A new digital service quality model and its strategic analysis in aviation industry using interval-valued intuitionistic fuzzy AHP

Today, airline companies have to provide digital products and services to ensure customer satisfaction. Accordingly, classical service quality models are not sufficient to capture customer expectations, and new digital service quality (DSQ) models should emerge. This study aims to propose a new and genuine DSQ model. The proposed model consists of digital tangibles, reliability, digital interaction, digital trust, and customer-centricity dimensions; and 35 criteria related to them. Importance degrees of criteria are determined with the IVIF AHP method. The validity of the proposed model is verified, employing a real case concerning the Turkish airline industry. The results show that the most significant dimension is digital trust, and the three most important criteria are proactive customer service, cybersecurity, and customer insight.     

An integrated SWOT based fuzzy AHP and fuzzy MARCOS methodology for digital transformation strategy analysis in airline industry

Driven by business strategies, digital transformation (DT) facilitates dramatic change in air passenger behavior. This study aims to determine and analyze different DT strategies (DTSs) with the help of an integrated SWOT-based fuzzy AHP-MARCOS methodology that is proposed for the first time in the literature for this purpose. This methodology is validated with a case study concerning the airline industry in Turkey. The weights of the SWOT factors are determined with the fuzzy AHP method. The fuzzy MARCOS approach is used to select the most suitable DTS. The most appropriate strategy is obtained as “focusing on differentiated digital customer experience and service quality by the adaptation of business models to DT to provide benefits”.     

FIPIA with information entropy: A new hybrid method to assess airline service quality

This study proposes “FIPIA with information entropy” as a new, hybrid method to assess airline service quality by identifying the most important priorities for airline passengers and producing recommendations to airline management for optimal resource allocation to improve service quality and customer satisfaction. The proposed method is an improvement over IPA, IPIA and FIPA methods, through the introduction of information entropy and fuzzy logic to the analysis of importance, performance and impact dimensions of airline service quality to improve interpretability and actionability of analysis results. This study also offers airline managers a list of what they should improve in resource allocation in order to increase service quality considering customer satisfaction and create value by managing the relational capital more effectively. The new hybrid method was field-tested by administering a 26-item questionnaire to passengers of a major airline operator, analyzing the responses using the Importance-Performance-Impact Analysis (IPIA) method, fuzzy logic and information entropy. The analysis revealed four main dimensions of airline service quality, namely reliability, assurance, tangibles, empathy and responsiveness with 17 constituent attributes. The case study revealed that (1) resource allocation was adequate only on four attributes; (2) seven service quality attributes were identified as needing further management focus on resource allocation; (3) six service quality attributes received more resources than necessary which should be shifted to other attributes; (4) dimensions of reliability and tangibles needed more focus than others. The proposed hybrid method of FIPIA with information entropy can be employed for any industry where service quality depends on multiple attributes.     

Airline alliance survival analysis: typology,strategy and duration

Alliances have become the organization model of choice in many industries. This study uses data from the Airline Business annual surveys of airline alliances to test a proposed typology predicting survival and duration in airline alliances. The Rhoades-Lush typologies classified key activities of airline alliances by their level of complexity and resource commitment in order to suggest a series of propositions on alliance stability and duration. The results of our analysis indicate that alliances containing joint purchasing and marketing activities had lower risk of termination than alliances involving equity. Furthermore, alliances spanning more than two typologies showed lower risk of termination than one and two typology alliances. This is explained as the ‘strategy effect’ or in other words the tendency of alliances wide in scope to be more resistant to early termination.     

A voice in the skies: Listening to airline passenger preferences

This study aims to identify the impact levels and priorities in the service expectations that passengers have when identifying a preferred airline. The results are based on probabilities and impacts, and can help airlines to accurately understand the preference criteria of their passengers. The priorities of the passengers may differ according to the airline chosen; therefore, the probabilities shown in this study can inform senior airline managers about the passenger perspective. This study uses the Stochastic Multicriteria Acceptability Analysis-2 (SMAA-2) method. SMAA-2 identifies the priorities and impact levels of passengers’ expectations on airline selection, and the ranking of alternative firms according to the probability. According to the obtained results, Airline 3 (AF₃) is the most preferred airline with the highest confidence rate. This airline operates based on a low-cost model that allows passengers to choose additional services for additional charges. The passenger expectations that have the highest impact on the preference when selecting an airline are 1) ticket prices, 2) punctuality, and 3) booking convenience. Free in-flight food and beverages, the variety and quality of the food and beverages, and voyager miles programs for loyal customers are found to have no impact on the ranking of airlines. The expectations identified by the passengers in this study are related to the outcome quality dimension, with services dominated by flexible features. The findings of this study define the passenger as a rational decision maker who is price sensitive.     

