Understanding the Influence of Wireless Communications and...

Research ArticleUnderstanding the Influence of Wireless Communications andWi-Fi Access on Customer Loyalty A Behavioral Model System

Ana Reyes-Menendez1 Pedro R Palos-Sanchez 2

Jose Ramon Saura 1 and Felix Martin-Velicia 3

1Department of Business and Economics Rey Juan Carlos University Madrid Spain2Department of Business Organization Marketing and Market Research International University of La Rioja Logrono Spain3Department of Business Management and Marketing University of Sevilla Sevilla Spain

Correspondence should be addressed to Jose Ramon Saura joseramonsauraurjces

Received 29 May 2018 Accepted 18 October 2018 Published 2 December 2018

Academic Editor Davide Mattera

Copyright copy 2018 Ana Reyes-Menendez et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

New technologies offer new possibilities to better understand complex consumer behavior points of sale The data obtainedusing wireless communications and Wi-Fi services available in restaurants and catering companies make it possible to acquirein-depth knowledge on consumer behavior complexity In the present study the PLS-SEM analysis was used to analyze theimpact of free wireless communications and Wi-Fi access on customer loyalty Our results demonstrate that client satisfactionwith wireless communications and Wi-Fi access networks services has a direct impact on customer loyalty Therefore wirelesscommunications and Wi-Fi networks and technologies available at the points of sale should be updated in order to better meetcustomersrsquo expectations

1 Introduction

In recent years advances in technology have spurred aconsiderable scholarly interest in the research on consumerbehavior [1] Some relevant studies have focused on retailstoresmaking consumer behavior simultaneouslymoremea-surable and more complex The results of this body of workdemonstrate that due to the use of digital devices customerexperience at points of sale has been considerably enhancedThe digital technology allows for registering the data forfurther storage tracking and use These data provide theresearch community with a deeper knowledge of consumerbehavior satisfaction loyalty and consumer consumption aswell as enabling the development of newmodels thereof [2 3]

An important landmark in the retail commerce revolu-tion within stores has been the appearance and democratiza-tion of new connectivity options for users [4 5] Along withthe mobile channel tablets with access to social media havealso become available at points of sale bringing about thenecessity to integrate these new channels into both online andoffline retail commerce [6ndash8]

Overall companies should be proactive in their adapta-tion to the market and provision of positive experiences totheir consumers One way of achieving this goal is providingin-store mobile devices (tablets) that clients can use to searchfor information about the companyrsquos products and even orderthem (eg Apple stores) Alternatively through in-storewireless communications and Wi-Fi networks companiescan communicate with their clients through mobile devicesas well as track client behavior [9]These information systemsrender themselves as complex concepts within companies[10 11]

In many cases in their search formore information abouta company eg showrooming discounts or more attractiveretail prices [12 13] consumers search for information bothinside the store and online using their mobile devices [8]Accordingly the concept of showrooming has recently beencomplemented by the emergence of the concept of web-rooming which refers to the phenomenon when consumerssearch for information online and buy offline Since the emer-gence of this concept web-rooming has become a valuablesource of information which allows for the development of

HindawiWireless Communications and Mobile ComputingVolume 2018 Article ID 3487398 16 pageshttpsdoiorg10115520183487398

2 Wireless Communications and Mobile Computing

predictive models of complex behavior of Internet usersThesemodels are based on the results of the analysis obtainedfrommobile devices (see eg [14 15])

However providing a good customer experience partic-ularly in the hospitality industry requires more than freewireless communications and Wi-Fi networks services andquality products [16] For instance to create a significantexperience inside a restaurant a major aspect that should betaken into account is the presentation of the restaurant envi-ronment Pleasant aroma nice music ambient temperaturelow noise levels and adequate lighting these and many otheraspects should be harmonized with each other to create aspace that will favorably influence clientsrsquo perception of therestaurant and help them evaluate their experiences in amorepositive manner [17ndash19] The entirety of these aspects thatrelated to the physical environment of the service transactionand have a meaningful impact on consumer behavior isreferred to as servicescape [20 21]

In the present study we seek to bridge the gap in previousliterature on the impact of some elements of the restaurantservicescape on consumer behavior To this end we buildon previous work on customer loyalty employing severalquestionnaires handed out at points of sale [22 23] Thissurvey-based approach is complemented with a focus ontechnology Here the technological element refers to freewireless communications and Wi-Fi networks access thatcontributes to a better consumer experience in restaurants[16 24] and enables for collecting relevant data on consumerbehaviorThese data can further be used to develop predictivemodels

The main aim of the present study was twofold On onehand through conducting a survey at the moment of con-sumption we aimed to assess the importance of factors thathave an impact on consumer perceptions of food chain storesTo this end a questionnaire was sent to those customers whobeing within the premises of the restaurant asked for accessto the free wireless communications and Wi-Fi networksoffered by the restaurant We additionally took into accountthat filling in the survey while using mobile devices couldaffect the outcome [30 31] Our second goal was to build apredictive model of the complex consumer behaviors withinthe retail point of sale and of consumer intentions towardsrestaurants To achieve this goal we employed the modelsproposed by DCunha et al [25] Zhang and He [27] andJahanshahi et al [32] These models predict the impact ofclient services on customer satisfaction and actual consumerbehavior the latter becomes tangible through for examplebrand loyalty

In the present study we also build on previous researchon using wireless communications and Wi-Fi networks forresearch purposes [16 24] or as part of a restaurantrsquos ser-vicescape [33 34] However compared to previous researchthe novelty of the present study lies in that we use wirelesscommunications and Wi-Fi networks both as an element ofour conceptual model and as a means of data collection Inwhat follows based on the revision of previously proposedmodels [35] we propose an updated model and present theresults of our exploratory analysis of applying this model to arestaurantrsquos servicescape

2 Theoretical Background

As long as the environment of an establishment can attractclients to shop once it strives to attract clients to buy repeat-edly According to Sharma and Stafford (2000) customerdecision-making process can be influenced more by theenvironment of retail points of sale than by the productitself Even though cognitive factors can explain the selectionof stores and most purchases planned within the store thestorersquos atmosphere and the emotional state of the buyers canexert a decisive impact on the complex behavior implied in apurchaseThere is a wide scholarly consensus that enhancingthe extent to which an environment is enjoyable and boostingthe degree to which it highlights innovation are essentialfactors that determine a companyrsquos success [36]

In this context an important concept to refer to isldquoatmosphericsrdquomdasha notion first used to reveal the distinctivecapacity of a physical environment to affect client behavior bystimulating customersrsquo perceptive and emotional responses[21] According to the results of several environmental psy-chology studies there is a strong link between emotionsand behavior and accordingly complex human behaviorsare strongly associated with physical environments [37ndash39]These findings are of vital importance in the services sectorsuch as hotels restaurants professional offices banks retailstores and hospitals [20 21 33 36] For that reason the effectof ldquoatmosphericsrdquo in the restaurants sector has been an issueof extensive research and analysis (eg [17 40ndash43])

Both pleasure (defined as the degree to which a per-son feels good happy or cheerful) and emotion (ie thedegree to which a person feels exited stimulated alert oractive which is considered to be one of the main moti-vations for experience-orientated customers) are importantvariables related to purchase intention Emotion interactswith pleasure to facilitate approaching pleasing consump-tion environments and avoiding unpleasant environments[44]

Consumer loyalty refers to the repeated choice of acatering service by a client so that the client becomesloyal to the service-providing company organization orbusiness [21] Oliver [45] defined customer loyalty as ldquoanentrenched repurchasing compromise or repeated promotionof a preferred productservice in a consistent way throughoutthe future triggering repetitive consumption behaviors of thesame brand regardless of how context or marketing effortswhich hold the potential to cause a change in behaviormay influence themrdquo The development of consumer loyaltyis influenced by perceived value habit trust and clientsatisfaction [46 47]

