Journal of Air Transport Managementdownload.xuebalib.com/xuebalib.com.31567.pdf · Department of Management Studies, IIT Roorkee, India article info Article history: Received 1 February

lable at ScienceDirect

Journal of Air Transport Management 52 (2016) 55e66

Contents lists avai

Journal of Air Transport Management

journal homepage: www.elsevier .com/locate/ ja ir t raman

A robust hybrid decision model for evaluation and selection of thestrategic alliance partner in the airline industry

Chandra Prakash GargDepartment of Management Studies, IIT Roorkee, India

a r t i c l e i n f o

Article history:Received 1 February 2015Received in revised form27 December 2015Accepted 27 December 2015Available online 8 January 2016

Keywords:Strategic allianceAirlineAHPFuzzy TOPSISIndia

E-mail address: [email protected].

http://dx.doi.org/10.1016/j.jairtraman.2015.12.0090969-6997/© 2015 Elsevier Ltd. All rights reserved.

a b s t r a c t

Owing to the cut throat competition and economic uncertainty in the market, airlines are focusing onstrategic alliances for satisfying customer needs, especially in the current time which is dominated byglobal integration, demanding customer and fast changing technologies. This strategy is widely adoptedby airlines. However, the selection of strategic alliance partner is a very decisive decision, and this se-lection process engrosses a number of complex processes which is result of compound reflection ofassociated various factors. In addition, the decision makers may be inconsistent in their views andpreferences, arising due to imperfect information or intrinsic conflict between various departments. Thispaper presents a model based approach of an analytic hierarchy process (AHP) for evaluation of criteriaand fuzzy technique for order performance by similarity to ideal solution (FTOPSIS) for the selection ofstrategic alliance partner. A case of Indian airline industry demonstrates the application of the proposedapproach. Eventually, robustness of the model is demonstrated by sensitivity analysis.

© 2015 Elsevier Ltd. All rights reserved.

1. Introduction

Airlines have joined their hands in response to changes in eco-nomic, market and regulatory conditions and formed strategic al-liances (Albers et al., 2005). An evidence of this can be seen byscores of alliances that have been established in the recent yearssuch as Star Alliance, Sky Team and One World etc. that constituteapproximately 70% of passengers and turnover in the entire worldmarket (Holtbrugge et al., 2006). Tangible benefits in makingstrategic alliance are attracting more passengers, integration ofnetwork through expansion of routes and cost reduction in airlineoperations through joint baggage handling, gates and check-incounters, code-sharing, joint use of lounges, and exchange offlight attendants (Merkert and Morrell, 2012; Zhang et al., 2004).Moreover connectivity of arrival and departure flights can beimproved by making connections with strategic partners. Coordi-nation among strategic partners is the key performance indicators.However, poor coordination could lead to the loss of the partners'that had been seen in the case of Swissair (Park and Cho, 1997). Ithas been seen that the performance of the strategic alliance directlydepend on the selection of partners (Mohr and Spekman, 1994).

One of the objectives of forming strategic alliances is to choose suchpartner who can share his resources and capability where theairline lacks. A healthy relationship between strategic partnerswould require trust, commitment, good culture and synergy amongmanagement.

Previous research studies in this area have suggested that se-lection of the partners is beneficial when strategic partnersassimilate their core competency, technology and share their re-sources. Airline should concentrate on those routes which havecomparative advantages apart from extension of her network viathe sharing routes with partner (Goh and Yong, 2006). Strategicpartners may invest enough time and efforts to effectively utilizethe synergy created through alliance. That will come through focuson the internal structure and the culture of the organizations toencounter diverse external requirements. Passengers experiencewill depend on the total package of services offered by carrierwithout knowing that these services are presented by airline withthe support of strategic partner and such partner would derivesynergy and will create winewin situation for both. Partner se-lection and evaluation is important because of the increasinglycritical role in a firm success. However evaluation of the criteria forselection of the strategic partner is strategic decision and has gotmuch attention recently. Since previous studies shows that manyresearchers have recognized the importance of the selection of

mailto:[email protected]

http://crossmark.crossref.org/dialog/?doi=10.1016/j.jairtraman.2015.12.009&domain=pdf

www.sciencedirect.com/science/journal/09696997

http://www.elsevier.com/locate/jairtraman

http://dx.doi.org/10.1016/j.jairtraman.2015.12.009



C.P. Garg / Journal of Air Transport Management 52 (2016) 55e6656

strategic partner but very few studies suggested empirical analysisfor the selection of the strategic partner. And in Indian contextthere is no study identified in literature so far for selection ofstrategic partner. However airline industry of India is a key player inglobal market and Indian economy has huge potential in terms ofdemand and growth.

This is new research paradigm and it has many research gapsstill to be explored (studies about strategic partner selection andevaluation are very limited in Indian context); and particularly, itbecomes important when airlines are looking towards growth ofthe business by expanding their networks. Therefore, the needarises to evaluate strategic partner selection to ensure sharedbenefits in the alliance.

1.1. Research motives

This study is an attempt to achieve the following objectives:

� To identify and prioritize the evaluation criteria for selection ofstrategic partner;

� To support in selecting the best strategic partner among possiblealternatives.

This study attempts to identify, evaluate the strategic alliancepartner criteria and to develop research framework to help inselecting the best strategic partner among existing alternatives.However, there are many criteria and their sub-criteria for partnerselection those include various dimensions which require coordi-nation and collaboration among different departments of selectedpartner. But consideration of criteria and their evaluation shouldfocus on operational environment and contextualized according togeographical dimension. Therefore, it is important that the selec-tion criteria should focus on cooperation, capability, commitmentand compatibility of the partners. Identification and evaluation ofcriteria, for partner selection along with their sub-criteria is doneby decision making group. Decision team has 4 members (1 fromAcademia and 3 from industry) these members have more than 10years of experience in their fields. After extensive literature reviewand experts opinions, the decision making group has finalizedcriteria the recent development of strategic airline alliances selec-tion criteria. Although each criterion is very important to maintainand sustain good customer service but presence of multiple criteriaand the views from the experts will increase the complexity inselection of the strategic partner. To show the real-life applicationof the proposed combined AHP-FTOPSIS approach, a case exampleof Indian airline industry is presented. Selection of the partnerwhile considering different criteria is multi-criteria decision mak-ing (MCDM) problem and involvement of fuzziness in decision;makes decision structure flexible. And this fuzzy based flexiblesystematic decision support tool provides flexibility for the selec-tion of the strategic partners. The application of AHP has been seenin many multi-criteria decision making (MCDM) problems. Butintegrating one MCDM with other decision support system wouldimprove our decisionmaking process. This study utilizes the AHP toget the weights for criteria and these weights are used in FuzzyTOPSIS approach to select the right strategic partner for airlines.The rest of this paper is planned as follows. Section 2 highlights thebackground of the research. Section 3 deals with the problem.Section 4 describes the methodology and application of the modelfor selection of the partner is given in Section 5. The results anddiscussions are reported in Section 6. Section 7 shows the sensi-tivity analysis outcome. Section 8 represents the managerial im-plications of the proposed framework and concluding remarks aregiven in Section 9.

