This article presents raw inferential statistical data that determined the how selected multi-sensory dimensions such as sight, sound and smell would influence consumer attitudes towards quick-service restaurants, restaurant patronage intention, food purchase decision, food consumption satisfaction, restaurant attachment, repurchase intention and positive word of mouth in South African quick-service restaurants. To test the conceptual model an online questionnaire was used to collect data from Generation Z restaurant consumers within the metropolitan area of Bloemfontein, South Africa. The data were analysed using a quantitative approach. Reliability and validity were confirmed. The data was presented using Structural Equation modeling (SEM) using the Smart PLS program. The analysis of the SEM path shows estimates of the interconnectivity of the major constructs in the data. The findings from this dataset show that sight, sound and smell had on consumer attitudes towards quick-service restaurants and restaurant patronage intention. In addition, consumer attitudes towards quick-service restaurants and restaurant patronage intention had a positive influence on food purchase decisions. Food purchase decisions positively and significantly influenced food consumption satisfaction. Additionally, food consumption satisfaction positively and significantly influenced restaurant attachment, repurchase intention and positive word of mouth. Furthermore, restaurant attachment had a positive influence on repurchase intention and repurchase intention had a positive influence on positive word of mouth. Moreover, surprisingly, restaurant attachment had a negative and an insignificant influence on positive word of mouth.http://www.elsevier.com/locate/dibhj2020Marketing Managemen

Chuchu, Tinashe

Maziriri, Eugine Tafadzwa

Rukuni, Tarisai Fritz

UPSpace at the University of Pretoria

Data in Brief 32 (2020) 106279 Contents lists available at ScienceDirect Data in Brief journal homepage: www.elsevier.com/locate/dib Data Article From selected multi-sensory dimensions to positive word of mouth: Data on what really drives generation z consumers to b e attache d to quick service restaurants in bloemfontein, south africa? Eugine Tafadzwa Maziriri a , ∗, Tarisai Fritz Rukuni a , Tinashe Chuchu b a Department of Business Management, University of the Free State, Bloemfontein, South Africa b Department of Marketing Management, University of Pretoria, Pretoria, South Africa a r t i c l e i n f o Article history: Received 22 June 2020 Revised 29 August 2020 Accepted 31 August 2020 Available online 7 September 2020 Keywords: Multi-sensory dimensions Consumer attitudes Restaurant patronage intention Food purchase decision Food consumption satisfaction Restaurant attachment Repurchase intention Positive word of mouth a b s t r a c t This article presents raw inferential statistical data that de- termined the how selected multi-sensory dimensions such as sight, sound and smell would influence consumer atti- tudes towards quick-service restaurants, restaurant patronage intention, food purchase decision, food consumption satisfac- tion, restaurant attachment, repurchase intention and posi- tive word of mouth in South African quick-service restau- rants. To test the conceptual model an online questionnaire was used to collect data from Generation Z restaurant con- sumers within the metropolitan area of Bloemfontein, South Africa. The data were analysed using a quantitative approach. Reliability and validity were confirmed. The data was pre- sented using Structural Equation modeling (SEM) using the Smart PLS program. The analysis of the SEM path shows es- timates of the interconnectivity of the major constructs in the data. The findings from this dataset show that sight, sound and smell had on consumer attitudes towards quick- service restaurants and restaurant patronage