Major studies that investigated the factors affecting con-sumer loyalty in catering establishments and other services-related institutions are shown in Table 1 In this body of workatmospherics and loyalty were key elements to consider [2528] Other essential factors involved in customer loyalty arethe quality of products [27] willingness to pay waiting time(Frank et al 2008 Sompie amp Pangemanan 2014) and qualityof service [26] However new technologies incorporated inthe offered services such as wireless communications andWi-Fi networks have rarely been studied

Wireless Communications and Mobile Computing 3

Table 1 Studies on servicescape and wireless communications andWi-Fi networks in relation to customer loyalty in restaurants

Study Research aim(s)

DCunha et al [25] To develop a PLS model to measure the influence of environmental elements design and social factors onthe perceived quality of servicescape affecting consumer satisfaction and behavior

Frank et al (2008)Sompie andPangemanan (2014)

Using the PLS-SEM methodology to test the hypothesis that willingness to pay and waiting time are amongthe main factors that affect consumersrsquo attitudes

Naidoo and Leonard[26]

To develop a PLS model that emphasizes a positive relation between quality of services and incentives forloyalty

Zhang and He [27] To develop a TAM-based PLS model that incorporates products and their prices as another central elementthat impacts customer satisfaction and consumer loyalty

Masri et al [28] Using the PLS-SEM methodology to examine the relationship between the Wi-Fi servicesrsquo attributes andtourist satisfaction All the attributes were found to be significant predictors of tourist satisfaction

As mentioned in Section 1 the main aim of the presentstudy was to investigate the impact of wireless communi-cations and Wi-Fi networks in the domain of restaurantsTheory-wise we develop a predictive model of complexconsumer behaviors within the retail points of sale andconsumer intentions towards restaurants

The proposed model derives from previous models ofthe relationship between customer satisfaction and customerloyalty [25 26 32] that have been widely used in variousdomains Based on our model we investigate the role ofwireless communications andWi-Fi networks on satisfactionand customer loyalty in the restaurants sector

3 Conceptual Model

The conceptual model proposed in the present study derivesfrom Zhang and Hersquos [27] model and establishes a linkbetween service quality and willingness to pay on the onehand and customer satisfaction on the other hand in turncustomer satisfaction exerts a direct impact on customerloyalty Another contributing factor to customer loyalty is theavailability of wireless communications and Wi-Fi networks

31Willingness to Pay According to Breidert et al [48] ldquotyp-ically the number of possible differentiated products is largeand not all candidates can be tested under justifiable budgetand time restrictionsrdquo The related concept of willingness topay refers to ldquothe maximum amount an individual is willingto hand over to procure a product or service The price ofthe transaction will thus be at a point somewhere between abuyerrsquos willingness-to-pay and a sellerrsquos willingness to acceptrdquo[29]

32 Service Quality Zeithaml et al [49] defined servicequality as ldquothe extent of discrepancy between the customersrsquoexpectations and perceptionsrdquo Furthermore according toDabholkar Shepherd and Thorpe [50] service qualityhas subdimensions of reliability and responsiveness Provid-ing a high level of service quality is essential for serviceproviders to be able to compete with other competitors [51ndash53]

33 Customer Satisfaction Customer satisfaction frequentlyconsidered an important factor that affects customer loyaltyrefers to ldquothe summary psychological state resulting when theemotion surrounding disconfirmed expectations is coupledwith the consumerrsquos prior feelings about the consumptionexperiencerdquo [54]

34 Customer Loyalty Customer loyalty is defined as ldquoadeeply held commitment to rebuy or re-promote a preferredproductservice consistently in the future thereby causingrepetitive same-brand or same brand set purchasing despitesituational influences andmarketing efforts having the poten-tial to cause switching behaviorrdquo [45] The ultimate goal ofthese efforts is customer satisfaction [55]

35 Wireless Communications and Wi-Fi Networks Consid-ering that the present study aims to establish the positionoccupied by the wireless communication and Wi-Fi servicesin the proposedmodel (see Figure 1) and assuming they maydirectly influence customer loyalty [16 24] we incorporatedthese two variables into the proposed model

In what follows all abovementioned constructs areexplained in further detail

36 Hypotheses First studies that focused on service qualityemerged in the field of marketing [56 57] afterwards thisconstruct has been extensively studied in the domain ofclient service [58 59] Service quality has proved to be ofgreat use in terms of measuring and predicting consumerresponses and reactions related to customer satisfaction [60ndash62] the increase of sales [63] or the willingness to pay apremium price [49] Several studies including Bitner [20]andWall and Berry [64] pointed out that that service qualityaffects consumers experiences In particular in their PLS-SEM study Wall and Berry [64] concluded that the physicalenvironment of restaurants has a direct influence on theperception of clients regarding the quality of client serviceFurthermore in their extensive review of 600 case studiesUlrich et al [65] demonstrated that design characteristicsof the space increase customer attendance which in turnimproves the results and quality of the offered client services


CustomerLoyalty Wi-Fi

ServiceQuality

Willingnessto pay

CustomerSatisfaction H4

H2

H3

H5

H1

Figure 1 Conceptual model proposed in the present study (based on [27])

In this respect Afshar Jahanshahi et al [32] developed amodel to demonstrate the existing positive relation betweenservice quality and customer satisfaction constructs

Based on our review of the literature the followinghypothesis can be formulated

H1 Service quality positively influences customer satis-faction

Furthermore as put forward in the PLS-SEM model bySompie and Pangemanan (2014) and Frank et al (2008)willingness to pay is a predictor of consumer behaviorSimilarly Jain and Bala [29] argued that both the priceand the intention to pay that price influence the quality ofoffered services In the present study we adopt this view andconsequently formulate the second hypothesis to be tested

H2Willingness to pay positively influences service qual-ity

Next several other studies such as Perutkova [66] andSaravanan and Veerabhadraiah (2003) argued that willing-ness to pay can positively influence customer satisfactionHence our third hypothesis is as follows

H3 Willingness to pay positively influences customersatisfaction

Furthermore as reported by Kursunluoglu [67] clientsatisfaction can explain 432of variance in customer loyaltyTherefore customer satisfaction can reasonably be expectedto have a positive impact on loyalty Therefore if sellers wishto improve customer loyalty customer satisfaction should beenhanced Accordingly several researchers argued that thereis a direct link between customer satisfaction and customerloyalty [25 26 32] Therefore the following hypothesis canbe formulated

H4 Customer satisfaction positively influences customerloyalty

Wireless communications and Wi-Fi networks speedimprovements result in an increased service demand [3468] In a study using multiple regression analysis Jeon [34]identified a relation between wireless communications and

Wi-Fi networks offered by restaurants and the profit earnedby those restaurants Accordingly the fifth hypothesis toexplore in the present study can be formulated as follows

H5 Quality and free access to wireless communicationsand Wi-Fi networks positively influence customer loyalty

Mediation occurs when the relation between the inde-pendent variable (X) and the dependent variable (Y) changeswhen a mediator variable (M) is introduced The followingtwo mediation hypotheses postulate how or through whichmeans the independent variable (willingness to pay) affectsthe dependent variable (customer satisfaction) through oneor more mediator variables (service quality)

H6 The relation between willingness to pay over cus-tomer satisfaction is mediated by service quality

H7 Wireless communications and Wi-Fi networks havea moderating effect on the relation between customer satis-faction and customer loyalty