2. Background of the research

Many research studies have been carried out and it was foundthat creating strategic alliance is winewin strategy for airlinepartners. Selection of the strategic partners involves various di-mensions that had identified and recognized in the literature(Mockler and Carnevali, 1997; Bissessur and Alamdari, 1998;Brouthers et al., 1995; Evans, 2001; Geringer, 1991; Luo, 1998;Park and Cho, 1997). Zhang et al. (2004) identified that due to thealliance; access to the different parts of the world made possible ateconomical rates and increased competition among airlines. GomesCasseres (1996) and Yoshino (1995) presented that the alliance as aventure between firms based on the cooperation and partnershipfollowed by sharing resources, risks and benefits but had limitedcontrol and incomplete contracts. Rhoades and Lush (1997) statedthat strategic alliances had reduced airlines operating cost signifi-cantly. Luo (1998) distinguished the selection criteria of strategicpartner into three broad areas they were operational efficiency,cooperation and financial stability. Holtbrugge et al. (2006) pre-sented that alliances achieved synergy through sharing infra-structure like lounges and check-in facilities, along with code-sharing agreements and the interchanging of flight-crewpersonnel and aircraft and also identified the importance of roleand responsibilities of HR aspect after strategic alliance. Priorempirical studies on the alliances were done by Gellman ResearchAssociates (1994), Park and Cho (1997), Oum et al. (2000), Park et al.(2001), and Zhang et al. (2004) and Proposed that profit, perfor-mance and productivity could be improved by forming alliances.Dev et al. (1996) studied alliances strategies from various per-spectives like theory of games, network benefits, economics andinternationalization of the firms. Brueckner (1997) examined thatcode sharing would beneficial in terms economic of density forairline. Fan et al. (2001) analyzed the factor behinds the strategicalliance and concluded that globalization and liberalization werethe main reason for forming the airline alliances along with otherfactors. Flores-Fillol and Moner-Colonques (2007) suggestedthrough strategic alliance airline can provide smooth services totheir passengers by adjusting flight schedules, gate procedures andjoint frequent flyers programs. Liou et al. (2011) proposed hybridfuzzy ANPmodel to select the strategic partners and considered thevarious criteria like marketing, product/service, computer systems,equipment servicing and logistics along with different sub-criteria.This model did not include one of the important criteria financialbenefits achieved through alliance and used single MCDM supportsystem. Airlines make strategic alliance to gain economic andfinancial incentives. In the existing literature few studies haveutilized integrated MCDMs approach to evaluate and select stra-tegic partner for airline industry like Liou et al. (2011) applied FuzzyDEMATEL and ANP based model for selection of the strategicpartners and used organization, service, computer systems,equipments, partner and strategy as criteria and defined varioussub-criteria under them. This model was proposed for Taiwaneseairline. However various studies are available which applied com-bined MCDMs in different areas.

Recently Jiang et al. (2015) presented airline alliance modelbased on game theory and identified that expected outcomes maybe multiple if four airlines players are involved in their case theyhave taken two local airlines and two global airlines. Chao and Kao(2015) proposed fuzzy AHP based model for selection of strategicalliance partner for Taiwan-based China Airlines and used 14 se-lection criteria and identified three important dimensions whileselecting appropriate strategic partner. These were enhanced flightroute and frequency, increased revenue, and improved load factor.This study utilized single MCDM support system and robustness ofthe presented approach is not verified.

C.P. Garg / Journal of Air Transport Management 52 (2016) 55e66 57

Since the selection criteria is subjective in nature and depend onthe particular region, but it can be well represented by combina-tions of the various criteria. Existing literature on selection of thestrategic partners shows that few studies utilized MCDMs as acombined approach for selection of the partners for airlines and nostudy presented sensitivity analysis. And moreover in Indiancontext there is not even single study presented in literature forselection of the strategic partner. Hence there is a need for clear,robust and rational scientific method for decision maker to take adecision (Table 1).

3. Problem statement

Airlines are looking to provide the best air transport service allaround the world and their presence in majority of air travel mar-ket. Airlines are keen to offer safe, efficient, economical and regulartransport services. But due to economic, operational, financial andregulatory constraints, it is very difficult for them to proffer analternative. This can be done by joining hands and forming strategicalliance with other airlines. But seeking strategic partners, whosestructure, culture and strategy should alignwith airline, are difficultand involvement of various selection criteria increase complexity indecision making. Identification of criteria and their evaluationshould consider operational environment and cater the geograph-ical need. Therefore, it is important that selection criteria shouldfocus on cooperation, capability, commitment and compatibility ofthe partners. This paper proposed selection criteria for the strategicpartner, which is supposed to be multi-criteria decision makingproblem. Evaluation criteria and sub-criteria have been shown inTable 2.

Due to the dynamic nature of the airline industry all around theworld today, criteria have been identified through developing ateam of experts in this area and existing literature. Therefore, adecision group was formed consists of 4 professionals (1 from ac-ademics and 3 from airline industry/consultancy). The expertsselected for the present study are highly skilled in their domains

Table 1Combined MCDMs techniques used in the recent literature in diverse stream.

S.N.

Researcher (year) Modeling techniques used Issues co

1 Liou et al. (2007) DEMAEL and ANP Safety m2 Büyük€ozkan et al.

(2008)Fuzzy AHP and Fuzzy TOPSIS Evaluati

partners3 Kannan et al. (2009) ISM and Fuzzy TOPSIS Selection

4 Liou and Chuang(2010)

DEMATEL, ANP and VIKOR Outsour

5 Kuo et al. (2010) DEA, ANP and ANN Green su6 Sasikumar and Haq

(2010)Fuzzy VIKOR and MILP Third-pa

7 Shaw et al. (2012) Fuzzy-AHP and Fuzzy multi-objective linearprogramming

Supplierchain

8 Ho et al. (2012) QFD and Fuzzy AHP 3rd party9 Bai and Sarkis (2013) Gray system and Rough set Sustaina10 Liou et al. (2013) DEMATEL, ANP and Gray relation theory Selection11 Akman (2014) Fuzzy c means and VIKOR Evaluati12 Senthil et al. (2014) AHP and Fuzzy TOPSIS 3PRL con13 Rezaei et al. (2014) Conjunctive screening method and Fuzzy AHP Supplier14 Ayhan and Kilic (2015) Fuzzy AHP and MILP Supplier15 Prakash and Barua

(2015a)Fuzzy AHP and Fuzzy TOPSIS Prioritiz

logistics16 Prakash and Barua

(2015e)AHP and Fuzzy TOPSIS Evaluati

17 Barros and Wanke(2015)

TOPSIS and Neural Networks Perform

and are proficient in decision-making. More importantly, all of theexperts have experience of more than ten years. In this studycombined AHP and Fuzzy TOPSIS has been utilized to select themost appropriate partner for strategic alliance and fuzzy environ-ment signifies, involvement of uncertainty and vagueness consid-eration of the decision makers.

4. Research methodology

In this paper three phase methodology has been applied forevaluation and selection of the strategic partners. In phase e I,decision group was formed. This group has identified evaluationcriteria for partner through extensive discussion and with thesupport of existing literature. In phase e II, after finalization ofcriteria, decision group has given rating to calculate the weights ofthe criteria by using AHP and in phase e III, again decision grouphas assigned final pre-defined rating for selection of strategicpartner by using FTOPSIS. The collected sample of rating was cho-sen from decision making group through questionnaire. This paperused AHP to get weights of criteria and prioritize to select partnersby Fuzzy TOPSIS. Although decision making can be done by AHPitself, but multi-criteria decisionmaking process can be improved ifit is integrated with many other decision support tools. By usingfuzzy framework impreciseness and uncertainty can be handled.The suitability of this approach in a complex multi-criteria decisionenvironment compels to select this method. Fig. 1 represents aschematic diagram of the research methodology (Fig. 2).