intention. In ad- dition, consumer attitudes towards quick-service restaurants and restaurant patronage intention had a positive influence ∗ Corresponding author. E-mail address: maziririet@ufs.ac.za (E.T. Maziriri). Social media: (E.T. Maziriri) https://doi.org/10.1016/j.dib.2020.106279 2352-3409/© 2020 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ) 2 E.T. Maziriri, T.F. Rukuni and T. Chuchu / Data in Brief 32 (2020) 106279 on food purchase decisions. Food purchase decisions pos- itively and significantly influenced food consumption sat- isfaction. Additionally, food consumption satisfaction posi- tively and significantly influenced restaurant attachment, re- purchase intention and positive word of mouth. Furthermore, restaurant attachment had a positive influence on repurchase intention and repurchase intention had a positive influence on positive word of mouth. Moreover, surprisingly, restaurant attachment had a negative and an insignificant influence on positive word of mouth. © 2020 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ) SV         1 r  a  t  pecifications Table Subject Business and Marketing Specific subject area Consumer behaviour, retailing, restaurant consumption behaviour Type of data Tables and figures How data were acquired Data was gathered significantly through the dissemination of online questionnaires to Generation Z consumers within the Bloemfontein Metropolitan region Data format Raw, analysed, descriptive and statistical data Parameters for data collection To qualify for inclusion in the sample the participants had to be Generation Z restaurant consumers within the Bloemfontein metropolitan area. Description of data collection An online questionnaire was used to collect data from 381 Generation Z restaurant consumers within the metropolitan area of Bloemfontein. The questionnaire is provided as a supplementary file. Data source location University of the Free State, Bloemfontein, South Africa. Data accessibility Data is included in this article alue of the Data • The data helps explain how multi-sensory dimensions such as sight, sound and smell wouldinfluence consumer attitudes towards quick-service restaurants, restaurant patronage inten-tion, food purchase decision, food consumption satisfaction, restaurant attachment, repur-chase intention and positive word of mouth in South African and African quick-servicerestaurants as a whole. • The data can be used to enlighten restaurant and marketing managers on the importanceof multi-sensory dimensions, as well as how they can be beneficial to enhancement of con-sumer attitudes towards and consumer behavioural intentions. • The data can be used as a springboard for further discourse on how restaurant and marketingmanagers could enhance positive word of mouth in quick-service restaurants. • Data presented in this data article provides retail strategies which might be utilised to winmarket share. • The data does not involve any control variables but further research could consider using anyone of the constructs of this study as control variables. . Data Description Raw data was collected on generation Z consumers’ behaviour regarding quick-service restau-ants. The data files comprise of two supplementary files, namely the dataset in Excel (file 1)nd the questionnaire in MS Word (file 2). The processed data is then presented through fourables and two figures. First, the researchers, drafted a conceptual model ( Fig 1 ) which servedE.T. Maziriri, T.F. Rukuni and T. Chuchu / Data in Brief 32 (2020) 106279 3 H7 (+)Restaurant aachmentSoundFoodpurchase decisionSightH9 (+)Consumer atudes towards quick-service restaurantsH6 (+)H5 (+)H2 (+)H1 (+)H3 (+)H4 (+)H12 (+)H8 (+)Smell Restaurant patronage intenonPosive word of mouthFood consumpon sasfaconRepurchase intenonH10 (+)H11 (+)H13 (+)H14 (+)H15 (+)Fig. 1. Maziriri sensory trigger model. Fig. 2. Structural model.       