4 Methodology and Research Data

To obtain the data we surveyed clients who went to a restau-rant with the capacity for 200 people located in downtownMadrid between March 8 2017 and January 13 2018 Therestaurant offers Mediterranean food and the average bill init amounts to 30 euro The questionnaires were distributed viathe mobile devices of those restaurant clients who connectedto the free wireless communications and Wi-Fi networksservices offered by the restaurant (see Hwang and Jang [69]for the use of this approach see also Contigiani et al [70] onthe importance of marketing on the use of the data extractedfrom wireless communications and Wi-Fi networks) In thepresent study the obtained data were later uploaded to thecloud On the other hand Al-Turjman [71] emphasized theimportance of mobile devices for data collection that arisesfrom the fact that these devices are always in the possessionof consumers


Table 2 Sample characteristics

Classifications Variable Number PercentageGender Female 64 547

Male 53 453Age 18-25 23 197

26-35 38 32536- 45 29 24846-55 12 10256-65 10 85gt65 5 43

Email permission No 2 17Si 115 983

Nationality Spain 57 487Portugal 5 43

United Kingdom 27 231Italy 8 68France 7 60Romania 5 43Nederland 2 17Germany 2 17Others 4 34

Connections 1-10 104 88911-20 7 6021-30 3 2631-50 1 0951-100 2 17

The length of the questionnaire and the number of itemsper construct were developed following the guidelines forthe questionnaires to be administered in stores using mobiledevices [31 72]

The final questionnaire (see the Appendix) included 7questions related to (1) the price of the products (2) thequality of the service (3) the client satisfaction with theamount of attention she received (4) engaging clients and(5) quality of wireless communications and Wi-Fi networksservices Following Stan and Saporta [73] the participantswere asked to rate each of the items on a 10-point scaleAnother reason to choose using the 10-point Likert scalewas that as highlighted by Awang et al [74] a 10-pointscale is more efficient than a 5-point one when measuringan equivalently sized sample in structural equations (SEM)Table 2 summarizes the characteristics of the participants

For data analysis and hypotheses testing we used struc-tural equation models based on variance (SEM) These mod-els allow one to statistically examine a series of interrelateddependency relations among the variables grounded in thetheory and their indicator variables this is done via measur-ing the directly observable variables [75] Among the SEMtechniques the technique of Partial Least Squares regression(PLS)was selectedThemodeling of the PLS trajectory can beunderstood as a complete SEM method which can managefactor models and the models composed for measuringconstructs estimate structural models and do adjustmenttrials of the model [76] The use of PLS is also recommended

when there is a low number of observations [77] In our casethis approach was applicable because the sample was small(n = 117) Another reason why we decided to use the PLS-SEM method was because the object of study is relativelynew and the theory on the matter has not yet consolidatedMoreover we also assumed an exploratory perspective [78] inwhich this data analysis technique is strongly recommendedFinally we decided to use PLS-SEM because one of the maingoals of the present study was to test whether or not ourmodel (see Figure 1) was predictive [78ndash80] To determinethe minimum size of the sample for the PLS model Hair etal [81] recommended using Cohenrsquos tables [82] We madeuse of these tables through the software GlowastPower [83] Inthe first place we checked the dependent construct or theone that had the highest number of predictors ie the onethat received the most number of arrows In our case suchconstruct was customer satisfaction which received the valueof 2 To calculate this score we used the following parametersthe power of the test (power = 1 - 120573 error prob II) andthe size of the effect (f 2) Cohen [84] and Hair et al [81]recommended the power of 080 and the average size of theeffect f 2 = 015 In our case the value of 2 was taken as thenumber of predictors obtained ie the number of constructsthat establish causality relations with customer satisfactionHence for PLS the accepted number of participants in thesample for the construct of customer satisfaction was 68Therefore the minimum calculated samples for this exampleshould be 68 cases which we surpassed using n=117 Finally



ServiceQuality

Willingnessto pay

CustomerSatisfaction 0197

0607

0509

0729

products

comfort0721

prices

wait time0895

0872

quality

1000

connectivity

1000

recommend

10000227

0945

Figure 2 Quality of the measurement model and the structural model

Table 3 Measurement items and correlations between the constructs

Constructs rho A Reliability comp (AVE) Customer Sat Service Q Customer Loy Willingness to pay Wi-FiCustomer Satisfaction 1000 1000 1000 1000Service Quality 0877 0826 0707 0535 0841Customer Loyalty 1000 1000 1000 0056 -0189 1000Willingness to pay 0724 0877 0780 0646 0607 0039 0883Wi-Fi 1000 1000 1000 -0193 -0182 0691 -0110 1000

the software SmartPLS 3 [85] was used for the PLS-SEM dataanalysis

5 Data Analysis and Results

The use of PLS unfolds in two steps [72 86 87] The first steprequires evaluating the measurement model which makes itpossible to specify the relations between observable variablesand theoretical concepts In the second phase the structuralmodel is evaluated to see to what extent the causal relationsspecified by the proposed model are consistent with theavailable data

51 First Phase Measurement Model First we analyzed theindividual reliability of the items observing the changes of(120582) Following Carmines and Zeller [88] the minimum levelwas established for its acceptance as part of the construct120582 gt= 0707 The commonality manifested by variable (1205822)is that of the variance which is explained by the factor orconstruct [89] Thus value 120582 gt= 0707 indicates that eachmeasurement represents at least 505 of the variance of thesubjacent construct [90]The indicators that did not reach theminimum were refined [91] The results of the measurementmodel are shown in Figure 2

Second we analyzed internal consistency The measure-ment of reliability of the construct and convergent validityrepresents internal consistency measures The reliability ofthe construct enables checking if the indicators actually

measure the constructs The results in Table 3 indicate that allconstructs are reliable since their compositejoint reliabilityis gt 07 These values are considered ldquosatisfactory to goodrdquobecause they are between 070 and 095 [92] On top ofthat the most recent developments indicate that the rho Acoefficient is the only consistent reliability measurement [93]In our case the variables also comply with the constructreliability requirements because their rho A coefficientswereover 07 level The most common measure to evaluate theconvergent validity in PLS-EM is the AVE Using the samebase as the one used with the individual indicators a valueor AVE of 50 or superior means that on average theconstruction explains more than a half of the variance of itsown indicator [81 94]

As shown in Figure 2 all indicators meet these criteriabecause the diagonal elements should be significantly greaterthan those that are multiform in the corresponding rowsand columns This condition is satisfied for each constructin relation to the remaining constructs (see column 5 inTable 3)

We also used a recently proposed criterion to evaluatethe discriminatory validity the Heterotrait-Monotrait Ratioof Correlations (HTMT) which is an estimation of thecorrelation of the factor (specifically a superior limit) Toclearly discriminate between two factors the HTMT shouldbe significantly lower than 1 [76]

Table 4 shows that all variables also reached discrimi-natory validity following the HTMT criteria Consequently


Table 4 Ratio HTMT

HTMT Customer Satisfaction Service Quality Customer Loyalty Willingness to pay Wi-FiCustomer SatisfactionService Quality 0601Customer Loyalty 0056 0268Willingness to pay 0757 0833 0046Wi-Fi 0193 0221 0691 0129

Table 5 Hypotheses testing

No Hypothesis Path coeff (120573) Statistics t (120573STDEV) P-value Confidence25 Intervals 975 Support

1 Service quality 997888rarr Customer satisfaction 0227 2339 0019 0036 041 Yeslowastlowast

2 Willingness to pay 997888rarr Service quality 0607 9193 0000 0469 0731 Yeslowastlowastlowast

3 Willingness to pay 997888rarr Customer satisfaction 0509 4174 0000 0249 0732 Yeslowastlowastlowast

4 Customer Satisfaction 997888rarr Customer loyalty 0730 9926 0000 0553 0845 Yeslowastlowastlowast

5 Wi-Fi 997888rarr Customer loyalty 0197 2720 0007 0067 0355 Yeslowastlowast

Table 6 R2 y collinearity (VIF values)

