The transformative power of recommender systems in enhancing citizens’ satisfaction: Evidence from the Moroccan public sector
-
Author(s)Ouissale El GharbaouiLink to ORCID Index: https://orcid.org/0009-0002-3943-5100
,
Hayat El BoukhariLink to ORCID Index: https://orcid.org/0009-0004-6829-6204
,
Abdelkader SalmiLink to ORCID Index: https://orcid.org/0009-0007-8964-9904
-
DOIhttp://dx.doi.org/10.21511/im.20(3).2024.18
-
Article InfoVolume 20 2024, Issue #3, pp. 224-236
- Cited by
- 191 Views
-
44 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
The study aims to specifically evaluate the potential impact of implementing AI-powered recommender systems on citizen satisfaction within Moroccan public services. As part of its ambitious digital transformation, Morocco is integrating digital technologies into its public sector to enhance service delivery. Recommender systems, by providing personalized, timely, and relevant recommendations, are hypothesized to significantly increase citizens’ satisfaction and transform public service delivery. The study highlights a comprehensive model that captures the complex and interrelated factors influencing recommender system success. This model was tested using Smart PLS (Partial Least Squares) on data collected from a diverse sample of 157 Moroccan citizens. These participants were randomly selected from various demographics and regions to represent the general population’s perspectives on the future implementation of AI-powered recommender systems in public services. The survey tested three hypotheses: the positive relationship between the potential use of recommender systems and anticipated citizen satisfaction (supported; b = 0.694, p = 0.000, t = 21.214), the impact of trust in AI-powered recommender systems on anticipated citizens’ satisfaction (supported; b = 0.543, p = 0.000, t = 14.230) ; and the moderating effect of trust on AI-powered recommender systems showing a positive effect on anticipated satisfaction (supported; b = 0.154, p = 0.000, t = 4.907). These findings suggest that the future integration of AI-powered recommender systems into public services can enhance citizens’ satisfaction, particularly where there is high trust in the technology.
Acknowledgment
This paper is partly supported by Sidi Mohamed ben Abdellah University, Morocco.
- Keywords
-
JEL Classification (Paper profile tab)H83, O33, M380
-
References52
-
Tables8
-
Figures2
-
- Figure 1. Research model
- Figure 2. Results of the conceptual model
-
- Table 1. Sample distribution across gender and age
- Table 2. Age and qualification level distribution
- Table 3. Hypotheses testing
- Table 4. Standardized loadings, reliability, and validity
- Table 5. Fornell-Larcker criterion for discriminant validity
- Table 6. Standardized root mean squared residual
- Table 7. HTMT criterion test result
- Table A1. Use of recommender systems, trust in AI-powered recommender systems and citizens’ satisfactions measures
-
- Alexander, W. (2022). Applying Artificial Intelligence to Public Sector Decision Making.
- Bannister, F., & Connolly, R. (2014). ICT, public values and transformative government: A framework and programme for research. Government Information Quarterly, 31(1), 119-128.
- Cardozo, R. (1965). An experimental study of customer effort, expectation, and satisfaction. Journal of Marketing Research, 2(3), 244-249.
- Cortés-Cediel, M., Cantador, I., & Gil, O. (2017). Recommender systems for e-governance in smart cities: State of the art and research opportunities. In Proceedings of the international workshop on recommender systems for citizens (pp. 1-6).
- Creswell, J. W., Plano Clark, V. L., Gutmann, M. L., & Hanson, W. E. (2003). Advanced Mixed Methods Research Designs. In Tashakkori, A. & Teddlie, C. (Eds.), Handbook of Mixed Methods in Social and Behavioral Research (pp. 209-240). Thousand Oaks, CA: Sage.
- Fields, B., Jones, R., & Cowlishaw, T. (2018). The case for public service recommender algorithms.