as a guide to test the data in a statistical manner. Table 1 presents the sample profile showingdemographic data of the participants. Measurement accuracy assessment data is described intable 2 , presenting the Cronbach’s alpha value, composite reliability, average variance extracted(AVE) and factor loadings. Fig 2 describes the structural model which depicts the research con-structs post-analysis. Table 3 provides the model fit summary while table 4 depicts the outcomesof structural equation model analysis where proposed hypotheses, path coefficients ( β) and p-values are presented. 4 E.T. Maziriri, T.F. Rukuni and T. Chuchu / Data in Brief 32 (2020) 106279 Table 1 Sample profile. Characteristics Frequency % Age 18 years old 28 7,4 19 years old 32 8,4 20 years old 14 3,7 21 years old 193 50,8 22 years old 43 11,3 23 years old 40 10,5 24 years old 21 5,5 25 years old 9 2,4 Total 380 100 Gender Male 212 55,8 Female 161 42,4 Prefer not to say 7 1,8 Total 380 100 Year of study 1st year 93 24,5 2nd year 110 28,9 3rd year 85 22,4 Post graduate study 92 24,2 Total 380 100 Allowance usually received per month Less than R500 35 9,2 R501 – R10 0 0 90 23,7 R1001-R1500 46 12,1 R1501-R20 0 0 135 35,5 More than R20 0 0 74 19,5 Total 380 100 How often do you eat from quick-service restaurants Everyday 2 0,5 A few times a week 61 16,1 A few times a month 116 30,5 Once in a while 201 52,9 Total 380 100 2 w  c  w  s  w  w  w  s  s  t  m  b2 f  o  . Experimental Design, Materials and Methods The data presented was based on a quantitative approach. A descriptive research designas adopted to obtain the opinions of consumers concerning the multi-sensory dimensions,onsumer attitudes towards and consumers behavioural intentions. An online survey methodas considered an appropriate data collection method because it allows for the collection oftandardised data that permits the researcher to produce information for answering the how,ho, what and when questions regarding the subject matter. Generation Z student consumersithin the Bloemfontein metropolitan area. To test the data, the researchers proposed the modelhereby sound, sight and smell were the predictor variables. Consumer attitudes towards quick-ervice restaurants, restaurant patronage intention, food purchase decision, food consumptionatisfaction, were the mediating variables. Moreover, restaurant attachment, repurchase inten-ion and positive word of mouth were the outcome variables. The researchers had to propose aodel to test the validity of the proposed model as well as to determine if the data, which haseen collected in the field, fits well with the proposed conceptual model. .1. Assessment of the goodness of fit (GoF) Overall, R ² for consumer attitudes, restaurant patronage intention, food purchase decision,ood consumption satisfaction, restaurant attachment, repurchase intention and positive wordf mouth in Fig. 2 indicate that the research model explains 65.6%, 81.5%, 59.3%, 55.8%, 71.9%,E.T. Maziriri, T.F. Rukuni and T. Chuchu / Data in Brief 32 (2020) 106279 5 Table 2 Measurement accuracy assessment. Research PLS Scale item Cronbach’s Composite Average variance Factor constructs code item Mean SD alpha value reliability extracted (AVE) loadings Sound SO1 3.868 0.777 0.958 0.965 0.754 0.762 SO2 3.958 0.717 0.789 SO3 3.974 0.757 0.782 SO4 3.932 0.729 0.808 SO5 3.871 0.796 0.857 SO6 3.892 0.730 0.945 SO7 3.892 0.727 0.948 SO8 3.887 0.722 0.948 SO9 3.895 0.721 0.944 Sight ST1 4.074 0.757 0.912 0.928 0.618 0.804 ST2 4.026 0.684 0.795 ST3 3.989 0.736 0.749 ST4 4.034 0.726 0.810 ST5 3.963 0.746 0.775 ST6 3.866 0.798 0.768 ST7 3.982 0.720 0.806 ST8 4.037 0.717 0.778 Smell SM1 4.047 0.702 0.801 0.883 0.716 0.803 SM2 3.892 0.762 0.871 SM3 3.868 0.784 0.863 Consumer attitudes CTA1 4.0 0 0 0.740 0.797 0.880 0.710 0.847 CTA2 4.0 0 0 0.764 0.842 CTA3 4.082 0.715 0.840 Food Purchase decision FPD1 3.937 0.730 0.962 