4.1. Phase I selection criteria

The selection criteria should understand organization's needsand operating strategy that's why decision making group is formedincluding experts, industry associates, and airline operators. Iden-tification of evaluation criteria are done by decision making groupthrough literature review and experts who have proficiency in thisarea. The team of 4 members 2 members of case airline personnel

vered Application, country

easurement Taiwanese airline, Taiwanon of e-logistics-based strategic alliance Electronics industry, Turkish logistics

sectorof third-party reverse logistics providers Battery manufacturing industry,

Indiacing partner selection Taiwanese airline, Taiwan

pplier selection Electronics industry, Taiwanrty reverse logistics provider selection Battery manufacturing industry,

Indiaselection for developing low carbon supply Garment manufacturing company,

Indialogistics service provider selection Hardware components, Hong Kong

bility into supplier selection Case company, Chinaof outsourcing partner Taiwanese airline, Taiwan

on of green supplier development programs Automobile company, Turkeytractor evaluation and selection Plastic industry, Indiaselection Airline industry, Europeselection Gear motor manufacturers, Turkeying solutions to overcome barriers of reverse Indian electronics industry

on and ranking of airports Aviation Industry, India

ance measurement Airline industry, Africa

Table 2Evaluation criteria and sub-criteria for strategic alliance.

S.No. Criteria Sub-criteria References

1 Jointequipments

Shared components & equipment, shared maintenance,shared ground handling procurement

Liou et al. (2011), Yoshino (1995), Chao and Kao (2015), Geringer (1991), Rhoados andLush (1997), Evans (2001), Fan et al. (2001), Morrish and Hamilton (2002)

2 Marketing &service

Code sharing, promotion, frequent flyer scheme, brands,adopted under license, exceeding freedom constraint

Liou et al. (2011), Goh and Yong (2006), Zhang et al. (2004), Albers et al. (2005), Chao andKao (2015), Rhoados and Lush (1997), Bilotkach and Hüschelrath (2012), Goetz andShapiro (2012), Merkert and Morrell (2012)

3 Finance Profit, economics, investment, shared cost & expenditures Chao and Kao (2015), Rhoados and Lush (1997), Oum et al. (2000), Evans (2001),Bilotkach and Hüschelrath (2012)

4 Integration &network

Flight & route expansions, increased frequencies, jointcoverage

Liou et al. (2011), Holtbrugge et al. (2006), Zhang et al. (2004), Chao and Kao (2015), Fanet al. (2001), Oum et al. (2000), Liou et al. (2011), Bilotkach and Hüschelrath (2012),Rhoados and Lush (1997), Merkert and Morrell (2012).

5 IT system Integrated system, shared & exchange information,standered operating procedures

Rhoados and Lush (1997), Evans (2001), Liou et al. (2011), Oum et al. (2000), Chao andKao (2015).

6 Logistics &resources

Shared terminals, shared offices, transportation Rhoados and Lush (1997), Oum et al. (2000), Morrish and Hamilton (2002), Liou et al.(2011), Zhang et al. (2004), Cassers (1996), Chao and Kao (2015)

7 Partner image& experience

Star alliances, sky team and one world, proficiencies Liou et al. (2011), Fan et al. (2001), Chao and Kao (2015), Stafford (1994)

Constructing Decision Group

Identifying partner selection criteria

Finalizing the evaluation criteria

Calculate Weights using AHP

Evaluation by Fuzzy TOPSIS

Selection of the strategic partner

Y

Phase ICriteria Selection

Phase IIAHP

Phase IIIFuzzy TOPSIS

Approve

Weights

N

Fig. 1. Proposed three phase methodology for strategic partner.

Strategic Partner - Selection

JE MS FI IN IT LR PE

SP1 SP2 SP7…………………

Fig. 2. Hierarchy model.


including 1 member; Associate Vice President (Strategic PlanningDeptt.); 1 Senior Airline manager (Commercial/Marketing- AeroDeptt.), 1 consultant; CEO of Aviation Consultancy, India and 1researcher in this field, They have more than 10 years of experiencein the aviation industry. After brainstorming sessions and with thehelp of past research in this area, team has finalized 7 evaluation

criteria along with sub-criteria. The decision makers finalized thevarious criteria like e Equipment's, Marketing & Service, Integra-tion & Network, Finance, IT systems and Logistics along with theirsub-criteria. Although these dimensions are compatible andfeasible but decision makers want to include another criteria alli-ance partnership and proposed three potential partners by

Table 4Linguistics variables ratings.


considering interdependence between partners and determents.This evaluation system is given in Table 2.

Linguistic variables Assigned TFN

Very low (1, 2, 3)Low (2, 3, 4)Medium (3, 4, 5)High (4, 5, 6)Very high (5, 6, 7)Excellent (7, 8, 9)

4.2. Phase II analytical hierarchy process: stepwise procedure

In phase e II, after finalization of criteria, decision group hasassigned rating to calculate theweights of the criteria by using AHP.The data of rating is collected from decision making group throughquestionnaire.

Saaty (1980) had given AHP; it was a widely used technique ofmulti criteria decision making. The application of AHP has beenseen in the decision making process for both quantitative as well asqualitative aspects. The general AHP process utilized in this paper isas follows:

Step 1: The matrix gives the relative importance used in pair-wise comparison and discrete number 1 to 9, are used in scalingscheme (see Table 3). The value 1 is assigned for same comparisonwith criteria so that the all diagonal elements become 1.

Recently this approach is used by Prakash and Barua (2015e).Step 2: Calculate the weights of criteriaThe geometric mean of the row has been used to get weight of

each criterion, and then it is normalized by the means of rows inmatrix A.

Step 3: Checking consistencyThe largest Eigen value of the matrix is calculated using eqn.

given below- AW ¼ lmaxW, where W is Principal Eigen Vector.

And Consistency Ratio CR ¼ CIRI

and CI ¼ lmax � nn� 1

(1)

where CI is Consistency Index and RI is random index, which isshown in below table.

N 1 2 3 4 5 6 7 8RI 0 0 0.58 0.9 1.12 1.24 1.32 1.41

As a thumb rule, value of the CR � 0.10, is acceptable, otherwisevalues need to be revised to get consistent matrix. The applicationof Satty's AHP has some limitation due to the usability of AHP inCrisp environment, Judgmental scale is unbalanced, and absence ofuncertainty, selection of judgment is subjective. So there is a needto utilize a fuzzy approach to solve such problem (Prakash et al.,2014; Prakash and Barua, 2015b).

4.3. Phase III fuzzy TOPSIS: stepwise procedure

TOPSIS is another MCDM method was presented by Hwang andYoon (1981). It is based on the selective attribute should be at theleast distance and longest distance from the positive ideal solutionand the negative ideal solution respectively. In the classical TOPSIS

Table 3Relative importance scale used for the pairwise the comparison.

Importance intensity Linguistic variables

1 Equally important3 Moderately important5 Strongly important7 Very strongly important9 Extremely important2,4,6,8 Intermediate valuesReciprocals Reciprocals for inverse comparison

Source: Satty (2000)

approach, individual preferences are assignedwith crisp values. Butin reality, a better approach is; which consider uncertainty andimpreciseness rather than crisp value. Fuzzy environment allowsincorporating uncertainty in decision making, that's why, and thefuzzy TOPSIS method is quite appropriate tool for the solution ofreal life problems (Prakash et al., 2015c, d). Uncertainty in decisionmaking can be reduced by using fuzzy approach, which was pio-neered by Zadeh (1965). In practice, the triangular fuzzy numbers(TFN) are commonly used. The Fuzzy TOPSIS approach used in thisresearch paper is as follows:

Step 1: Assign rating values for the linguistic variables withrespect to criteria. The scale used for rating is given in belowTable 4. And construct matrix for alternatives in fuzzy form.