- Ge, Y., Liu, S., Fu, Z., Tan, J., Li, Z., Xu, S., Li, Y., Xian, Y., & Zhang, Y. (2024). A survey on trustworthy recommender systems. ACM Transactions on Recommender Systems (in press).
- Gefen, D. (2003). TAM or just plain habit: A look at experienced online shoppers. Journal of Organizational and End User Computing, 15(3), 1-13.
- Heeks, R. (2003). Most eGovernment-for-development projects fail: how can risks be reduced? (iGovernment Working Paper no. 14).
- Hildén, J. (2022). The public service approach to recommender systems: Filtering to cultivate. Television & New Media, 23(7), 777-796.
- Hu, L.-T., & Bentler, P. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55.
- Hult, G. T., Hair Jr, J., Proksch, D., Sarstedt, M., Pinkwart, A., & Ringle, C. M. (2018). Addressing endogeneity in international marketing applications of partial least squares structural equation modeling. Journal of International Marketing, 26(3), 1-21.
- Islam, M. M. & Ehsan, M. (2013). E-Governance as a Paradigm Shift in Public Administration: Theories, Applications, and Management. In Halpin, E., Griffin, D., Rankin, C., Dissanayake, L., & Mahtab, N. (Eds.), Digital Public Administration and E-Government in Developing Nations: Policy and Practice (pp. 97-111). IGI Global.
- Jeddou, E., & Oulhadj, B. (n.d.). Public Communication at the Service of Moroccan Citizens in the Era of Public Services Digitalization: the Case of the Service “watiqa. ma”.
- Kim, D., Ferrin, D., & Rao, H. (2009). Trust and satisfaction, two stepping stones for successful e-commerce relationships: A longitudinal exploration. Information Systems Research, 20(2), 237-257.
- Kline, R. (2023). Principles and practice of structural equation modeling.
- Komiak, S., & Benbasat, I. (2004). Understanding customer trust in agent-mediated electronic commerce, web-mediated electronic commerce, and traditional commerce. Information Technology and Management, 5, 181-207.
- Kumar, D., Grosz, T., Rekabsaz, N., Greif, E., & Schedl, M. (2023). Fairness of recommender systems in the recruitment domain: an analysis from technical and legal perspectives. Frontiers in Big Data, 6, 1-14.
- Kunkel, J., & Ziegler, J. (2023). A comparative study of item space visualizations for recommender systems. International Journal of Human-Computer Studies, 172.
- Lin, C.-P., Tsai, Y., & Chiu, C.-K. (2009). Modeling customer loyalty from an integrative perspective of self-determination theory and expectation–confirmation theory. Journal of Business and Psychology, 24, 315-326.
- Lin, Z. (2014). An empirical investigation of user and system recommendations in e-commerce. Decision Support Systems, 68, 111-124.
- Lindgren, I., & Jansson, G. (2013). Electronic services in the public sector: A conceptual framework. Government Information Quarterly, 30(2), 163-172.
- Liu, W., & Wang, Y. (2024). Evaluating Trust in Recommender Systems: A User Study on the Impacts of Explanations, Agency Attribution, and Product Types. International Journal of Human–Computer Interaction, 1-13.
- Madan, R., & Ashok, M. (2023). AI adoption and diffusion in public administration: A systematic literature review and future research agenda. Government Information Quarterly, 40(1).
- Mayer, R., Davis, J., & Schoorman, F. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709-734.
- Lee, M. K., & Turban, E. (2001). A trust model for consumer internet shopping. International Journal of electronic commerce, 6(1), 75-91.
- Norman, D. (2013.). The design of everyday things: Revised and expanded edition. Basic books.
- O’Donovan, J., & Smyth, B. (2005). Trust in recommender systems. In Proceedings of International Conference on Intelligent User Interfaces (pp. 167-174).
- Pavlou, P. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101-134.
- Portugal, I., Alencar, P., & Cowan, D. (2018). The use of machine learning algorithms in recommender systems: A systematic review. Expert Systems with Applications, 97, 205-227.