The application of inventory transshipment modeling to air cargo revenue management

This paper applies an inventory transshipment modeling approach to investigate the air cargo revenue management problem for an airline operating in a two-segment network. Building upon an extension of the classic two-location inventory transshipment model, we develop a framework to optimize an airline’s cargo overbooking decisions in a two-segment network setting. We find consistent evidence indicating that network-based global optimization always leads to greater expected profits than does local (i.e., market by market) optimization. Further, the magnitude of profit improvement is found to be most significant when local shipments have a relatively higher freight yield compared to flow-through shipments. Finally, our results indicate that global optimization contributes to greater profit improvement as offloading penalty costs become higher.     

Mergers and acquisitions in aviation – Management and economic perspectives on the size of airlines

This paper reviews literature and management perspectives on airline mergers and acquisitions. We find that M&A/consolidation is seen as a “game-changer” and mandatory to survive in aviation markets. We, therefore, apply DEA models to 66 airlines to evaluate whether big is indeed always beautiful. Our results suggest that the optimal airline size is between 34 and 52 bn available seat kilometre capacity and that airlines with more than 200 bn ASK are definitely too large to operate efficiently. This also applies when revenues are included in the DEA models, which is central as yield management and ancillary revenues are increasingly important.     

Predicting airline passengers’ loyalty using artificial neural network theory

The study explores a model for predicting airline loyalty using the antecedents indicated in previous studies. Data was collected using a questionnaire distributed to 614 domestic air passengers using the snowball sampling method. The measurement tool had 16 scale items constructed on the recommendations of previous studies. Passenger satisfaction, airline service quality, passenger perceived value, and airline image are identified as determinants for airline loyalty. The predictive analytical approach of Artificial Neural Network theory and covariance-based Structural Equation Modelling for determining causality is employed in the study. The artificial neural network model predicts airline loyalty with 89% accuracy. Sensitivity analysis suggests passenger satisfaction as the most significant predictor of airline loyalty. The causal study supports that passenger satisfaction mediates the relationship between airline service quality and airline loyalty.     

Towards a maturity model for big data analytics in airline network planning

The evaluation, acquisition and use of newly available big data sources has become a major strategic and organizational challenge for airline network planners. We address this challenge by developing a maturity model for big data readiness for airline network planning. The development of the maturity model is grounded in literature, expert interviews and case study research involving nine airlines. Four airline business models are represented, namely full-service carriers, low-cost airlines, scheduled charter airlines and cargo airlines. The maturity model has been well received with seven change requests in the model development phase. The revised version has been evaluated as exhaustive and useful by airline network planners. The self-assessment of airlines revealed low to medium maturity for most domains. Organizational factors show the lowest average maturity, IT architecture the highest. Full-service carriers seem to be more mature than airlines with different business models.     

An airline maintenance manpower planning model with flexible strategies

An effective maintenance manpower supply plan not only reduces operating costs but ensures greater aviation safety and punctuality. A variety of flexible management strategies have been widely applied in other industries; however, little research has stressed flexible management strategies for airline crew scheduling problems. Here, we introduce a model that includes various flexible strategies so that an airline can effectively manage its maintenance manpower supply. The model is formulated as a mixed integer program that is solved using a commercial software package, CPLEX. In order to evaluate the model performance, we use the operating data from a leading Taiwan airline.     

Airline alliances and partner firms’ outputs

This paper analyzes the effects of airline alliances on partner airlines’ outputs by comparing traffic changes on alliance routes with those on non-alliance routes. A theoretical model of an airline alliance is developed, and an empirical analysis is then conducted using panel data from four major alliances in North Atlantic markets during the 1992–1994 period. It is found that most of the partners have greater traffic increases on their alliance routes than those on their non-alliance routes, supporting a hypothesis derived from the theoretical model.     

A Multi-Objective Goal Programming airport selection model for low-cost carriers’ networks

This paper examines the practicability and validity of the Multi-Objective Goal Programming (MOGP) model in designing a point-to-point airline network. The MOGP mathematical model is used in this study to identify and select a best central airport and its connecting airports providing the best overall optimal performance. By using the proposed model in the selection process, one can expect to achieve the following objectives: (1) an airline network designed at less cost, (2) an airline network that generates more revenues, and (3) an airline network that serves more passengers. Route data from Taipei to the Southeast Asian region were selected for this experiment to validate the model’s practicability.     

Evaluating airline efficiency: An application of Virtual Frontier Network SBM

In this paper, airline efficiency is divided into three stages: Operations Stage, Services Stage and Sales Stage. The new three-stage strategic operating framework of airline efficiency is a modification of existing models. A new model, Virtual Frontier Network SBM, is proposed to evaluate the efficiency of 22 international airlines from 2008 to 2012. The results demonstrate the following: 1. The new model can apply to a new benchmarking airline such as Scandinavian Airlines. 2. Although passenger traffic, cargo traffic and revenue decreased from 2008 to 2009, most airlines’ overall efficiency increased in the period.     