Step 2: Normalized fuzzy decision matrixTo get comparable scale by utilizing linear scale transformation,

data is normalized. It is given by B�where:

B�¼hpijimxn

Where i ¼ 1;2;3;……m and j ¼ 1;2;3;……n

p�

ij ¼ aijc�j;bijc�j;cijc�j

!and c�j ¼ max cij ðbenefit criteriaÞ (4.1)

p�

ij ¼ a�jcij

;a�jbij

;a�jaij

!and a�j ¼ min aij ðcost criteriaÞ (4.2)

Step 3: Construct the weighted normalized matrix by usinggiven Eq.

V�¼�v�ij

�mxn

Where i ¼ 1;2;3;……m and j

¼ 1;2;3;……n where v�ij ¼ p

�

ij5wj (4.3)

Step 4: Determine the ideal and fuzzy negative ideal solution(FNIS) and positive ideal solution (FPIS) as follows respectively:

Aþ ¼nvþ1 ; ……:; vþn

o; where vþj

¼ �max�vij�if jεJ; min

�vij�if jεJ0

�; j ¼ 1……n (4.4)

A� ¼nv�1 ;……:; v�n

o; where v�j

¼ �min�vij�if jεJ; max

�vij�if jεJ0

�; j ¼ 1……n (4.5)

Step 5: Calculate the distance of each alternative from FNIS andFPIS is computed as follows:

dþi ¼8<:Xnj¼1

�vij � vþij

29=;1 =

2

; i ¼ 1………::m

Table 6Obtained value from AHP.

Highest eigen value (lmax) 7.755251Consistency index (CI) 0.125875Random index (RI) 1.32Consistency ratio (CR) 0.09536


dþi ¼Xnj¼1

vij � vþij� 28<

:9=;

1=2

; i ¼ 1………::m

d�i ¼Xnj¼1

vij � v�ij� 28<

:9=;

1=2

; i ¼ 1………::m

(4.6)

Step 6: Closeness coefficient (CCi) of each alternative is calcu-lated by using Eq. below-

CCi ¼d�i

d�i þ dþii ¼ 1………m: CCiεð0;1Þ (4.7)

Step 7: Rank the alternatives to select partner as per closenessrating by using CCi in descending order.

5. Application of the model through case analysis

In order to demonstrate the application of the framework, anempirical case of low cost airline of India (named as ABC) was takeninto account. The case company is growing tremendously in the airtransport service and located in northern region of India. The casecompany vision is to become market leader in airline industry andprovide air services in domestic and international markets. Atpresent, company's turnover is more than INR 90 billion and haspresence in majority of markets all around India and Asia. Companyoffers only economy class service and covers more than 40 desti-nations within the country and more than 15 foreign destinations.Case company also provides special packages to their customers.Now company is aiming to increase its presence in other marketsand expand her networks. Therefore case company is looking toform a strategic alliance with other airlines those can provide suf-ficient support in network expansion, sharing resources and riskequally and work together with full commitment. For successfulalliance there must be effective control from partners to contributeto the alliance. Effective leadership and management could be veryimportant aspect for the control of the alliance and in achievementof the objectives by concentrating on the alliance strategies. Thegeographical constraints cannot be ruled out for the selection of thestrategic partner. Therefore before forming strategic alliance,partner should do sufficient analysis by considering various di-mensions. The management is also seeking prioritization of thepartner selection criteria and wants to select the best partneramong alternatives. After analyzing the problem and discussing theaim of this research, the proposed AHP Fuzzy TOPSIS basedframework is applied to the discussed case. Now methodologydiscussed above will follow in sub-sections.

5.1. Application of AHP to evaluate weights of the criteria

After determining the criteria and sub-criteria of strategicpartner, decision group assigned the value given in Table 4 by pair-

Table 5Pair wise comparison matrix.

Criteria EQ MS FI

Joint equipments' (JE) 1.00 0.25 1.00Marketing & service (MS) 4.00 1.00 4.00Finance (FI) 1.00 0.25 1.00Integration & network (IN) 4.00 2.00 4.00IT system (IT) 3.00 1.00 0.5Logistics & resources (LR) 4.00 0.33 3.00Partner image & experience (PE) 2.00 0.5 3.00

wise comparison matrix for each criteria and the weights arecalculated as shown in Table 5 is given below. Consistency ratio iscalculated by using Eqns 1 and it is less than 0.10 (see Table 6),shows that weights in matrix is consistent and can be used forfurther calculations. Entire process has been followed as discussedin Phase II of AHP. These calculations can be done with MS-ExcelSheet.

5.2. Application of fuzzy TOPSIS to select the strategic partner

Fuzzy evaluation matrix is constructed by using linguistic vari-ables as shown in Table 4 and given in TFN corresponding fuzzyevaluation matrix is given in Table 7. Then it is normalized by usingEqns 4.1 and 4.2 as shown in Table 8. After that by using Eqns 4.3,weighted normalized fuzzy decisionmatrix is obtained as shown inTable 9. This study considers all enablers as cost criteria and allo-cated the fuzzy positive ideal solution as v

�þ1 ¼ ð0; 0; 0Þ And the

fuzzy negative ideal solution as v��1 ¼ ð1; 1; 1Þ, thereafter each

alternative distance is calculated by using Eqns 4.6 and coefficientof the closeness is obtained by using Eqns 4.7 and according to CCiValues the selection of the strategic partners have been done,which is shown in Table 10. This entire process has been done as permethodology discussed in phase III.

6. Results and discussions

Strategic alliance can improve airline performance in areas likeroutes and network expansion, sharing resources, cost reduction,higher revenue and more customer satisfaction; therefore selec-tion of such partner plays crucially important role in making aconscious decision. This research identifies and evaluates variouscriteria from the relevant literature and industrial experts' viewsfor selection of strategic alliance partner for Indian airline in-dustry. Decision making group has participated in idea generationseminar for identification, evaluation and selection of strategicpartners. Seven criteria along with various sub-criteria have beenfinalized by decision making group for partner selection. It is veryhard to say which of the criteria for strategic alliance partner se-lection is more important than others, but prioritizing them byusing this approach made it more logical, flexible and supportivefor management. AHP has been used for evaluation and ranking ofthe criteria and FTOPSIS is applied to select the strategic partner.The ranking of the criteria have been done by observing thehighest weightage values. Based on Table 5 shows Integration &

IN IT LO PE Weights Rank

0.25 0.33 0.25 0.5 0.0493 70.5 1.00 3.00 2.00 0.2146 20.25 2.00 0.33 0.33 0.0758 61.00 3.00 1.00 2.00 0.2512 10.33 1.00 1.00 2.00 0.1292 41.00 1.00 1.00 3.00 0.1786 30.5 0.5 0.33 1.00 0.1012 5

Table 7Fuzzy evaluation matrix.


Strategic partner 1 (2, 3, 4) (0.333, 0.5, 1) (1, 2, 3) (0.333, 0.5, 1) (2, 3, 4) (0.333, 0.5, 1) (1, 2, 3)Strategic partner 2 (1, 2, 3) (1, 2, 3) (0.2, 0.25, 0.333) (2, 3, 4) (0.2, 0.25, 0.333) (3, 4, 5) (0.333, 0.5, 1)Strategic partner 3 (0.333, 0.5, 1) (3, 4, 5) (2, 3, 4) (3, 4, 5) (1, 2, 3) (1, 2, 3) (0.333, 0.5, 1)Strategic partner 4 (2, 3, 4) (1, 2, 3) (0.2, 0.25, 0.333) (1, 2, 3) (3, 4, 5) (0.333, 0.5, 1) (2, 3, 4)Strategic partner 5 (0.333, 0.5, 1) (3, 4, 5) (0.333, 0.5, 1) (0.2, 0.25, 0.333) (1, 2, 3) (1, 2, 3) (0.2, 0.25, 0.333)Strategic partner 6 (1, 2, 3) (0.2, 0.25, 0.333) (2, 3, 4) (1, 2, 3) (0.333, 0.5, 1) (2, 3, 4) (3, 4, 5)Strategic partner 7 (0.2, 0.25, 0.333) (1, 2, 3) (1, 2, 3) (0.333, 0.5, 1) (0.333, 0.5, 1) (0.2, 0.25, 0.333) (1, 2, 3)

Table 8Normalized Fuzzy decision matrix.


