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Criticality of the third-party partnership in the UTAUT2 framework: an empirical examination of restaurant self-service technology applications

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Abstract

This study aims to examine the critical role of the third-party partnership and extend the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) framework for restaurant customers’ adoption of self-service technologies (SSTs). The study employs a systematic process and develops a full set of scales for constructing the third-party partnership. In addition, this study collects two sets of data for analysis purposes, with one from during the COVID-19 lockdown period and the other from the post-COVID-19 lockdown period. It uses comparison analysis in addition to PLS-SEM for model testing. Findings revealed that the third-party partnership significantly impacts perceived brand image both during and post-COVID-19 lockdown periods. In contrast, UTAUT2 factors (i.e., performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, and habit) have positive effects only on the perceived brand image during the post-COVID 19 lockdown period. The perceived brand image mediates between the third-party partnership and word-of-mouth (WOM) behaviors during and post-COVID-19 lockdown periods; in contrast, in the post-COVID-19 lockdown period, perceived brand image mediated the effects of UTAUT 2 factors on WOM behaviors and intentions to pay a premium price. This study contributes to the knowledge on restaurant brand image through the study of brand image from the perspective of technology applications, that is, by conducting an assessment of technology applications that combined UTAUT2 with third-party partnership SSTs regarding customers’ brand image identification in the restaurant industry.

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References

  1. Pendrill, K.: How self-service technology can help restaurants promote physical distancing. https://www.touchbistro.com/blog/self-service-technology. Accessed 19 Oct 2022

  2. Statista: Year-over-year daily change in seated restaurant diners due to the coronavirus (COVID-19) pandemic worldwide from February 24 to November 28, 2020 (2020). https://www.statista.com/statistics/1103928/coronavirus-restaurant-visitation-impact. Accessed 9 June 2021

  3. Kim, J., Kim, J., Wang, Y.: Uncertainty risks and strategic reaction of restaurant firms amid COVID-19: evidence from China. Int. J. Hosp. Manag. 92, 1–23 (2020)

    Google Scholar 

  4. Sumagaysay, L.: The pandemic has more than doubled food-delivery apps’ business. Now what? (2020). https://www.marketwatch.com/story/the-pandemic-has-more-than-doubled-americans-use-of-food-delivery-apps-but-that-doesnt-mean-the-companies-are-making-money-11606340169. Accessed 9 June 2021

  5. Khalilzadeh, J., Ozturk, A.B., Bilgihan, A.: Security-related factors in extended UTAUT model for NFC based mobile payment in the restaurant industry. Comput. Hum. Behav. 70, 460–474 (2017)

    Article  Google Scholar 

  6. Gupta, A., Dogra, N.: Tourist adoption of mapping apps: a UTAUTt2 perspective of smart travellers. Tour. Hosp. Manag 23(2), 145–161 (2017)

    Article  Google Scholar 

  7. Alalwan, A.A.: Mobile food ordering apps: an empirical study of the factors affecting customer e-satisfaction and continued intention to reuse. Int. J. Inf. Manag. 50, 28–44 (2020)

    Article  Google Scholar 

  8. Liu, C.R., Wang, Y.C., Huang, W.S., Tang, W.C.: Festival gamification: Conceptualization and scale development. Tour. Manag. 74, 370–381 (2019)

    Article  Google Scholar 

  9. Meuter, M.L., Ostrom, A.L., Roundtree, R.I., Bitner, M.J.: Self-service technologies: understanding customer satisfaction with technology-based service encounters. J. Mark. 64(3), 50–64 (2000)

    Article  Google Scholar 

  10. Chandler, A., Jr.: Scale and Scope: The Dynamics of Industrial Capitalism. Belknap Press of Harvard University Press, Cambridge (1990)

    Book  Google Scholar 

  11. Lin, J.S.C., Hsieh, P.L.: Assessing the self-service technology encounters: development and validation of SSTQUAL scale. J. Retail. 87(2), 194–206 (2011)

    Article  Google Scholar 

  12. Choi, S.: What promotes smartphone-based mobile commerce? Mobile-specific and self-service characteristics. Internet Res. 28(1), 105–122 (2018)

    Article  Google Scholar 

  13. Ahn, J.A., Seo, S.: Customer responses to interactive restaurant self-service technology (IRSST): the role of gadget-loving propensity. Int. J. Hosp. Manag. 74, 109–121 (2018)