Constructs R2 R2 adjusted Item VIFCustomer satisfaction 0450 0440 Quality 1000

Service quality 0368 0363 Products 1263Comfort 1263

Wireless communications andWi-Fi networks - - Connectivity 1000

Willingness to pay - - Prices 1461Wait time 1461

Customer loyalty 0657 0651 Recommend 1000

each construct is more strongly related to its own measuresthan to the those of other constructs

52 Second Phase Structural Model The evaluation of thestructural model implies an analysis of the predictive capac-ities of the model and the relation between the studiedconstructs We evaluated the structural model by evaluatingcollinearity the algebraic sign the magnitude and thesignification of the coefficients of the path coefficients (120573)the R2 values (explained variance) the size of the effect f 2and the test Q2 (validated crossed redundancy) for predictiverelevance [87]

For these evaluations bootstrapping was used and theresults where later compared to the statistics obtained finallythe signification statistic of the coefficientrsquos path was evalu-ated

As can be seen in the results summarized in Table 5all hypotheses were supported by our data analyses withp-values being the highest for Hypotheses 2-4 (so that theobserved differences were significant at 0001 level) Hypoth-esis 1 (120573=0227 t=2339) and Hypothesis 5 on the impactof wireless communications and Wi-Fi on customer loyalty(120573=0197 t=2720) obtained the lowest levels of trust (99)The strongest relationwas for the impact of customer satisfac-tion on customer loyalty (120573=0730 t=9926) followed by thatof willingness to pay on service quality (120573=0607 t=9193)

The weakest relation was between wireless communicationsand Wi-Fi and customer loyalty (120573=0197 t=2720)

To verify the problem of collinearity we examined thevalues of VIF of all the predictor constructions (see Table 6)All VIF values were under the conventional standard of 5therefore collinearity between constructs was not a criticalproblem in the structural model

Table 6 shows the results obtained from the codetermi-nation coefficient R2 The test performed shows the maindependent construct R2Customer Loyalty=657 of the varianceIn previous research [78 81 90] the cut-off points were setat 075 050 and 025 for the same three levels (relevantmoderate and weak)

As suggested by the results this model is moderatelyapplicable in the context of the factors that affect loyaltytowards fast food restaurants Customer satisfaction andservice quality had substantial R2 values of 045 and 368respectively Regarding the size of the f 2 effect to relate tothe structural model customer loyalty was found to have abig-sized effect in service quality and willingness to pay thecorresponding values were 0059 and 0297 respectively Atthe same time service quality was found to have a big effectin willingness to pay (0583) Finally the size of the effectproduced by customer loyalty with wireless communicationsand Wi-Fi networks was small (0103)

Next the bandages were used to evaluate the model withthe redundancy index with crossed (Q2) validation for the


Table 7 PLS Predict assessment

Items PLS LM PLS-LMRMSE MAE Q2 RMSE MAE Q2 RMSE MAE Q2

Complete Sample ModelCustomer Loyalty(Recommend) 1528 0948 0485 149 0971 051 0038 -0023 -0025

Customer Satisfaction(Quality) 1751 1097 0396 174 1121 0404 0011 -0024 -0008

Service Quality(Comfort) 1163 0832 0075 1164 0845 0075 -0001 -0013 0

Service Quality(Products)

1177 0878 0361 1148 0827 0393 0029 0051 -0032

endogenous variables Chin [86] suggested this measurementto examine predictive relevance of theoretical structuralmodels The Q2 values above zero imply that a modelhas predictive relevance The results obtained for customerloyalty (Q2=0440) confirm that the structural model has asatisfactory predictive relevance The remaining endogenousconstructions confirm this result as well service quality(Q2=0226) and customer satisfaction (Q2=0409)

With respect to the approximate adjustment measure-ments of the model [95] the value obtained from thestandardized root mean square residual (SRMR) [96 97]measures the difference between the matrix of the observedcorrelations implied by the model Therefore the SRMRreflects the average magnitude of such differences the lowerthe SRMR the better the fit In our case SRMR=008 ie onlyslightly below the recommendation of SRMR lt 008 [96]Therefore our adjustment can properly fit in the exact limit ofthe composed factor model constituting hence a compoundconfirmatory analysis [76]

53 Predictive Validity Evaluation We analyzed the set ofitems of the endogenous constructs of the model to measurethe predictive capacity of the proposed model customerloyalty (Recommend) customer satisfaction (Quality) ser-vice quality (Comfort) and service quality (Products) Theobjective was to predict each item or dependent variable [98]In our case the final dependent variable was customer loyaltyThe approach suggested by Shmueli et al [99]was used in thisstudy For this it was evaluated through cross validation withretained samples

Using the studies of other authors [100 101] the currentPLS prediction algorithm was used in the SmartPLS software[85] This software gave results like the R Mean Square Error(RMSE) and the Mean Absolute Error (MAE) As can be seeninTable 7 we evaluated the predictive performance of the PLSmodel for indicators of dependent constructs [100 101]

The Q2 value in ldquoPLS Predictrdquo indicates that the predic-tion errors of the PLS model were compared with the simplemean predictions If the value of Q2 is negative the predictionerror of the PLS-SEM results is greater than the predictionerror of simply using the mean values As a result the PLS-SEMmodel offered inappropriate predictive performance Aswe can see in Table 7 all the values of the last column are very

close to 0 and negative inmost of the items whichmeans thatwe obtained poor prediction results

The Linear Regression Model (LM) approach is a regres-sion of all exogenous indicators in each endogenous indicatorWhen this comparison is made an estimate of where betterprediction errors can be obtained is the result This is shownwhen the RMSE and MAE value of PLS are lower thanthe values of the LM model The results only indicate thispredictive capacity (see Table 7) in the case of the servicequality (Products) indicator since it is the only item withnegative RMSE and MAE values which indicated a goodpredictive power

The density diagrams of the residues within the sampleand outside the sample are given in Figure 3

As a result of the different analyses this research didnot find sufficient evidence to support the predictive validity(out-of-sample prediction) of our research model in orderto predict values for new cases of recommend in CustomerLoyaltyTherefore the model can not predict the intention ofrecommending additional samples that are different from thedata set that was used to test the theoretical research model[102]

54 Considerations for Customer Loyalty through Wi-Fi Net-works (IPMA) In line with the research that studied theheterogeneity of the data [103] the IPMA-PLS technique wasused to find more precise recommendations for customerloyalty through Wi-Fi networks IPMA is a grid analysis thatuses matrices that allows combining the total effects of theldquoimportancerdquo of PLS-SEM estimation with the average valuescore for ldquoperformancerdquo [103 104] The results are presentedin an importance-performance graph For Groszlig [103] theinterpretation of Figure 4 is to demonstrate the recommen-dation attributes (Customer Loyalty) that are highly valuedfor performance and importanceThe results obtained will beof great interest for Restaurants and Hospitality for those thatare developing loyalty techniques in which one of the factorsused is the Wi-Fi network

The results show that most of the factors are in quadrants1 and 2 (upper left and right in Figure 4) Quadrant 1 showsattributes of recommendation of great importance but oflow performance which must be improved In this caseloyalty techniques should be based on Wi-Fi and servicequality which show an average performance Quadrant 2


250

225

200

175

150

125

100

75

50

25

00

Freq

uenc

y

minus35 minus30 minus25 minus20 minus15 minus10 minus05 00 05 10 15PLS LV Prediction Residuals