Strategic partner 1 (0.25, 0.333, 0.5) (0.2, 0.4, 0.606) (0.333, 0.666, 1) (0.2, 0.4, 0.6) (0.25, 0.333, 0.5) (0.2, 0.4, 0.6) (0.333, 0.666, 1)Strategic partner 2 (0.333, 0.666, 1) (0.333, 0.666, 1) (0.6, 0.8, 1) (0.25, 0.333, 0.5) (0.6, 0.8, 1) (0.2, 0.25, 0.333) (0.2, 0.4, 0.6)Strategic partner 3 (0.2, 0.4, 0.6) (0.2, 0.25, 0.333) (0.25, 0.333, 0.5) (0.2, 0.25, 0.33) (0.333, 0.666, 1) (0.333, 0.666, 1) (0.2, 0.4, 0.6)Strategic partner 4 (0.25, 0.333, 0.5) (0.333, 0.666, 1) (0.6, 0.8, 1) (0.333, 0.666, 1) (0.2, 0.25, 0.333) (0.2, 0.4, 0.6) (0.25, 0.333, 0.5)Strategic partner 5 (0.2, 0.4, 0.6) (0.2, 0.25, 0.333) (0.2, 0.4, 0.6) (0.6, 0.8, 1) (0.333, 0.666, 1) (0.333, 0.666, 1) (0.6, 0.8, 1)Strategic partner 6 (0.333, 0.666, 1) (0.6, 0.8, 1) (0.25, 0.333, 0.5) (0.333, 0.666, 1) (0.2, 0.4, 0.6) (0.25, 0.333, 0.5) (0.2, 0.25, 0.333)Strategic partner 7 (0.6, 0.8, 1) (0.333, 0.666, 1) (0.333, 0.666, 1) (0.2, 0.4, 0.6) (0.2, 0.4, 0.6) (0.6, 0.8, 1) (0.333, 0.666, 1)

Table 9Weighted normalized Fuzzy decision matrix.


Strategic partner 1 (0.012, 0.016, 0.024) (0.042, 0.086, 0.13) (0.025, 0.051, 0.076) (0.05, 0.1, 0.151) (0.032, 0.043, 0.06) (0.035, 0.07, 0.11) (0.034, 0.07, 0.1)Strategic partner 2 (0.016, 0.032, 0.049) (0.071, 0.143, 0.215) (0.045, 0.061, 0.076) (0.062, 0.083, 0.126) (0.078, 0.103, 0.13) (0.035, 0.04, 0.06) (0.02, 0.04, 0.06)Strategic partner 3 (0.009, 0.019, 0.029) (0.042, 0.054, 0.072) (0.019, 0.025, 0.038) (0.050, 0.062, 0.084) (0.043, 0.086, 0.13) (0.059, 0.12, 0.18) (0.02, 0.04, 0.06)Strategic partner 4 (0.012, 0.016, 0.024) (0.071, 0.143, 0.215) (0.045, 0.061, 0.076) (0.083, 0.167, 0.251) (0.026, 0.032, 0.040) (0.035, 0.07, 0.11) (0.025, 0.03, 0.05)Strategic partner 5 (0.009, 0.019, 0.029) (0.042, 0.054, 0.072) (0.015, 0.03, 0.045) (0.152, 0.20, 0.251) (0.043, 0.086, 0.13) (0.059, 0.12, 0.18) (0.061, 0.08, 0.1)Strategic partner 6 (0.016, 0.032, 0.049) (0.13, 0.172, 0.215) (0.019, 0.025, 0.038) (0.083, 0.167, 0.251) (0.026, 0.052, 0.08) (0.044, 0.06, 0.09) (0.02, 0.025, 0.033)Strategic partner 7 (0.029, 0.039, 0.049) (0.071, 0.143, 0.215) (0.025, 0.051, 0.076) (0.05, 0.1, 0.151) (0.026, 0.052, 0.08) (0.107, 0.14, 0.18) (0.034, 0.07, 0.1)Aþ, A� v

�þ1 ¼ ð0; 0;0Þ v

�þ1 ¼ ð0; 0; 0Þ v

�þ1 ¼ ð0;0; 0Þ v

�þ1 ¼ ð0;0;0Þ v

�þ1 ¼ ð0;0;0Þ v

�þ1 ¼ ð0;0;0Þ v

�þ1 ¼ ð0;0;0Þ

v��1 ¼ ð1; 1;1Þ v

��1 ¼ ð1; 1; 1Þ v

��1 ¼ ð1;1; 1Þ v

��1 ¼ ð1;1;1Þ v

��1 ¼ ð1;1;1Þ v

��1 ¼ ð1;1;1Þ v

��1 ¼ ð1;1;1Þ

Table 10Closeness coefficient values and final ranking.

Alternatives dþi d�i CCi Rank

Strategic partner 1 0.47358 6.57318 0.932795 2Strategic partner 2 0.54324 6.5126 0.923009 3Strategic partner 3 0.43947 6.60321 0.937599 1Strategic partner 4 0.56557 6.48342 0.919766 4Strategic partner 5 0.62106 6.42491 0.911856 6Strategic partner 6 0.57044 6.4705 0.918983 5Strategic partner 7 0.6325 6.41762 0.910285 7


Network (IN) criterion has maximum weightage value (0.2512)and got 1st rank, Marketing & Service (MS) criterion has 2nd rankwith weightage value (0.2146) and Logistics & Resources (LR)criterion has 3rd rank with weightage value (0.1786). Further,ranking of various criteria are in descending order with weightagevalues IT System (0.1292) > Partner Image and Experience(0.1012) > Finance (0.0758) >Joint Equipments (0.0493). Thisshows Integration & Network criterion is the most importantcriterion among other criteria and Joint Equipments criterion isthe least important criterion while choosing appropriate strategicpartner. And importance of the other criteria can be understood asper ranking obtained in Table 5. It indicates that integration andnetwork criterion is the top most important in Indian scenariowhile making decision to select strategic partner. Expansion of

destinations, mutual mobility and more flights are the primaryexpectations from the selected strategic partner. Therefore airlinemanager should understand and evaluate the strategic partnerabilities under the dimensions of integration and network crite-rion. The importance of this criterion was also mentioned by oneof the experts of decision making group, e.g.:

‘Our airline has focused the network expansion capabilities ofstrategic partners so we can increase frequencies as well as desti-nations in order to boost business opportunity’.

Marketing & service criterion comes second and it includesstrategic partner's ability of sharing airline code, promotional ac-tivities, customer base, brand image, regulation capabilities andmarket coverage. Hence Indian airline management should analyzeand assess strategic partner skills and competencies accordingly.This was also revealed in the interview by one of the aviationanalyst:

‘Airlines do strategic partnership in India; the motive behind thatthey can promote and market their business propositions bysharing brand image of partner. This strategy has profound impactin the development of corporate image of those airlines’.

The third rank is obtained by logistics & resources criterion, it


means sharing ability of strategic partner in terms of infrastructure,resources and facilities. Therefore airline service providers mustjudge partner capabilities and encourage new, innovative andcustomized facilities & services that will improve its serviceultimately.