Modeling Airline Crisis Management Capability: Brand attitude,brand credibility and intention

Crisis management consists of a number of capabilities which evaluate crisis signals and implements actions to minimize damage. Both identifying these capabilities and understanding their influence on perceptions of brand attitude, credibility and intention to purchase offers significant theoretical and managerial insights. This paper, therefore, proposes a cause-effects model of perceived airline crisis management capabilities and their influence on brand credibility, brand attitude and purchase intention in the aftermath of large-scale Taiwanese airline strikes. Using data collected via an online survey, the study makes three contributions to airline crisis management. It, firstly, identifies a set of crisis management capabilities for strike-hit airlines, secondly, it offers a causal chain of perceived airline crisis management capabilities, brand attitude, brand credibility and purchase intention. Furthermore, it the full mediating effects of brand credibility and brand attitude in the causal chain. This study, thus, makes conceptual and methodological contributions to crisis management and purchase intention research and provides practical insights into effective airline crisis management and brand management for the airline industry.     

Drivers of airline loyalty: Evidence from the business travelers in China

This paper examines the key factors that determine business traveler loyalty toward full-service airlines in China. Based on literature review and panel interview, ten airline attributes under three categories were derived: (a) operational factors: safety, punctuality, and aircraft; (b) competitive factors: frequency of flights, schedule, frequent flyer program, ticket price, and reputation; and (c) attractive factors: in flight food & drinks and in flight staff service. We surveyed 2000 Chinese business travelers on domestic flights, obtaining 462 usable questionnaires. Hierarchical regression analysis reveals that reputation, in-flight service, frequent flyer program, and aircraft have the greatest influence in driving airline loyalty.     

Airline demand distributions: passenger revenue management and spill

Both revenue management and airline schedule optimization need to characterize the distribution of likely demand outcomes. Sources have proposed both Gamma and Normal shapes for these distributions. Data suggests that a model combining both distributions is appropriate. The model explains when the Gamma shape will dominate and when the Normal will determine the shape. One consequence of this understanding is that Gamma shapes are probably better for revenue management and Normal for spill modeling. However, it takes a compound process combining the two to generate all the observed characteristics of various cases.     

Structuring critical success factors of airline safety management system using a hybrid model

With the global trend of legal requirement for a performance-based Safety Management System (SMS), to develop and implement an SMS to deliver services has become the most important goal within the airline industry. Yet there found some discrepancies concerning the use and the manner that SMS was being explained and taught. Therefore, this research aims to develop a quantitative evaluation model, which identifies the key components of airline SMS and considers the interaction between key components. To explore the core value of SMS, an extensive review regarding SMS components are firstly conducted and summarized from major aviation organizations and authorities, and then Grey Relational Analysis is used to group and select key components. After the critical components are derived, Decision Making Trial Evaluation Laboratory and Analytic Network Process are employed to analyze and map out all kinds of interactions among critical components and dimensions systematically. An empirical study is presented to illustrate the application of the proposed methods. From the results of the combined approaches, Organization is the most important dimension in SMS, which begins with Policies that convey to all staff the top managers’ vision on safety.     

Methodology for assessing dependencies between factors influencing airline pilot performance reliability: A case of taxiing tasks

Pilot performance reliability is critical to civil air transportation safety. Internal and external factors influencing pilot performance reliability are important elements to estimate human error probability. However, dependencies between these factors, including state dependencies and effect dependencies, are always ignored or only partially considered in most human reliability analysis (HRA) method. This paper attempts to develop a methodology incorporating both state and effect dependencies between contributory factors into HRA for airline pilot tasks based on fuzzy logic and the Cognitive Reliability and Error Analysis Method (CREAM). Anchor points were determined for each common performance condition (CPC) state levels through airline pilot elicitation process and then corresponding state and effect fuzzy sets were constructed for each CPC. The CPC dependency fuzzy inference rules were constructed and transformed with reference to the CPC dependency assessing rule in CREAM. And a cognitive failure probability (CFP) calculation method based on the final CPC effect fuzzy sets and the extended CREAM was proposed. To illustrate the use of the proposed methodology, a case study about commercial airline pilot taxiing task was given. The results demonstrate the validity of the proposed methodology and its capability of representing the ambiguity and fuzziness of expert judgement.     

The impact of an aircraft's service environment on perceptions of in-flight food quality

Airlines are currently striving to improve the quality and quantity of in-flight food, because research has shown that catering is a key attribute for a customer's satisfaction with airline service quality. But the role of an airline's service environment in forming customer perceptions about food quality has not yet been properly investigated. Using electronic word-of-mouth data from N = 3996 airline passengers, this study deploys a linear regression model at multiple levels to relate perceived in-flight food quality with both the overall service environment and its formative components. The results clearly unveil the importance of an aircraft's service environment on perceived in-flight catering quality; perceptions of food quality are primarily influenced by the quality of cabin staff service, followed by entertainment and seat quality. Instead of continuing with the current practice of signing up top chefs to improve menus, airlines may instead consider putting their management focus on service improvements.