    Article  Google Scholar 

  14. Okumus, B., Bilgihan, A., Ozturk, A.: Factors affecting the acceptance of smartphone diet applications. J. Hosp. Mark. Manag. 25(6), 726–747 (2016). https://doi.org/10.1080/19368623.2016.1082454

    Article  Google Scholar 

  15. Wei, W., Torres, E.N., Hua, N.: Help yourself: are self-service technologies doing enough to elicit a positive response from customers? Rosen Res. Rev. 1(1), 4 (2020)

    Google Scholar 

  16. Okumus, B., Bilgihan, A.: Proposing a model to test smartphone users’ intention to use smart applications when ordering food in restaurants. J. Hosp. Tour. Technol. 5(1), 31–49 (2014). https://doi.org/10.1108/JHTT-01-2013-0003

    Article  Google Scholar 

  17. Zhang, T., Lu, C., Torres, E., Cobanoglu, C.: Value co-creation and technological progression: a critical review. Eur. Bus. Rev. 32(4), 687–707 (2020). https://doi.org/10.1108/EBR-08-2019-0149

    Article  Google Scholar 

  18. Ozturk, A., Bilgihan, A., Salehi-Esfahani, S., Hua, N.: Understanding the mobile payment technology acceptance based on valence theory: a case of restaurant transactions. Int. J. Contemp. Hosp. Manag. 29(8), 2027–2049 (2017). https://doi.org/10.1108/IJCHM-04-2016-0192

    Article  Google Scholar 

  19. Noone, B., Maier, M.: A decision framework for restaurant revenue management. J. Revenue Pricing Manag. 14(4), 231–244 (2015)

    Article  Google Scholar 

  20. Kattara, H.S., El-Said, O.A.: Customers’ preferences for new technology-based self-services versus human interaction services in hotels. Tour. Hosp. Res. 13(2), 67–82 (2014)

    Article  Google Scholar 

  21. Sur, S.: Technology-based remote service encounters: understanding customer satisfaction and sustainability. J. Foodserv. Bus. Res. 11(3), 315332 (2008)

    Article  Google Scholar 

  22. Chen, S.C., Elston, J.A.: Entrepreneurial motives and characteristics: an analysis of small restaurant owners. Int. J. Hosp. Manag. 35, 294–305 (2013)

    Article  Google Scholar 

  23. Law, R., Leung, R., Buhalis, D.: Information technology applications in hospitality and tourism: a review of publications from 2005 to 2007. J. Travel Tour. Mark. 26(5–6), 599–623 (2009)

    Article  Google Scholar 

  24. Dabholkar, P.A., Bagozzi, R.P.: An attitudinal model of technology-based self-service: moderating effects of customer traits and situational factors. J. Acad. Mark. Sci. 30(3), 184–201 (2002)

    Article  Google Scholar 

  25. Curran, J.M., Meuter, M.L., Surprenant, C.F.: Intentions to use self-service technologies: a confluence of multiple attitudes. J. Serv. Res. 5(3), 209–224 (2003)

    Article  Google Scholar 

  26. Montoya-Weiss, M.M., Voss, G.B., Grewal, D.: Determinants of online channel use and overall satisfaction with a relational, multichannel service provider. J. Acad. Mark. Sci. 31(4), 448–458 (2003)

    Article  Google Scholar 

  27. Meuter, M.L., Bitner, M.J., Ostrom, A.L., Brown, S.W.: Choosing among alternative service delivery modes: an investigation of customer trial of self-service technologies. J. Mark. 69(2), 61–83 (2005)

    Article  Google Scholar 

  28. Oh, H., Jeong, M., Baloglu, S.: Tourists’ adoption of self-service technologies at resort hotels. J. Bus. Res. 66(6), 692–699 (2013)

    Article  Google Scholar 

  29. Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D.: User acceptance of information technology: toward a unified view. MIS Q. 425–478 (2003)

  30. Venkatesh, V., Thong, J., Xu, X.: Customer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q. 36(1), 157–178 (2012)

    Article  Google Scholar 

  31. Baba, N., Shahril, A.M., Hanafiah, M.H.: Self-ordering kiosk usage and post-purchase behavior in quick service restaurant. J. Tour. Hosp. Culin. Arts 12(1), 360–376 (2020)

    Google Scholar 

  32. Zhao, Y., Bacao, F.: What factors determining customer continuingly using food delivery apps during 2019 novel coronavirus pandemic period? Int. J. Hosp. Manag. 91, 102683 (2020)

