Vol. 3 No. 4
In Progress

Research Article

Co-Productive Engagement, Service-Quality Perception, and Sustained Use of Fresh-Grocery Digital Platforms in Guangdong: A Boundary-Conditional Mediation Model with Technology-Related Apprehension

Published 2026-07-12

  • Qiuying Yue
    • ,
  • Weixiang Gan
    • ,
  • Tara Ahmed Mohammed
    • ,
  • Shashi Kant Gupta
    • ,
  • Mengfei Xiao
    • ,
  • Xiaolin Song
    • ,
  • Jialin Liu

Qiuying Yue
SEGi University image/svg+xml

Weixiang Gan
Lincoln University College image/svg+xml

Tara Ahmed Mohammed
SEGi University image/svg+xml

Shashi Kant Gupta
Lincoln University College image/svg+xml

Mengfei Xiao
Lincoln University College image/svg+xml

Xiaolin Song
Nanjing Tech University image/svg+xml

Jialin Liu
Taylor's University image/svg+xml

Abstract

The rapid growth of China’s online fresh-grocery market has not translated into consistent user retention. Although prior research recognises user engagement on digital platforms, it remains unclear which forms of engagement foster sustained platform use and through what psychological mechanisms. Drawing on the stimulus-organism-response framework, this study examines four dimensions of co-productive engagement—task cognition, information seeking, effort expenditure, and human-computer interaction—as separate antecedents of continuance intention. Perceived service quality is modelled as a mediator, and technology-related apprehension as a moderator. Survey data from 550 fresh-grocery users in Guangdong Province were analysed using partial least squares structural equation modelling (SmartPLS 4.1.1). All four engagement dimensions positively influence perceived service quality, which in turn increases continuance intention and partially mediates these relationships. Effort expenditure shows the strongest effect. Technology-related apprehension negatively affects perceived service quality and significantly weakens only the information-seeking pathway. Within the limits of a cross-sectional, non-probability sample, the findings extend the participatory perspective of the stimulus-organism-response framework, identify service quality as a key transmission mechanism, and indicate that technology-related apprehension influences engagement selectively rather than uniformly. The results suggest that platforms should simplify information processing for apprehensive users while encouraging forms of engagement that make user effort more rewarding.

Keywords

Fresh-grocery e-commerce, Co-productive engagement, Service-quality, Continuance intention, Technology-related apprehension, Partial-least-squares structural equation modelling (PLS-SEM)

