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Purpose: This study explored the role of market mavens for young tourists and existential authenticity for young tourist co-creation experience. It determines intentions to revisit and intentions to recommend tourism service providers. The theoretical foundation of this study is based upon the macro-micro theory approach of service-dominant (S-D) logic and customer engagement, which explains the idea of tourist co-creation interactivity and value creation between the service provider and tourist. Design/methodology/approach: The positivist deductive approach, survey-based methodology was followed for this study. Data was collected from 349 young tourists who have traveled and contributed to travel services through collaboration and interaction with the service provider. The data were analyzed in SmartPLS, and structured equation modeling technique was used for hypothesis testing. Findings: It was found that market mavens have a positive impact on young tourist's co-creation process, hence depicting intention to revisit and recommend the tourism service provider. Existential authenticity was also found having positive impact on tourist co-creation and intention to revisit and recommend the service provider. The results show that young tourists plan domestic trips in Pakistan and co-create with service providers. Research limitations/implications: This study theoretically and practically contributed in the literature and enhanced the literature concerning mainly the young tourists co-creation. It has also enhanced the knowledge about intentions to revisit and recommend with special focus on market mavens. Also, existential authenticity of the tourist spots was keenly considered in the underlying research. Originality/value: This study highlights value-creation phenomenon in tourism for younger tourists. Their input is important in the form of creativity and innovation and by incorporating these young tourists in decision-making process. They ensure their ability to participate and design, resulting in loyalty towards service provider.

This paper proposes a generalized class of difference-cum-exponential-type estimators for population mean under simple random sampling without replacement through known conventional and non-conventional auxiliary information. It is observed that some well-known estimators are the members of our proposed class. Moreover, proposed class of estimators behaves efficiently than competing estimators under some simple conditions. Theoretical findings are confirmed with numerical illustration by using six real-life datasets. In addition, Monte Carlo simulation study on four real populations also approved the potential of the proposed class against competing estimators.

This paper proposes an improved general family of ratio-type estimators using the coefficient of skewness of an auxiliary variable for estimating finite population mean under simple random sampling. Bias, mean squared error (MSE) and minimum MSE of the proposed family are derived up to the first degree of approximation. It is found that proposed family behaves better than competing estimators under some simple conditions. Theoretical findings are confirmed with numerical illustration using three natural populations. In addition, Monte Carlo simulation study on a real data set also proves the potential of the proposed family against the common unbiased estimator, traditional ratio estimator and estimators by Yan and Tian (CCIS 106:103–110, 2010).

This study suggests a new optimal family of exponential-type estimators for estimating population mean in stratified random sampling. These estimators are based on the traditional and nontraditional measures of auxiliary information. Expressions for the bias, mean square error, and minimum mean square error of the proposed estimators are derived up to first order of approximation. It is observed that proposed estimators perform better than the traditional estimators (unbiased, combined ratio, and combined regression) and other recent estimators. A real dataset is used to highlight the applicability of proposed estimators. In addition, a simulation study is carried out to assess the performance of new family as compared to other estimators.