An advanced analytical framework for improving customer satisfaction: A case of air passengers

Having an appropriate and advanced analytical framework is essential for transport service managers to optimize resource allocation to improve customer satisfaction. This study proposes a novel analytical framework, the “Importance-Performance-Impact Analysis” (IPIA), which aims to overcome several conceptual and methodological shortcomings associated with Importance–Performance Analysis (IPA). The IPIA framework integrates advanced analytical techniques, such as Back Propagation Neural Network and Decision-Making Trial and Evaluation Laboratory (DEMATEL/ANP). We illustrate IPIA in one of the ‘Big Four’ airlines in China. IPIA Table and IPIA Matrix help transportation managers to allocate resources better than IPA to improve customer satisfaction.

[1]  Frank Englert,et al.  Improving service quality in public transportation systems using automated customer feedback , 2016 .

[2]  Gwo-Hshiung Tzeng,et al.  A Novel Hybrid MCDM Model Combined with DEMATEL and ANP with Applications , 2008 .

[3]  R. Nash,et al.  A critical evaluation of importance - performance analysis , 2013 .

[4]  Chih-Hung Tsai,et al.  Using BPNN and DEMATEL to modify importance-performance analysis model - A study of the computer industry , 2009, Expert Syst. Appl..

[5]  Alessandro Arbore,et al.  Rejuvenating importance‐performance analysis , 2011 .

[6]  T. Saaty Decision making — the Analytic Hierarchy and Network Processes (AHP/ANP) , 2004 .

[7]  Juan de Oña,et al.  Neural networks for analyzing service quality in public transportation , 2014, Expert Syst. Appl..

[8]  Hayri Baraçli,et al.  An integrated novel interval type-2 fuzzy MCDM method to improve customer satisfaction in public transportation for Istanbul , 2013 .

[9]  Eleni I. Vlahogianni,et al.  Statistical methods versus neural networks in transportation research: Differences, similarities and some insights , 2011 .

[10]  Ying-Feng Kuo,et al.  IPA–Kano model: A new tool for categorising and diagnosing service quality attributes , 2012 .

[11]  Zhibin Lin,et al.  Drivers of airline loyalty: Evidence from the business travelers in China , 2014 .

[12]  Ivan Sever,et al.  Importance-performance analysis: A valid management tool? , 2015 .

[13]  Keng-Boon Ooi,et al.  An SEM-artificial-neural-network analysis of the relationships between SERVPERF, customer satisfaction and loyalty among low-cost and full-service airline , 2015, Expert Syst. Appl..

[14]  Kurt Matzler,et al.  The asymmetric relationship between attribute-level performance and overall customer satisfaction: a reconsideration of the importance–performance analysis , 2004 .

[15]  Gwo-Hshiung Tzeng,et al.  Brand marketing for creating brand value based on a MCDM model combining DEMATEL with ANP and VIKOR methods , 2012, Expert Syst. Appl..

[16]  Josip Mikulic,et al.  Accounting for dynamics in attribute-importance and for competitor performance to enhance reliability of BPNN-based importance-performance analysis , 2012, Expert Syst. Appl..

[17]  Suihuai Yu,et al.  A hybrid approach based on fuzzy AHP and 2-tuple fuzzy linguistic method for evaluation in-flight service quality , 2017 .

[18]  Zhibin Lin,et al.  Airline passengers’ continuance intention towards online check-in services: The role of personal innovativeness and subjective knowledge , 2015 .

[19]  Anming Zhang,et al.  Will China's airline industry survive the entry of high-speed rail? , 2012 .

[20]  Kurt Matzler,et al.  The factor structure of customer satisfaction , 2002 .

[21]  Jillian Anable,et al.  Performance, importance and user disgruntlement: a six-step method for measuring satisfaction with travel modes. , 2007 .

[22]  M. Caetano,et al.  Airport level of service: A model according to departing passengers’ perceptions at a small-sized airport , 2017 .

[23]  Eleanor T. Loiacono,et al.  The Classification of Extranet Attributes in Terms of Their Asymmetric Influences on Overall User Satisfaction , 2013 .

[24]  John A. Martilla,et al.  Importance-Performance Analysis , 1977 .

[25]  Abdallah Shanableh,et al.  Modeling roadway traffic noise in a hot climate using artificial neural networks , 2017 .

[26]  F. Pan Practical application of importance-performance analysis in determining critical job satisfaction factors of a tourist hotel , 2015 .

[27]  Li-Fei Chen,et al.  A novel approach to regression analysis for the classification of quality attributes in the Kano model: an empirical test in the food and beverage industry , 2012 .

[28]  Gwo-Hshiung Tzeng,et al.  Improving tourism policy implementation – The use of hybrid MCDM models , 2012 .

[29]  Xuening Chu,et al.  A new importance-performance analysis approach for customer satisfaction evaluation supporting PSS design , 2012, Expert Syst. Appl..

[30]  Ming-Shin Kuo,et al.  A novel interval-valued fuzzy MCDM method for improving airlines’ service quality in Chinese cross-strait airlines , 2011 .

[31]  Nezir Aydin,et al.  A fuzzy-based multi-dimensional and multi-period service quality evaluation outline for rail transit systems , 2017 .

[32]  Yan Dong,et al.  Linkages between customer service, customer satisfaction and performance in the airline industry: Investigation of non-linearities and moderating effects , 2012 .

[33]  R. Oliver A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions , 1980 .

[34]  Hsiu-Yuan Hu,et al.  Applying the IPA and DEMATEL models to improve the order-winner criteria: A case study of Taiwan's network communication equipment manufacturing industry , 2011, Expert Syst. Appl..

[35]  Carl Goves,et al.  Short Term Traffic Prediction of the UK Motorway Network Using Neural Networks , 2015 .

[36]  Haemoon Oh,et al.  Revisiting importance–performance analysis , 2001 .

[37]  Wen-Chin Chen,et al.  Back-propagation neural network based importance-performance analysis for determining critical service attributes , 2008, Expert Syst. Appl..

[38]  Michal Tkác,et al.  Artificial neural networks in business: Two decades of research , 2016, Appl. Soft Comput..

[39]  Yunpeng Wang,et al.  Long short-term memory neural network for traffic speed prediction using remote microwave sensor data , 2015 .

[40]  W. Tsai,et al.  AN EFFECTIVE EVALUATION MODEL AND IMPROVEMENT ANALYSIS FOR NATIONAL PARK WEBSITES: A CASE STUDY OF TAIWAN , 2010 .

[41]  Donald R. Bacon,et al.  Understanding Priorities for Service Attribute Improvement , 2012 .