**6. Conclusions and Future Research Directions**

The current study aims to examine customers' behavioral intentions related to dronedelivery food services. This study has integrated subjective norms and domain-specific innovativeness constructs: product processing innovativeness and information processing innovativeness into TAM to predict customers' behavioral intention. Further, this study assessed the impact of customers' attitudes on behavioral intention constructs: intention to use, word of mouth, and willingness to pay more. The cross-sectional data of 354 restaurants customers from five main cities of Pakistan has been collected for this study. The result of ten hypotheses indicates that the proposed theoretical model possesses adequate relevancy and predictive power in the context of drone delivery food services in the Pakistani market. Although the current study has several theoretical and managerial implications, it has some limitations that need careful consideration. First, the data of respondents were collected from a developing market context, therefore, generalizability would be an issue because the respondents of developed countries may have a different opinion regarding drone delivery services. Therefore, it is recommended for future research to collect data from advanced countries' customers and compare the findings with the developing countries for a comprehensive understanding of novel technology adoption. Second, this study has only used quantitative techniques to evaluate the behavioral intention of the respondents. The findings of the quantitative study can be generalized but it covers the specific dimensions under study. To comprehensively understand customers' perceptions regarding drone food delivery services, future researchers should conduct in-depth interviews with the respondents. Third, this study has used the purposive sampling technique for the collection of data that may result in biases in data. Future studies can use different types of sampling techniques to avoid biases in data. Fourth, the data of this study has been collected at the same time which may cause common methods. Therefore, Harman single factor was conducted to ensure that common method bias is not a threat. The result depicts that a single factor contributed very low variance [94]. Therefore, Podsakoff [83] suggested to collect data at different times to avoid common method bias issues.

**Author Contributions:** Conceptualization, I.W., R.A. and A.N.; methodology, I.W.; software, I.W. and R.A.; validation, I.W., R.A. and M.B.; formal analysis, R.L. and I.H.; investigation, I.W.; resources, I.H.; data curation, I.W.; writing—original draft preparation, I.W.; writing—review and editing, R.L., I.H. and R.A.; visualization, I.H.; supervision, R.A. and A.N.; project administration, M.B. and I.H.; funding acquisition, M.B., A.N. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Taif University Researchers Supporting Project number (TURSP-2020/239), Taif University, Taif, Saudi Arabia.

**Institutional Review Board Statement:** This statement if the study did not require ethical approval.

**Informed Consent Statement:** Informed consent was obtained from the participants of the study.

**Data Availability Statement:** The datasets analyzed during the current study are available from the corresponding authors on reasonable request.

**Acknowledgments:** Authors would like to thank for the support of Taif University Researchers Supporting Project number (TURSP-2020/239), Taif University, Taif, Saudi Arabia.

**Conflicts of Interest:** The authors declare no conflict of interest.