PDF

References

  1. Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation–confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921 DOI: https://doi.org/10.2307/3250921
  2. Brochado, A., Rita, P., Oliveira, C., & Oliveira, F. (2019). Airline passengers’ perceptions of service quality: Themes in online reviews. International Journal of Contemporary Hospitality Management, 31(2), 855–873. https://doi.org/10.1108/IJCHM-09-2017-0572 DOI: https://doi.org/10.1108/IJCHM-09-2017-0572
  3. Chattaraman, V., Kwon, W.-S., & Gilbert, J. E. (2012). Virtual agents in retail web sites: Benefits of simulated social interaction for older users. Computers in Human Behavior, 28(6), 2055–2066. https://doi.org/10.1016/j.chb.2012.06.009 DOI: https://doi.org/10.1016/j.chb.2012.06.009
  4. Cheng, H., Chen, H. Q., & Liu, M. R. (2024). Research on the influencing factors of users’ willingness to continuously use discipline service platform based on S-O-R framework: Case study of FULink. Information Research, (12), 105–114. https://doi.org/10.3969/j.issn.1005-8095.2024.12.014
  5. Chi, J. Y., Wang, M. H., & Sun, L. (2024). Influencing factors of SMEs’ continuance usage intention of software as a service (SaaS) enterprise resource planning system: A study based on expectation confirmation model of information systems continuance. Science and Technology Management Research, 44(24), 129–135. https://doi.org/10.3969/j.issn.1000-7695.2024.24.015
  6. Cui, Y. (2025). Research on the dual risk transmission mechanism of China’s fresh e-commerce supply chain. Review of Industrial Economics. Advance online publication. https://doi.org/10.19313/j.cnki.cn10-1223/f.20250722.003
  7. Davis, P. T., & Chandrasekar, T. (2025). Understanding the impact of push-pull-mooring factors on the switching and continued usage intentions for electric three-wheelers in public transport. Sustainable Futures, 9, Article 100554. https://doi.org/10.1016/j.sftr.2025.100554 DOI: https://doi.org/10.1016/j.sftr.2025.100554
  8. Espino-Rodríguez, T. F., & Rodríguez-Díaz, M. (2021). The influence of outsourcing activities on the perception of service quality: An empirical study based on online reviews by hotel customers. Journal of Hospitality and Tourism Technology, 12(4), 689–711. https://doi.org/10.1108/JHTT-03-2020-0064 DOI: https://doi.org/10.1108/JHTT-03-2020-0064
  9. Fei, X. J., & Chen, M. K. (2025). The influence of fresh e-commerce circulation mode on service value creation: Based on the moderating effect of customer switching cost. Journal of Commercial Economics, (15), 134–137. https://doi.org/10.3969/j.issn.1002-5863.2025.15.031
  10. Fu, W. D., & Zhang, S. (2025). Study on the influencing factors of primary and middle school students’ continuous use intention in national smart education platform: Based on a survey of 10,674 students in seven provinces in eastern, central and western China. Journal of Hebei University (Philosophy and Social Science), 50(4), 124–137. https://doi.org/10.3969/j.issn.1005-6378.2025.04.011
  11. Gan, W., Xiao, M., & Zhang, N. (2025). “The more it is performed, the less it sticks”: The erosive mechanism of excessive scripted performative content on user stickiness on China’s Douyin platform: A moderated mediation perspective of user trust and algorithmic regulation. Hong Kong Financial Bulletin, 1(6), 1–17. https://doi.org/10.71052/hkfb2025/NHBS8569 DOI: https://doi.org/10.71052/hkfb2025/NHBS8569
  12. Gao, L., Waechter, K. A., & Bai, X. (2015). Understanding consumers’ continuance intention towards mobile purchase: A theoretical framework and empirical study - A case of China. Computers in Human Behavior, 53, 249–262. https://doi.org/10.1016/j.chb.2015.07.014 DOI: https://doi.org/10.1016/j.chb.2015.07.014
  13. Hackman, J. R., & Oldham, G. R. (1976). Motivation through the design of work: Test of a theory. Organizational Behavior and Human Performance, 16(2), 250–279. https://doi.org/10.1016/0030-5073(76)90016-7 DOI: https://doi.org/10.1016/0030-5073(76)90016-7
