(Please get in touch with myself (Adyb Siddiquee @ firstname.lastname@example.org), with clear formal identification, corporate/business/organizational representation, referee status, methodology of review, and client-writing requirements)
As an industry observant, ancillary revenues have exploded to $58 Billion-USD, an average of $17.49 for 2014, up 10% year-on-year (Travelweekly.com, 2015). Ancillary revenue is cited as revenue generated from a goods/service beyond business-revenue framework, and redeemed by airlines beyond the legal transaction of an associated service (Investopedia, 2005). Drawing on theoretical and extrapolated conceptual frameworks, marketplace-demand for quantitative correlatory controlled, rigorous, systematic and critical study between ancillary revenue share and customer satisfaction would deem effective industry-wide feedback mechanisms to conventional impact-understanding of ancillary-revenue share growth to customers and demand.
The inherent theoretical framework (from which the directional hypothesis is derived) suggests that the leverage of airlines to attain returns to inherent sunk costs and overheads is by unbundling airfares, rationalizing and selling/valuing inherent fragments of the business (Foundations of Airline Finance, 2013). This is often cited within carrier’s annual report (as ancillary/other revenue). Elements of ancillary revenue includes (Ideaworkscompany.com, 2015):
- Commission-Based Products (commissions from contracts; hotels, car-rentals, travel insurance, duty-free)
- La Carte (purchase options of foods and beverages, checked/excess baggage fees, assigned seats, call-centre/website booking charges, internet access, In-flight-entertainment, early-boarding, credit-card fees are few of many)
- Frequent-Flier Programs: purchase/sale of points/products (capacity-fill for airlines)
- Advertising (placement of products/samples, advertising messages and inflight magazine)
Customer-satisfaction as review of airline’s product delivery, is the meeting/surpass of customer expectation of product/services (Scholar.google.com.au: Quality-and-customer-satisfaction, 2015). While this theoretical framework is defined, a conceptual framework is whereby facilitation of low-tier differentiation in product-offerings of airlines (with ancillaries or ancillary-oriented product-placement on the market) suffers in reviews, NPS, and customer-satisfaction (Strauss, 2010) (Airlineindustryreview.com, 2011) (Worldwide-Review-of-Ancillary-Revenue-Innovation (IDEAWORKS, 2008). Hence the objectives is to utilize unbiased/broad airline-review platforms to quantify and identify if ancillary revenues impact customer-satisfaction.
The given facilitative-structure would be (Kumar, 2012): Formulating Research-Problem, Conceptualizing Research-Design, Constructing Instrument for-data-collection, Selecting Sample, Data-Collection management, Processing-data facilitation.
The professional application of the information would facilitate industry-macroeconomic analytical facilitation regarding customer-satisfaction associated to the itinerary/fare breakdown and revenue-optimization associated to incremental-fees. The study also underpins variables tweaking the data which could influence airlines to review customer-feedback management and quantify correlations to inter-disciplinary study-feedbacks to following fields:
- Operations: Amend operating structure around the customer
- Cost/revenue-management: Identify cost and revenue sources and quantify inputs to business case of ventures, current non-operating structures, and inventories
- Economics/finance: Alter cost and revenues to margins of core business-areas, and identify alterations to return-on-investment and the bottom-line.
- Business-structure: Alteration of portfolio to diversify product-offerings through input-facilitations of the above (Consolidate? Develop/grow subsidiaries? Fragmentize and drive ancillaries? Drop/collate fees?)
Hence from the conceptual and theoretical framework, it is viable and accurate for the theoretical-operationalisation of the dependant variables to be the agglomeration of reviews on Skytrax as per fluctuation in sample shares of ancillary revenue (as a legitimate platform against sample’s independent-variables).