IT system criterion receives fourth rank it means partnerinvolvement in having unified information system and pro-cedures, common practices and joint information portal. It alsoincludes common communication and information system. Thisdimension is also plays important role while selecting strategicpartner. Fifth rank is acquired by partner image and experiencecriterion it connotes partner's availability in market and its brandvalue as well as current membership with alliances. Airlinemanagement personnel of India have to look this dimensioncarefully while choosing strategic partner. Finance criterion hasgained sixth rank; it assesses financial ability of the partner. Itscapital expenditure, investment capabilities, returns and overallprofit so partner can share cost, investment and expenses infuture. Planners have to evaluate financial statements carefullywhile making decision to select strategic partner. Last rank isacquired by joint equipments criterion it measures sharing abilityof partner in equipment, maintenance, ground handling activitiesand joint purchasing & procurement. Managers have to look thisdimension also while selecting strategic partner. The weights ofthe criteria obtained through AHP are used in Fuzzy TOPSIS forselection of the strategic alliance partner. In this paper sevenpotential strategic partners (SP) have been identified and with thehelp of decision making group, evaluation matrix of strategicpartner selection alternatives are constructed. The selection of thestrategic partner has done by observing highest closeness coeffi-cient values. According to Table 10 alternative SP3 is the beststrategic partner with high CCi value (0.9375) and SP7 is thelowest rating partner with less CCi value (0.9102). The ranking ofother alternatives with CCi values based on Table 10 are SP1(0.9327) > SP2 (0.9230) > SP4 (0.9197) > SP6 (0.9189) > SP5(0.9118) in descending orders. Results show that SP3, SP1 and SP2can be three potential strategic partners for case company. Byusing this approach decision makers can identify and selectstrategic partner for Indian airlines industry.

7. Sensitivity analysis

Sensitivity analysis is performed to connote the effect on theevaluation process and selection of the strategic partner by vari-ation in the priority weights. This has been done by replacing theweights for two decision attributes while putting the otherweights are constant. In the sensitivity analysis run 2, weight ofthe criteria 1 i.e. C1 (Joint Equipments) has changed with criteria 2i.e. C2 (Marketing & Services) and weights of all others criteria i.e.C3 (Finance), C4 (Integration & Network), C5 (IT System), C6(Logistics & Resources), C7 (Partner Image and Experience) re-mains constant. Then CCi scores are calculated by using FuzzyTOPSIS method. That shows SP1 is the best rating partner withhigh CCi value (0.9341) and SP7 is the lowest rating partner withless CCi value (0.9079). Other strategic partners ranking are SP3(0.9337) > SP4 (0.9277) > SP2 (0.9230) > SP6 (0.9213) > SP5(0.9080) in descending order are shown in Table 10. Again in thesensitivity analysis run 3, weight of the criteria 3 i.e. C3 (Finance)has changed with criteria 1 i.e. C1 (Joint Equipments) and weightsof all others criteria i.e. C2 (Marketing & Services), C4 (Integration& Network), C5 (IT System), C6 (Logistics & Resources), C7 (Part-ner Image and Experience) remains constant and CCi values arecalculated to get final rank. Which shows SP3 is again selected the

best strategic partner with high CCi value 0.9373 and SP7 is thelowest rating partner with less CCi value 0.9099. Other strategicpartners ranking are SP1(0.9340)-SP2 (0.9233)-SP4(0.9214)-SP6(0.9177)-SP5(0.9118) in descending order. The details of theexperiment are given in the Table 10. The results of the sensitivityanalysis is shown in Table 11 and Fig. 3 shows that SP3 has highestvalue in five experiments and has to be select for strategic part-nership, SP1 has highest score in remaining experiments and SP7is consistently has lowest score in all the experiments. It indicatesthat proposed framework is robust and less sensitive to thecriteria weights.

8. Managerial implications

Airlines are looking to expand their network and service inmajority of air travel market. This can be done by joining handswith other airlines. Increased competition has given attention forthe selection of strategic partner in the industry. It can reduce thecost and improve customer service quality significantly. In thispaper benchmarking framework is presented to select strategicpartner to achieve efficiency and effectiveness in air transportservice. It was found that while selecting strategic partner, airlineshave to focus more on Integration & Network, Marketing & Service,and Logistics & Resources. The proposed approach allows man-agers/practitioners to make decision about strategic partners' se-lection in their organizations. The result obtained is discussed withthe industry and they found it meaningful according to the usedcriteria. In this study sensitivity analysis is also performed to getfurther insights to the causes of selection of appropriate strategicpartner for case airline. This work is helpful to industry to selectoptimized partners who are aiming to for strategic alliance. Airlinesor other allied service industries may use our proposed approach toevaluate and select strategic partners in most effective and efficientway.

9. Conclusions and unique contributions

In a nutshell, it can be said that in today's competitive sce-nario, it is indispensable for an airline to offer more consistent,safe, economical, regular and efficient air transport service totheir passengers/customers. Such goal demands top managementcommitment as it involves financial, operational and strategicoutlook that require an airline can increase its presence indifferent market. This is only possible if its strategic partnerwould share its resources and derive synergy by commitment.Partner should cooperate and capable enough so both can ach-ieve their objectives. Now the question is how airline can selectstrategic partner. This study presents a robust multi criteria de-cision making method for evaluating the strategic partner andselection of the partner. This has been done through the identi-fication of criteria of strategic partnership based on relevantliterature, industry experts and industry associates and thenlinguistic ratings to the criteria are assigned by decision makingteam and prioritization of criteria have been done by using AHP.It was found that Integration & Network, Marketing & Service,and Logistics & Resources criteria are three important criteria forselection of strategic Partner. And Fuzzy TOPSIS is applied toselect the strategic partner. The selection of the strategic partnerhas done by observing highest closeness coefficient value whichshows SP3, SP1, SP2, SP4, SP6, SP5 and SP7 strategic partners arein descending orders. The results of this study shows SP3 is thebest strategic partner with high CCi value and SP7 is the lowestrating partner with less CCi value. At last we work out on the

0.87

0.88

0.89

0.9

0.91

0.92

0.93

0.94

0.95

1 2 3 4 5 6 7

SP1 SP2 SP3 SP4 SP5 SP6 SP7

Fig. 3. Results of sensitivity analysis.

Table 11Sensitivity analysis.

Sensitivity analysis run Weight of criteria CCi score of airports using TOPSIS Ranking

1 C1 ¼ 0.0493 SP1 ¼ 0.932795 SP3>SP1>SP2>SP4>SP6>SP5>SP7C2 ¼ 0.2146 SP2 ¼ 0.923009C3 ¼ 0.0758 SP3 ¼ 0.937599C4 ¼ 0.2512 SP4 ¼ 0.919766C5 ¼ 0.1292 SP5 ¼ 0.911856C6 ¼ 0.1786 SP6 ¼ 0.918983C7 ¼ 0.1012 SP7 ¼ 0.910285









sensitivity analysis to determine the effect on the decisions of thechange in the criteria weights. The results of the sensitivityanalysis is shown in Table 11 and Fig. 3 which shows that SP3 hashighest value in five experiments, SP1 has highest score inremaining experiments and SP7 is consistently has lowest scorein all the experiments. It indicates that SP3 is the best strategicpartner for case company. This method considered the vague-ness/impreciseness of expert opinions in the evaluation processthat makes this method is a powerful tool in multi criteria de-cision making process. From a managerial perspective, thecredibility and validity of the proposed robust hybrid model isshown by taking a case of Indian aviation industry for airlinesoperators but it can be extend for the suppliers/partners selec-tion for other allied services industries due to flexibility andmalleability of this approach.