- Pu, P., Chen, L., & Hu, R. (2011). A user-centric evaluation framework for recommender systems. In Proceedings of the Fifth ACM Conference on Recommender Systems (pp. 157-164).
- Rahi, S., Bisui, S., & Misra, S. (2017). Identifying the moderating effect of trust on the adoption of cloud-based services. International Journal of Communication Systems, 30(11), 3253.
- Ricci, F., Rokach, L., & Shapira, B. (2010). Introduction to recommender systems handbook. Boston, MA: Springer.
- Rubens, N., Elahi, M., Sugiyama, M., & Kaplan, D. (2015). Active learning in recommender systems. In Ricci, F., Rokach, L., & Shapira, B. (Eds.), Recommender Systems Handbook (pp. 809-846).
- Saguin, K. (2013). Critical challenges in implementing the citizen’s charter Initiative: Insights from selected local government units. Journal of Public Administration, 57(1).
- Seth, N., Deshmukh, S., & Vrat, P. (2005). Service quality models: a review. International Journal of Quality & Reliability Management, 22(9), 913-949.
- Siegrist, M., & Cvetkovich, G. (2000). Perception of hazards: The role of social trust and knowledge. Risk Analysis, 20(5), 713-720.
- Špaček, D., & Špačková, Z. (2023). Issues of e-government services quality in the digital-by-default era – the case of the national e-procurement platform in Czechia. Journal of Public Procurement, 23(1), 1-34.
- Stoker, G. (2006). Public value management: A new narrative for networked governance? The American Review of Public Administration, 36(1), 41-57.
- Susanto, T. D., & Goodwin, R. (2013). User acceptance of SMS-based e-government services: Differences between adopters and non-adopters. Government Information Quarterly, 30(4), 486-497.
- Tassabehji, R. (2005). Information security threats. In Encyclopedia of Multimedia Technology and Networking (pp. 404-410).
- Toreini, E., Aitken, M., & Coopamootoo, K. (2020). The relationship between trust in AI and trustworthy machine learning technologies. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 272-283).
- Torfing, J., Sørensen, E., & Røiseland, A. (n.d.). Transforming the public sector into an arena for co-creation: Barriers, drivers, benefits, and ways forward. Administration & Society, 51(5), 795-825.
- Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.
- Wanner, J., Herm, L.-V., Heinrich, K., & Janiesch, C. (2022). The effect of transparency and trust on intelligent system acceptance: Evidence from a user-based study. Electronic Markets, 32(4), 2079-2102.
- Wang, S., Zhang, X., Wang, Y., & Ricci, F. ( 2022). Trustworthy recommender systems. ACM Transactions on Intelligent Systems and Technology, 15(4), 1-20.
- Wang, W., & Benbasat, I. (2016). Empirical assessment of alternative designs for enhancing different types of trusting beliefs in online recommendation agents. Journal of Management Information Systems, 33(3), 744-775.
- Wang, W., & Benbasat, I. (2008). Attributions of trust in decision support technologies: A study of recommendation agents for e-commerce. Journal of Management Information Systems, 24(4), 249-273.
- Welch, E. W., Hinnant, C. C., & Moon, M. J. (2005). Linking citizen satisfaction with e-government and trust in government. Journal of public administration research and theory, 15(3), 371-391, 2005.
- West, D. (2004). E-government and the transformation of service delivery and citizen attitudes. Public administration review, 64(1), 15-27.
- Yakhchi, S. (2021). Learning Complex Users’ Preferences for Recommender Systems (arXiv preprint).
- Zhang, Q., Lu, J., & Jin, Y. (2021). Artificial intelligence in recommender systems. Complex & Intelligent Systems, 7(1), 439-457.