Review of literature and feedback to the study was consistently done throughout the formulation of the research-problem, its associated-theory, conceptualization, construction of dependant-variable data-collection instruments, selecting samples and general analytical pieces. The literature-review facilitated background context provisions, variables (analysis, strengths, utilization and mitigation-processes), theoretical and conceptual frameworks, gaps/findings, clarity to problem(s), and improve methodology. Due to lack of quantitative/empirical analysis on the tentative relationship identification, pieces were literature-reviewed (searched, found, read and adopted to the proposal-structure), and included inter-disciplinary and commercial-beta analysis, journals, and conference papers as follows (Scopus.com, 2015):
- 52nd-AGIFORS: Branded-fares, product-design & pricing
- Services-Engineering: Design/Pricing/Service Features
- Product-debundling trends (US-airline industry): Customer-service/public-policy implications
- The airline-retail industry: A customer’s-perspective
- Low-fare airlines back-off ancillary-revenue opportunities
- 30 years of frequent flyer programs
- Research on passenger interest model for civil aviation ancillary services
- Deterministic price setting rules to guarantee profitability of unbundling in the airline industry
The objective of the study is to attain measureable data to demonstrate impact of ancillary-revenue share in customer satisfaction (with operational definitions in theoretical/conceptual frameworks). Subobjectives are to ascertain experimental-controlled cultures in developing correlatory/analytical study to ratify/verify quantitative analysis explored historically (seen in Literature Review), while also utilizing prospective-beta facilitation of the current platform for longitudinal/repetitive facilitation. Main variables and characteristics to be measured will be – (with prospective extrapolation):
- Independent: The ancillary-revenue share of year (Airline’s(ancillary-revenue/total-revenue))
- Dependant: The mean sum of ratings for year (Skytrax stars + overall ratings of year + foods-ratings + IFE-ratings + seat-comfort ratings + service-lounge-ratings) (note: value not included; discussed below)
- Extraneous and intervening variables(to be discussed)
- Control-experiment as-per subobjectives would be to identify and analyse the full-service carrier with lowest ancillary revenue share as the control sample, and analysis determined from it will be the compared to samples, independent variables associated and dependant variables compared.
FORMULATING RESEARCH PROBLEM
The concept of the demand-driven inquiry came through rigorous analysis of current issues facing industry-wide interests associated to airline-economics and evolving customer-demands and satisfactions. Finding out empirical numerical and conclusive data to establish a relationship in a tentative manner is inspiration for formulation of the research problem and conceptual framework; whether ancillary-revenue share in an airline impacts customer-satisfaction. The phenomena and problem is largely sourced from the growth, and media coverage associated to airlines adopting high-ancillary portfolios and revenue-structures (Travelweekly.com, 2015) (news.com, 2015) (DailyMail.co.uk, 2015). Parallel cross-reference material for structure and analysis to tackle the problems (and give distinct features) were noted in the conceptual/theoretical frameworks were also identified (Scopus.com, 2015; 52nd-AGIFORS: Branded-fares, product-design & pricing, 30 years of frequent flyer programs). Formulation of research problem included:
- Magnitude: Possibilities were narrowed down to just mean-Skytrax agglomeration and ancillary revenue-share, while available-seat-kilometre data and yield information will be supplementary information to narrow the study from an independent standpoint. Skytrax value ratings were contradictory to the study (higher ancillary is higher assumed value, contrary to maximcon requirements of independent-variable maximization (Scopus.com, 2015; the airline-retail industry: A customer’s-perspective)).
- Measurement of concepts: While sample data is highly objective, high intervening variables are associated to uncontrollable biases underpinning customer-satisfaction reviews (variability in services of operating profiles, biases in perceptions, data not fully filled out, and utilization of mean data, macro-influence and environmental factors). This is hence why the use of the largest airline customer-review platform (Skytrax) was used, along with leverage management and identification of inaccurate-extremities in sampling resultant data.
- Expertise: Highly-accurate, verified and audited sampled independsnt-variable data, while dependant data leveraged between low-expertise opinions and high-expertise data (Scopus.com, 2015; Product-debundling trends (US-airline industry): Customer-service/public-policy implication).
- Availability of data: Sample was narrowed by lack of available data in context of ancillary revenue share (commercially sensitive information for many airlines and some airlines also not on Skytrax customer-review platform). Careful selection and verification to facilitate objectives and prospects were required. Narrowed to 17 airlines.
- Ethical issues: Low intervention (sample not treated, purely observed), public unanimous data utilization.
Given contexts, phenomena and problems listed, it’s deemed the higher the ancillary revenue share, the lower the customer-satisfaction (with aid of literature (Scopus.com, 2015; Research on passenger interest model for civil aviation ancillary services)). This hypothesis will be for verification, clarity to problem, and operationalisable-capability (null/alternate-hypothesis identification)
- FIND SAMPLE AIRLINES AND IATA CODES
- GATHER REVENUE, ANCILLARY REVENUE AND REVENUE PASSENGER KILOMETERS
- CONVERT CURRENCY AND LEVEL OUT INFLATION TO LATEST YEAR (2013)
- CALCULATE A-R SHARE, YEILD
- OBTAIN SKYTRAX STARS, RATINGS, AND CALCULATE SUMS (WITH/WITHOUT VALUE)
- COMPARE A-R SHARE AND SUM (RATINGS-V), GRAPH, DISPLAY, ANALYZE AND DISCUSS.
