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This study examines the influence of restaurant location and innovative restaurant practices on the performance of restaurant. The study further explores the moderating role of innovative restaurant practices in the relationship between restaurant location and performance.

The study employs a cross-sectional survey design. Data were gathered via structured questionnaires from 281 restaurant managers in Tanzania. Confirmatory factor analysis (CFA) was employed to assess the validity of the measurement model, while hypotheses were tested with the PROCESS macro.

The results indicate that both restaurant location and innovative practices significantly influence restaurant performance. Moreover, the implementation of innovative practices not only directly enhances performance but also strengthens the positive effect of a location on performance. This highlights the critical role of innovation in optimizing the benefits of a strategic location.

Restaurant owners should carefully choose locations for their business to enhance performance. They should also prioritize innovation through unique menu items, technology and creative marketing strategies to enhance performance. Additionally, owners and managers should focus on integrating innovation with location strategy, as innovative practices strengthen the effect of location on overall restaurant performance.

This research contributes to the limited empirical evidence on the influence of location and innovative practices on restaurant performance. Additionally, the study adds to the existing literature by examining the moderating effect of innovative restaurant practices on the relationship between restaurant location and performance, an aspect not previously explored in prior research.

The goal of this research is to analyse the obstacles to the implementation of mobile health (mHealth) in India and to gain an understanding of the contextual inter-relationships that exist amongst those obstacles.

Potential barriers and their interrelationships in their respective contexts have been uncovered. Using MICMAC analysis, the categorization of these barriers was done based on their degree of reliance and driving power (DP). Furthermore, an interpretive structural modeling (ISM) framework for the barriers to mHealth activities in India has been proposed.

The study explores a total of 15 factors that reduce the efficiency of mHealth adoption in India. The findings of the Matrix Cross-Reference Multiplication Applied to a Classification (MICMAC) investigation show that the economic situation of the government, concerns regarding the safety of intellectual technologies and privacy issues are the primary obstacles because of the significant driving power they have in mHealth applications.

Promoters of mHealth practices may be able to make better plans if they understand the social barriers and how they affect each other; this leads to easier adoption of these practices. The findings of this study might be helpful for governments of developing nations to produce standards relating to the deployment of mHealth; this will increase the efficiency with which it is adopted.

At this time, there is no comprehensive analysis of the factors that influence the adoption of mobile health care with social cognitive theory in developing nations like India. In addition, there is a lack of research in investigating how each of these elements affects the success of mHealth activities and how the others interact with them. Because developed nations learnt the value of mHealth practices during the recent pandemic, this study, by investigating the obstacles to the adoption of mHealth and their inter-relationships, makes an important addition to both theory and practice.