0.971 0.872 0.827 FPD2 3.874 0.791 0.907 FPD3 3.895 0.732 0.976 FPD4 3.889 0.728 0.977 FPD5 3.897 0.724 0.973 Restaurant patronage intention RPI1 3.868 0.777 0.835 0.901 0.752 0.855 RPI2 3.961 0.719 0.875 RPI3 3.974 0.757 0.872 Food Consumption satisfaction FCS1 3.897 0.720 0.849 0.892 0.625 0.730 FCS2 4.079 0.757 0.851 FCS3 4.032 0.684 0.829 FCS4 3.995 0.736 0.736 FCS5 4.029 0.723 0.798 Restaurant attachment RA1 3.963 0.750 0.875 0.906 0.616 0.749 RA2 3.868 0.800 0.769 RA3 3.987 0.720 0.837 RA4 4.034 0.716 0.817 RA5 4.050 0.703 0.801 RA6 3.895 0.764 0.733 Repurchase intention RI1 3.871 0.786 0.830 0.887 0.662 0.805 RI2 3.995 0.740 0.831 RI3 4.003 0.766 0.803 RI4 4.092 0.714 0.816 Positive word of mouth PWM1 3.895 0.725 0.853 0.900 0.693 0.856 PWM2 3.897 0.724 0.847 PWM3 4.076 0.759 0.830 PWM4 4.045 0.689 0.796   66.8% and 88.6% respectively, of the variance in the endogenous variables. The following for-mulae given by [1] , the global GoF statistic for the research model was calculated using theequation: Goodness of Fit = 2 √ (average of all AVEs values ∗ average of all R 2 )2 √ 0 . 701 ∗0 . 400 = 0 . 53 6 E.T. Maziriri, T.F. Rukuni and T. Chuchu / Data in Brief 32 (2020) 106279 Table 3 Model fit summary. Estimated Model SRMR 0.070 d_ULS 1.727 d_G1 0.941 d_G2 0.783 Chi-Square 1919.097 NFI 0.851 Table 4 Outcomes of structural equation model analysis. Path Hypothesis Path coefficients ( β) T- Statistics P-value Decision Sound - > Consumer attitudes towards quick-service restaurants H1( + ) 0.110 2.284 0.023 Positive and significant Sound - > Restaurant patronage intention H2( + ) 0.727 22.212 0.0 0 0 Positive and significant Sight - > Consumer attitudes towards quick-service restaurants H3( + ) 0.391 7.379 0.0 0 0 Positive and significant Sight - > Restaurant patronage intention H4 ( + ) 0.084 2.148 0.032 Positive and significant Smell - > Consumer attitudes towards quick-service restaurants H5 ( + ) 0.381 6.824 0.0 0 0 Positive and significant Smell - > Restaurant patronage intention H6 ( + ) 0.146 3.526 0.0 0 0 Positive and significant Consumer attitudes towards quick-service restaurants - > Food purchase decisions H7 ( + ) 0.076 1.618 0.106 Positive and insignificant Restaurant patronage intention - > Food purchase decisions H8 ( + ) 0.715 15.425 0.0 0 0 Positive and significant Food purchase decisions_ - > Food Consumption satisfaction H9 ( + ) 0.747 24.861 0.0 0 0 Positive and significant Food Consumption satisfaction - > Restaurant attachment H10 ( + ) 0.848 40.196 0.0 0 0 Positive and significant Food Consumption satisfaction - > Positive word of mouth H11 ( + ) 0.952 21.966 0.0 0 0 Positive and significant Food Consumption satisfaction - > Repurchase intention H12 ( + ) 0.313 4.687 0.0 0 0 Positive and significant Restaurant attachment - > Repurchase intention H13( + ) 0.535 8.461 0.0 0 0 Positive and significant Repurchase intention - > Positive word of mouth H14 ( + ) 0.082 2.304 0.022 Positive and significant Restaurant attachment - > Positive word of mouth H15 ( + ) −0.088 1.736 0.083 Negative and insignificant w  t  e  r2 t  S  here AVE represents the average of all AVE values for the research variables while R ² representshe average of all R ² values in the full path model. The calculated global GoF is 0.53, whichxceeds the threshold of GoF > 0.36 suggested by [2] . Therefore, it can be concluded that theesearch model has a good overall fit. .2. The standardized root mean square residual (SRMR) The SRMR is an index of the average of standardized residuals between the observed andhe hypothesized covariance matrices [3] . The SRMR is a measure of estimated model fit. WhenRMR = < 0.08, then the study model has a good fit [4] , with a lower SRMR being a better fit.E.T. Maziriri, T.F. Rukuni and T. Chuchu / Data in Brief 32 (2020) 106279 7                   Table 3 shows the theoretical model’s SRMR was 0.07, which revealed that the model had a goodfit, whereas the Chi-Square was equal to 1919.097 and NFI equal to 0.851 was also measured,meeting the recommended threshold for NFI [5] . 