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Mobile payment services are the most growing business in recent days and it is the most cost‐effective tool as well. This study aimed to explore the factors affecting the adoption of m‐payment services in emerging economies, which was attained by implementing the descriptive research design. Accordingly, the collection of 440 responses was carried out via the e‐mail method, wherein the respondents were selected by using the convenience sampling method. To this end, the research adapted the dimensions of UTAUT, ISS model, trust, and perceived security to examine one's adoption behavior toward mobile banking services. It was thus found that system quality yielded the strongest and significant influence on the intention to adopt, whereby the latter element served as a mediator and fully mediated the relationships between effort expectancy, facilitating conditions, and intention to recommend. Henceforth, the findings obtained in this study would aid industry practitioners in exploring pertinent strategies toward encouraging the adoption of mobile banking services not only among individuals but also for microentrepreneurs and SMEs. This is further achieved via the combination of the dimensions included in the UTAUT and ISS models along with the elements of trust and perceived security, thereby offering tremendous value addition to the existing literature. This particular study will further make several contribution toward mobile banking and mobile payment services related literature.

The growing importance of Big data in the food industry enables businesses to leverage information to gain a competitive advantage. This paper provides a systematic literature review (SLR) to provide an insight into the use of state-of-art of Big data applications in the food industry. The SLR relies on available literature that provides the context, theoretical construct and identifies gaps. Based on the findings, we suggest recommendations, identify limitations and suggest policy implications and future directions. Using search databases were examined and 38 relevant studies were identified for retrospective analysis. The review shows that Big Data supports the food industry in ways that enable using Artificial Intelligence to manage restaurants and mobile based applications in supporting consumers with restaurant selection. This SLR open new avenues for future research in the importance of Big data in the food industry, which will surely help researchers/practitioners in effective utilization of big data.

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