  14. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage. DOI: https://doi.org/10.1007/978-3-030-80519-7
  15. He, Z., Hu, H., & Zhang, M. (2023). Literature review of service quality and customer satisfaction in the context of self-service technology. Industrial Engineering and Management, 28(3), 199–206. https://doi.org/10.19495/j.cnki.1007-5429.2023.03.020
  16. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8 DOI: https://doi.org/10.1007/s11747-014-0403-8
  17. Huang, Z., & Benyoucef, M. (2013). From e-commerce to social commerce: A close look at design features. Electronic Commerce Research and Applications, 12(4), 246–259. https://doi.org/10.1016/j.elerap.2012.12.003 DOI: https://doi.org/10.1016/j.elerap.2012.12.003
  18. Jia, P. (2024). The impact of artificial intelligence (AI) and big data analytics on supply chain performance of SMEs in Chinese fresh food e-commerce industry. SHS Web of Conferences, 208, Article 01018. https://doi.org/10.1051/shsconf/202420801018 DOI: https://doi.org/10.1051/shsconf/202420801018
  19. Jiao, Y. Y., Yan, X., Luo, Y. Q., & Kamilia, A. (2025). The impact of information cocoon on continuance intention among e-commerce platform users: A mediated moderation model. Journal of Industrial Engineering and Engineering Management, 39(4), 47–60. https://doi.org/10.13587/j.cnki.jieem.2025.04.004
  20. Kim, J., & Yum, K. (2024). Enhancing continuous usage intention in e-commerce marketplace platforms: The effects of service quality, customer satisfaction, and trust. Applied Sciences, 14(17), Article 7617. https://doi.org/10.3390/app14177617 DOI: https://doi.org/10.3390/app14177617
  21. Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101 DOI: https://doi.org/10.4018/ijec.2015100101
  22. Larivière, B., Bowen, D. E., Andreassen, T. W., Kunz, W., Sirianni, N. J., Voss, C., Wünderlich, N. V., & De Keyser, A. (2017). “Service Encounter 2.0”: An investigation into the roles of technology, employees and customers. Journal of Business Research, 79, 238–246. https://doi.org/10.1016/j.jbusres.2017.03.008 DOI: https://doi.org/10.1016/j.jbusres.2017.03.008
  23. Li, X., Li, G., Wu, H., & Tang, O. (2025). Service portfolio design for ship-then-shop subscription in online retailing. Transportation Research Part E: Logistics and Transportation Review, 199, Article 104152. https://doi.org/10.1016/j.tre.2025.104152 DOI: https://doi.org/10.1016/j.tre.2025.104152
  24. Li, Z. H., & Jiang, G. R. (2025). The influence of digital economy on the development of e-commerce of fresh agricultural products. Co-Operative Economy & Science, (14), 73–75. https://doi.org/10.13665/j.cnki.hzjjykj.2025.14.030
  25. Lin, J. Q. (2025). Research on the sustained usage intention in young users of social networking apps dominated by acquaintances [Master’s thesis, Communication University of Zhejiang]. CNKI-China Doctoral Dissertations/Masters’ Theses Full-text Database. https://doi.org/10.27852/d.cnki.gzjcm.2025.000147
  26. Liu, T., Li, W., & Jia, X. (2023). Consumer data vulnerability, peer privacy concerns and continued usage intention of sharing accommodation platforms: The moderating roles of perceived benefits. Information Technology & People, 36(6), 2234–2258. https://doi.org/10.1108/ITP-05-2021-0365 DOI: https://doi.org/10.1108/ITP-05-2021-0365
  27. Liu, W. J., & Liu, J. H. (2025). Study on the promotion and pricing strategy of fresh brand based on e-commerce mode: Taking Xinjiang salmon as an example. Trade Fair Economy, (12), 66–69. https://doi.org/10.19995/j.cnki.CN10-1617/F7.2025.12.066
  28. Liu, X., & Chen, Y. (2025). Research on the business model of community fresh e-commerce under the new retail background. Journal of Commercial Economics, (11), 135–138. https://doi.org/10.3969/j.issn.1002-5863.2025.11.033