    Article  PubMed  PubMed Central  Google Scholar 

  33. Li, C., Mirosa, M., Bremer, P.: Review of online food delivery platforms and their impacts on sustainability. Sustainability 12(14), 5528 (2020)

    Article  Google Scholar 

  34. Yang, F.X., Li, X., Lau, V.M.C., Zhu, V.Z.: To survive or to thrive? China’s luxury hotel restaurants entering O2O food delivery platforms amid the COVID-19 crisis. Int. J. Hosp. Manag. 94, 102855 (2021)

    Article  PubMed  PubMed Central  Google Scholar 

  35. Ray, A., Dhir, A., Bala, P.K., Kaur, P.: Why do people use food delivery apps (FDA)? A uses and gratification theory perspective. J. Retail. Cust. Serv. 51, 221–230 (2019)

    Article  Google Scholar 

  36. Weijters, B., Rangarajan, D., Falk, T., Schillewaert, N.: Determinants and outcomes of customers’ use of self-service technology in a retail setting. J. Serv. Res. 10(1), 3–21 (2007)

    Article  Google Scholar 

  37. Holloway, B.B., Beatty, S.E.: Satisfiers and dissatisfiers in the online environment: a critical incident assessment. J. Serv. Res. 10(4), 347–364 (2008)

    Article  Google Scholar 

  38. Meuter, M.L., Ostrom, A.L., Bitner, M.J., Roundtree, R.: The influence of technology anxiety on customer use and experiences with self-service technologies. J. Bus. Res. 56(11), 899–906 (2003)

    Article  Google Scholar 

  39. Kallweit, K., Spreer, P., Toporowski, W.: Why do customers use self-service information technologies in retail? The mediating effect of perceived service quality. J. Retail. Cust. Serv. 21(3), 268–276 (2014)

    Article  Google Scholar 

  40. Selnes, F., Hansen, H.: The potential hazard of self-service in developing customer loyalty. J. Serv. Res. 4(2), 79–90 (2001)

    Article  Google Scholar 

  41. Waheed, M., Kaur, K., Ain, N., et al.: Emotional attachment and multi-dimensional self-efficacy: extension of innovation diffusion theory in the context of eBook reader. Behav. Inf. Technol. 1–13 (2015)

  42. Al-Shafi, S., Weerakkody, V., Janssen, M.: Investigating the adoption of eGovernment services in Qatar using the UTAUT model. In: Fifteenth Americas Conference on Information Systems (AMCIS), 6–9 August 2009, Paper 260. AIS, California (2009)

  43. Yeo, S.F., Tan, C.L., Teo, S.L., Tan, K.H.: The role of food apps servitization on repurchase intention: a study of FoodPanda. Int. J. Prod. Econ. 108063 (2021)

  44. Ryu, S.: Online luxury goods with price discount or onsite luxury goods with luxury services: role of situation-specific thinking styles and socio-demographics. J. Retail. Cust. Serv. 57, 102253 (2020)

    Article  Google Scholar 

  45. Park, S., Lehto, X., Lehto, M.: Self-service technology kiosk design for restaurants: an QFD application. Int. J. Hosp. Manag. 92, 102757 (2021)

    Article  Google Scholar 

  46. He, Z., Han, G., Cheng, T.C.E., Fan, B., Dong, J.: Evolutionary food quality and location strategies for restaurants in competitive online-to-offline food ordering and delivery markets: an agent-based approach. Int. J. Prod. Econ. 215, 61–72 (2019)

    Article  Google Scholar 

  47. Chathoth, P., Altinay, L., Harrington, R.J., Okumus, F., Chan, E.S.: Co-production versus co creation: a process-based continuum in the hotel service context. Int. J. Hosp. Manag. 32, 11–20 (2013)

    Article  Google Scholar 

  48. Pokhriyal, R.: Enhance the Customer Experience of Food Delivery Applications, Customer think (2020). https://customerthink.com/enhance-the-customer-experience-of-food-delivery-applications. Accessed 19 Oct 2022

  49. Torres, E.N., Ronzoni, G.: The evolution of the customer delight construct: prior research, current measurement, and directions for future research. Int. J. Contemp. Hosp. Manag. 30(1), 57–75 (2018)

    Article  Google Scholar 

  50. Zhang, S.N., Li, Y.Q., Liu, C.H., Ruan, W.Q.: Reconstruction of the relationship between traditional and emerging restaurant brand and customer WOM. Int. J. Hosp. Manag. 94, 102879 (2021)