9.1. Unique contributions

This study offers unique theoretical as well as practical contri-bution in context to strategic alliance partner selection in airlineindustry, given as follows:

� This study explores possible criteria of strategic alliance partnerselection under seven dimensions and incorporates the industryrequirement.

� This study has selected optimized partner using combined AHP-Fuzzy TOPSIS framework. Airlines may use developed frame-work to evaluate and select their strategic partners in mosteffective and efficient way.

� The sensitivity analysis is carried out in this research in order toanalyze the evaluation and selection process of the strategicpartner and verification of the robustness of the proposedcombined framework.

Questionnaire form to facilitate the comparison of criteria with respect to goal:

Criteria JE MS FI

Equally important:1

Equally important:1

Equally important:1

Moderatelyimportant: 3



Strongly Important:5



Very StronglyImportant: 7



ExtremelyImportant: 9



IntermediateValues: 2.4.6.8



Joint equipments' (JE) e

Marketing & service(MS)

e

Finance (FI) e

Integration & network(IN)

IT system (IT)Logistics & resources

(LR)Partner image &

experience (PE)

9.1.1. Limitations of the study and scope of future workThis study used combined AHP-Fuzzy TOPSIS for strategic alli-

ance partner selection in the airline industry. From the relevantliterature and experts views in detail, various criteria of strategicpartner selection have been identified and ranked. The identifiedcriteria are specific to one industry and other criteria and di-mensions have not been identified. All the pair comparisons inAHP-Fuzzy TOPSIS have been done by decision group. Hence, it isnatural; views of decision makers may be subjective. Several ex-tensions of this study are possible by inculcating any number ofquantitative and qualitative attributes of strategic partner selectionand framework can developed for stochastic environment. Thisstudy can also extend and explore by using other approaches suchas VIKOR, ELECTRE, PROMETHEE, DEMATEL and Rough Set Theoryeither utilizing single and integrated approach for the similarproblem and outcomes/results can be matched in the furtherstudies.

Acknowledgments

The author is very much thankful to the Prof. Dr. A. Graham(Editor-in-chief, JATM) and Prof. Dr. R. Merkert (Associate Editor,JATM) to provide opportunity and motivated me to submit thepaper. I am also grateful to two anonymous reviewers of the paperfor their constructive and helpful comments that improved thequality of the paper.

Appendix A

IN IT LR PE

Equally important:1

Equally important:1

Equally important:1

Equally important:1





















e

e

e

e


Appendix B

Questionnaire form to evaluate the comparison of strategic partners with respect to goal:


Equal (1, 1, 1) Equal (1, 1, 1) Equal (1, 1, 1) Equal (1, 1, 1) Equal (1, 1, 1) Equal (1, 1, 1) Equal (1, 1, 1)

Very low (1, 2, 3) Very low (1, 2, 3) Very low (1, 2, 3) Very low (1, 2, 3) Very low (1, 2, 3) Very low (1, 2, 3) Very low (1, 2, 3)

Low (2, 3, 4) Low (2, 3, 4) Low (2, 3, 4) Low (2, 3, 4) Low (2, 3, 4) Low (2, 3, 4) Low (2, 3, 4)

Medium (3, 4, 5) Medium (3, 4, 5) Medium (3, 4, 5) Medium (3, 4, 5) Medium (3, 4, 5) Medium (3, 4, 5) Medium (3, 4, 5)

High (4, 5, 6) High (4, 5, 6) High (4, 5, 6) High (4, 5, 6) High (4, 5, 6) High (4, 5, 6) High (4, 5, 6)

Very high (5, 6, 7) Very high (5, 6, 7) Very high (5, 6, 7) Very high (5, 6, 7) Very high (5, 6, 7) Very high (5, 6, 7) Very high (5, 6, 7)

Excellent (6, 7, 8) Excellent (6, 7, 8) Excellent (6, 7, 8) Excellent (6, 7, 8) Excellent (6, 7, 8) Excellent (6, 7, 8) Excellent (6, 7, 8)

Strategic partner 1Strategic partner 2Strategic partner 3Strategic partner 4Strategic partner 5Strategic partner 6Strategic partner 7

References

Albers, S., Koch, B., Ruff, C., 2005. Strategic alliances between airlines and airportstheoretical assessment and practical evidence. J. Air Transp. Manag. 11 (2),49e58.

Akman, G., 2014. Evaluating suppliers to include green supplier developmentprograms via fuzzy c-means and VIKOR methods. Comput. Ind. Eng. http://dx.doi.org/10.1016/j.cie.2014.10.013. Online available at:

Ayhan, M.B., Kilic, H.S., 2015. A two stage approach for supplier selection problem inmulti-item/multi-supplier environment with quantity discounts. Comput. Ind.Eng. 85, 1e12.

Bai, C., Sarkis, J., 2013. Flexibility in reverse logistics: a framework and evaluationapproach. J. Clean. Prod. 47 (1), 306e316.

Barros, C.P., Wanke, P., 2015. An analysis of African airlines efficiency with two-stageTOPSIS and neural networks. J. Air Transp. Manag. 44e45, 90e102.

Bissessur, A., Alamdari, F., 1998. Factors affecting the operational success of strategicairline alliances. Transportation 25 (4), 331e355.

Bilotkach, V., Hüschelrath, K., 2012. Airline alliances and antitrust policy: the role ofefficiencies. J. Air Transp. Manag. 21, 76e84.

Brouthers, K.D., Brouthers, L.E., Wilkinson, T.J., 1995. Strategic alliances: choose yourpartners. Long. Range Plan. 28 (3), 18e25.

Brueckner, J.K., 1997. The Economics of International Code Sharing: an Analysis ofAirline Alliances. working paper 97e0115. University of Illinois at Urbana-Champaign, IL.

Büyük€ozkan, G., Feyzio�glu, O., Nebol, E., 2008. Selection of the strategic alliancepartner in logistics value chain. Int. J. Prod. Econ. 113, 148e158.

Casseres, B.G., 1996. The Alliance Revolution: the New Shape of Business Rivalry.Harvard University Press, Cambridge, MA.

Chao, Ching-Cheng, Kao, Ko-Ting, 2015. Selection of strategic cargo alliance byairlines. J. Air Transp. Manag. 43, 29e36.

Dev, C.S., Klein, S., Fisher, R.A., 1996. A market-based approach for partner selectionin marketing alliances. J. Travel Res. 11e17 (Summer).

Evans, N., 2001. Collaborative strategy: an analysis of the changing world of inter-national airline alliances. Tour. Manag. 22 (3), 229e243.

Fan, T., Vigeant-Langlois, L., Geissler, C., Bosler, B., 2001. Evolution of global air-linestrategic alliance and consolidation in the twenty-first century. J. Air Transp.Manag. 7 (6), 349e360.

Flores-Fillol, R., Moner-Colonques, R., 2007. Strategic formation of airline alliances.J. Transp. Econ. Policy 41, 427e449.

Geringer, M., 1991. Strategic determinants of partner selection criteria in interna-tional joint ventures. J. Int. Bus. Stud. 22 (1), 41e63.

Goetz, C.F., Shapiro, A.H., 2012. Strategic alliance as a response to the threat of entry:evidence from airline code sharing. Int. J. Ind. Organ. 30, 735e746.

Goh, M., Yong, J., 2006. Impacts of code-share alliance on airline cost structure: atruncated third-order translog estimation. Int. J. Ind. Organ. 24 (4), 835e866.

Holtbrugge, D., Wilson, S., Berg, N., 2006. Human resource management at StarAlliance: pressures for standardization and differentiation. J. Air Transp. Manag.12 (2), 306e312.