-
Public finances, governance control and economic growth: a macroeconomic history approach
Ola Honningdal Grytten 
doi: http://dx.doi.org/10.21511/imfi.16(1).2019.15
Investment Management and Financial Innovations Volume 16, 2019 Issue #1 pp. 189-202 Views: 2580 Downloads: 510 TO CITE АНОТАЦІЯThe size of the public sector is an important tool in public governance. Public sector size may fuel both economic growth and political influence over the economy.
By compiling and processing data from different sources of public accounts the paper aims at mapping the development of key financial indicators for the Norwegian central government sector during the transition period from the mid 19th to the mid 20th century. The data enable us to give measures of the size of the public sector alone and compared to the overall economy.
It is found that the sector started its continuous growth before politicians deliberately started to increase the sector’s size of the total economy. The paper also finds that an increase of the public sector often, but not always, reflects political economy regimes. Persistent growth in public finances as a tool for economic policy making did not take place before the introduction of the social-democratic regime in 1935.
The paper also concludes that economic growth started before the growth in the public sector, suggesting that public sector growth might as well be a result of economic growth or vice versa. -
The development of talent management in Malaysian public sector: a comprehensive review
Sharmaine Sakthi Ananthan ,
Halimah Abdul Manaf ,
Mega Hidayati ,
Dian Suluh Kusuma Dewi
doi: http://dx.doi.org/10.21511/ppm.17(2).2019.18
Problems and Perspectives in Management Volume 17, 2019 Issue #2 pp. 242-253 Views: 2273 Downloads: 1138 TO CITE АНОТАЦІЯThe aim of this current study was to analyze recent development of talent management among public service managers in Malaysia. The study brings understanding on talent management and broader human capital issues within Malaysia context by analysing the comprehensive literature. Government of Malaysia is emphasizing on development of talent in the public sector, as it is the most significant resource for the public service. The study found that the government has invested in various programs and initiatives to develop and produce key talented human capital within the public sector. However, despite the initiatives taken by the government, Malaysia is still short of talented people and struggling to develop talent, and talented skilful people are leaving. The study also indicated that year after year increasing number of graduates are being produced at the public universities, but the education system is not producing the right skilful individuals for the particular position or task, resulting in talent mismatch. Another key issue is the incapability to retain talented people that led to poor innovation and creativity within the country. Is key issue is part of the factors discussed in the 10th and 11th Malaysia Plan, and is a concern, as it plays a role in preventing the progress of Malaysia in achieving a developed nation by 2020. As one of the key aspirations of Malaysia is to enrich public service in Malaysia as a citizen centre that is having talented human capital. The study revealed that there is a need for in-depth quantitative and qualitative further research to discover new insights on talent management and retain talent among the public sector managers in Malaysia.
-
The role of content marketing in enhancing customer loyalty: an empirical study on private hospitals in Saudi Arabia
Ahmed Suhail Ajina
doi: http://dx.doi.org/10.21511/im.15(3).2019.06
Due to increasing competition in the private hospital sector in Saudi Arabia, the organizations need to adopt the new ways to foster the brand loyalty and repeated visits. In this context, content marketing may have pleasant effects on a business in terms of customer engagement, trust and loyalty. The present research attempts to investigate the multi-dimensional relationships among content marketing, loyalty, trust and customer engagement in the private hospital sector using primary data collected through a questionnaire from 400 randomly selected respondents. A simple random sampling technique is utilized to select the patients or their family members visiting the private hospital to have their perception regarding hypothesized variables. The findings indicate that content marketing has a positive impact on the factors like customer engagement, trust and loyalty. In comparison, content marketing is found more effective in persuading the loyalty of the customers in comparison of trust and loyalty. Further, the customers’ trust towards a brand has a strong positive relationship with the loyalty. Moreover, the positive effect of customer engagement on the trust has also been found. Based on the results, this study suggests that the private healthcare institutions should adopt a multi-channel content marketing approach to exploit its maximum benefit. Multiple social media platforms and popular healthcare blog sites should be chosen for an effective content marketing advertisement.