    Article  Google Scholar 

  51. Berens, G., Riel, C.B.V., Bruggen, G.H.V.: Corporate associations and customer product responses: the moderating role of corporate brand dominance. J. Mark. 69(3), 35–48 (2005)

    Article  Google Scholar 

  52. Keller, K.L.: Strategic Brand Management: Building, Measuring, and Managing Brand Equity, 4th edn. Pearson, New York (2012)

    Google Scholar 

  53. Thomson, M., MacInnis, D.J., Whan Park, C.: The ties that bind: measuring the strength of customers’ emotional attachments to brands. J. Cust. Psychol. 15(1), 77–91 (2005)

    Google Scholar 

  54. Wu, W.Y., Quyen, P.T.P., Rivas, A.A.A.: How e-servicescapes affect customer online shopping intention: the moderating effects of gender and online purchasing experience. IseB 15(3), 689–715 (2017)

    Article  Google Scholar 

  55. Kim, D., Magnini, V.P., Singal, M.: The effects of customers’ perceptions of brand personality in casual theme restaurants. Int. J. Hosp. Manag. 30(2), 448–458 (2011)

    Article  Google Scholar 

  56. Brunner, T.A., Stöcklin, M., Opwis, K.: Satisfaction, image and loyalty: new versus experienced customers. Eur. J. Mark. (2008)

  57. Kim, J.H.: The impact of memorable tourism experiences on loyalty behaviors: the mediating effects of destination image and satisfaction. J. Travel Res. 57(7), 856–870 (2018)

    Article  Google Scholar 

  58. Ryu, K., Han, H., Kim, T.H.: The relationships among overall quick-casual restaurant image, perceived value, customer satisfaction, and behavioral intentions. Int. J. Hosp. Manag. 27(3), 459–469 (2008)

    Article  Google Scholar 

  59. Liu, K.N., Hu, C., Lin, M.C., Tsai, T.I., Xiao, Q.: Brand knowledge and non-financial brand performance in the green restaurants: mediating effect of brand attitude. Int. J. Hosp. Manag. 89, 102566 (2020)

    Article  Google Scholar 

  60. Lin, Z., Chen, Y., Filieri, R.: Resident-tourist value co-creation: the role of residents’ perceived tourism impacts and life satisfaction. Tour. Manag. 61, 436–442 (2017)

    Article  Google Scholar 

  61. Morosan, C., DeFranco, A.: It’s about time: revisiting UTAUT2 to examine customers’ intentions to use NFC mobile payments in hotels. Int. J. Hosp. Manag. 53, 17–29 (2016)

    Article  Google Scholar 

  62. Landis, J.R., Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics (1977)

  63. Efron, B.: Bootstrap methods: another look at the jackknife. In: Breakthroughs in Statistics, pp. 569–593. Springer, New York (1992)

    Chapter  Google Scholar 

  64. Kaiser, H.F.: The application of electronic computers to factor analysis. Educ. Psychol. Measur. 20(1), 141–151 (1960)

    Article  Google Scholar 

  65. MacKenzie, S.B., Podsakoff, P.M., Podsakoff, N.P.: Construct measurement and validation procedures in MIS and behavioral research: integrating new and existing techniques. MIS Q. 35(2), 293–334 (2011)

    Article  Google Scholar 

  66. Kock, N., Lynn, G.: Lateral collinearity and misleading results in variance-based SEM: an illustration and recommendations. J. Assoc. Iinf. Syst. 13(7) (2012)

  67. Hair, J.F., Jr., Matthews, L.M., Matthews, R.L., Sarstedt, M.: PLS-SEM or CB-SEM: updated guidelines on which method to use. Int. J. Multivar. Data Anal. 1(2), 107–123 (2017)

    Article  Google Scholar 

  68. Fornell, C., Larcker, D.F.: Structural equation models with unobservable variables and measurement error: algebra and statistics. 382–388 (1981)

  69. USA Today. Coronavirus closures: Map of where US states are tightening restrictions. COVID-19 Restrictions (2022) https://www.usatoday.com/storytelling/coronavirus-reopening-america-map.