  29. Liu, X., Gou, X., & Xu, Z. (2024). Multi-objective last-mile vehicle routing problem for fresh food e-commerce: A sustainable perspective. International Journal of Information Technology & Decision Making, 23(6), 2335–2363. https://doi.org/10.1142/S0219622024500020 DOI: https://doi.org/10.1142/S0219622024500020
  30. Liu, X. N., Zhang, S. Y., Song, F. Y., & Guo, R. (2025). Study on the continuous use intention and influencing factors of patients with Internet diagnosis and treatment: Based on the analysis of the D&M model and ECM-ISC model. Chinese Hospital Management, 45(4), 16–23. https://d.wanfangdata.com.cn/periodical/zgyygl202504004
  31. Liu, Y. C., Zhao, M. H., & Xia, M. S. (2025). New model of fresh produce e-commerce: Exploration and breakthrough of the integration of production, sales and tourism. China Journal of Commerce, 34(9), 63–66. https://doi.org/10.19699/j.cnki.issn2096-0298.2025.09.063
  32. Mehrabian, A., & Russell, J. A. (1974). An approach to environmental psychology. MIT Press.
  33. Meijerink, J., & Schoenmakers, E. (2021). Why are online reviews in the sharing economy skewed toward positive ratings? Linking customer perceptions of service quality to leaving a review of an Airbnb stay. Journal of Tourism Futures, 7(1), 5–19. https://doi.org/10.1108/JTF-04-2019-0039 DOI: https://doi.org/10.1108/JTF-04-2019-0039
  34. Meuter, M. L., Ostrom, A. L., Bitner, M. J., & Roundtree, R. (2003). The influence of technology anxiety on consumer use and experiences with self-service technologies. Journal of Business Research, 56(11), 899–906. https://doi.org/10.1016/S0148-2963(01)00276-4 DOI: https://doi.org/10.1016/S0148-2963(01)00276-4
  35. Monoarfa, T. A., Sumarwan, U., Suroso, A. I., & Wulandari, R. (2024). The crucial role of e-logistic service quality to integrated theories to predict continuance intention on fresh produce e-commerce. Cogent Business & Management, 11(1), Article 2379569. https://doi.org/10.1080/23311975.2024.2379569 DOI: https://doi.org/10.1080/23311975.2024.2379569
  36. Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12–40.
  37. Preacher, K. J., & Selig, J. P. (2012). Advantages of Monte Carlo confidence intervals for indirect effects. Communication Methods and Measures, 6(2), 77–98. https://doi.org/10.1080/19312458.2012.679848 DOI: https://doi.org/10.1080/19312458.2012.679848
  38. Pu, B., Hu, J. W., Ma, C. Y., & Phau, I. (2025). An empirical study on the willingness to reuse smart tour guides in scenic spots: A combined analysis of hierarchical regression and fsQCA. Tourism Tribune, 40(6), 80–95. https://doi.org/10.19765/j.cnki.1002-5006.2025.00.005
  39. Punj, G. N., & Staelin, R. (1983). A model of consumer information search behavior for new automobiles. Journal of Consumer Research, 9(4), 366–380. https://doi.org/10.1086/208931 DOI: https://doi.org/10.1086/208931
  40. Qi, X., Lv, X., Li, Z., Yang, C., Li, H., & Ploeger, A. (2024). Factors influencing young adults’ organic food purchase intention on fresh food e-commerce platforms. British Food Journal, 126(12), 4277–4303. https://doi.org/10.1108/BFJ-04-2024-0417 DOI: https://doi.org/10.1108/BFJ-04-2024-0417
  41. Ren, Y., Qu, Y., Liang, J., & Zhao, F. (2025). Development and validation of a framework on consumer satisfaction in fresh food e-shopping: The integration of theory and data. Journal of Theoretical and Applied Electronic Commerce Research, 20(2), Article 114. https://doi.org/10.3390/jtaer20020114 DOI: https://doi.org/10.3390/jtaer20020114
  42. Sadighha, J., Pinto, P., Guerreiro, M., & Campos, A. C. (2025). Value reciprocity mechanism in hotels: Testing customer participation behaviour and value co-creation as mediators and previous experience as a moderator. Journal of Hospitality and Tourism Insights, 8(6), 2206–2231. https://doi.org/10.1108/JHTI-06-2024-0627 DOI: https://doi.org/10.1108/JHTI-06-2024-0627
