development of the hospitality quality consciousness scale - Asbbs.org

Proceedings of ASBBS Volume 16 Number 1Taoban Japanese restaurants.FIRST STUDYIn first study, the respondents are front-line employees in Tasty steak restaurants. One hundred front-lineemployees participated in this study. The survey method used in this study is mail questionnaire survey.Ten branches of Tasty steak restaurant in Northern Taiwan were picked and mailed questionnaire to itsbranch manager for helping the execution of survey. After excluding questionnaires with missing values,a total of 76 valid and usable questionnaires were retained for analysis. The demographic profiles of thefront-line employees are as follows. The sample included 16 (21%) males and 60 (79%) females amongthe respondents. The main age group was 21-30 and represented 69% of the respondents. Most of therespondents (75.6%) had a college or university level education. Most of the respondents (67%) had 1-3years of work experience, with an average length of 1.6 years.In the beginning of scale refinement, Churchill (1979) suggested that the purification of ameasurement instrument should begin with the computation of the coefficient α. Items with correcteditem-to-total correlation lower than 0.35 were discarded (Parasuraman et al., 1988). Moreover, if item isdeleted will increase the whole scale’s Cronbach’s α value then it should delete from scale. As individualitems were deleted, each Cronbach’s α value was recomputed for the remaining items and the newcorrected correlations were evaluated for further deletion of items. Item number 5, 22, 25 and 32 weredeleted from the analysis in first round (Table 1). Item number 21, 23 and 29 were deleted from theanalysis in second round (α value of whole scale is 0.9726). No item was deleted from the analysis inthirdly round (α value of whole scale is 0.9737). Therefore, total seven items were deleted.Table 1Refinement results of reliability analysis in first roundItemnumberCorrected item-to-total correlationCronbach’s Αlpha valueIf item deleted1 .7391 .97412 .6643 .97433 .8628 .97364 .7602 .97405 .5340 .97496 .7325 .97407 .7331 .97408 .6486 .97439 .7175 .974110 .7093 .974211 .6513 .974312 .8041 .973813 .8101 .973914 .5745 .974615 .7617 .974016 .6706 .974317 .7633 .973918 .6600 .974319 .7313 .974120 .7486 .974022 .4968 .975024 .8246 .973725 .4791 .9750ASBBS Annual Conference: Las Vegas February 2009

Proceedings of ASBBS Volume 16 Number 126 .8473 .973627 .6523 .974328 .6001 .974630 .7101 .974131 .7004 .974232 .5130 .974933 .7041 .974234 .8898 .973535 .6659 .974336 .6808 .974237 .6500 .974438 .7081 .974139 .7573 .973940 .8312 .9737Cronbach’s Αlpha value of whole scale 0.9748A total of 33 items were retained for further unidimensionality examination. Exploratory factoranalysis (EFA) was performed to identify and confirm the underlying structure of the items and to furtherreduce their number. The Kaiser–Meyer–Olkin (KMO) measure and Bartlett’s test of sphericity were usedto ensure that the data had inherent sufficient correlations to perform EFA. In EFA analysis of this study,the principal component analysis with varimax rotation, eigenvalue exceeding 1 and factor loadingsexceeding 0.5 are used and set. The KMO index of EFA analysis under 33 items was 0.808, and Bartlett’stest of sphericity was significant at a level of 0.00, which justified the use of EFA.The first round of factor analysis was resulted in a five factors solution, which explained 74.25% ofvariance. To achieve a more meaningful solution, items were deleted if they loaded equally heavily intomore than one factor, and their loadings were smaller than 0.55, in consideration of the small sample size(Hair et al., 1998). Each time items were deleted from the analysis, the factor analysis was re-run andcoefficient α was re-computed until a satisfactory result was achieved. In first round, Item number 10, 24and 40 were deleted because their factor loading were smaller than 0.55. Item number 1, 3, 7, 20, 34 and38 were deleted because these items loaded equally heavily into more than one factor (Table 2). No itemwas deleted in second round. Therefore, total nine items were deleted in the EFA analysis. The process ofscale purification in this initial stage reduced the number of items from 40 to 24 items. Among these 24items, the factor analysis extracted four factors which explained 73.23% of variance with item loadingsexceeding 0.55 (Table 3). Cronbach’s α value for each dimension ranged from 0.7518 to 0.9419. Thisdemonstrates that the scales had considerable reliability (Cronbach’s α value for each dimension greaterthan 0.7) (Hair et al., 1998; Nunnally, 1978).Table 2Refinement results of factor analysis in first roundItem numberFactor loading1 2 3 4 58 .82327 .79130 .74515 .71113 .7046 .7032 .64328 .6253 b .624 .609ASBBS Annual Conference: Las Vegas February 2009