Customer Loyalty

Figure 3 Residue density within the sample and outside the sample

Importance-Performance Map100

90

80

70

60

50

40

30

20

10

0

Customer Loyalty

00 01 02 03 04 05 06 07Total Effects

Customer Satisfaction Service Quality Wi-Fi Willingness to pay

Figure 4 Importance-performance maps

shows recommendation attributes of high importance andperformance These are mainly customer satisfaction and toa lesser extent performance willingness to pay

The results obtained show two different situations Theseconsumers and Wi-Fi users rated the importance gt75 in allthe constructs while the performance varied from025 inWi-Fi 03 in service quality 055 in willingness to pay and 07 inthe case of customer satisfaction

The results indicate that Wi-Fi has the highest valuationalthough it is the one that obtains the least performancewithin the model studied Therefore it is in this constructthat improvements in performance must be made Mostmarketing actions should be taken emphasizing the useof Wi-Fi as an element that can contribute to customerloyalty

55 Mediation Effect of Service Quality The causal effectof the variable X can be divided in an indirect effect overY through M (a lowast b) and a direct effect on Y (path c1015840)Confidence intervals (CI) are reported in Table 7 and if value0 was obtained then the mediation was not considered tobe statistically significant If the signification was significantthen we would calculate the total effect of X over Y= c wherec = c1015840 + ab X turned out to be significant (120573=0607 t=9010)at the confidence level of 99

The first step was to test the mediating hypothesis(Hypothesis 6) to determine the level of significance of theindirect effects (ai x bi) as c1015840 (willingness to pay997888rarr customersatisfaction) yielded significant results At the same time twotypes of partial mediation were found the complementaryone where a x b y c1015840 had the same direction (positive in our


ServiceQuality

Willingnessto pay

CustomerSatisfaction

(a) H2 (b) H1

(c lsquo) H3

Figure 5 Mediation of service quality in the relationWP 997888rarr CS

Table 8 Mediating effects

Path coeff(120573)

CI (25)LOWER

CI (975)UPPER

a Willingness to pay 997888rarrService quality 0607 0479 0733

b Service quality 997888rarrCustomer satisfaction 0227 0031 0414

c Willingness to pay 997888rarrCustomer satisfaction 0509 0263 0739

alowastb Indirect effects 0309 0148 0485

case) and the competitive one where a x b y c1015840 had differentdirections (one positive and the other negative or vice versain our case a x b x c1015840 997888rarr negative [105]

Table 8 reports the main parameters obtained for thestudied submodel in the structural model in which thehypothesis of mediation H6 refers to the following Therelation of willingness to pay over customer satisfaction ismediated by service quality (see Figure 5)

Therefore willingness to pay 997888rarr customer satisfaction(c1015840= 0509lowastlowastlowast) was found to be compatible when it wassignificant Consistently it was still significant in willingnessto pay 997888rarr service quality (a=0607lowastlowastlowast) and service quality997888rarr customer satisfaction (b=0227lowastlowast)

Consequently customer satisfaction in the dimensionof willingness to pay decreased when quality service wasincluded (a x b=0309) Furthermore we found significantpaths such as a and b therefore the significant incrementmanifested in the direct state (c1015840) since the significationof the regression coefficients (a and b) suggests a possibleexistence of an indirect effect of service quality on the partialrelation between both constructs with service quality as themediating variable

However the key condition to determine the impliedeffect is to prove the importance of a times b = path of willingnessto pay 997888rarr service quality times path service quality 997888rarr customersatisfaction [106] With this in mind we obtained values forthis indirect effect (a x b = 0309 see Table 9) of SmartPLS

Table 9 Moderating effects

Path coeff(120573)

Statistics t(120573STDEV) p Support

Customer satisfaction997888rarr Customer loyalty 0683 7097 0000 Yeslowastlowastlowast

Moderating effects997888rarr Customer loyalty -0106 1459 0145 ns

Wi-Fi networkservices 997888rarrCustomer loyalty

0187 2671 0008 Yeslowastlowast

This indirect effect was significant as confidence interval(CI) was not 0 confirming the mediation effect (Hypothesis6) All situationswere under the condition that both the directeffect c1015840 and the indirect effect a x b y c1015840 had the same positivedirection [105]

According to Hair et al [72] the decision should notdepend on the significance of one of the parameters There-fore the criteria should be established as shown in (1)depending on what part of the total effect of the independentvariable over the dependent one is due to mediation (VAF =Variance Accounted For) Our results were as follows VAFgt 80 Complete Mediation 20 le VAR le 80 Partialmediation andVAFlt 20Therefore therewas nomediation[72]

VAF

=(120573WP 997888rarr SQ x 120573 SQ 997888rarr CS)

((120573WP 997888rarr SQ x 120573 SQ 997888rarr CS) + 120573WP 997888rarr CS)

(1)

The result was VAF= 0377 accordingly we concluded therewas a partial mediation with a 377 magnitude [72]

56 Moderation Effect of Wireless Communications andWi-FiNetworks Services Themoderator effects are very importantin the study of the PLSModel just as the one proposed in thisstudy Generally amoderator can be defined as a variable thataffects the direction andor the force of the relation betweenan independent variable or a predictor and a dependentvariable or criteria [107]

In our model one of the main goals was to understandthe influence of the construct of free wireless communica-tions and Wi-Fi access on customer loyalty To this endwe proposed wireless communications and Wi-Fi networksas a moderation variable of the model (see Figure 6) Toevaluate it the product perspective [108 109] was used Thisperspective is used only when the independent variable andthemoderation variable are factors ormeasurements for onlyone indicator as in our case when the three interveningconstructs only had one item [110]

After multiplying each indicator of the exogenic variableper the moderating variable we proceeded to use the productindicators to create the moderating term (see Table 9)

The obtained results suggest that there was no mediatingeffect of wireless communications andWi-Fi networks accessover the relation between customer satisfaction 997888rarr customerloyalty as p =0145 (see Table 9)Therefore Hypothesis 7 had


CustomerLoyalty

Wi-Fi


H5

Figure 6 Moderation of Wi-Fi access on the relation CS 997888rarr CL

to be rejected Thus H7 was not supported and it can beconcluded that no moderation effect exists To determine theforce (not signification) of the mediating effects the f 2 indexwas used

To this end we compared the model of direct effect withthe moderating relation and with the model of moderatingterms The conventions are as follows f 2 gt 002 = weakmoderator effect f 2 gt 015 =moderatemediating effect and iff 2 gt 035 = strong moderative effect In our case the f 2 indexof the moderate effect was 0047 which is considered to be aweak effect

6 Discussion

In the present study we investigated the relation betweenrestaurant client behavior and the factors that influencecustomer loyalty In recent years new technologies in thecatering sector have started to be widely used so as to developtactics to increase customer loyalty and customer satisfactionIn line with the growing importance of wireless communi-cations and Wi-Fi networks in the perception of users topromote returning our results demonstrate the strong impactthat wireless communications and Wi-Fi networks have onrepeated use of restaurant services Furthermore our findingsalso suggest that the quality of the service influences customersatisfaction making clients perceive a higher quality in thedelivered service From the applied perspective our worksuggests the need to further investigate the impact of wirelesscommunications and Wi-Fi networks access on customerloyalty since the latter promotes repeated use of restaurantservices

Our first hypothesis on the positive impact of the qualityof service in restaurants on consumer satisfaction (H1) wassupported by the data analysis This is congruent withSaravanan and Veerabhadraiahrsquos (2003) finding that servicequality is directly perceived by consumers and can increasetheir loss of satisfaction with respect to the products andservices