  43. Sharma, V., Payal, R., Dutta, K., Poulose, J., & Kapse, M. (2024). A comprehensive examination of factors influencing intention to continue usage of health and fitness apps: A two-stage hybrid SEM-ML analysis. Cogent Business & Management, 11(1), Article 2391124. https://doi.org/10.1080/23311975.2024.2391124 DOI: https://doi.org/10.1080/23311975.2024.2391124
  44. Shen, Y. J. (2025). Influence of visual situation in live broadcasting room on consumers’ online purchase intention of fresh agricultural products. Marketing Management Review, (6), 59–61. https://doi.org/10.19932/j.cnki.22-1256/F.2025.02.059
  45. Singh, M., Tandon, U., & Mittal, A. (2023). Modeling users’ and practitioners’ intention for continued usage of the Internet of Medical Devices (IoMD): An empirical investigation. Information Discovery and Delivery, 51(3), 306–321. https://doi.org/10.1108/IDD-02-2022-0016 DOI: https://doi.org/10.1108/IDD-02-2022-0016
  46. Song, J., Wang, Q., & Yi, H.-T. (2025). The impact of AR app characteristics on telepresence, customer experience, and continued app usage intention: The moderating effect of consumer innovativeness. Journal of Retailing and Consumer Services, 87, Article 104385. https://doi.org/10.1016/j.jretconser.2025.104385 DOI: https://doi.org/10.1016/j.jretconser.2025.104385
  47. Sun, Y. G. (2025). Research on e-commerce marketing strategy of fresh agricultural products based on 4C marketing theory: Taking JD Fresh as an example. Modern Agricultural Science and Technology, (4), 188–190. https://doi.org/10.3969/j.issn.1007-5739.2025.04.046
  48. Tao, L. M. (2025). Research on the optimization of e-commerce supply chain of fresh agricultural products in the digital economy environment. Agricultural Development & Equipments, (6), 47–49. https://doi.org/10.3969/j.issn.1673-9205.2025.06.016
  49. Tarafdar, M., Cooper, C. L., & Stich, J.-F. (2019). The technostress trifecta, Techno eustress, techno distress and design: Theoretical directions and an agenda for research. Information Systems Journal, 29(1), 6–42. https://doi.org/10.1111/isj.12169 DOI: https://doi.org/10.1111/isj.12169
  50. Tarhini, A., Alalwan, A. A., & Al-Qirim, N. (2021). The influence of technology anxiety on the adoption of e-learning systems: An integrated model. Education and Information Technologies, 26, 6119–6137. https://doi.org/10.1007/s10639-021-10547-1
  51. Verhagen, T., Van Nes, J., Feldberg, F., & Van Dolen, W. (2014). Virtual customer service agents: Using social presence and personalization to shape online service encounters. Journal of Computer-Mediated Communication, 19(3), 529–545. https://doi.org/10.1111/jcc4.12066 DOI: https://doi.org/10.1111/jcc4.12066
  52. Wang, H., Good, V., & Lim, J. H. (2025). Online retail formats and product sales performance: The moderating role of product characteristics. Journal of Business Research, 198, Article 115509. https://doi.org/10.1016/j.jbusres.2025.115509 DOI: https://doi.org/10.1016/j.jbusres.2025.115509
  53. Wang, J. X., & Wei, F. X. (2011). An empirical study on the influence of customer participation on the service quality of tourism enterprises. Social Scientist, (8), 87–90. https://doi.org/10.3969/j.issn.1002-3240.2011.08.022
  54. Wang, M., Lee, J.-Y., Liu, S., & Hu, L. (2023). The role of emotional responses in the VR exhibition continued usage intention: A moderated mediation model. International Journal of Environmental Research and Public Health, 20(6), Article 5001. https://doi.org/10.3390/ijerph20065001 DOI: https://doi.org/10.3390/ijerph20065001
  55. Wang, S. Y., & Li, Q. Q. (2025). Research on fresh food e-commerce platform user post-purchase reviews based on topic mining and sentiment analysis. Journal of Liaoning Technical University (Social Science Edition), 27(2), 130–137. https://doi.org/10.11955/j.issn.1008-391X.20250207
  56. Wu, D. W., & Liu, G. L. (2025). Research on the construction of rural e-commerce low-loss smart fresh food logistics network system. Industry and Information Technology Education, (6), 91–94. https://doi.org/10.3969/j.issn.2095-5065.2025.06.019