Proceedings of ASBBS Volume 16 Number 131 .60519 .5931 b .551 .53911 .71839 .67312 .64233 .64017 .61236 .5999 .57737 .55520 b .553 .5024 .55110 a .46826 .78934 b .519 .71735 .63838 b .579 .5087 b .558 .57324 a .51516 .73518 .70914 .68140 a .486Variance23.58 19.94 13.90 13.57 3.26explained (%)Note:a Indicate that the item was deleted because its low factor loadingb Indicate that the item was deleted because its multiple loadingTable 3Refinement results of factor analysis in second roundFactor loadingItem number8 .84127 .77230 .7506 .69515 .69213 .6852 .68328 .66731 .62511 .74539 .67636 .65612 .6549 .64533 .6331 2 3 4Variance Cronbach’s αexplained (%) value25.63 0.94222.73 0.942ASBBS Annual Conference: Las Vegas February 2009

Proceedings of ASBBS Volume 16 Number 14 .63237 .60317 .58714 .75416 .70919 .61018 .60726 .78935 .64415.71 0.8439.16 0.752SECOND STUDYIn second study, researcher sought to confirm the validity of the refined scale by analyzing a new data setobtained from another survey. Using the 24-item HQCS, data were collected from respondents employedin Taoban Japanese restaurants that use same quality management system. Three hundred forty front-lineemployees from seventeen Taoban Japanese restaurants were asked to fill out the questionnaire. A total of304 available questionnaires were analyzed. The profile of the respondents included that they were, in themajority, female (76%), fewer than 40 years old (81%), with 1-3 years’ tenure at current positions (47%).Most of the respondents (72%) had a college or university level education.Confirmatory factor analysis (CFA) with maximum likelihood estimation in LISREL 8.80 wasutilized to examine the factor structure of the HQCS. Research postulated a priori measurement modellinking observed variables with latent factors, and then tested that model for its ability to fit the data using2CFA. The fit of the measurement model for the data was based on the χ statistic, standardized rootmean square residual (SRMR), comparative fit index (CFI), and goodness-of-fit index (GFI). In the eventthat poorly fitting models emerged from the initial series of analyses, further model re-specificationwould be needed to improve the model fit based on standardized residuals and modification indices.Large standardized residuals and modification indices identified pairs of indicators in which thecovariance was either over predicted or under predicted by the model. One of the two items was deletedbased on redundant content, salience, or content that was ambiguous for determining its placement withinthe model. The CFA was then rerun to determine whether the modification resulted in an improved fit.This process was continued until a reasonable model was generated.The initial estimation of the 24 item of two-factor structure emotional labor model did not generate a2satisfactory result ( χ =565.92, SRMR=0.053, CFI=0.97, GFI=0.76). This indicated a poor fit betweenthe sample data and the model. The data were subsequently subjected to a specification search aspreviously described. This search resulted in a final model consisting of 19 items (item 9, 13, 14, 31 and37 had been deleted), with 10 items loaded into the service quality factor, and 9 items loaded into the2TQM factor. The final model provided an improved and reasonable fit for the data ( χ =296.63,SRMR=0.045, CFI=0.98, GFI=0.91). Table 4 lists the dimension and attribute factor loadings andreliability scores. Both service quality and TQM indicators with loadings ranging from 0.72 to 0.87demonstrated that the HQCS attributes were moderately strong measures of employee’s qualityconsciousness. In addition, the composite reliability for the service quality and TQM factors were 0.94and 0.93, respectively, which exceeded the recommended 0.70 (Hair et al., 1998; Nunnally, 1978), andtherefore, evidenced the good internal consistency of the HELS.ASBBS Annual Conference: Las Vegas February 2009

