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The purpose of this paper, a cross-cultural study, is twofold: first, to identify personal, social, demographical and marketing facets that imply fast food addiction in Pakistan and America, and second to explore the reasons of reduction in fast food consumption in Pakistan.

The present study followed an explanatory sequential mixed-methods research design. Both quantitative and qualitative data collection methods were used to obtain supportive results of fast food consumption with the help of defining logical relations between independent and dependent variables. Partial least square based structural equation modeling technique was used to analyze quantitative data. For qualitative data, NVivo 11 was used to explore themes.

Quantitative findings of Pakistani setting suggested that craving and impulsiveness have a significant relation with addiction and have no mediation effect in both the countries. However, these results contradict with US study. Qualitative findings explored many personal, social, marketing and health factors that have affected fast food market of Pakistan which include low income, price-sensitive market, low quality, poor services, PFA, word of mouth, mood, obesity, environment and cultural influence on the consumption behavior of Pakistani consumers.

This study is restricted to the consumption behavior of fast food only in two countries the USA and Pakistan. Hence, the results of this study cannot be generalized to other countries’ cultural and traditional values. In addition, this study only focuses on personal, social and marketing factors that implicate fast food addiction.

The consumption of fast food was the focus of analysis. Therefore, research adds value to service industry and helps in developing growth and marketing strategies for the fast food industry.

Empirical examinations of initial public offering (IPO) initial returns often rely heavily on linear regression models. However, these models can prove inefficient owing to their susceptibility to outliers, a common occurrence in IPO data. This study introduces a machine learning method, known as random forest, to address issues that linear regression may struggle to resolve.

The study’s sample comprises 352 fixed-priced IPOs from the year 2004 until 2021. A unique aspect of this research is its application of the random forest method. The accuracy of random forest in comparison to other methods is evaluated. The findings indicate that the random forest model significantly outperforms other methods in all of the evaluated aspects.

The variable importance measure indicates that investors’ demand, divergence of opinion among investors and offer price are the most crucial predictors of IPO initial returns. These determinants hold particular significance due to the widespread use of the fixed-price method in Malaysia, as this method amplifies the information asymmetry in the IPO market.

To the best of the authors’ knowledge, this study is among the pioneering works in Malaysian literature to apply the random forest method to address the constraints of conventional linear regression models. This is achieved by considering a more extensive array of factors and acknowledging the influence of outliers. Additionally, this study adds value to Malaysian literature by ranking and identifying the ex-ante information that best signals the issuing firm’s quality. This contribution facilitates prospective investors’ decision-making processes and provides issuing firms with effective means to communicate their value and quality to the IPO market.