Ho, W., He, T., Lee, C.K.M., Emrouznejad, A., 2012. Strategic logistics outsourcing: anintegrated QFD and fuzzy AHP approach. Expert Syst. Appl. 39 (12),10841e10850.

Hwang, C.L., Yoon, K., 1981. Multiple Attributes Decision Making Methods andApplications. Springer, Berlin.

Jiang, C., Wan, Y., D'Alfonso, T., 2015. Strategic Choice of Alliance Membership underLocal Competition and Global Networks. J. Transp. Econ. Policy 49 (2), 316e337.

Kannan, G., Pokharel, S., Kumar, P.S., 2009. A hybrid approach using ISM and fuzzyTOPSIS for the selection of reverse logistics provider. Resour. Conserv. Recycl. 54(1), 28e36.

Kuo, R.J., Wang, Y.C., Tien, F.C., 2010. Integration of artificial neural network andMADA methods for green supplier selection. J. Clean. Prod. 18 (12), 1161e1170.

Liou, James J.H., Tzengb, G.H., Chang, H.-C., 2007. Airline safety measurement usinga hybrid model. J. Air Transp. Manag. 13, 243e249.

Liou, J.J.H., Chuang, Y.T., 2010. Developing a hybrid multi-criteria model for selectionof outsourcing providers. Expert Syst. Appl. 37 (5), 3755e3761.

Liou, James J.H., Tzengb, G.H., Tsai, C.Y., Hsu, C.C., 2011. A hybrid ANP model in fuzzyenvironments for strategic alliance partner selection in the airline industry.Appl. Soft Comput. 11, 3515e3524.

Liou, James J.H., 2013. Developing an integrated model for the selection of strategicalliance partners in the airline industry. Knowl. Based Syst. 28, 59e67.

Luo, Y., 1998. Joint venture success in China: how should we select a good partner?J. World Bus. 33 (2), 145e167.

Merkert, R., Morrell, P.S., 2012. Mergers and acquisitions in aviationemanagementand economic perspectives on the size of airline. Transp. Res. Part E 48,853e862.

Mockler, R.A., Carnevali, N., 1997. Type and structure of multinational strategic al-liances. Airl. Ind. J. Strateg. Change 249e260.

Mohr, J., Spekman, R., 1994. Characteristics of partnership success: partnership at-tributes, communication behavior and conflict resolution techniques. Strateg.Manag. J. 15 (2), 135e152.

Morrish, S.C., Hamilton, R.T., 2002. Airline alliances e who benefits? J. Air Transp.Manag. 8 (6), 401e407.

Oum, T.H., Park, J.H., Zhang, A., 2000. Globalization and Strategic Alliances: the Caseof the Airline Industry. Pergamon Press, Oxford.

Park, J.H., Zhang, A., Zhang, Y., 2001. Analytical models of international alliances inthe airline industry. Transp. Res. Part B 35 (9), 865e886.

Park, N.L., Cho, D., 1997. The effect of strategic alliance on performance: a study ofinternational airline industry. J. Air Transp. Manag. 3 (3), 155e164.

Prakash, C., Barua, M.K., Vishwakarma, V., Balon, V., 2014. A multi criteria decisionmaking approach to overcome its barriers in green supply chain managementunder fuzzy environment. In: Proceedings of the International Conference onResearch & Sustainable Business on March, 08-09, 2014 in DoMS, IIT Roorkee,pp. 965e976. ISBN: 9789383842193.

Prakash, C., Barua, M.K., 2015a. Integration of AHP-TOPSIS method for prioritizingthe solutions of reverse logistics adoption to overcome its barriers under fuzzyenvironment. J. Manuf. Syst. 37 (2015), 599e615.

Prakash, C., Barua, M.K., 2015b. Flexible modelling approach for evaluating reverselogistics adoption barriers using fuzzy AHP and IRP framework. Int. J. Oper. Res.(in press).

Prakash, C., Barua, M.K., Pandya, V.K., 2015c. Barriers analysis for reverse logisticsimplementation in Indian electronics industry using fuzzy analytic hierarchyprocess. In: Procedia e Social and Behavioral Sciences of the XVIII Annual In-ternational Conference of the SOM14, 189, pp. 91e102.

Prakash, C., Barua, M.K., Balon, V., 2015d. Prioritizing TQM enablers to improveIndian airlines performance under fuzzy environment. Ind. Eng. J. 8 (8), 28e34.

Prakash, C., Barua, M.K., 2015e. A robust multi-criteria decision making frameworkfor evaluation of the airport service quality enablers for ranking the airports.

http://refhub.elsevier.com/S0969-6997(15)00144-1/sref2




http://dx.doi.org/10.1016/j.cie.2014.10.013

http://dx.doi.org/10.1016/j.cie.2014.10.013















































































































































J. Qual. Assur. Hosp. Tour. (in press).Rezaei, J., Fahim, P.B.M., Tavasszy, L., 2014. Supplier selection in the airline retail

industry using a funnel methodology: conjunctive screening method and fuzzyAHP. Expert Syst. Appl. 41, 8165e8179.

Rhoades, D.L., Lush, H., 1997. A typology of strategic alliances in the airline industry:propositions for stability and duration. J. Air Transp. Manag. 3 (3), 109e114.

Saaty, T.L., 1980. Analytic Hierarchy Process. McGraw Hill, New York.Sasikumar, P., Haq, A.N., 2010. Integration of closed loop distribution supply chain

network and 3PRLP selection for the case of battery recycling. Int. J. Prod. Res.49 (11), 3363e3385.

Senthil, S., Srirangacharyulu, B., Ramesh, A., 2014. A robust hybrid multi-criteriadecision making methodology for contractor evaluation and selection inthird-party reverse logistics. Expert Syst. Appl. 41, 50e58.

Shaw, K., Shankar, R., Yadav, S.S., Thakur, L.S., 2012. Supplier selection using fuzzyAHP and fuzzy multi-objective linear programming for developing low carbonsupply chain. Expert Syst. Appl. 39 (9), 8182e8192.

Stafford, E.R., 1994. Using co-operative strategies to make alliances work. Long.Range Plan. 27 (3), 64e74.

Yoshino, M.Y., 1995. U.S. Rangan, Strategic Alliances: an Entrepreneurial Approachto Globalization. Harvard Business School Press, Boston.

Zadeh, L.A., 1965. Fuzzy sets. Inf. Control 8 (2), 338e353.Zhang, A., Hui, Y.V., Leung, L., 2004. Air cargo alliances and competition in passenger

markets. Transp. Res. Part E 40 (2), 83e100.

Chandra Prakash Garg, doctoral student in Department ofManagement Studies, IIT Roorkee, India. His current areasof research are Aviation Management, Reverse Logistics,Green Supply Chain Management and Inventory Control.He has completed Master in Math and MBA. He has pub-lished/presented papers in journals of reputes and inconference proceedings.
































本文献由“学霸图书馆-文献云下载”收集自网络，仅供学习交流使用。

学霸图书馆（www.xuebalib.com）是一个“整合众多图书馆数据库资源，

提供一站式文献检索和下载服务”的24 小时在线不限IP

图书馆。

图书馆致力于便利、促进学习与科研，提供最强文献下载服务。

图书馆导航：

图书馆首页文献云下载图书馆入口外文数据库大全疑难文献辅助工具

http://www.xuebalib.com/cloud/

http://www.xuebalib.com/

http://www.xuebalib.com/cloud/


http://www.xuebalib.com/vip.html

http://www.xuebalib.com/db.php

http://www.xuebalib.com/zixun/2014-08-15/44.html


Documents

Journal of Air Transport Managementdownload.xuebalib.com/xuebalib.com.31567.pdf · Department of Management Studies, IIT Roorkee, India article info Article history: Received 1 February