Similarly our prediction on the positive influence ofwillingness to pay on quality of service (Hypothesis 2) wasalso supported as consumers correlated the quality of servicewith what they were willing to pay for Furthermore insupport of Hypothesis 3 our results also demonstrate thatconsumersrsquo willingness to pay in a restaurant has a positiveinfluence on their satisfaction due to among other factorsthe customersrsquo decision-making in relation to what they arewilling to pay

Furthermore our hypothesis that customer satisfactionwould positively influence consumer loyalty (Hypothesis 4)was also supported by our results as satisfaction with theproducts and services acquired at the point of sale was foundto increase loyalty and engagement of consumers insidethe restaurant One of the factors perceived by customersto positively increase the quality of service was wirelesscommunications and Wi-Fi Similarly a previous study alsodemonstrated that consumers valued the speed of the con-nection n a retail selling point [16]

Nevertheless according to our results consumers donot realize that their appreciation of free wireless com-munications and Wi-Fi causes an increase in their loyalty(Hypothesis 5) While consumers perceive wireless commu-nications and Wi-Fi networks as a complementary servicethis additional service might even increase their desire toreturn to the establishment if the experience was positive[22] Likewise it should be emphasized that willingness topay over customer satisfaction wasmeasured through servicequality (H6) which implies that consumers were satisfiedafter comparing product price product quality perception aswell as with the quality of the service [24]

The results of the present study are meaningful formanagers in catering establishments as our findings suggestmeaningful implementations that can enhance the quality ofclient service and ultimately improve the general conditionsof their retail point of sale [23]

Taken together our results convincingly demonstrate thattechnologies such as wireless communications and Wi-Finetworks offer great opportunities in terms of understandingcustomer behavior in retail points of sale particularly inthe catering domain The environment of the retail pointsof sale and establishments has become a measuring devicefor product attractiveness and consumer satisfaction with theoffered servicesTherefore wireless communications andWi-Fi networks should be considered as key identifiers of anappropriate development of a relationship between restaurantclients and the catering establishment more specifically freehigh-quality wireless communications and Wi-Fi networksservices generate long-term clientele and promote customerloyalty (Saravanan amp Veerabhadraiah 2003) [23]

7 Conclusions

The results of the present study provide meaningful insightsfor company managers in the catering sector regarding theways to improve their client services and consequentlyincrease the number of loyal customers Our findings demon-strate the existence of a correlation between quality and price


Table 10

ConstructsAuthors Items PathWillingness to pay [29](Kemeny 2015)

It was easy to pay for the products 0872The waiting time was reasonable 0895

Service quality(Francis 2009)

The restaurant was visually appealing 0721The range of the offered products was good 0945

Customer loyalty(Yang amp Peterson 2004)

I would recommend this restaurant to those who seek my advice onthe issue 1000

Customer satisfaction(Anderson amp Srinivasan 2003) The restaurant is willing and ready to respond to customer needs 1000

Wireless communication andWi-Fi services (Chang et al2009)

Wi-Fi access is fast and reliable 1000

of wireless communications and Wi-Fi networks on the onehand and customer loyalty on the other hand

Next it is interesting to point out the established relationbetween willingness to pay and the quality of service whichis directly related to variables such as price Similarly theinfluence of willingness to pay on customer satisfaction wasalso confirmed by our findings In this respect restaurantmanagers should take into account that customer predisposi-tion to pay for a product or service arising from a customerrsquosconviction of having ldquoa good dealrdquo influences customersatisfaction Taken together our results demonstrate thatcustomer satisfaction positively influences customer loyaltyThis conclusion provides a meaningful insight for restaurantmanagers specifically free wireless communications andWi-Fi networks services linked to the retail point of sale canincrease long-term customer loyalty and therefore shouldbe among the priorities to be considered by restaurantmanagers

Wi-Fi networks serve as a mean to promote customerloyalty so if marketers find the way to create value forcustomers they can use the Wi-Fi networks for severalpurposes that range from strategic content communicationwith the access pages to branding

Similarly our results suggest that the relation betweenwillingness to pay and customer satisfaction is mediated bythe effect of service quality Said differently clients perceivewillingness to pay through the satisfaction they get froma given company or brand which allows them to directlyperceive an optimal service quality Therefore customersrsquowillingness to pay determines satisfaction that clients getfrom the obtained products and services Moreover it isimportant to highlight the mediation effect of wireless com-munications and Wi-Fi networks in the relation betweencustomer satisfaction and customer loyalty whichmakesWi-Fi and wireless communications key antecedents of clientsrsquosatisfaction and purchase intention In this respect themodel proposed in the present study is particularly usefulas it can serve as a basis for future studies to implementimprovements in consumer loyalty in restaurants throughwireless communications andWi-Fi networks or to elaboraterelevant strategies to increase satisfaction and quality ofservice

In future studies the analysis developed can be conductedwith a larger sample This could be extended to otherrestaurants and future studies could focus on comparativeanalysis in order to find advantages in terms of marketingimplications

The limitations of the present study relate to the samplesize and the selection of the restaurant Our results can beused in the future to inform other theories andmodels of userloyalty and new technologies like wireless communicationsand Wi-Fi networks

Appendix

Items and Constructs

The questionnaire items have been adapted from studiesspecified in Table 10 as well as from Kemeny (2015) andSharma and Stafford (2000)The table includes the questionson which the indicators of each construct are based

Data Availability

The data survey used to support the findings of this study isincluded within the article

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

References

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[2] R J-H Wang E C Malthouse and L Krishnamurthi ldquoOnthe Go How Mobile Shopping Affects Customer PurchaseBehaviorrdquo Journal of Retailing vol 91 no 2 pp 217ndash234 2015

[3] D CyrMHead andA Ivanov ldquoDesign aesthetics leading tom-loyalty in mobile commercerdquo Information amp Management vol43 no 8 pp 950ndash963 2006


[4] W Xiaoliang K Xu and Z Li ldquoSmartFix Indoor LocatingOptimization Algorithm for Energy-Constrained WearableDevicesrdquoWireless Communications and Mobile Computing vol2017 Article ID 8959356 13 pages 2017

[5] LWu ldquoUnderstanding the Impact ofMedia Engagement on thePerceived Value and Acceptance of Advertising Within MobileSocial Networksrdquo Journal of Interactive Advertising vol 16 no1 pp 59ndash73 2016

[6] J Yim S Ganesan and B H Kang ldquoLocation-Based MobileMarketing Innovationsrdquo Mobile Information Systems vol 2017Article ID 1303919 3 pages 2017

[7] K Dong Z Ling X Xia H Ye W Wu and M YangldquoDealingwith Insufficient Location Fingerprints inWi-Fi BasedIndoor Location Fingerprintingrdquo in Wireless Communicationsand Mobile Computing 2017

[8] Y H Kim D J Kim and K Wachter ldquoA study of mobileuser engagement (MoEN) Engagement motivations perceivedvalue satisfaction and continued engagement intentionrdquoDeci-sion Support Systems vol 56 no 1 pp 361ndash370 2013

[9] J V Doorn K N Lemon V Mittal et al ldquoCustomer Engage-ment Behavior Theoretical Foundations and Research Direc-tionsrdquo Journal of Service Research vol 13 no 3 Article ID1094670510375599 pp 253ndash266 2010

[10] A Backlund ldquoThe concept of complexity in organisations andinformation systemsrdquo Kybernetes vol 31 no 1 pp 30ndash43 2002

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[104] E E Rigdon C M Ringle M Sarstedt and S P Guder-gan ldquoAssessing heterogeneity in customer satisfaction studiesrdquoAdvances in International Marketing vol 22 pp 169ndash194 2011