CONCEPTUALZING RESEARCH DESIGN
The study of identifying the relationship between ancillary revenue share and customer satisfaction will be hypothetico-inductive research utilizing a robust scientific method (as per mentioned). With the operational research-problem formally-formulated; a plan, a valid structure and strategy of the investigation will be produced to answer whether ancillary revenue shares do impact customer-satisfaction:
- Independent: The ancillary-revenue share of year (Airline’s (ancillary-revenue/total-revenue)). The independent variable is extracted for the sample-element’s financials to preview ancillary-revenue share for a carrier.
- Operationalised-Dependant: The mean sum of ratings for year (Skytrax stars + overall ratings of year + foods-ratings + IFE-ratings + seat-comfort ratings + service-lounge-ratings). Validated by Likert-scale structure, high-volumes and expertise/opinion leverage.
- The study will constitute 17 airlines dissected into research associated to classification of airlines as pure full-service, pure Low-Cost Carriers, high ancillary-revenue share mid-FSC, and regional airlines (proportionate to volumes (Timmons and Pasick, 2015)
- The study population will be identified through transparent ancillary-revenue data of each airline, and Skytrax ratings of large samples to accumulate volume-induced accuracy
- Sample will be selected, with shares reflective of whole populations (FSC, LCC, mid-FSC and regional airline shares by passenger kilometres carried; 35%, 29%, 29% and 7% respectively)
- Sample will be contacted through the Skytrax website (SKYTRAX, 2015), and their respective annual-reports posted (refer in “selecting sample” segment)
- Consent is inherently provided, given proprietary requirements for publication of results in publicly-listed companies taken as sample. Skytrax information also public.
Theory of causality in the research design was blueprint-designed in the conceptual framework of the study. Whether the hypothesis is strong/weak or incorrect, will be verified through the data collection and presentation stages of the study. The Maximcon principal (the process of maximizing the independent variable impact on the dependant-variable data output (Kumar, 2012)), will be attained through collation of extremities in data (from very low ancillary revenue with high-customer feedback ratings, to high ancillary LCCs with low-customer feedback ratings). These extremities also broadens parameters and identifies skewage in data, anomalies, and strength of relationship (as inherently outlines as objectives/subobjectives listed previously).
The total independent-variable impact/effect equals to the dependant variable of results subtracted by the control. This equates to the highest ratings on Skytrax (control as listed), subtracted by the lowest-rating airline, which approximately equates to (36-11=25) or rating-percentage discrepancy of 62.5% (maximum achievable). Extraneous/random-variables are eliminated through judgemental-decision making to the selection of this sample (while under parameters of maximizing the maximcon effect and requirements listed above). This is qualitatively described as an effective trade-off.
Extraneous Variables (mostly nominal with high-influence) include (with mitigation/management):
- Currency (currency was analysed, where if airline’s annual-report was in foreign currency, it converted to average US-dollar of that year. Along with that, the US-dollar was then converted to the 2013 values, where it mitigated inflation impacts).
- Available seat kilometres was standardized (hence requiring conversions from miles and nautical miles)
- Yields data required conversion and inflation (calculated as function of revenue/ASK)
- Perceptions (mentioned below, hence tweaking and skewage towards LCCs. Value perceptions were ignored to provide direct and accurate transparency to the data)
- Skytrax stars have qualitative and inaccurate perceptive biases in regards to stars (Businesstraveller.com, 2015). This cannot be mitigated or managed
- Agglomeration of short-haul and long-haul arms, which may be differentiated (eg; ANA, Qantas, Norwegian etc.)
- Varied airline-portfolios (some have no inflight entertainment, no food, no lounges etc.)
- Biases associated to perceptions and stars of business-segments (the mean sum of ratings for year (Skytrax stars + overall ratings of year + foods-ratings + IFE-ratings + seat-comfort ratings + service-lounge-ratings)). This is managed through use of large sample data associated to each airline each year, along with removal of perceived-value data to provide accuracy and reliability to the information.