  70. Glaeser, E.L., Jin, G.Z., Leyden, B.T., Luca, M.: Learning from deregulation: the asymmetric impact of lockdown and reopening on risky behavior during COVID-19. J. Reg. Sci. 61(4), 696–709 (2021). Accessed 19 October, 2021

  71. Panzone, L.A., Larcom, S., She, P.-W.: Estimating the impact of the first COVID-19 lockdown on UK food retailers and the restaurant sector. Glob. Food Sec. 28, 100495 (2021)

    Article  PubMed  PubMed Central  Google Scholar 

  72. Kandampully, J., Zhang, T.C., Bilgihan, A.: Customer loyalty: a review and future directions with a special focus on the hospitality industry. Int. J. Contemp. Hosp. Manag. 27(3), 379–414 (2015)

    Article  Google Scholar 

  73. Prahalad, C.K., Ramaswamy, V.: Co-opting customer competence. Harv. Bus. Rev. 78(1), 79–90 (2000)

    Google Scholar 

  74. Reddy, M.V., Boyd, S.W., Nica, M.: Towards a post-conflict tourism recovery framework. Ann. Tour. Res. 102940 (2020)

  75. Toral, S.L., Martínez-Torres, M.R., Gonzalez-Rodriguez, M.R.: Identification of the unique attributes of tourist destinations from online reviews. J. Travel Res. 57(7), 908–919 (2018)

    Article  Google Scholar 

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Funding

This paper has been supported by the Fujian Provincial Federation of Social Sciences, China (FJ2021B160).

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Authors and Affiliations

  1. Tourism Management Department, Business School, Minnan Normal University, Zhangzhou, Fujian, China

    Bin Li

  2. Rosen College of Hospitality Management, University of Central Florida, Orlando, FL, USA

    Jeffrey Weinland, Tingting Zhang & Nan Hua

Authors
  1. Bin Li
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  2. Jeffrey Weinland
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  3. Tingting Zhang
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  4. Nan Hua
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Contributions

Bin Li wrote the main manuscript text; Jeffrey Weinland wrote the literature review; Tingting Zhang finished the methodology; and Nan Hua reviewed the manuscript.

Corresponding author

Correspondence to Bin Li.

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Appendices

Appendix A: Participants’ profile

Variable

Options

During COVID-19 lockdown (331 responses) (%)

Post-COVID-19 lockdown (210 responses) (%)

Gender

Men

28.1

28.1

Women

71.9

71.9

Marital status

Single

52.4

52.6

Widowed

0

0.6

Divorced

2.4

1.5

Married

45.2

45.3

Age

18–25

44.3

43.5

26–34

21

20.5

35–54

34.3

35

55–64

0.5

0.6

65 and above

0

0.3

Employment status

Student

23.8

26.9

Professional

37.1

31.1

Managerial

18.6

16.3

Sales

6.7

9.1

Homemaker

4.3

7.3

Other

9.5

9.3

Education status

Less than high school

4.8

4.8

High school graduate/G.E.D.

7.6

8.5

Associate degree/certificate

21.9

26.3

Bachelor’s degree

52.9

46.2

Master’s degree

9.0

11.8

PHD

2.4

2.1

Others

1.4

0.3

Household income per month

 < 1500¥ (< 214$)

13.3

18.4

1500–1999¥ (214–285$)

2.9

3.0

2000–2999¥ (286–428$)

12.9

8.5

3000–4999¥ (429–714$)

31.4

29.6

5000–9999¥ (715–1428$)

28.6

28.4

10,000–19,999¥ (1428–2857$)

7.6

7.9

 > 20,000¥ (> 2858$)

3.3

4.2

Appendix B: EFA results

Factor

Initial eigenvalues

Extraction sums of squared loadings

Total

% of variance

Cumulative %

Total

% of variance

Cumulative %

1

11.477

76.515

76.515

11.248

74.985

74.985

2

0.859

5.725

82.240

   

3

0.699

4.663

86.903

   

4

0.506

3.376

90.280

   

5

0.317

2.112

92.392

   

6

0.227

1.516

93.907

   

7

0.180

1.198

95.105

   

8

0.160

1.066

96.171

   

9

0.138

0.920

97.091

   

10

0.114

0.760

97.850

   

11

0.102

0.683

98.534

   

12

0.076

0.503

99.037

   

13

0.063

0.417

99.454

   

14

0.047

0.314

99.768

   

15

0.035

0.232

100.000

   
  1. Extraction Method: Principal Axis Factoring

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Li, B., Weinland, J., Zhang, T. et al. Criticality of the third-party partnership in the UTAUT2 framework: an empirical examination of restaurant self-service technology applications. Univ Access Inf Soc (2024). https://doi.org/10.1007/s10209-024-01088-0

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