeable clusters , 2018, Inf. Fusion.

[65]  S. H. Amin,et al.  A multi-objective mathematical model integrating environmental concerns for supplier selection and order allocation based on fuzzy QFD in beverages industry , 2018, Expert Syst. Appl..

[66]  Anca Draghici,et al.  Evaluation of Methods for Customer Integration to the Quality of IT Services , 2014 .

[67]  H. Houthakker.  Revealed Preference and the Utility Function , 1950 .

[68]  Huayou Chen,et al.  Approaches to group decision making with linguistic preference relations based on multiplicative consistency , 2017, Comput. Ind. Eng..

[69]  Zhang Xiaoqiang,et al.  Dynamic pricing for passenger groups of high-speed rail transportation , 2017 .

[70]  Robert Schmitt,et al.  Sensory QFD: Matching Sensation with Measurement , 2014 .

[71]  Helmut Krcmar,et al.  Criteria for Selecting Cloud Service Providers: A Delphi Study of Quality-of-Service Attributes , 2018, Inf. Manag..

[72]  Ahmad Mumani,et al.  An integration of a QFD model with Fuzzy-ANP approach for determining the importance weights for engineering characteristics of the proposed wheelchair design , 2020, Appl. Soft Comput..

[73]  K. Stolarsky,et al.  The Power and Generalized Logarithmic Means , 1980 .

[74]  Guy De Tré,et al.  A large scale consensus reaching process managing group hesitation , 2018, Knowl. Based Syst..

[75]  Monica Leba,et al.  QFD – Method for eLearning Systems Evaluation , 2013 .

[76]  Ozlem Alpu,et al.  A methodology for evaluating satisfaction with high-speed train services: A case study in Turkey , 2015 .

[77]  Hosna Pakizehkar,et al.  The Application of Integration of Kano's Model, AHP Technique and QFD Matrix in Prioritizing the Bank's Substructions , 2016 .

[78]  Li Pheng Khoo,et al.  Towards a QFD-based expert system: A novel extension to fuzzy QFD methodology using rough set theory , 2010, Expert Syst. Appl..

[79]  Wann-Yih Wu,et al.  The Integration between Service Value and Service Recovery in the Hospitality Industry: An Application of QFD and ANP , 2018, International Journal of Hospitality Management.

[80]  Glenn Lyons,et al.  Changing times – a decade of empirical insight into the experience of rail passengers in Great Britain , 2016 .

[81]  Young-joon Choi,et al.  Exploring Airbnb service quality attributes and their asymmetric effects on customer satisfaction , 2019, International Journal of Hospitality Management.

[82]  Mi-Jin Noh,et al.  Customer acceptance of IPTV service quality , 2011, Int. J. Inf. Manag..

[83]  J. Hauser,et al.  The House of Quality , 1988 .

[84]  Luis Martínez-López,et al.  An Overview on Fuzzy Modelling of Complex Linguistic Preferences in Decision Making , 2016, Int. J. Comput. Intell. Syst..

[85]  Chin-Shan Lu,et al.  Effects of service quality and customer satisfaction on customer loyalty in high-speed rail services in Taiwan , 2014 .

[86]  Nicolas Haber,et al.  A practical ANP-QFD methodology for dealing with requirements' inner dependency in PSS development , 2019, Comput. Ind. Eng..

[87]  Yucheng Dong,et al.  On consistency measures of linguistic preference relations , 2008, Eur. J. Oper. Res..

[88]  Jiuping Xu,et al.  Multi-criteria group individual research output evaluation based on context-free grammar judgments with assessing attitude , 2015 .