Intervening variables (with mitigation) are (Scopus.com, 2015; Research on passenger interest model for civil aviation ancillary services & Services-Engineering: Design/Pricing/Service Features):
- Attitude-contagion in consumer ratings and opinion platforms, whereby negative inputs associated to a product have cascade-impacts on reviews (Link.springer.com, 2014). This is active, attributal, constant, and taken on-board (discussed in analaysis)
- Growth in ancillary revenue share also tied with drops in yield. This means an airline with low yields may be dependent on ancillaries to be profitable. This is also active, attributal, and taken on-board of the study (logarithmic impacts and maximcon amplification could suggest skewage to extremities)
Perceptions associated to the study is very strong in the dependant-variable analysis. The transparency of fare-structures, high volumes of budget-economy travellers, strong misalignment of full-service carriers introducing fees, low-profitability of many airlines (mostly Full-service), and an evolving portfolio of airlines, and aviation-presence in evolving markets today. Despite inability to be unquantifiable, this will impact the study (although mitigation and management is through selection and appropriation of an effective sample, along transparency of information/data, being both of independent and measured data.
The operationalisation-process underpins conceptual/theoretical betas with intervening variables which are adopted and taken on-board during quantitative-analysis. This may produce “non-linear and non-sequential” characteristics in result data (as discussed and its agglomeration with dependant-extraneous variables), and is seen in variety of other studies (Kumar, 2012) (Scopus.com, 2015; 52nd-AGIFORS: Branded-fares, product-design & pricing & the airline-retail industry: A customer’s-perspective & 30 years of frequent flyer programs). Operationalization is validated by the Likert-scale and high volumes of the dependant-variable data.
SELECTING SAMPLE AND INSTRUMENTS OF DATA COLLECTION
Selection of samples for independent-variable analysis were purposed for the objective of quantifying the relationship between ancillary-revenue share and customer-satisfaction (with subobjective facilitation). There is a very large population (240 IATA airlines (Iata.org, 2015)), of which all IATA-representing airlines have ancillary-revenue streams. Samples were drawn as many of those airlines don’t have transparent information on ancillary costs (samples only taken where ancillaries were identifiable). Further-narrowed intake of samples were conducted to facilitate and longitudinal study in conjunction with conceptualization of research designs to provide high accuracy in dependant/independent-variables, robust and distinct results, a non-treatment control, precision, and bias mitigation with extrapolation-capability. Selection was judgemental non-random sample draw to facilitate the maximcon-principal, trend/skewage verification and nature of sample independent/dependant-variable dynamic.
Computer, excel and secondary-sources are the only equipment needed. The data analysed is to produce a non-participant retrospective natural (with near-0 treatment of ANA as control) experimental cross-sectional analysis of the industry (1 contact across the board for 2012 and 2013 (dual-concurrent analysis for reliability/validity); no differentiated-treatment). These are public data on the Skytrax database and annual-reports. An example is ANA, where annual report is retrieved (ANA-Annual report, 2013), analysed, and compared to Skytrax-reviews online (ANA-SKYTRAX et al., 2013) This is done through dissected formulated-selection (Scopus.com; Product-debundling trends (US-airline industry): Customer-service/public-policy implications):
- Qualitative-extraction (judgemental): identification of sample diversity of airlines under study, categorization and profiling, total revenue findings, ancillary revenues, total passenger kilometres travelled, Skytrax stars, overall ratings of year, foods/IFE/Seat-comfort/service-lounge/value ratings and Skytrax Rankings.
- Numerical extraction (see in Instruments for data collection)
- Sample size of 17 airlines. Qualitative research associated to classification of airlines as pure full-service (ANA, Emirates, Jet Airways, Qantas, Aer-Lingus, China Eastern ), pure Low-Cost Carriers (Jet-2, Air Asia, Allegiant, Ryanair, Spirit), high ancillary-revenue share mid-FSC (Norwegian, JetBlue, Delta, United, American-Airlines), and regional airlines (ExpressJet) (proportionate to volumes (Timmons and Pasick, 2015))
- Sample-elements’ independent-variables retrieved will include IATA code, Revenue, Yield, Ancillary-Revenue, Ancillary-revenue share, Ancillary-Revenue per revenue seat kilometre (dual-concurrent experiments: 2012/2013). Calculation for ancillary revenue share.
- Dependant-variables of sample-statistic validated/operationalized by high-volume and Likert scale (stars). Dependant-variable retrieved include Skytrax Stars, customer-ratings, food/beverage-ratings, IFE-ratings, seat/comfort-ratings, service/lounge ratings, value-ratings, rankings (2012/2013). Calculations include sums of respondent-population means (without value-ratings); (Skytrax stars + overall ratings of year + foods-ratings + IFE-ratings + seat-comfort ratings + service-lounge-ratings). Maximum-attainable result is 40.