  57. Wu, W. Z., & Chen, Q. J. (2017). Impacts of customer participation behavior on customer satisfaction and behavioral intentions based on co-creation perspective. Management Review, 29(9), 167–180. https://doi.org/10.14120/j.cnki.cn11-5057/f.2017.09.015
  58. Xiao, B., & Wang, L. N. (2025). High-quality development of fresh food e-commerce in digital economy. Food & Machinery, 41(4), 256. https://d.wanfangdata.com.cn/periodical/spyjj202504039
  59. Xiao, M., Gan, W., Yue, Q., Hashim, F. B., & Mohammed, T. A. (2025). Understanding the loyalty effects of AI-driven personalisation: Consumer engagement as a mediator and algorithmic trust as a boundary condition. Scientific Research Bulletin, 2(3), 39–58. https://doi.org/10.71052/srb2024/WISX5526 DOI: https://doi.org/10.71052/srb2024/WISX5526
  60. Xie, C., Bagozzi, R. P., & Troye, S. V. (2008). Trying to prosume: Toward a theory of consumers as co-creators of value. Journal of the Academy of Marketing Science, 36(1), 109–122. https://doi.org/10.1007/s11747-007-0060-2 DOI: https://doi.org/10.1007/s11747-007-0060-2
  61. Xu, L., Jiang, Z. J., & Guo, J. (2025). Emotion oriented or function oriented: The influencing mechanism of customers’ continuance intention using chatbots. Science and Technology Management Research, 45(2), 206–216. https://doi.org/10.3969/j.issn.1000-7695.2025.2.023
  62. Yang, F. M. (2025). A study on the influencing factors of consumers’ continuous purchase intention on fresh e-commerce platforms. Journal of Zhenjiang College, 38(3), 38–43. https://doi.org/10.3969/j.issn.1008-8148.2025.03.007
  63. Yang, J. (2025). Research on optimization countermeasures of fresh e-commerce logistics terminal distribution system in new retail environment. China Logistics & Purchasing, (13), 45–46. https://doi.org/10.16079/j.cnki.issn1671-6663.2025.13.040
  64. Yang, Z. Y. (2025). Factors influencing continuous usage intention of AI virtual social platforms: A case study of Xingye app [Master’s thesis, Yantai University]. CNKI-China Doctoral Dissertations/Masters’ Theses Full-text Database. https://doi.org/10.27437/d.cnki.gytdu.2025.000690
  65. Yi, C. H., & Zhu, W. J. (2025). Research on the influencing factors of the continuous use intention of mobile government app users. Information Research, (3), 1–8. https://doi.org/10.3969/j.issn.1005-8095.2025.03.001
  66. Yu, F. H. (2025). Research on the impact of AI’s self-disclosure on consumers’ continuance intention [Master’s thesis, Xi’an Technological University]. CNKI-China Doctoral Dissertations/Masters’ Theses Full-text Database. https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CMFD&dbname=CMFDTEMP&filename=1025432105.nh
  67. Yu, H. X. (2025). Analysis of influencing factors on users’ willingness to continue using Internet medical platform. Quality & Market, (5), 114–116. https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CJFQ&dbname=CJFDLAST2025&filename=ZLSC202505037
  68. Yue, Q., Gan, W., Mohammed, T. A., Xiao, M., & Liu, J. (2026). How customer participation drives continuance intention in fresh food e-commerce: The mediating role of perceived service quality and the moderating role of technology anxiety. Advances in Management and Intelligent Technologies, 2(2), 1–13. https://doi.org/10.62177/amit.v2i2.1283 DOI: https://doi.org/10.62177/amit.v2i2.1283
  69. Zhang, Q., Wei, J. Q., Luo, J., Liu, X., & Xu, K. Q. (2025). A study on the audience’s experience quality and willingness to continue to use in virtual exhibition: Taking China-ASEAN Expo on the cloud as an example. Popular Science & Technology, 27(1), 193–197. https://doi.org/10.3969/j.issn.1008-1151.2025.01.046
  70. Zhou, J. H. (2025). Research on user intention of AIGC mobile application [Master’s thesis, Zhengzhou University of Aeronautics]. CNKI-China Doctoral Dissertations/Masters’ Theses Full-text Database. https://cdmd.cnki.com.cn/Article/CDMD-10485-1025447606.htm