Proceedings of ASBBS Volume 16 Number 1Table 4CFA results of HQCS (n=304)Dimension and attributeCompletelystandardized loadingsDimension/attributereliabilityErrorvarianceService quality .94 .182 Neat appearance of front-line employee will .736 .678 .24increase service quality4 Moment of truth will significantly affect service .768 .713 .23quality6 Appropriate Provision of customer required .866 .826 .12service will increase service quality8 Precise execution of role-prescribed service will .795 .743 .21increase service quality11 Short on-line waiting time will increase service .817 .766 .18quality12 Courtesy and friendliness of front-line employee .851 .807 .13will increase service quality15 Communication between customer andemployee is an important factor for affectingservice quality.811 .759 .1616 Personal attention given by front-line employeewill increase service quality17 Listening and understanding the specific needsof customers will increase service quality18 Never give any excuse for responding customerrequests will increase service quality.818 .769 .17.793 .740 .18.829 .782 .16Total quality management .93 .2319 Continuous service quality related training of .848 .797 .16company employee will increase qualitycompetitiveness26 Effective communication between manager and .731 .658 .26employee is an important factor for affectingquality competitiveness27 Every employee willingly helps others who have .825 .765 .17work related service problems will affectquality competitiveness28 Every employee has the belief that quality.872 .825 .13management is everyone’s duty will affectquality competitiveness30 Every employee has the belief that quality is the .837 .784 .23most important source of businesscompetitiveness will affect qualitycompetitiveness33 Every employee has the belief that deliver the .717 .644 .38ASBBS Annual Conference: Las Vegas February 2009

Proceedings of ASBBS Volume 16 Number 1service right at first time will affect qualitycompetitiveness35 Every employee willingly provides serviceimprovement idea to company will affectquality competitiveness36 Every employee positively collects relatedcustomer opinions and provides to companywill affect quality competitiveness39 Customer evaluates the consuming value byconsidering cost and the quality of wholeservice delivering system (includes service,employee and place).830 .777 .15.734 .663 .29.758 .692 .23Fit statistics2χ =296.63, SRMR=0.045, CFI=0.98, GFI=0.91Note: the number of attribute is the original item numberCONCLUSIONSIn highly global and competitive marketplace, every service enterprise really needs to make endeavor ofemployee management in service system to pursue the competitive advantage of excellent service qualityand customer satisfaction. The quality consciousness of front-line employee affects his service attitudeand service behavior. Furthermore, service quality and customer satisfaction are affected by front-lineemployees’ service behavior and performance. Usually employee with high positive quality consciousnessis likely to be willing to participate in quality improvement efforts and to take on more responsibility forservice quality. Therefore, when manager try to make endeavor of service quality and customersatisfaction improvement, employees’ quality consciousness definitely is the critical management topic inpresent human resource management and service management.This research proposed a novel hospitality quality consciousness scale to help restaurant forunderstanding the present situation of its employee quality consciousness. The Major dimensions ofemployee quality consciousness have been identified as TQM and service management, and a pool ofmeasurement items has been confirmed. An instrument has also been formed that exhibits a high degreeof validity and reliability. Furthermore, the HQCS is most valuable when it is used periodically to trackrestaurant employees’ quality consciousness performance and to pinpoint areas requiring moremanagerial attention and action for improving employees’ quality consciousness. In addition, humanresource manger in restaurant can use the HQSC as an assessment tool to identify the more “suitable” jobapplicant.Since research scope of this research was limited in restaurant industry, this HQCS requiresfurther replication in different hospitality contexts to ensure its consistency and generalization. Therefore,researchers can apply this pilot HQCS and propose a general hospitality quality consciousness scale orapply this pilot HQCS in the relationship study with quality consciousness.REFERENCESAhearne, M., R. and E. Jones (2007) Examining the effect of salesperson service behavior in acompetitive context. Journal of the Academy of Marketing Science, Volume 35, Number 4, 603-616.Balaguer, L. and M. Cantavella-Jorda (2002) Tourism as a long-run economic growth factor: the Spanishcase. Applied Economics, Volume34, Number 7, 877-884.ASBBS Annual Conference: Las Vegas February 2009

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