- Formula and hypothesis strength nominal ratio.
- Sample frame is 1 year snapshot, longitudinally-repeated. Qualitative-prospective analysis/discussion.
Face-validity is attained through the provision of rationale, robust structure, results of the dependant-variables (mean of Skytrax consumer-satisfaction), its relation with the sample’s independent variable (ancillary-revenue share), and an identifiable relationship. For content-validity, the method is appropriated with strategies of extraneous/intervening variable mitigation procedures to provide accurate data (Scopus.com, 2015; Product-debundling trends (US-airline industry): Customer-service/public-policy implications):
- Concurrent/predictive validity: The dependant-variable’s scale is balanced between opinionated-means, high-volume, and associated to official-rating structures, and can be extrapolated to today’s airline-industry (and short-term future).
- Construct-validity: Consistent-calculation and formulation on Excel. Study is simple-construct with few anomalies in data.
- Internal-consistency: strong with simple data, simple formulas/functions, and visible data.
Due to niche-structure, inherent collection of data on only largest platform, few samples legible for study, and lack or reference-areas, external consistency can only be found through 3rd party sources. This is because test-retest capability and parallel testing can only be done for new-samples on the market, and testing year-by-year. The information’s reliability is seen:
- Credibility: Airlines, in-theory, agree under theoretical/conceptual-construct, that fragmentation of fares impacted their image structures (Travelweekly.com, 2015) (news.com, 2015) (DailyMail.co.uk, 2015).
- Transferability: Is contextualized to other settings (see “literature-review”)
- Dependability: Attainted through doing 2 years of analysis. Since independent-variable data is extensive/encapsulating of business-fragments, whether lowering ancillary-revenue share will improve customer satisfaction is dependent on sample-element’s business/portfolio/operations/revenue-management (see theoretical/conceptual framework)
- Confirmability: Independent/dependant data confirmable.
EXPECTED RESULTS AND FINDINGS
The expected results are driven by findings of the dependant-variable data correlated with sample’s independent-variable characteristics (Scopus.com, 2015; Low-fare airlines back-off ancillary-revenue opportunities). Editing had very little requirement thanks to low inference, recall and “back-to-respondent” requirements. Answer-requirements are fully-transparent/transferrable, with encapsulated data readily available.
Coding of data on excel was simple and straightforward, developed, coded, tested, verified, and as follows (Scopus.com, 2015; The airline-retail industry: A customer’s-perspective &Low-fare airlines back-off ancillary-revenue opportunities);
- USD = Foreign currency * exchange rate
- Inflation to year = USD * (inflation rate ^ year)
- yield = R/RPK = (revenue/(revenue-pax-kilometres))
- Ancillary-revenue share = AR-share = Ancillary-revenue / Total-revenue
- Ancillary-revenue per RPK = yield * AR-share
- Mean-ratings = (sum of ratings)/(population-observed)
- Sum of ratings – value = (Skytrax stars + (mean) overall ratings of year + (mean) foods-ratings + (mean) IFE-ratings + (mean) seat-comfort ratings + (mean) service-lounge-ratings)
- Sum of ratings = (Sum of ratings – value) + (mean) value & “%”=sum of rating over total max.
PROPOSED STRUCTURE OF REPORT
- Theoretical/Conceptual Framework
- Literature Review
- Formulating Research Problem
- Conceptualizing Research design
- Selecting Sample and Instruments of Data Collection
- Presented Data
- Processed Data (Graphs, charts, formulation results)
- Analysis and Case studies
Airlineindustryreview.com, (2011). Airline Industry Review Roofing. [online] Available at: http://www.airlineindustryreview.com/ancillaryart/ [Accessed 19 Sep. 2015].
ANA-SKYTRAX, A., Sahni, R., Bonari, L., McGrath, M., Roosenzweig, M., Mazza, N., Halperin, N., Waldman, T., Brooke, T., Santana, G. and Pearson, J. (2014). ANA All Nippon Airways Customer Reviews | SKYTRAX. [online] SKYTRAX. Available at: http://www.airlinequality.com/airline-reviews/ana-all-nippon-airways/page/13/ [Accessed 21 Sep. 2015].
Businesstraveller.com, (2015). Discussion – Business Traveller. [online] Available at: http://www.businesstraveller.com/discussion/topic/Is-Skytrax-selective-in-its-reviews [Accessed 21 Sep. 2015].
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Kumar, R. (2012). Research-Methodology: A Step-By-Step. 3rd ed.
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