[105] J L Roldan and G Cepeda PLS-SEM CFP Universidad deSevilla 4th edition 2017

[106] FHernandez-Perlines JMoreno-Garcıa and B Yanez-AraqueldquoThe mediating role of competitive strategy in internationalentrepreneurial orientationrdquo Journal of Business Research 2016

[107] R M Baron and D A Kenny ldquoThe moderator-mediator vari-able distinction in social psychological research conceptualstrategic and statistical considerationsrdquo Journal of Personalityand Social Psychology vol 51 no 6 pp 1173ndash1182 1986


[108] D A Kenny and C M Judd ldquoEstimating the nonlinear andinteractive effects of latent variablesrdquo Psychological Bulletin vol96 no 1 pp 201ndash210 1984

[109] W W Chin B L Marcelin and P R Newsted ldquoA partialleast squares latent variable modeling approach for measuringinteraction effects results from aMonte Carlo simulation studyand an electronic-mail emotionadoption studyrdquo InformationSystems Research vol 14 no 2 pp 189ndash217 2003

[110] G Fassott J Henseler and P S Coelho ldquoTesting moderatingeffects in PLS path models with composite variablesrdquo IndustrialManagementampData Systems vol 116 no 9 pp 1887ndash1900 2016

International Journal of

AerospaceEngineeringHindawiwwwhindawicom Volume 2018

RoboticsJournal of

Hindawiwwwhindawicom Volume 2018


Active and Passive Electronic Components

VLSI Design



Shock and Vibration


Civil EngineeringAdvances in

Acoustics and VibrationAdvances in



Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawiwwwhindawicom

Volume 2018

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

Control Scienceand Engineering

Journal of



Journal ofEngineeringVolume 2018

SensorsJournal of



RotatingMachinery


Modelling ampSimulationin EngineeringHindawiwwwhindawicom Volume 2018


Chemical EngineeringInternational Journal of Antennas and

Propagation




Navigation and Observation


Hindawi

wwwhindawicom Volume 2018

Advances in

Multimedia

Submit your manuscripts atwwwhindawicom


predictive models of complex behavior of Internet usersThesemodels are based on the results of the analysis obtainedfrommobile devices (see eg [14 15])

However providing a good customer experience partic-ularly in the hospitality industry requires more than freewireless communications and Wi-Fi networks services andquality products [16] For instance to create a significantexperience inside a restaurant a major aspect that should betaken into account is the presentation of the restaurant envi-ronment Pleasant aroma nice music ambient temperaturelow noise levels and adequate lighting these and many otheraspects should be harmonized with each other to create aspace that will favorably influence clientsrsquo perception of therestaurant and help them evaluate their experiences in amorepositive manner [17ndash19] The entirety of these aspects thatrelated to the physical environment of the service transactionand have a meaningful impact on consumer behavior isreferred to as servicescape [20 21]

In the present study we seek to bridge the gap in previousliterature on the impact of some elements of the restaurantservicescape on consumer behavior To this end we buildon previous work on customer loyalty employing severalquestionnaires handed out at points of sale [22 23] Thissurvey-based approach is complemented with a focus ontechnology Here the technological element refers to freewireless communications and Wi-Fi networks access thatcontributes to a better consumer experience in restaurants[16 24] and enables for collecting relevant data on consumerbehaviorThese data can further be used to develop predictivemodels

The main aim of the present study was twofold On onehand through conducting a survey at the moment of con-sumption we aimed to assess the importance of factors thathave an impact on consumer perceptions of food chain storesTo this end a questionnaire was sent to those customers whobeing within the premises of the restaurant asked for accessto the free wireless communications and Wi-Fi networksoffered by the restaurant We additionally took into accountthat filling in the survey while using mobile devices couldaffect the outcome [30 31] Our second goal was to build apredictive model of the complex consumer behaviors withinthe retail point of sale and of consumer intentions towardsrestaurants To achieve this goal we employed the modelsproposed by DCunha et al [25] Zhang and He [27] andJahanshahi et al [32] These models predict the impact ofclient services on customer satisfaction and actual consumerbehavior the latter becomes tangible through for examplebrand loyalty

In the present study we also build on previous researchon using wireless communications and Wi-Fi networks forresearch purposes [16 24] or as part of a restaurantrsquos ser-vicescape [33 34] However compared to previous researchthe novelty of the present study lies in that we use wirelesscommunications and Wi-Fi networks both as an element ofour conceptual model and as a means of data collection Inwhat follows based on the revision of previously proposedmodels [35] we propose an updated model and present theresults of our exploratory analysis of applying this model to arestaurantrsquos servicescape

2 Theoretical Background

As long as the environment of an establishment can attractclients to shop once it strives to attract clients to buy repeat-edly According to Sharma and Stafford (2000) customerdecision-making process can be influenced more by theenvironment of retail points of sale than by the productitself Even though cognitive factors can explain the selectionof stores and most purchases planned within the store thestorersquos atmosphere and the emotional state of the buyers canexert a decisive impact on the complex behavior implied in apurchaseThere is a wide scholarly consensus that enhancingthe extent to which an environment is enjoyable and boostingthe degree to which it highlights innovation are essentialfactors that determine a companyrsquos success [36]

In this context an important concept to refer to isldquoatmosphericsrdquomdasha notion first used to reveal the distinctivecapacity of a physical environment to affect client behavior bystimulating customersrsquo perceptive and emotional responses[21] According to the results of several environmental psy-chology studies there is a strong link between emotionsand behavior and accordingly complex human behaviorsare strongly associated with physical environments [37ndash39]These findings are of vital importance in the services sectorsuch as hotels restaurants professional offices banks retailstores and hospitals [20 21 33 36] For that reason the effectof ldquoatmosphericsrdquo in the restaurants sector has been an issueof extensive research and analysis (eg [17 40ndash43])

Both pleasure (defined as the degree to which a per-son feels good happy or cheerful) and emotion (ie thedegree to which a person feels exited stimulated alert oractive which is considered to be one of the main moti-vations for experience-orientated customers) are importantvariables related to purchase intention Emotion interactswith pleasure to facilitate approaching pleasing consump-tion environments and avoiding unpleasant environments[44]

Consumer loyalty refers to the repeated choice of acatering service by a client so that the client becomesloyal to the service-providing company organization orbusiness [21] Oliver [45] defined customer loyalty as ldquoanentrenched repurchasing compromise or repeated promotionof a preferred productservice in a consistent way throughoutthe future triggering repetitive consumption behaviors of thesame brand regardless of how context or marketing effortswhich hold the potential to cause a change in behaviormay influence themrdquo The development of consumer loyaltyis influenced by perceived value habit trust and clientsatisfaction [46 47]

Major studies that investigated the factors affecting con-sumer loyalty in catering establishments and other services-related institutions are shown in Table 1 In this body of workatmospherics and loyalty were key elements to consider [2528] Other essential factors involved in customer loyalty arethe quality of products [27] willingness to pay waiting time(Frank et al 2008 Sompie amp Pangemanan 2014) and qualityof service [26] However new technologies incorporated inthe offered services such as wireless communications andWi-Fi networks have rarely been studied













3 Conceptual Model











ServiceQuality

Willingnessto pay


H2

H3

H5

H1






















Male 53 453Age 18-25 23 197

26-35 38 32536- 45 29 24846-55 12 10256-65 10 85gt65 5 43




Connections 1-10 104 88911-20 7 6021-30 3 2631-50 1 0951-100 2 17







ServiceQuality

Willingnessto pay


0607

0509

0729

products

comfort0721

prices

wait time0895

0872

quality

1000

connectivity

1000

recommend

10000227

0945














Table 4 Ratio HTMT































1177 0878 0361 1148 0827 0393 0029 0051 -0032













250

225

200

175

150

125

100

75

50

25

00

Freq

uenc

y


Customer Loyalty



90

80

70

60

50

40

30

20

10

0

Customer Loyalty

00 01 02 03 04 05 06 07Total Effects









ServiceQuality

Willingnessto pay


(a) H2 (b) H1

(c lsquo) H3



Path coeff(120573)