2.3. Path model The PLS estimation path coefficients values as well as the item loadings for the researchconstruct are shown in Fig. 2 . The Microsoft Excel spreadsheet worksheet was used to enter all data and draw conclusionsfrom the data obtained. The Statistical Packages for Social Sciences (SPSS) and the Smart PLSsoftware for structural equation modelling (SEM) technique were used to code data and to runthe statistical analysis [6] . Moreover, Smart PLS supports both exploratory and confirmatory re-search; it is robust to deviations for multivariate normal distributions and is good for a smallsample size [6] . 3. Ethical considerations This research acted in accordance with the ethical standards of academic research. Hence, anethical clearance certificate (Ethical clearance number: UFS-HSD2020/0261/1805) was obtained from the University of the Free State General or Human Research Ethics Committee. 4. Academic, practical and policy implications of this data article The present data article offers implications for academicians. The data describes, most no-tably the relationship between food consumption satisfaction and positive word of mouth. Thisdata is represented by a path coefficient of ( β = 0.952), a T-Statistic of 21.966 and a P valueof 0.0 0 0. This discovery enhances the comprehension of retail marketing in terms of the foodconsumption. Policy makers and practitioners in the retail space stand to benefit from under-standing factors associated with quick service restaurants. Declaration of competing Interest The authors declare that they have no known competing financial interests or personal rela-tionships which have, or could be perceived to have, influenced the work reported in this article.Acknowledgements The authors of this data article express their sincere gratitude to the University of the FreeState for funding the publication of this research work. Supplementary materials Supplementary material associated with this article can be found, in the online version, atdoi: 10.1016/j.dib.2020.106279 . 8 E.T. Maziriri, T.F. Rukuni and T. Chuchu / Data in Brief 32 (2020) 106279 R[  [  [  [  [   [  eferences 1] M. Tenenhaus , V.E. Vinzi , Y.M. Chatelin , C. Lauro , PLS path modeling, Comput. Stat. Data Anal. 48 (1) (2005) 159–205 .2] M. Wetzels, G. Odekerken-Schröder, C. Van Oppen Using PLS path modeling for assessing hierarchical construct mod-els: guidelines and empirical illustration MIS Q. (2009), pp. 177–195 3] F.F. Chen , Sensitivity of Goodness of Fit Indexes to Lack of Measurement Invariance, Struct. Equ. Model. 14 (2007)464–504 . 4] L. Hu , P.M. Bentler , Fit Indices in Covariance Structure Modeling: sensitivity to Under parameterized Model Misspec-ification, Psychol. Methods 3 (1998) 424–453 . 5] T. Chininga, E. Rungani, N. Chiliya, T. Chuchu, Facebook Communication and Marketing Influence on Decision-Makingand Choice of University Student Representatives: a Student’s Perspective, Rom. J. of Comm and Pub Rel 21 (2) (2019)7–21 https://doi.org/10.21018/rjcpr.2019.2.274 . 6] E.T. Maziriri, N.W. Madinga, Data to model the prognosticators of luxury consumption: a partial least squares-structural equation modeling approach (PLS-SEM), Data Br. (2018) https://doi.org/10.1016/j.dib.2018.10.032 . 

From selected multi-sensory dimensions to positive word of mouth : data on what really drives generation z consumers to be attached to quick service restaurants in Bloemfontein, South Africa?

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From selected multi-sensory dimensions to positive word of mouth : data on what really drives generation z consumers to be attached to quick service restaurants in Bloemfontein, South Africa?

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