CI (25)LOWER

CI (975)UPPER











Path coeff(120573)








VAF



(1)







CustomerLoyalty

Wi-Fi


H5




6 Discussion








7 Conclusions



Table 10
















Appendix



Data Availability




References





















































































































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia














3 Conceptual Model











ServiceQuality

Willingnessto pay


H2

H3

H5

H1






















Male 53 453Age 18-25 23 197

26-35 38 32536- 45 29 24846-55 12 10256-65 10 85gt65 5 43




Connections 1-10 104 88911-20 7 6021-30 3 2631-50 1 0951-100 2 17







ServiceQuality

Willingnessto pay


0607

0509

0729

products

comfort0721

prices

wait time0895

0872

quality

1000

connectivity

1000

recommend

10000227

0945














Table 4 Ratio HTMT































1177 0878 0361 1148 0827 0393 0029 0051 -0032













250

225

200

175

150

125

100

75

50

25

00

Freq

uenc

y


Customer Loyalty



90

80

70

60

50

40

30

20

10

0

Customer Loyalty

00 01 02 03 04 05 06 07Total Effects









ServiceQuality

Willingnessto pay


(a) H2 (b) H1

(c lsquo) H3



Path coeff(120573)

CI (25)LOWER

CI (975)UPPER











Path coeff(120573)








VAF



(1)







CustomerLoyalty

Wi-Fi


H5




6 Discussion








7 Conclusions



Table 10
















Appendix



Data Availability




References





















































































































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia




ServiceQuality

Willingnessto pay


H2

H3

H5

H1






















Male 53 453Age 18-25 23 197

26-35 38 32536- 45 29 24846-55 12 10256-65 10 85gt65 5 43




Connections 1-10 104 88911-20 7 6021-30 3 2631-50 1 0951-100 2 17







ServiceQuality

Willingnessto pay


0607

0509

0729

products

comfort0721

prices

wait time0895

0872

quality

1000

connectivity

1000

recommend

10000227

0945














Table 4 Ratio HTMT































1177 0878 0361 1148 0827 0393 0029 0051 -0032













250

225

200

175

150

125

100

75

50

25

00

Freq

uenc

y


Customer Loyalty



90

80

70

60

50

40

30

20

10

0

Customer Loyalty

00 01 02 03 04 05 06 07Total Effects









ServiceQuality

Willingnessto pay


(a) H2 (b) H1

(c lsquo) H3



Path coeff(120573)

CI (25)LOWER

CI (975)UPPER











Path coeff(120573)








VAF



(1)







CustomerLoyalty

Wi-Fi


H5




6 Discussion








7 Conclusions



Table 10
















Appendix



Data Availability




References





















































































































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia





Male 53 453Age 18-25 23 197

26-35 38 32536- 45 29 24846-55 12 10256-65 10 85gt65 5 43




Connections 1-10 104 88911-20 7 6021-30 3 2631-50 1 0951-100 2 17







ServiceQuality

Willingnessto pay


0607

0509

0729

products

comfort0721

prices

wait time0895

0872

quality

1000

connectivity

1000

recommend

10000227

0945














Table 4 Ratio HTMT































1177 0878 0361 1148 0827 0393 0029 0051 -0032













250

225

200

175

150

125

100

75

50

25

00

Freq

uenc

y


Customer Loyalty



90

80

70

60

50

40

30

20

10

0

Customer Loyalty

00 01 02 03 04 05 06 07Total Effects









ServiceQuality

Willingnessto pay


(a) H2 (b) H1

(c lsquo) H3



Path coeff(120573)

CI (25)LOWER

CI (975)UPPER











Path coeff(120573)








VAF



(1)







CustomerLoyalty

Wi-Fi


H5




6 Discussion








7 Conclusions



Table 10
















Appendix



Data Availability




References





















































































































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia




ServiceQuality

Willingnessto pay


0607

0509

0729

products

comfort0721

prices

wait time0895

0872

quality

1000

connectivity

1000

recommend

10000227

0945














Table 4 Ratio HTMT































1177 0878 0361 1148 0827 0393 0029 0051 -0032













250

225

200

175

150

125

100

75

50

25

00

Freq

uenc

y


Customer Loyalty



90

80

70

60

50

40

30

20

10

0

Customer Loyalty

00 01 02 03 04 05 06 07Total Effects









ServiceQuality

Willingnessto pay


(a) H2 (b) H1

(c lsquo) H3



Path coeff(120573)

CI (25)LOWER

CI (975)UPPER











Path coeff(120573)








VAF



(1)







CustomerLoyalty

Wi-Fi


H5




6 Discussion








7 Conclusions



Table 10
















Appendix



Data Availability




References





















































































































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia



Table 4 Ratio HTMT































1177 0878 0361 1148 0827 0393 0029 0051 -0032













250

225

200

175

150

125

100

75

50

25

00

Freq

uenc

y


Customer Loyalty



90

80

70

60

50

40

30

20

10

0

Customer Loyalty

00 01 02 03 04 05 06 07Total Effects









ServiceQuality

Willingnessto pay


(a) H2 (b) H1

(c lsquo) H3



Path coeff(120573)

CI (25)LOWER

CI (975)UPPER











Path coeff(120573)








VAF



(1)







CustomerLoyalty

Wi-Fi


H5




6 Discussion








7 Conclusions



Table 10
















Appendix



Data Availability




References





















































































































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia









1177 0878 0361 1148 0827 0393 0029 0051 -0032













250

225

200

175

150

125

100

75

50

25

00

Freq

uenc

y


Customer Loyalty



90

80

70

60

50

40

30

20

10

0

Customer Loyalty

00 01 02 03 04 05 06 07Total Effects









ServiceQuality

Willingnessto pay


(a) H2 (b) H1

(c lsquo) H3



Path coeff(120573)

CI (25)LOWER

CI (975)UPPER











Path coeff(120573)








VAF



(1)







CustomerLoyalty

Wi-Fi


H5




6 Discussion








7 Conclusions



Table 10
















Appendix



Data Availability




References





















































































































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia



250

225

200

175

150

125

100

75

50

25

00

Freq

uenc

y


Customer Loyalty



90

80

70

60

50

40

30

20

10

0

Customer Loyalty

00 01 02 03 04 05 06 07Total Effects









ServiceQuality

Willingnessto pay


(a) H2 (b) H1

(c lsquo) H3



Path coeff(120573)

CI (25)LOWER

CI (975)UPPER











Path coeff(120573)








VAF



(1)







CustomerLoyalty

Wi-Fi


H5




6 Discussion








7 Conclusions



Table 10
















Appendix



Data Availability




References





















































































































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia



ServiceQuality

Willingnessto pay


(a) H2 (b) H1

(c lsquo) H3



Path coeff(120573)

CI (25)LOWER

CI (975)UPPER











Path coeff(120573)








VAF



(1)







CustomerLoyalty

Wi-Fi


H5




6 Discussion








7 Conclusions



Table 10
















Appendix



Data Availability




References





















































































































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia



CustomerLoyalty

Wi-Fi


H5




6 Discussion








7 Conclusions



Table 10
















Appendix



Data Availability




References





















































































































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia



Table 10
















Appendix



Data Availability




References





















































































































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia



















































































































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia

















































































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia














































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia








RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia




RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia


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